SAGE Directions in Educational Psychology (SAGE Library of Educational Thought & Practice)

How to go to your page This eBook contains five volumes. Each volume has its own page numbering scheme, consisting of a volume number and a page number, separated by a colon. For example, to go to page ii of Volume I, type I:ii in the "page #" box at the top of the screen and click "Go." To go to page 186 of Volume II, type II:186 … and so forth.

SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY

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The SAGE Library of Educational Thought and Practice major works series encapsulates and disseminates the seminal works in the field of educational science and collects together those articles and essays which have been most influential in shaping and driving the discipline. Each multivolume set presents readers with a collection of both classical and contemporary published works sourced from the foremost publications in the field by an internationally renowned editor or editorial team. Each set includes a full introduction, presenting a rationale for the selection and which contextualizes the major work within the discipline, giving students, researchers and academics insight into the past, present and likely future of that area of research. The series covers both key approaches to studying education theory and the primary sub-fields which form the focus of educational practitioners’ work. The SAGE Library of Educational Thought and Practice is an essential addition for all libraries throughout the world with an interest in education. Neil J. Salkind has been teaching at the University of Kansas for 30 years, in the Department of Psychology and Research in Education. He has published more than 80 professional papers and is the author of several college-level textbooks, including Statistics for People Who (Think They) Hate Statistics (now in the third edition), Child Development, Exploring Research, and Introduction to Theories of Human Development (SAGE 2004). He was editor of Child Development Abstracts and Bibliography from 1989 through 2002 and is active in the Society for Research in Child Development.

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SAGE LIBRARY OF EDUCATIONAL THOUGHT AND PRACTICE

SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY VOLUME I

Edited by

Neil J. Salkind

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Introduction and editorial arrangement © Neil J. Salkind 2011 First published 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Every effort has been made to trace and acknowledge all the copyright owners of the material reprinted herein. However, if any copyright owners have not been located and contacted at the time of publication, the publishers will be pleased to make the necessary arrangements at the first opportunity. SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1, Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 33 Pekin Street #02-01 Far East Square Singapore 048763 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN: 978-0-85702-178-6 (set of five volumes) Library of Congress Control Number: 2010923776 Typeset by Mukesh Technologies Pvt. Ltd., Pondicherry, India. Printed on paper from sustainable resources Printed by MPG Books Group, Bodmin Cornwall

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Contents Appendix of Sources Editor’s Introduction Neil J. Salkind

xiii xxiii

Volume I Section I: Human Development 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

11. 12.

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Aging and Human Performance Neil Charness Violence and Human Development Elton B. McNeil The Life-course and Human Development: An Ecological Perspective Glen H. Elder, Jr and Richard C. Rockwell The Family Conference: The Social Control of Human Development David R. Buckholdt From Childhood to the Later Years: Pathways of Human Development Robert Crosnoe and Glen H. Elder Jr The Developmental Niche: A Conceptualization at the Interface of Child and Culture Charles M. Super and Sara Harkness Conceptualizing Adult Development Calvin F. Settlage, John Curtis, Marjorie Lozoff, Milton Lozoff, George Silberschatz and Earl J. Simburg Early Child Care and Children’s Development Prior to School Entry: Results from the NICHD Study of Early Child Care NICHD Early Child Care Research Network A Developmental Approach to Language Acquisition: Two Case Studies M. Bamberg, N. Budwig and B. Kaplan Promoting Positive Youth Development: New Directions in Developmental Theory, Methods, and Research William M. Kurtines, Laura Ferrer-Wreder, Steven L. Berman, Carolyn Cass Lorente, Wendy K. Silverman and Marilyn J. Montgomery Children Have More Need of Models than Critics: Early Language Experience and Brain Development Travis Thompson Development: Transfer of Technology, Transfer of Culture Jacques Binet (Translated by Jeanne Ferguson)

3 15 25 43 57 81 103

119 151 171

181 191

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vi

13. 14. 15. 16. 17. 18.

Contents

The Clinical Study and Treatment of Normal and Abnormal Development: A Psychological Clinic Lightner Witmer Self-Motivation for Academic Attainment: The Role of Self-Efficacy Beliefs and Personal Goal Setting Barry J. Zimmerman, Albert Bandura and Manuel Martinez-Pons The Dangerous and the Good? Developmentalism, Progress, and Public Schooling Bernadette Baker The Scientific Humanism of G. Stanley Hall Donald H. Meyer Growing Old – Or Older and Growing Carl R. Rogers Maturational Timing and the Development of Problem Behavior: Longitudinal Studies in Adolescence Rainer K. Silbereisen, Anne C. Petersen, Helfried T. Albrecht and Bärbel Kracke

207 231 245 287 299 311

Volume II Section I: Human Development (Continued) 19. 20. 21.

Motor Development as Foundation and Future of Developmental Psychology Esther Thelen Physical Growth Kai Jensen Mental Development during the Preadolescent and Adolescent Periods Gordon Hendrickson

3 31 79

Section II: Curriculum, Instruction and Learning 22. 23. 24. 25. 26.

Making Sense of Curriculum Evaluation: Continuities and Discontinuities in an Educational Idea David Hamilton Psychology of Learning Environments: Behavioral, Structural, or Perceptual? Herbert J. Walberg Thought and Two Languages: The Impact of Bilingualism on Cognitive Development Rafael M. Diaz Components of a Psychology of Instruction: Toward a Science of Design Robert Glaser The Emergence of Cognitive Psychology Robert R. Holt

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93 123 159 189 211

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Contents

27.

The Advancement of Learning Ann L. Brown 28. Paradigms of Knowledge and Instruction S. Farnham-Diggory 29. Health Promotion by Social Cognitive Means Albert Bandura 30. Models of the Learner Jerome Bruner 31. Child’s Talk: Learning to Use Language Jerome Bruner 32. The Reflexivity of Cognitive Science: The Scientist as Model of Human Nature Jamie Cohen-Cole 33. History, Culture, Learning, and Development Patricia M. Greenfield, Ashley E. Maynard and Carla P. Childs 34. Biology and Cognition Jean Piaget (Translated by Martin Faigel) 35. Neural Bases of Intelligence and Training Mark R. Rosenzweig

vii

227 249 267 291 299 303 333 351 369

Volume III Section II: Curriculum, Instruction and Learning (Continued) 36. 37. 38. 39. 40. 41.

Human Intelligence: An Introduction to Advances in Theory and Research David F. Lohman Cognitive Demands of New Technologies and the Implications for Learning Theory Richard J. Torraco Cognitive Conceptions of Learning Thomas J. Shuell Meaning in Complex Learning Ronald E. Johnson Phases of Meaningful Learning Thomas J. Shuell Growth, Development, Learning, and Maturation as Factors in Curriculum and Teaching William C. Trow

3 51 79 109 141 161

Section III: Motivation 42. 43. 44.

Maslow, Monkeys and Motivation Theory Dallas Cullen Maslow’s Theory of Motivation: A Critique Andrew Neher Caught on Fire: Motivation and Giftedness Ann Robinson

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175 195 215

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viii

45. 46. 47. 48. 49. 50. 51.

52. 53.

54. 55. 56.

57.

Contents

An Empirical Test of Maslow’s Theory of Motivation Eugene W. Mathes and Linda L. Edwards Meaningfulness, Commitment, and Engagement: The Intersection of a Deeper Level of Intrinsic Motivation Neal Chalofsky and Vijay Krishna Motivation and Human Growth: A Developmental Perspective M.S. Srinivasin Evolutionary Perspectives on Human Motivation Jutta Heckhausen The Debate about Rewards and Intrinsic Motivation: Protests and Accusations Do Not Alter the Results Judy Cameron and W. David Pierce A Comprehensive Expectancy Motivation Model: Implications for Adult Education and Training Kenneth W. Howard The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education Robert J. Vallerand, Luc G. Pelletier, Marc R. Blais, Nathalie M. Brière, Caroline Senécal and Evelyne F. Vallières Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again Edward L. Deci, Richard Koestner and Richard M. Ryan Beyond the Rhetoric: Understanding Achievement and Motivation in Catholic School Students Janine Bempechat, Beth A. Boulay, Stephanie C. Piergross and Kenzie A. Wenk Dimensions of School Motivation: A Cross-cultural Validation Study Dennis M. McInerney and Kenneth E. Sinclair Achievement Motivation in Children of Three Ethnic Groups in the United States Manuel Ramirez III and Douglass R. Price-Williams Motivation and Learning Environment Differences between Resilient and Nonresilient Latino Middle School Students Hersholt C. Waxman, Shwu-yong L. Huang and Yolanda N. Padrón Attracting and Retaining Teachers: A Question of Motivation Karin Müller, Roberta Alliata and Fabienne Benninghoff

219 223 237 247 263 279 291

305 333

345 361 369 387

Volume IV Section III: Motivation (Continued) 58.

Interpersonal Relationships, Motivation, Engagement, and Achievement: Yields for Theory, Current Issues, and Educational Practice Andrew J. Martin and Martin Dowson

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Contents

59. 60. 61.

62. 63. 64. 65. 66.

Classroom and Individual Differences in Early Adolescents’ Motivation and Self-Regulated Learning Paul R. Pintrich, Robert W. Roeser and Elisabeth A.M. De Groot Atkinson’s Theory of Achievement Motivation: First Step toward a Theory of Academic Motivation? Martin L. Maehr and Douglas D. Sjogren Motivation and Engagement across the Academic Life Span: A Developmental Construct Validity Study of Elementary School, High School, and University/College Students Andrew J. Martin Motivation and Achievement: A Quantitative Synthesis Margaret E. Uguroglu and Herbert J. Walberg Academic Motivation and Achievement among Urban Adolescents Joyce F. Long, Shinichi Monoi, Brian Harper, Dee Knoblauch and P. Karen Murphy Intrinsic Motivation and School Misbehavior: Some Intervention Implications Howard S. Adelman and Linda Taylor Reinforcement, Reward, and Intrinsic Motivation: A Meta-Analysis Judy Cameron and W. David Pierce Motivation in Transition Barbara Stauber

ix

45 67

87 121 135

157 179 241

Section IV: Research Design, Measurement and Statistics and Evaluation 67. 68. 69. 70. 71. 72.

Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing Raymond Hubbard and R. Murray Lindsay Alphabet Soup: Blurring the Distinctions between p’s and a ’s in Psychological Research Raymond Hubbard Research Methods: Experimental Design Julian C. Stanley What Can We Learn from International Assessments? Robert J. Mislevy Power, Control, and Validity in Research Randall M. Parker Testing Reasoning and Reasoning about Testing Walt Haney

263 283 313 325 353 371

Volume V Section IV: Research Design, Measurement and Statistics and Evaluation (Continued) 73.

Magnitudes of Experimental Effects in Social Science Research Lee Sechrest and William H. Yeaton

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x

Contents

74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93.

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Hypothesis Testing in Relation to Statistical Methodology Cherry Ann Clark On Examinee Choice in Educational Testing Howard Wainer and David Thissen Historical Views of Invariance: Evidence from the Measurement Theories of Thorndike, Thurstone, and Rasch George Engelhard, Jr If Statistical Significance Tests Are Broken/Misused, What Practices Should Supplement or Replace Them? Bruce Thompson Musical Aptitude Testing: From James McKeen Cattell to Carl Emil Seashore Jere T. Humphreys The Life and Labors of Francis Galton: A Review of Four Recent Books about the Father of Behavioral Statistics Brian E. Clauser Regression towards the Mean, Historically Considered Stephen M. Stigler Karl Pearson and Statistics: The Social Origins of Scientific Innovation Bernard J. Norton A History of Effect Size Indices Carl J. Huberty The Role of Assessment in a Learning Culture Lorrie A. Shepard The Place of Theory in Educational Research Patrick Suppes Curriculum-based Measures: Development and Perspectives Stanley L. Deno Tests as Research Instruments Robert L. Thorndike My Current Thoughts on Coefficient Alpha and Successor Procedures Lee J. Cronbach and Richard J. Shavelson Handbook of Evaluation Research Lee Ross and Lee J. Cronbach A Model for Studying the Validity of Multiple-Choice Items Lee J. Cronbach and Jack C. Merwin Assisted Assessment: A Taxonomy of Approaches and an Outline of Strengths and Weaknesses Joseph C. Campione Standardized Testing Roger T. Lennon The Place of Statistics in Psychology Jum Nunnally Education in Statistics and Research Design in School Psychology Steven G. Little, Howard B. Lee and Angeleque Akin-Little

23 43 83 99 115 131 137 151 179 193 215 231 243 257 285 305 319 351 359 367

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Contents

94. 95.

The Role of Measurement Error in Familiar Statistics Malcolm James Ree and Thomas R. Carretta Qualitative Methods and the Development of Clinical Assessment Tools Jane F. Gilgun

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xi

377 393

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Appendix of Sources All articles and chapters have been reproduced exactly as they were first published, including textual cross-references to material in the original source. Grateful acknowledgement is made to the following sources for permission to reproduce material in this book. 1.

‘Aging and Human Performance’, Neil Charness Human Factors: The Journal of the Human Factors and Ergonomics Society, 50 (2008): 548–555. Published by SAGE Publications, Inc. Reprinted with permission.

2.

‘Violence and Human Development’, Elton B. McNeil The ANNALS of the American Academy of Political and Social Science, 364 (1966): 149–157. Published by SAGE Publications, Inc. Reprinted with permission.

3.

‘The Life-Course and Human Development: An Ecological Perspective’, Glen H. Elder, Jr and Richard C. Rockwell International Journal of Behavioral Development, 2 (1979): 1–21. Published by SAGE Publications Ltd. Reprinted with permission.

4.

‘The Family Conference: The Social Control of Human Development’, David R. Buckholdt Journal of Family Issues, 4(4) (1983): 613–631. Published by SAGE Publications, Inc. Reprinted with permission.

5.

‘From Childhood to the Later Years: Pathways of Human Development’, Robert Crosnoe and Glen H. Elder Jr Research on Aging, 26(6) (2004): 623–654. Published by SAGE Publications, Inc. Reprinted with permission.

6.

‘The Developmental Niche: A Conceptualization at the Interface of Child and Culture’, Charles M. Super and Sara Harkness International Journal of Behavioral Development, 9 (1986): 545–569. Published by SAGE Publications Ltd. Reprinted with permission.

7.

‘Conceptualizing Adult Development’, Calvin F. Settlage, John Curtis, Marjorie Lozoff, Milton Lozoff, George Silberschatz and Earl J. Simburg Journal of the American Psychoanalytic Association, 36 (1988): 347–369. Published by SAGE Publications, Inc. Reprinted with permission.

8.

‘Early Child Care and Children’s Development Prior to School Entry: Results from the NICHD Study of Early Child Care’, NICHD Early Child Care Research Network American Educational Research Journal, 39(1) (2002): 133–164. Published by SAGE Publications, Inc. Reprinted with permission.

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xiv

Appendix of Sources

9. ‘A Developmental Approach to Language Acquisition: Two Case Studies’, M. Bamberg, N. Budwig and B. Kaplan First Language, 11 (1991): 121–141. Published by SAGE Publications Ltd. Reprinted with permission. 10. ‘Promoting Positive Youth Development: New Directions in Developmental Theory, Methods, and Research’, William M. Kurtines, Laura Ferrer-Wreder, Steven L. Berman, Carolyn Cass Lorente, Wendy K. Silverman and Marilyn J. Montgomery Journal of Adolescent Research, 23(3) (2008): 233–243. Published by SAGE Publications, Inc. Reprinted with permission. 11. ‘Children Have More Need of Models than Critics: Early Language Experience and Brain Development’, Travis Thompson Journal of Early Intervention, 19(3) (1995): 264 – 272. Published by SAGE Publications, Inc. Reprinted with permission. 12. ‘Development: Transfer of Technology, Transfer of Culture’, Jacques Binet (Translated by Jeanne Ferguson) Diogenes, 32 (1984): 19–38. Published by SAGE Publications Ltd. Reprinted with permission. 13. ‘The Clinical Study and Treatment of Normal and Abnormal Development: A Psychological Clinic’, Lightner Witmer The ANNALS of the American Academy of Political and Social Science, 34 (1909): 141–162. Published by SAGE Publications, Inc. Reprinted with permission. 14. ‘Self-Motivation for Academic Attainment: The Role of Self-Efficacy Beliefs and Personal Goal Setting’, Barry J. Zimmerman, Albert Bandura and Manuel Martinez-Pons American Educational Research Journal, 29(3) (1992): 663–676. Published by SAGE Publications, Inc. Reprinted with permission. 15. ‘The Dangerous and the Good? Developmentalism, Progress, and Public Schooling’, Bernadette Baker American Educational Research Journal, 36(4) (1999): 797–834. Published by SAGE Publications, Inc. Reprinted with permission. 16. ‘The Scientific Humanism of G. Stanley Hall’, Donald H. Meyer Journal of Humanistic Psychology, 11 (1971): 201–213. Published by SAGE Publications, Inc. Reprinted with permission. 17. ‘Growing Old – Or Older and Growing’, Carl R. Rogers Journal of Humanistic Psychology, 20(4) (1980): 5 –16. Published by SAGE Publications, Inc. Reprinted with permission. 18. ‘Maturational Timing and the Development of Problem Behavior: Longitudinal Studies in Adolescence’, Rainer K. Silbereisen, Anne C. Petersen, Helfried T. Albrecht and Bärbel Kracke The Journal of Early Adolescence, 9(3) (1989): 247–268. Published by SAGE Publications, Inc. Reprinted with permission.

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Appendix of Sources

xv

19. ‘Motor Development as Foundation and Future of Developmental Psychology’, Esther Thelen International Journal of Behavioral Development, 24(4) (2000): 385–397. Published by SAGE Publications Ltd. Reprinted with permission. 20. ‘Physical Growth’, Kai Jensen Review of Educational Research, XXV(5) (1955): 369–414. Published by SAGE Publications, Inc. Reprinted with permission. 21. ‘Mental Development during the Preadolescent and Adolescent Periods’, Gordon Hendrickson Review of Educational Research, XX(5) (1950): 351–360. Published by SAGE Publications, Inc. Reprinted with permission. 22. ‘Making Sense of Curriculum Evaluation: Continuities and Discontinuities in an Educational Idea’, David Hamilton Review of Research in Education, 5 (1977): 318–347. Published by SAGE Publications, Inc. Reprinted with permission. 23. ‘Psychology of Learning Environments: Behavioral, Structural, or Perceptual?’, Herbert J. Walberg Review of Research in Education, 4 (1976): 142–178. Published by SAGE Publications, Inc. Reprinted with permission. 24. ‘Thought and Two Languages: The Impact of Bilingualism on Cognitive Development’, Rafael M. Diaz Review of Research in Education, 10 (1983): 23–54. Published by SAGE Publications, Inc. Reprinted with permission. 25. ‘Components of a Psychology of Instruction: Toward a Science of Design’, Robert Glaser Review of Educational Research, 46(1) (1976): 1–24. Published by SAGE Publications, Inc. Reprinted with permission. 26. ‘The Emergence of Cognitive Psychology’, Robert R. Holt Journal of the American Psychoanalytic Association, 12 (1964): 650–665. Published by SAGE Publications, Inc. Reprinted with permission. 27. ‘The Advancement of Learning’, Ann L. Brown Educational Researcher, 23 (1994): 4 –12. Published by SAGE Publications, Inc. Reprinted with permission. 28. ‘Paradigms of Knowledge and Instruction’, S. Farnham-Diggory Review of Educational Research, 64(3) (1994): 463–477. Published by SAGE Publications, Inc. Reprinted with permission. 29. ‘Health Promotion by Social Cognitive Means’, Albert Bandura Health Education & Behavior, 31(2) (2004): 143–164. Published by SAGE Publications, Inc. Reprinted with permission. 30. ‘Models of the Learner’, Jerome Bruner Educational Researcher, 14 (1985): 5–8 Published by SAGE Publications, Inc. Reprinted with permission.

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Appendix of Sources

31. ‘Child’s Talk: Learning to Use Language’, Jerome Bruner Child Language Teaching and Therapy, 1 (1985): 111–114. Published by SAGE Publications Ltd. Reprinted with permission. 32. ‘The Reflexivity of Cognitive Science: The Scientist as Model of Human Nature’, Jamie Cohen-Cole History of the Human Sciences, 18(4) (2005): 107–139. Published by SAGE Publications Ltd. Reprinted with permission. 33. ‘History, Culture, Learning, and Development’, Patricia M. Greenfield, Ashley E. Maynard and Carla P. Childs Cross-Cultural Research, 34(4) (2000): 351–374. Published by SAGE Publications, Inc. Reprinted with permission. 34. ‘Biology and Cognition’, Jean Piaget (Translated by Martin Faigel) Diogenes, 14 (1966): 1–22. Published by SAGE Publications Ltd. Reprinted with permission. 35. ‘Neural Bases of Intelligence and Training’, Mark R. Rosenzweig The Journal of Special Education, 15(2) (1981): 105–123. Published by SAGE Publications, Inc. Reprinted with permission. 36. ‘Human Intelligence: An Introduction to Advances in Theory and Research’, David F. Lohman Review of Educational Research, 59(4) (1989): 333–373. Published by SAGE Publications, Inc. Reprinted with permission. 37. ‘Cognitive Demands of New Technologies and the Implications for Learning Theory ’, Richard J. Torraco Human Resource Development Review, 1(4) (2002): 439–466. Published by SAGE Publications, Inc. Reprinted with permission. 38. ‘Cognitive Conceptions of Learning’, Thomas J. Shuell Review of Educational Research, 56(4) (1986): 411–436. Published by SAGE Publications, Inc. Reprinted with permission. 39. ‘Meaning in Complex Learning’, Ronald E. Johnson Review of Educational Research, 45(3) (1975): 425–459. Published by SAGE Publications, Inc. Reprinted with permission. 40. ‘Phases of Meaningful Learning’, Thomas J. Shuell Review of Educational Research, 60(4) (1990): 531–547. Published by SAGE Publications, Inc. Reprinted with permission. 41. ‘Growth, Development, Learning, and Maturation as Factors in Curriculum and Teaching’, William C. Trow Review of Educational Research, XXI(3) (1951): 186–195. Published by SAGE Publications, Inc. Reprinted with permission. 42. ‘Maslow, Monkeys and Motivation Theory ’, Dallas Cullen Organization, 4(3) (1997): 355–373. Published by SAGE Publications Ltd. Reprinted with permission.

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Appendix of Sources

xvii

43. ‘Maslow’s Theory of Motivation: A Critique’, Andrew Neher Journal of Humanistic Psychology, 31(3) (1991): 89–112. Published by SAGE Publications, Inc. Reprinted with permission. 44. ‘Caught on Fire: Motivation and Giftedness’, Ann Robinson Gifted Child Quarterly, 40(4) (1996): 177–178. Published by SAGE Publications, Inc. Reprinted with permission. 45. ‘An Empirical Test of Maslow’s Theory of Motivation’, Eugene W. Mathes and Linda L. Edwards Journal of Humanistic Psychology, 18(1) (1978): 75–77. Published by SAGE Publications, Inc. Reprinted with permission. 46. ‘Meaningfulness, Commitment, and Engagement: The Intersection of a Deeper Level of Intrinsic Motivation’, Neal Chalofsky and Vijay Krishna Advances in Developing Human Resources, 11(2) (2009): 189–203. Published by SAGE Publications, Inc. Reprinted with permission. 47. ‘Motivation and Human Growth: A Developmental Perspective’, M.S. Srinivasin Journal of Human Values, 14(1) (2008): 63–71. Published by SAGE Publications India. Reprinted with permission. 48. ‘Evolutionary Perspectives on Human Motivation’, Jutta Heckhausen American Behavioral Scientist, 43(6) (2000): 1015–1029. Published by SAGE Publications, Inc. Reprinted with permission. 49. ‘The Debate about Rewards and Intrinsic Motivation: Protests and Accusations Do Not Alter the Results’, Judy Cameron and W. David Pierce Review of Educational Research, 66(1) (1996): 39–51. Published by SAGE Publications, Inc. Reprinted with permission. 50. ‘A Comprehensive Expectancy Motivation Model: Implications for Adult Education and Training’, Kenneth W. Howard Adult Education Quarterly, 39(4) (1989): 199–210. Published by SAGE Publications, Inc. Reprinted with permission. 51. ‘The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education’, Robert J. Vallerand, Luc G. Pelletier, Marc R. Blais, Nathalie M. Brière, Caroline Senécal and Evelyne F. Vallières Educational and Psychological Measurement, 52 (1992): 1003–1017. Published by SAGE Publications, Inc. Reprinted with permission. 52. ‘Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again’, Edward L. Deci, Richard Koestner and Richard M. Ryan Review of Educational Research, 71(1) (2001): 1–27. Published by SAGE Publications, Inc. Reprinted with permission.

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xviii

Appendix of Sources

53. ‘Beyond the Rhetoric: Understanding Achievement and Motivation in Catholic School Students’, Janine Bempechat, Beth A. Boulay, Stephanie C. Piergross and Kenzie A. Wenk Education and Urban Society, 40(2) (2008): 167–178. Published by SAGE Publications, Cor. Reprinted with permission. 54. ‘Dimensions of School Motivation: A Cross-Cultural Validation Study’, Dennis M. McInerney and Kenneth E. Sinclair Journal of Cross-Cultural Psychology, 23(3) (1992): 389–406. Published by SAGE Publications, Inc. Reprinted with permission. 55. ‘Achievement Motivation in Children of Three Ethnic Groups in the United States’, Manuel Ramirez III and Douglass R. Price-Williams Journal of Cross-Cultural Psychology, 7(1) (1976): 49–60. Published by SAGE Publications, Inc. Reprinted with permission. 56. ‘Motivation and Learning Environment Differences between Resilient and Nonresilient Latino Middle School Students’, Hersholt C. Waxman, Shwu-yong L. Huang and Yolanda N. Padrón Hispanic Journal of Behavioral Sciences, 19(2) (1997): 137–155. Published by SAGE Publications, Inc. Reprinted with permission. 57. ‘Attracting and Retaining Teachers: A Question of Motivation’, Karin Müller, Roberta Alliata and Fabienne Benninghoff Educational Management Administration & Leadership, 37(5) (2009): 574–598. Published by SAGE Publications Ltd. Reprinted with permission. 58. ‘Interpersonal Relationships, Motivation, Engagement, and Achievement: Yields for Theory, Current Issues, and Educational Practice’, Andrew J. Martin and Martin Dowson Review of Educational Research, 79(1) (2009): 327–365. Published by SAGE Publications, Inc. Reprinted with permission. 59. ‘Classroom and Individual Differences in Early Adolescents’ Motivation and Self-Regulated Learning’, Paul R. Pintrich, Robert W. Roeser and Elisabeth A.M. De Groot The Journal of Early Adolescence, 14(2) (1994): 139–161. Published by SAGE Publications, Inc. Reprinted with permission. 60. ‘Atkinson’s Theory of Achievement Motivation: First Step Toward a Theory of Academic Motivation?’, Martin L. Maehr and Douglas D. Sjogren Review of Educational Research, 41(2) (1971): 143–161. Published by SAGE Publications, Inc. Reprinted with permission. 61. ‘Motivation and Engagement across the Academic Life Span: A Developmental Construct Validity Study of Elementary School, High School, and University/College Students’, Andrew J. Martin Educational and Psychological Measurement, 69(5) (2009): 794–824. Published by SAGE Publications, Inc. Reprinted with permission.

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Appendix of Sources

xix

62. ‘Motivation and Achievement: A Quantitative Synthesis’, Margaret E. Uguroglu and Herbert J. Walberg American Educational Research Journal, 16(4) (1979): 375–389. Published by SAGE Publications, Inc. Reprinted with permission. 63. ‘Academic Motivation and Achievement among Urban Adolescents’, Joyce F. Long, Shinichi Monoi, Brian Harper, Dee Knoblauch and P. Karen Murphy Urban Education, 42(3) (2007): 196–221. Published by SAGE Publications, Cor. Reprinted with permission. 64. ‘Intrinsic Motivation and School Misbehavior: Some Intervention Implications’, Howard S. Adelman and Linda Taylor Journal of Learning Disabilities, 23(9) (1990): 541–550. Published by SAGE Publications, Inc. Reprinted with permission. 65. ‘Reinforcement, Reward, and Intrinsic Motivation: A Meta-Analysis’, Judy Cameron and W. David Pierce Review of Educational Research, 64(3) (1994): 363–423. Published by SAGE Publications, Inc. Reprinted with permission. 66. ‘Motivation in Transition’, Barbara Stauber Young: Nordic Journal of Youth Research, 15(1) (2007): 31–47. Published by SAGE Publications India. Reprinted with permission. 67. ‘Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing’, Raymond Hubbard and R. Murray Lindsay Theory & Psychology, 18(1) (2008): 69–88. Published by SAGE Publications Ltd. Reprinted with permission. 68. ‘Alphabet Soup: Blurring the Distinctions between p’s and α’s in Psychological Research’, Raymond Hubbard Theory & Psychology, 14(3) (2004): 295–326. Published by SAGE Publications Ltd. Reprinted with permission. 69. ‘Research Methods: Experimental Design’, Julian C. Stanley Review of Educational Research, XXVII(5) (1957): 449–459. Published by SAGE Publications, Inc. Reprinted with permission. 70. ‘What Can We Learn from International Assessments?’, Robert J. Mislevy Educational Evaluation and Policy Analysis, 17(4) (1995): 419–437. Published by SAGE Publications, Inc. Reprinted with permission. 71. ‘Power, Control, and Validity in Research’, Randall M. Parker Journal of Learning Disabilities, 23(10) (1990): 613–620. Published by SAGE Publications, Inc. Reprinted with permission. 72. ‘Testing Reasoning and Reasoning about Testing’, Walt Haney Review of Educational Research, 54(4) (1984): 597–654. Published by SAGE Publications, Inc. Reprinted with permission.

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73. ‘Magnitudes of Experimental Effects in Social Science Research’, Lee Sechrest and William H. Yeaton Evaluation Review, 6(5) (1982): 579–600. Published by SAGE Publications, Inc. Reprinted with permission. 74. ‘Hypothesis Testing in Relation to Statistical Methodology’, Cherry Ann Clark Review of Educational Research, XXXIII(5) (1963): 455–473. Published by SAGE Publications, Inc. Reprinted with permission. 75. ‘On Examinee Choice in Educational Testing’, Howard Wainer and David Thissen Review of Educational Research, 64(1) (1994): 159–195. Published by SAGE Publications, Inc. Reprinted with permission. 76. ‘Historical Views of Invariance: Evidence from the Measurement Theories of Thorndike, Thurstone, and Rasch’, George Engelhard, Jr Educational and Psychological Measurement, 52 (1992): 275–291. Published by SAGE Publications, Inc. Reprinted with permission. 77. ‘If Statistical Significance Tests Are Broken/Misused, What Practices Should Supplement or Replace Them?’, Bruce Thompson Theory & Psychology, 9(2) (1999): 165–181. Published by SAGE Publications Ltd. Reprinted with permission. 78. ‘Musical Aptitude Testing: From James McKeen Cattell to Carl Emil Seashore’, Jere T. Humphreys Research Studies in Music Education, 10 (1998): 42–53. Published by SAGE Publications Ltd. Reprinted with permission. 79. ‘The Life and Labors of Francis Galton: A Review of Four Recent Books about the Father of Behavioral Statistics’, Brian E. Clauser Journal of Educational and Behavioral Statistics, 32(4) (2007): 440–444. Published by SAGE Publications, Inc. Reprinted with permission. 80. ‘Regression towards the Mean, Historically Considered’, Stephen M. Stigler Statistical Methods in Medical Research, 6 (1997): 103–114. Published by SAGE Publications Ltd. Reprinted with permission. 81. ‘Karl Pearson and Statistics: The Social Origins of Scientific Innovation’, Bernard J. Norton Social Studies of Science, 8 (1978): 3–34. Published by SAGE Publications Ltd. Reprinted with permission. 82. ‘A History of Effect Size Indices’, Carl J. Huberty Educational and Psychological Measurement, 62(2) (2002): 227–240. Published by SAGE Publications, Inc. Reprinted with permission. 83. ‘The Role of Assessment in a Learning Culture’, Lorrie A. Shepard Educational Researcher, 29(7) (2000): 4–14. Published by SAGE Publications, Inc. Reprinted with permission. 84. ‘The Place of Theory in Educational Research’, Patrick Suppes Educational Researcher, 3 (1974): 3–10. Published by SAGE Publications, Inc. Reprinted with permission.

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85. ‘Curriculum-based Measures: Development and Perspectives’, Stanley L. Deno Assessment for Effective Intervention, 28(3–4) (2003): 3–12. Published by SAGE Publications, Inc. Reprinted with permission. 86. ‘Tests as Research Instruments’, Robert L. Thorndike Review of Educational Research, XXI(5) (1951): 450–462. Published by SAGE Publications, Inc. Reprinted with permission. 87. ‘My Current Thoughts on Coefficient Alpha and Successor Procedures’, Lee J. Cronbach and Richard J. Shavelson Educational and Psychological Measurement, 64(3) (2004): 391–418. Published by SAGE Publications, Inc. Reprinted with permission. 88. ‘Handbook of Evaluation Research’, Lee Ross and Lee J. Cronbach Educational Researcher, 5 (1976): 9–19. Published by SAGE Publications, Inc. Reprinted with permission. 89. ‘A Model for Studying the Validity of Multiple-Choice Items’, Lee J. Cronbach and Jack C. Merwin Educational and Psychological Measurement, 15 (1955): 337–352. Published by SAGE Publications, Inc. Reprinted with permission. 90. ‘Assisted Assessment: A Taxonomy of Approaches and an Outline of Strengths and Weaknesses’, Joseph C. Campione Journal of Learning Disabilities, 22(3) (1989): 151–165. Published by SAGE Publications, Inc. Reprinted with permission. 91. ‘Standardized Testing’, Roger T. Lennon NASSP Bulletin: National Association of Secondary-School Principals, 39 (1955): 34 – 40. Published by SAGE Publications, Inc. Reprinted with permission. 92. ‘The Place of Statistics in Psychology’, Jum Nunnally Educational and Psychological Measurement, XX(4) (1960): 641–650. Published by SAGE Publications, Inc. Reprinted with permission. 93. ‘Education in Statistics and Research Design in School Psychology’, Steven G. Little, Howard B. Lee and Angeleque Akin-Little School Psychology International, 24(4) (2003): 437–448. Published by SAGE Publications Ltd. Reprinted with permission. 94. ‘The Role of Measurement Error in Familiar Statistics’, Malcolm James Ree and Thomas R. Carretta Organizational Research Methods, 9(1) (2006): 99–112. Published by SAGE Publications, Inc. Reprinted with permission. 95. ‘Qualitative Methods and the Development of Clinical Assessment Tools’, Jane F. Gilgun Qualitative Health Research, 14(7) (2004): 1008–1019. Published by SAGE Publications, Inc. Reprinted with permission.

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Editor’s Introduction Neil J. Salkind

I

f you walk into almost any educational setting or institution, you will see a variety of activities taking place, ranging perhaps from an early intervention program for very young children to traditional classroom teaching, to reviewing students’ work via a distance learning activity, and more. In the most general sense, the focus of educational psychology is the scientific basis of what occurs in these, and many other, settings. Educational psychology is a broad combination of the study of many disciplines, which together has the goal of better understanding the processes through which change takes place in such settings and how scientists, teachers, researchers and practitioners (and these categories are surely mutually inclusive of one another) can help facilitate that change. As a discipline, educational psychology might have its origins in John Dewey’s presidential address to the American Psychological Association in 1899 where he expressed concern about the need for developing a science that links theory in areas such as learning, cognitive processes and human development and practical application of such work. He emphasized linking theory and practice – the essence of the educational psychologist’s universe. With that in mind, this five-volume set of Sage Directions in Educational Psychology undertakes to familiarize the reader with important references from four areas of study including • • • •

human development, curriculum, instruction and learning, motivation, and research design, measurement and statistics and evaluation.

Each of these topics contributes to a better understanding of what goes on when children and adults are participating in educational activities and in educational settings be it school, home or even work.

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Within Sage Directions in Educational Psychology, each topic will be introduced and accompanied by a set of resources that are easily accessible through online library databases. These collections of articles are an overview of the important topics within each of the four areas and serve as an introduction to the critical issues that the field is facing. These citations alone, cannot of course, cover the entire field of educational psychology but they provide an accurate overview of what the most important topics are and in many cases, who the people are who are involved in the efforts to better understand how the educational process works.

The Study of Human Development: Understanding Change Over Time A Definition of Development and Some Influential Factors An understanding of the process of human development is essential to the educational psychologist. Development can be defined as a progressive series of changes that occur in a predictable pattern and as the result of interactions between biological and environmental factors. Anyone involved in the educational process well recognizes that the two sources of these factors (biology or nature and the environment or nurture) have to be taken into account, both in theory and in practice. Not only does the individual’s innate abilities need to be considered, but of course, the environments in which these qualities, attributes and characteristics flourish (or suffer), are modified and reinvented. And while there have been endless discussions as to whether it is ‘nature’ or ‘nurture’ that controls the processes that result in developmental outcomes, it is generally accepted by most educational psychologists that it is the presence of both types of factors all the time. It is not additive such as 60 percent heredity and 40 percent environmental factors (or any combination thereof), but rather it is of an interactive nature where the results are multiplicative and both forces are always operating 100 percent of the time.

The Course of Human Development As Buckholdt (1983) points out, among other viewpoints, there are three somewhat distinct ways of viewing the process of human development. The first focuses on individual traits such as memory, aggression, social behavior, and perception. These abilities, traits or characteristics are studied over time and often age groups are compared with one another. Such studies are usually conducted using research designs which fall in the general

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categories of cross-sectional or longitudinal although there are many alternatives recently developed that may better answer the questions at hand. Another viewpoint is that offered by life-span psychologists such as K. Warner Schaie, in which the process of human development is viewed as a series of stages that have the following characteristics. 1. Stages occur in a defined and invariant order. 2. Each stage is based in the characteristics of the previous stage but is qualitatively distinct. 3. Stages cannot by skipped. 4. Stages result in structural change that are characterized by a push and pull between equilibrium and disequilibrium. This stage approach was made popular by the early and very influentialbiologist-turned-epistemologist-turned-developmentalist Jean Piaget and his thorough study of cognitive development, but was also suggested by the pediatrician and Yale Child Study Center founder, Arnold Gesell, who thought of age as a way to organize descriptions of behavior. Gesell’s work developed in parallel fashion with that of G. Stanley Hall (Meyer, 1971), a pioneer American psychologist who proposed a ‘genetic’ view of development, the word not meant as is used today to represent the study of the transmission of traits, but more as a developmental theme signifying change over time. A final viewpoint is where age is thought of as a correlate of development, rather than playing a causal role. This is where experience accounts for more of the variability than age in understanding differences in outcomes and many educational psychologists view this as an age irrelevant view of development, a phrase coined by Don Baer. The importance here for the educational psychologist is that much of any curriculum can be taught at any age, as long as the structural elements (that Gesell often mentioneds), were present. So in fact, adhering to this perspective, one would believe that higher level thinking skills can be taught to very young children if an environment is created that supports such learning.

Trends and Issues Generic to our discussion here is also a set of six common trends and seven issues (discussion points) that characterize the process of human development as applied in an educational setting. The trends, and what they encompass are as follows: • Early experiences and events matter. For example, the introduction of harmful substances during a pregnancy or the involvement in early

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•

•

•

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stimulation programs have powerful impacts on later events. Crosnoe and Elder (2004) discuss pathways of human development and the impact that early experiences can have on later outcomes. Movement from global to discrete response systems. For example, a child’s early language is characterized by generalities (‘dog’ for any four-legged animal) whereas later in development, specific words are used for highly specific experiences (‘puppies’ for small and cute dogs). Increase in complexity. For example, in the most basic form, a simple collection of cells leads to a complex set of systems or early crying representing hunger, pain or discomfort, leads to more complex emotional responses to unpleasant events (such as specific types of cries). With change, students of all ages can better consolidate and integrate experiences and knowledge. Increasing integration and differentiation. For example, cognitive schemes or blueprints of what the individual’s world (such as safety, peers, relationships) become more finely discrete and well-defined while also becoming integrated into a useful whole. Decrease in egocentrism. For example, as growth and development proceed, the individual becomes less focused on him or herself and more focused on integrating personal characteristics and experiences with the surrounding culture. Development of social autonomy. And, as the individual grows, he or she becomes more socially autonomous and better able to function as an individual in increasingly complex environments.

The issues characterizing development, and the questions they pose are as follows. Many of these are addressed in Elder and Rockwell (1978) and their discussion of a ‘life-course’ view of the developmental process mentioned earlier. • The nature of development. Is development a function of environmental or biological factors or some combination of both? • Important developmental processes. What are the relative roles of learning (behavior that changes as function of experience) versus maturation (behavior that originates in biological changes)? • What role does age play as a marker of development? Are developmental changes a function of age or simply an accompanying event or correlate? • The rate of development. What role do sensitive or critical periods, such as adolescence, play in the developmental process? • The shape of development. Is the process of development characterized by a gradual shift or by more abrupt and qualitative changes? • The origin of individual differences. How do individual differences arise under the same, and different, circumstances?

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• The study of development. What are the methods used to study development and how does the question being asked influence the method selected?

Theories of Development These trends and issues are often the source of differences between different theoretical perspectives that have developed over the past 200 years (and often have their roots in philosophical themes). There are four separate families of traditional developmental theories which can guide educational psychologists in their understanding of how learning and change takes place in educational settings. 1. Maturation and biological theories emphasize the sequences and content of development as determined by biological factors and the evolutionary history of the species. Arnold Gessell’s work and that of current-day evolutionary psychologists are characterized by this approach. In recent years, the study of motor development as the foundation for developmental psychology (Thelen, 2000) has become more popular as well. 2. Psychodynamic theories contend that individuals are conflicted beings and that differences between them are the result of how these conflicts are resolved. The work of Sigmund Freud and Erik Erikson are characterized by this approach. 3. Behavioral theories focus on development being a function of the laws of learning and how the environment can have a pronounced impact on developmental outcomes. The learning theory of B. F. Skinner and social learning theory of Albert Bandura characterize this approach. 4. Cognitive-developmental theories see development as the result of structural change based on the individual’s active participation in the developmental process. The theoretical works of Jean Piaget, Lev Vygotsky, and Jerome Bruner characterize this approach. Also, the early work on John Dewey (Trow, 1951) echoes this reliance on ‘inner motivation’. However, beyond these theories, educational psychologists and others have made significant strides in articulating specific positions regarding the developmental process. For example, Super and Harkness (1986) discuss ‘the developmental niche’ focusing on the ‘interface’ between the child and culture. They contend that the study of development and the theories that underlie the process, have focused too much on outcomes rather than the process itself and the three components of this niche; the physical and social settings in which the child lives, the customs of child-rearing that surround the child, and the psychological approach of the caretakers.

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Their approach is only one of many where different theoretical perspectives result in an understanding of the influence of the child’s surroundings on his or her development. This approach also characterizes a more applied side of educational psychology.

New Perspectives on Aging Another significant trend over the past 100 years has been an increased interest in aging and development through the later years of adulthood. What was once known as old age (post 65 years) is now considered ‘the new 40’ or as the young-old or at least towards the end of what was once termed middle age. This is in part due to extended lifespans where people are living longer, but it is also due to changing demographics where the spike in births in the years following World War II resulted in a generation commonly known as baby boomers. Regardless of one’s theoretical perspective about the importance of aging, older is a relative term. This point is well made by the humanistic psychologist Carl Rogers (1980) in ‘Getting Old – or Older and Growing’ where he emphasizes that the personal changes he is experiencing in all aspects of his life are noticeable, but not as domineering as he expected (or as the culture at large expects when one ‘ages’). As cited by Rogers, perhaps Chief Justice Oliver Wendell Holmes best captures the spirit of this, upon leaving a burlesque house at age 80; ‘Oh to be 70 again!’. Earlier, the importance of age was discussed, and how as a variable, it correlates with many measures of performance, but has relatively low explanatory power. And, as it does correlate, Charness (2008) points out how it is a moderate predictor of performance on most laboratory tasks and important life management skills such as driving, the use of computers, and success using training materials. He goes on to conclude how aging reduces adaptive capacity so that achieving a successful environment-person fit requires extra effort and attention to the design of environments by planners. Ever since the systematic study of adulthood (and aging) became popular beginning in the 1960s, several different approaches have been taken to explain the developmental process that extends beyond the traditional period of adolescence, once seen as the ‘last’ stage of development. Calvin Settlage and his colleagues (1988) take a very interesting approach in moving away from the traditional stage model and moving towards a model that emphasizes what they call developmental process. They define this as ‘the function and structure forming process that parallels and derives from developmental interaction’. That is, through the interaction of the individual with the environment, different structures serve certain functions. This view can explore such concepts as challenge, tension and conflict as ways in which these structures are formed.

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Interestingly, the last 50 years of understanding development within a broader context of educational psychology has provided the tools needed to address issues facing adults within an educational context including everything from managing professional and family life, to differences in learning strategies, to distance learning and the use of technology.

The Importance of Early Experience At least one major theme that has resulted from over 100 years of understanding the important theme of continuity and discontinuity in development and later, its application to educational endeavors, is the importance of early experience. While theorists have almost always explored the implications of this, it was not until the advent of early childhood interventions and educational programs (such as Head Start) was there a corpus of data that could be used to evaluate the effectiveness of such efforts. The NICHD Early Child Care Research Network (2002), examined the effect of early childcare on children’s functioning at 4½ years. This very ambitious longitudinal study of over 1,000 children revealed that children’s later states were predictable by their participation in early childhood activities. Specifically, such factors as high quality child care predicted better academic outcomes in important markets such as academic performance and language skills. While educators traditionally would not have the resources or the infrastructure to begin the educational process so early, changing social practices and demands require the inclusion of such populations in the overall educational goals of any community. More children than ever are enrolled in early care programs, and it is early childhood educators who often apply the lessons learned from the research of developmental psychologists. Travis Thompson (1995) in his discussion of early language experience and brain development (in his review of Hart and Risley’s book Meaningful Differences in the Everyday Experiences of Young American Children), makes the same point regarding language development, a primary interest of those who study child development and those who are interested in facilitating educational growth. Even over 100 years ago (Witmer), in 1909, reported how psychologists bemoaned the loss of early experience in the treatment of a truant child who ‘lost three years of the invaluable six or eight years of school life’. For educators, early experience and the impact the lack of, or presence of, has on the growing individual will always be paramount.

An Increased Interest in Adolescence and Young Adulthood Culture and society both play an important role in what becomes an important topic of focus for educational psychologists. Just as there has been

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increased interest in early childhood by developmental and then educational psychologists, so the same pattern has evolved for the period known as adolescence and young adulthood including the newest concept of emerging adulthood coined by Jeffrey Arnett from Clark University. The importance of understanding the developmental and educational needs of the adolescent cannot be overestimated given that they are the heir apparent to the culture in which they live as well as the costs of inaction in terms of displaced young children at a time in their lives when structure and guidance are most important. William Kurtine and his colleagues (2008) address many of these concerns. In their work on promoting positive youth development in a Miami, Florida-based project. They draw on the importance of understanding ‘developmental intervention science’ (DIS) within an outreach program for the development of strategies to promote change and healthy outcomes. The Miami Youth Development Project (YDP), started in 1988, emphasizes community-based interventions through the use of practical communitybased research. While much of their work discusses the DIS model and its application, the results are aimed at working with the children in the community to avert the negative impacts of a rapidly growing multicultural community while preserving all the benefits – both challenges to school systems suddenly facing an influx of children from different cultures. Additionally, there has been a great deal of attention paid to the role of maturational timing on psychological functioning, especially in adolescence, given the extent of biological change that takes place during this time of growth (Silbereisen, et al., 1989). Interests such as these, have in turn, led to an increased interest in neuroscience made possible through the development of sophisticated imaging and other noninvasive experimental techniques.

Self-efficacy and Academic Success In the past, educational psychologists have realized the importance of belief systems and how they can influence outcomes. In particular, there has been a sizeable number of studies and interest in how such beliefs by students, parents and teachers can affect academic performance. This is clearly demonstrated based on the results of studies by Barry Zmmerman and his colleagues (1992) where the causal role of students’ self-efficacy beliefs and academic goals were examined. They found that the quality and level of goals set by parents and the personal goals of students at the beginning of the semester predicted students’ final grades in social studies. Such research has important implications for education. It not only reflects the belief that individuals can, in part, mitigate negative circumstances by changing their belief systems (how well they can do in school, for example) but also lends credibility to social cognitive theories that

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emphasize growth and learning to be a result of internal, as well as external, factors and influences. In sum, the study of human growth and development informs educational psychologists regarding the influence of certain factors (such as maturational processes and early experiences) and how these factors and their complex interactions can facilitate an understanding of educational participation, achievement and accomplishment.

Curriculum, Instruction and Learning: Sharing Knowledge and Skills Curriculum, instruction and learning focus on the transmission of information and abilities from one part of a culture to another, be it from generation to generation, from teacher to student, or from peer to peer. This area may indeed be one of the most applied for educational psychologists in that the majority of this work focuses on the everyday concerns of how to teach better and more efficiently. As we use the terms here, curriculum is the content of a teaching episode, instruction is the activity that takes place on the part of the teacher or the transmitter of information and skills, and learning is the process through which changes in behavior occur. Note that the words ‘teacher’ and ‘learner’ are both used in the broadest of terms. Traditionally, we think of teachers as college educated and credentialed employees of an educational institution and learners, their students. Rather, the broadest of educational contexts allows us to think of a teacher as anyone who transmits or shares any aspect of a culture and a student as anyone who receive such ‘knowledge’ be it in the form of rote memory to a family tradition.

The Nature of Curriculum An important point in any discussion of curriculum is that it no longer follows past perceptions of public school experience of reading, writing and arithmetic. Rather, the study and design of curriculum as it matches the learner’s abilities and the program’s goals, has become a discipline in and of itself in its application to almost any area that needs to be taught or shared. But at its most basic (and perhaps most important) level, curriculum is about learning and how the content can be designed to facilitate such. One excellent example of how this transmission of information might take place is the study of cultural apprenticeship by Patricia Greenfield and her colleagues (2000). Over a 24-year period, they studied an ‘ecocultural’ transition from a society based on agricultural activities to one based on commerce and in doing so, looked at the impact of this change on two generations of children.

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They found that within the space of a single generation, the primary skill taught, weaving, made the transition from an interdependent to an independent style of learning and that the work itself became more abstract. The results of this study show how transitions take place and how the curriculum of learning (in the broadest sense) often reflects the cultural influences that surround the learner. They also very effectively show that as with many other traits and characteristics of humans and the groups they live in, change and adaptation are not a one-way street. Rather, curriculum design is characterized by reciprocal effect where the conditions in a society (say agricultural or commercial) exert a profound effect on what and how newer generations will learn, while in turn, later generations are learning new things and developing new skill profiles that fit their new environments. Greenfield’s conclusion that ‘processes of cultural learning and cultural transmission change as culture changes over time’ are incredibly important as literate societies face the impact of accommodating (through newly designed curriculum) new cultures due to reform in immigration and other important social and political policies.

The Nature of Instruction and Teaching Few of the topics in the study of educational psychology stand alone. For example, understanding the child or adult’s pathways of development including their history and current circumstances can certainly have an impact on our understanding of their motives to learn. This is certainly the case when it comes to teaching, be it the recurring themes in Homer’s Odyssey or the rationale behind the scientific method. It is therefore difficult (and some would say, impossible) to discuss the act of instructing or teaching without paying heed to the intricacies of the learner and what impact such instruction might have on the individual. Indeed, Shuell (1990) posited that the learner proceeds through a series of phases where the process used to learn and the variables that might affect the final outcome, vary greatly. Shuell used the word ‘phase’ rather than stage since he felt that stage was too tied to a fixed time period. And he generally identifies an initial phase, an intermediate phase and a terminal phase as the last in his model where a great deal of emphasis is placed on understanding the transitions between them. Once that understanding is achieved, the teacher’s teaching activities can focus on the various factors affecting learning at different points during the teaching process. The challenge to the teacher is two-fold. First, of course, is to identify what phase the individual learner might be in and tailor the curriculum to fit the developmental and cognitive needs of the learner. Second, is to

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address the complex and difficult topic of understanding the transitions between phases (and incidentally, the same issue that developmental psychologists have always faced in dealing with stages). Over the last 50 years, the effort has been towards development of a psychology of instruction (Glaser, 1976) – where there is an ongoing effort to link the science of learning and the educational application of that knowledge. His classic paper, ‘Components of a Psychology of Instruction: Toward a Science of Design’, is must reading for any student of educational psychology since it illuminates how basic and applied sciences can work together to produce effective teaching (and learning) outcomes. He also discusses four elements of any teaching environment that are essential to success. The first is the development of competence of the students which reflects their cognitive and intellectual skills. The second is a description of the initial state from where instruction begins. The third is the identification of the conditions that can facilitate change from a relatively naïve learner to a competent one, and the last is the assessment of the learning to evaluate whether the strategies applied have short- and long-term consequences of any substance. If indeed there is a reciprocity between the methods used to teach and the content being taught; advances in technology open up new worlds of possibilities. Distance learning, for example, could not take place without the advent and intense use of the Internet and the tools it has made available to teachers and students including everything from the most simple of web browsers to the use of social networking. Yet, in spite of the advances in technology, the inclusion of it as a part of curriculum does not necessarily ensure successful teaching or learning. Rather, some of the most basic elements seem to continue to be important, almost beyond that of the tools used to convey knowledge. For example, Upvall, Decker and Eilerson (2000) examined the quality of distance learning in a cohort of nurses using two-way instructional television. They found that the three most important categories of elements, broadly defined, which emerged from the model they used were learning (including recognition of others, increasing independence and increased creativity), teaching (including feedback and the encouragement of interactions) and support for both of these activities (including emotional and financial support and scheduling). It was revealed through both questionnaires and focus groups that the importance of these elements and their presence led to a positive experience for the students enrolled.

The Nature of Learning Learning is most basically defined as a measurable change in behavior and through the history of understanding how humans learn, there has been

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considerable emphasis on the nature of the underlying process that accounts for this change. Early theories such as those offered by Edward Thorndike focused on the creation of associations between events and how they might be strengthened over time with repetition. This idea eventually grew to represent what we know today as classical and operant conditioning, two strong foundations of the behavioral views of learning. The principles underlying these types of learning were species, age, and domain specific and independent of any context (Brown, 1994). The most important implication of this for the educational psychologist is that the human (or animal, as was often the case) is not the active member in the learning setting, but rather is acted on. The human is also not responsible for not learning – rather, it is the design of the environment that accounts for success or failure. While classical conditioning is basically characterized by uncontrollable autonomic nervous system responses (such as knee-jerk reflexes), the operant conditioning paradigm offered by B. F. Skinner and other behaviorists during the 1950s expanded the approach towards understanding the best way to learn. Operant conditioning is a very specific type of learning that posits that the likelihood of events occurring is a function of what follows them. Most simply, behavior is a function of its consequences. If the frequency of a desired behavior increases, it must be the result of what follows that behavior. For example, a simple example is if a teacher wants children to arrive at school on time, the children should be rewarded for such behavior. Likewise, if unnecessary hand raising has become a problem in a teaching setting, being selective as to who is called on and why and how often, may help to decrease the frequency of that behavior. What followed this period of engagement with such mechanistic theories as classical and operant conditioning was the introduction of cognitive components (hence the revolution that characterized social learning theory) and also the cognitive evolution in understanding learning processes. What the cognitive revolution introduced during the 1960s and 1970s was the notion of the individual as an active learner – or, as Brown wrote, an active constructor – who is simply not a receiver of information but one who constructs his or her own learning experiences to match (not just meet) the demands of the environment. No longer passive, learning was now thought of as an active endeavor and classroom practices and curriculum materials and teaching methods reflected that. Efforts at better understanding the process of learning in a general cultural context have also reflected some of the theoretical positions we have talked about in other parts of this introduction. For example, Albert Bandura (2004) discusses the use of a social cognitive model to promote positive health outcomes. His particular model (in this specific application to health promotion)

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focuses on the individual’s self-efficacy to control ‘how psychosocial influences affect health functioning’. Holt writes, in his 1964 review (a date right in the middle of this revolution) of several books that have strong cognitive themes, how the cognitive approach emphasizes concepts that one may ordinarily think are outside those of ‘simple’ school learning such as perceiving, judging, forming concepts, imagining, and solving problems. As is clear, the path from simple associations (characterizing a behavioral view) to creative behavior (characterizing a cognitive view) can be seen as a long and indirect, but very rich, one. And while the process of learning certainly deserves adequate coverage, a less well defined, but as important, piece in the puzzle of what makes learning work, is what a learner should be and how a culture of learning can be cultivated (Bruner, 1985). But, as with most disciplines involving the social and behavioral sciences, notions of how to teach which curriculum run in cycles with trends coming and going as the Zeitgeist changes. One perspective that has not changed over time and has been resilient to calls for the newest fad is that of the knowledge and skills a teacher must have to be successful. More than 60 years ago, William Trow (1951) echoed John Dewey’s beliefs that teachers cannot know ‘what to teach, how to teach or when to teach’ unless they know who they are teaching, and from what homes, groups and cultures their students come from. Interestingly, if this knowledge becomes part of curriculum and development philosophies, Trow believes that learners will not be viewed as being free and capricious, but rather, a part of a ‘dynamic’ and lawful system that effectively interacts with the environment. And if this were not enough of a challenge, Dewey also felt that the teaching experience is not complete, until the teacher also can ‘identify the student’s current needs against the future needs of a dynamic society’. Quite an assignment.

Motivation: Why We Do What We Do No educational endeavor, be it teaching a child to swim or providing a foundation in molecular biology for a future physician can happen without the learner being sufficiently motivated to master the task at hand. Indeed motivation describes the process through which goal-oriented behavior becomes activated. It should come as no surprise to the student of educational psychology that this concept is critical to understanding why people do what they do and how educators can be informed by science to motivate (or ‘mover’ – Latin for move which is the root of the word ‘motivation’) them towards useful and attainable goals.

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Theories of Motivation: Driving Forces and Fulfilling Needs As with any approach to better understanding human behavior and in our case the entirety of the educational process, theories abound that drive empirical research that in turn drives educational policy. In the case of motivation this too is the case.

From Internal to External From the earliest days of understanding what motivates humans to act, there has been general agreement that motivation takes one of two forms (Srinivasin, 2008) with a simple distinction between the two. Intrinsic motivation explains behavior that is done for its own sake and not for other obvious rewards. Extrinsic motivation motivates behavior that is done for a more obvious reward, which usually has its origins external to the individual. For example, learning for its own sake would be motivated intrinsically and while there may be a reward (satisfaction and mastery), those rewards are often not at all obvious. Another more sophisticated form of intrinsic motivation is self-determination theory where it is posited that individual behavior is self-motivated and self-determined. The humanists bring to the table the notion that humans are preprogrammed with an inherent need to do work that is meaningful. On the other hand, learning a new set of skills to get an upgrade at work and an increase in salary would be motivated extrinsically. The intrinsic/extrinsic distinction is a relatively simple one that is somewhat superficial on a first reading. However, researchers have discussed other levels of intrinsic motivation such as meaningfulness (Chalofsky and Krishna, 2009). Although most of the literature they review has to do with human resources and employment, their distinction between the meaning of work and the meaning of at work is useful for those who study educational processes including achievement and especially vocational counseling (a sub-area of counseling psychology which in turn is often a sub-area of educational psychology). One works towards accomplishing a certain goal, but within this model, it is the work itself that provides the individual satisfaction and it is the process and action involved in the work that is motivating. Jean Piaget was famously quoted as saying, ‘A child’s work is play’ and so it may very well be intrinsically motivating for the student (and once again across any subject and of any age) to find that learning is a form of play as well and, in and of itself, motivating. The importance of extrinsic motivation being recognized, there is a growing body of literature and commentary that changes the idea that rewards

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(as extrinsic motivators) decrease intrinsic motivation (Cameron and Pierce, 1996). This is a huge issue for educators since much of the scientific literature has contended (and some question whether these contentions are based on fact or bias) that as motivation becomes extrinsic, the task at hand is less enjoyed. In other words, rewards and reinforcers do not work to further motivation, but instead may dampen it. Cameron and Pierce (1994) conducted a meta-analysis (where the results of many studies are combined) and concluded that in all but one of 100 empirical studies, this position is not supported and that external motivation can have a bearing on the enjoyment and pleasure associated with undertaking a particular task. In educational settings where external motivators are often are easily provided, knowing that such activity on the part of the teacher helps the student to be more highly motivated, can be a great step forward in reaching both the students’ and the teacher’s goal.

Maslow’s Hierarchy of Needs Some of the earliest thinking regarding motivation was done by Abraham Maslow in the early 1940s (Cullen, 1997) as he posited what he called a ‘needs hierarchy’ and even though discussions about this idea are well into their seventh decade, this may be the most widely recognized and most wellknown of all theories of motivation given its ubiquitous place among textbooks and as the focus of scientific articles. Perhaps most interesting about the development of Maslow’s theory, was that he was the first doctoral student of Harry Harlow, the Wisconsin-based psychologist whose early work on attachment theory tested on mother-child monkey dyads framed the continuing discussion about the role and importance of early experiences in development. The needs hierarchy posits a set of needs that all organisms have, hence the democratic nature of the theory, which also reflected Maslow’s humanistic approach toward understanding why people do what they do. Specifically, this set of needs progresses as a hierarchy with the most basic needs at the base of a triangle expanding to the most advanced at the top. These sets of needs (all of which are qualitatively distinct yet grounded in previous levels) are as follows:

More Advanced

More Basic

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Characteristics and Behaviors

Self-actualization

Individual potentialities reached and realized

Esteem

Self-esteem and respect of community and family members

Love and Belonging

Social support and family and group identity

Safety

Safety of self and family and general well being

Physiological

Provision of basic needs such as food and shelter

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All these needs are a prerequisite to the next higher level need. This means that early needs must be met before individuals can proceed through higher and more advanced needs. Quite interestingly, Maslow, for the humanist that he was, started his early career by studying animals in a quest to develop insights into ‘general humanness’. He believed that such study would provide insights into the behavior of humans’ earliest ancestors and how and what behaviors have evolved to help insure human success and survival as a species. He also believed that a better understanding of animal behavior (especially primates) provides us with an insight into behaviors that present-day humans share, independent of the culture in which they are shared – almost a quest of universals. Indeed, his hierarchy of needs shown earlier in this introduction is characterized by its universal nature in that all humans have this set of needs, mostly expressed in the same way. But there has’ as well, been a significant amount of criticism directed at Maslow’s theory of needs satisfaction or self-actualization as well. Neher (1991) writes that contrary to Maslow’s assumptions, some basic needs might be present at birth and innate in nature, but for the most part, the higher needs, such as those for self-esteem, are driven by cultural experiences. as said in another way, the influence of the environment is given more importance than Maslow’s ideas provide. And, the hierarchy where one level invariably follows another, really is not as structured as one might believe. In fact, Neher points out where certain societies are characterized by members who may go hungry and be in physical peril, but nonetheless, have strong family ties and high self-esteem. There have also been extended empirical tests of his theory, such as the one by Mathes and Edwards (1978) where they found that five levels might be too ambitious, suggesting that three would be a better number. The implications of Maslow’s theory, even given some reservations, for the educational psychologist should be clear. No matter the effectiveness of the teaching methods or the relevance of the curriculum, changes in behavior or learning are unlikely to take place unless basic needs are met. For the policy maker, children coming to school hungry or adults outside of meaningful personal relationships make unsuccessful students.

The Evolution of Motivation Over the past two decades there has been an increasing interest in the role that evolution plays in the development of different types of human behaviors. What was once the province of ethnologists and sociobiologists, is now the center of work by evolutionary psychologists. And, as it should not be to anyone’s surprise, several in this new wave of psychologists have focused on how motivation has evolved (Heckhausen, 2000).

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After all, any organism must be sufficiently motivated to satisfy essential needs and as with other critical behaviors, these strategies have changed through the process of natural selection as the human species has evolved. If we can better understand the mechanism through which motivation is actuated (when and what goals humans seek) and deactivated (when and what goals humans do not seek), a much more clear and ambitious picture of what motivates learners, for example, could result. There are three underlying (and somewhat overlapping) innovations in all of psychology, which led to this interest in an evolutionary basis. The first was that basic instincts guide behavior. When people are hungry, they seek out food. When they are aware of what needs to be done to accomplish a goal, they devise a strategy to achieve it. Second, much in the tradition of psychodynamic theory, behavior (both physical and cognitive) seeks to reduce conflict through the satisfaction of both physical and psychological instincts. Finally, and most relevant to our discussion, when environmental conditions change (as they do over time) humans can adjust instinctual behavior or, at least, how instincts are satisfied. When humans once fought for food, the orderly and now ‘civilized’ way of satisfying the instincts that accompany hunger is to go shopping at the market or at least figuratively ‘battle’ for the best job and procure the best resources. Plundering and ravaging are out; neighborhoods and play dates are in. What is so critical about understanding motivation from an evolutionary psychology theoretical perspective for the educational psychologist? Clearly evolution does not invent behaviors, but rather brings to the table those already created through the process of evolution and better allows us to better understand how the origin of behaviors has been formed. Should educational psychologists be better able to understand the origins of motivational forces, they should also be better able to harness those forces and more effectively use them to reach for more satisfying and complete educational outcomes.

Motivation moves Forward By any measure, the topic of motivation and its study have permeated far beyond the walls of the laboratory and even beyond that of the classroom’s typical charges. Today, principles of motivation developed by educational and other psychologists have found their way into the business world (first suggested by Maslow very early on) as well as into classrooms for exceptional students. For example, Ann Robinson (1996) points out how the leading scholars in the world of the gifted found ‘falling in love with an idea’ as a primary motive for achievement. Here, motivation becomes an emotional component of the individual’s behavior rather than simply a goal-driven behavior.

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Interestingly, with many other topics in the area of educational psychology having their origins in concerns about educating children, the study of motivation was never far away from both younger and older humans. With an increasing emphasis on distance learning and millions of adults continuing, or returning to school, there is even more reason to look at specific models of motivation that have implications for adults. One such model is proposed by Kenneth Howard (1989) referred to as an expectancy motivation model, which has its bases in the social learning theory that we mentioned earlier. The model describes expectancy motivation as part of a dynamic process which includes a host of related variables such as past experiences, level of motivation, effort, performance, rewards and level of need satisfaction. The expectancy theory sees humans as being purposeful and who interact in a proactive way with the environment based on their likelihood that their efforts will result in a positive outcome or an outcome that they value. If they are successful, one could say they were highly motivated. This theory has its origins in the classic theory of social psychologist Kurt Lewin and experimental psychologist Edward Tolman during the 1930s. Later work by Julian Rotter led to increased refinement of the expectancy theory and it was not until the 1990s that research turned towards understanding motivation from this perspective in terms of what is known as the valenceinstrumentality-expectancy (or VIE) theory. VIE theory makes three assumptions about behavior and notes how related these can be to adult achievement. First, anticipation of rewards increases an individual’s motivation. For example, knowing that good performance in class results in a higher grade. Second, perceived value of various outcomes provides direction for behaviors. For example, attaching values to outcomes helps the individual distinguish those potentially motivating behaviors from those that are not. And, third, connections between a behavior and the expectancy that behavior will work (or not) become stronger over time and as they are exercised. For the older individual (that is, the adult in an educational context) these three elements can effectively help predict specific outcomes but more importantly provide a framework as to how these adults might be motivated to reach their goals. One of the latest incantations of VIE is known as the expectancy motivation model. The variables that are most important and the sequence in which they occur are… Effort → Performance → Reward → Need Satisfaction It is not very difficult to speculate how such a model might work. Effort and performance are called E→P expectancy and is defined as an individual’s perception that efforts will lead to successful performance, P→R expectancy is the individual’s perception that performance will be rewarded, and

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R→N expectancy is the likelihood that the rewards will satisfy a need. And, of course, this model is cyclical in that need satisfaction leads to new, more advanced needs and additional efforts to fulfill them. Howard believes that the most important implications for adult education and training is that adults need highly structured environments and specific learning objectives allowing them to make informed choices regarding the likelihood of success. Simply put by Howard, ‘Learners that believe the learning goals are achievable and will result in personal rewards that met their individual needs will be more motivated than those who do not.’

Research Design, Measurement and Statistics, and Evaluation: Keeping Track – Tools and Methods The educational psychologist strives to capture a body of knowledge that can inform educators as how best to maximize the effectiveness of the educational experience. Given that these professionals have knowledge of human development, curriculum and its design and theories of motivation and their application, the next step is accountability. The disciplines of research design (how to ask and answer questions), measurement and statistics (how to assess outcomes and make sense of data) and evaluation (the science of determining the value or worth of outcomes) provide the tools to help understand the knowledge accrued through the study of the first three areas we introduced earlier.

Research Design: Answering Questions through Experiments and Inquiries Research design focuses on the way in which educational psychologists propose to ask and answer the questions they deem important. These designs fall into two very general categories; qualitative and quantitative methods.

Qualitative Research Designs Qualitative research designs focus on investigations that examine the how and why of different phenomena. They are distinctly different from other methods in that they often focus on individual entities, be they individual people or individual institutions. For example, one such qualitative method would be the case study, where investigators examine the characteristics of a school collecting data that are consistent with the questions that are being asked. An in-depth study of the nature and changing mission of a charter

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school (‘How did this school receive its charter and how has the school changed since?’) might be one such example. In turn, a common strategy in qualitative research is to use the answers to initial questions as the basis for later and more refined subsequent questions, until the researchers believe they have the initial inquiry answered. Sources of data for qualitative inquiries can include such diverse origins as archival records, physical artifacts, direct observation, participant observation and focus groups. The qualitative approach and methods as applied to the social sciences is relatively new, but cogent arguments have been made how it might work in tandem with more traditional quantitative methods. Gilgun (2004) effectively argues that given the pressure that practitioners face to demonstrate the effectiveness of a particular treatment and to do such in a timely manner, the wants and preferences of patients are critical to incorporate into what is important when it comes to evaluating an outcome. While traditional standardized tools are very valuable, this review shows how qualitative tools add additional dimensions (and information) such that the different methods can inform one another and that the evaluation of clinical outcomes using standardized methods are incomplete without their including a qualitative component.

Quantitative Research Methods Quantitative research designs can be broken down into three general categories; pre-experimental, quasi-experimental and experimental. Pre-experimental designs are those that do not include a control group, an element that is critical for understanding the effects of a treatment. Preexperimental designs also do not have any random assignment of treatments to groups thereby hindering the generalizability of any findings from a sample to a population (a hallmark of inferential statistics). For example, a pre-experimental design would be where an older adult was taught to improve his or her balance. The fact that there is no control group (other seniors) who do not receive the treatment and positive or negative outcomes could be due to many other factors. In addition, since these seniors were not selected at random from the larger population of seniors, there is little credibility as to how effectively the results can be generalized to the larger population of seniors. Quasi-experimental designs can involve a control group but also involve participants who are members of preassigned groups. For example, if one were to examine the effectiveness of the balance training on groups of both men and women, this would be quasi-experimental in nature because these groups come to the experiment already assigned to a group. The same would be the case if political party, age, grade, neighborhood of residence or preexisting medical conditions were factors being studied. While one may have

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confidence in differences between groups within such factors (such as gender), the level of confidence is somewhat limited because it is unclear as to what these participants bring to the experiment that occurred prior to the experiment’s beginning and what effect those prior experiences might have on ultimate outcomes. The purest form of research design is experimental where participants are randomly selected from a population, assigned to groups at random, and there is a control group present. For example, if one was to compare seniors who receive balance training with those who do not and the individuals in both groups were selected from a large population of seniors who are relatively similar to one another (in age, income, and previous experience, for example), that would be an experimental design.

Evaluating Experimental Methods The classic work of Campbell and Stanley defined two sets of criteria for the evaluation of experimental designs; internal and external validity. Internal validity is the quality of an experiment such that what was manipulated can be clearly shown to have an impact on the outcome of interest. For example, if one can clearly show that no other factors are responsible for changes in balance in a group of seniors, one could say that the experiment has internal validity. An example of a threat to internal validity could be exposure to exercises outside the experiment that would provide additional balance exercise for some participants, but not all. External validity is the quality of an experiment where the results are generalizable to other participants or settings similar to the original. For example, if one can generalize the results of the balance experiment from a sample of seniors to a larger population, one could say that the experiment had external validity. However, a threat to the external validity of this experiment would have been if the seniors selected from the experiment are, perhaps, not representative of those to whom the researchers would like to generalize. In almost all cases, threats to internal and external validity can be addressed through the use of a control group included in the original design of the experiment.

Measurement and Statistics: Beyond Numbers Types of Statistics Statistics are tools used to make sense of small and large sets of data and generally fall into two categories.

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Descriptive statistics describe outcomes such as when the average level of income is reported for families within a certain time frame or the range of scores on a history test. Examples of such measures of central tendency are the mean, median, and mode and examples of such measures of dispersion or spread are the standard deviation and the variance. Taken together, these descriptive measures of central tendency and dispersion can describe any collection or distribution of data. Inferential statistics are quite a bit different. They are the tools that educational psychologists (and other scientists) use to infer from a small sample of observations to a much larger population of observations. For example, if it is observed that an intervention program helps a small group of adolescents stop smoking, one might be interested in seeing whether this finding is generalizable to a larger population of the same type of participants. There are hundreds of inferential statistics tests with names such as t-test between independent means, chi Square and multivariate analysis of variance. What they all do is test a hypothesis and allow one to conclude whether one can accurately infer from those finding to a population in general and whether those findings are significant.

The Importance of Significance It is difficult to scan any journal in the field of educational psychology and not find a discussion about significance levels and their importance. Statistical significance is a central theme to all scientific research and that certainly is the case in educational psychology as well. There is also an important distinction between significance and meaningfulness, but that distinction will be left for a later part of this introduction. Statistical significance is the probability that is assigned to an outcome that it is a ‘true’ outcome beyond a certain level of doubt. For example, a researcher is examining the difference between two groups of adolescents in their distance learning success; one group participates in the program and the other does not. As a measure of success, the lead researcher tests all the adolescents on what they have learned. Assuming that the two groups are equal at the start of the experiment and at the end (she can hypothesize some other outcome but has to assume equality at the beginning since she is being as objective as possible), if there is a difference between the groups (given that all other relevant factors are controlled) she will attribute the difference to the presence of the learning program. All well and good, but there is always the chance that she is wrong in her conclusions. The degree of risk that she is willing to take that she is wrong (and conventionally that risk is set at .01 or 1% or .05 of 5%) is what is known as statistical significance. It technically is the likelihood of rejecting the fact that there is no difference between groups (in this case), when there actually is one.

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While ‘p’ for probability values is very useful, its objectivity is often questioned (Hubbard, 2004; Hubbard and Lindsay, 2008). With increasing frequency when examining the results of statistical analysis, these statements of probability are weighed against other evidence including subjective impressions and as you shall read next, meaningfulness.

Significance versus Meaningfulness Especially for beginning students of statistics, there is no issue that may need more attention than whether findings are meaningful in addition to being statistically significant and there are two strategies one may take in addressing this topic. The first is of a substantive nature. For example, let us say that a wide scale experiment has been conducted where it has been shown that test scores in reading can be increased 7 points over a year’s time using a new instructional program. This increase in 7 points is statistically significant at the .05 level of significance. Once again, this means that the likelihood of an error is quite low (less than 5%) that indeed there is not a difference between the scores of children who participated in the reading program and those who did not. Now, the question that the administrator has to address, is whether that 7-point difference is worth it. How much did the program cost? What does a 7-point advantage mean? Do such children (as examined in other settings where the same program was used) seem to have long lasting effects of this boost in reading score? What about unintended consequences? Do the children who participate do better in other subjects? Parents and caretakers more involved? It should be clear by now that using statistical significance, in and of itself, as a measure of success, is inadequate beyond the concerns of basic science. The implications and meaning of that significance have to be explored as well. The second strategy is by examining effect size. Effect size (or ES) is a relatively new idea formerly present by Jacob Cohen in the late 1960s (Cohen, 1969). Basically, effect size is a measure of how different two groups are from one another. It is not just a measure of differences between scores, however, but a measure of the magnitude of the difference – very handy when you have to determine if a treatment indeed had an impact, or was meaningful, beyond simple statistical significance. Effect size is easily calculated and in the case of group comparisons, compares the difference between groups as a function of the amount of variability within them. The larger the effect size, the larger the ‘true’ group difference relative to the amount of variability or the larger the ‘true’ effect of the treatment. And, the larger the effect size, the better.

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Measuring Outcomes: ‘If You Can, Measure It’ It is not exactly clear who first said, ‘If you can, measure it.’, but it may have very well been S. S. Stevens, the famous experimental psychologist who came up with a model of levels of measurement (nominal, ordinal, interval and ratio) that progress from the least precise (the nominal where things are named) to the most precise (the ratio level where there is a true zero present). Because there are increasing demands for assessing outcomes (be they job performance or college entrance exams) the world of measurement has expanded widely over the past 50 years.

The Basics There are many different topics in measurement, but the most important to know about as one reads a collection of papers such as those contained here is the distinction between reliability and validity and their application. Reliability is the quality of an assessment tool, be it a classroom test or a shipping mall questionnaire that insures the test does what it does reliably. Reliability can be assessed over time, between different forms of the same test and even estimates of the internal qualities of a test (do all the questions reliably ask about the same topic or sample the same personality disorder, for example). Each of these different types of reliability are sought out depending upon the purpose of the test. Validity is the quality of an assessment tool that indicates a test does what it is supposed to. For example, if one is designing a test of world history, then one would assume it is valid if the items on the test reflect the content covered in the general universe of what is known about world history. As reliability, there are several different types of validity ranging from face validity (where, on the surface, the test does what it should such as the world history test), to much more complex forms of validity such as construct validity, where one focuses on how well an instrument directly reflects the theory on which it is based. The relationship between reliability and validity is as follows. One can have a test that is reliable but not valid, but one cannot have a test that is valid without being reliable. Quite simply, a test must be able to do what it does repeatedly for it to be valid and a test cannot be valid unless it is reliable time after time. ‘What is the currency of great Britain?’ is certainly a reliable item (since it can be replicated time after time), but if one calls it a one-time test of basic math, it is widely off the mark (and not at all valid). Within the set of papers contained in these volumes, almost every empirical study will use some measure to assess outcomes. One should look carefully at the reliability and validity data reported in such studies for

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evidence of care and authenticity in collecting the data and the conclusions that are drawn.

Keeping Score: The New Sciences of Assessment and Evaluation Assessment and evaluation are the sciences of determining the worth of a particular outcome according to a set of criteria. For example, if one were to evaluate the efficiency of an early intervention program or a semester-at-sea educational experience, one would define the criteria beforehand (acquisition of language skills or development of international awareness) to be evaluated during and after the educational experience. In the past 50 years, assessment and evaluation have assumed a huge importance where individuals and institutions are having to be more accountable for their actions and the outcomes of their research, experiments and demonstration projects. In fact, the first substantive handbook regarding anything to do with evaluation was not published until 1975 – reflecting disciplines still in their infancy. Assessment and evaluation are also the sciences that reflect the increasing globalization of the educational community which also encouraged the development of strategies for improving approaches to difficult educational issues across countries and cultures. This is well demonstrated by Mislevy (1995) in his paper on inference in international assessment. He asks the important question regarding what can be learned from international assessments and how might that knowledge bear on what educators believe is important to learn? He concludes that spending resources on international assessments provides invaluable information from different perspectives that can form the basis for educational policy. Throughout this discussion, one should keep in mind that there are many different roles that assessment and evaluation play in an educational context, but perhaps the most important is how well these tools facilitate learning. There are many different views of how this might happen and Lorrie Shepard (2000) presents one of the most interesting. She does not contend that assessment should focus alone on the assignment of grades or even the evaluation of outcomes to satisfy external requirements or demands. Rather, she sees assessment as part of an instructional strategy that enhances learning. Where instruction and assessment were once seen as being ‘curiously separate’, she contends that the traditional separation of the two has hindered the integration of these two processes which when working together, reveals new possibilities for instructional design and evaluation. Others have integrated the various aspects of educational psychology covered in this collection of papers. For example, Stanley Deno (2003) writes about how assessment measures should be curriculum based, further integrating the

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disciplines of curriculum and assessment and evaluation. He believes that the Curriculum Based Measures (CBM) movement is an excellent tool for evaluating the effectiveness of instruction. Most interestingly, CBM was first used with students with learning disabilities and as with many ‘new’ approaches to education, the positive outcomes have been further applied to the mainstream. Earlier, we mentioned the utility of effect size in understanding the magnitude of experimental differences, which might be observed. Interestingly, in the evaluation literature there is a similar concern effectively summarized by Sechrest and Yeaton (1982). They acknowledge how effect size is very important to any evaluation effort and review the many questions that still surround the use of this tool.

Reference Cohen, J. (1969). Statistical Power Analysis for the Behavioral Sciences, 1st Edition, Lawrence Erlbaum Associates.

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Section I: Human Development

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1 Aging and Human Performance Neil Charness

H

uman factors practitioners are advised to understand the characteristics of user populations. Most populations are aging. Developed countries owe this change to increased fertility rates from 1946 to 1964 (“baby boom” cohorts), followed by rapid declines in population fertility coupled with increasing longevity (He, Sengupta, Velkoff, & DeBarros, 2005). Increased longevity is also occurring in developing countries. At the turn of the 21st century, Japan had the oldest population: a median age of 41 years compared with the world median of 26 years (World Population Ageing, 2001). Furthermore, older populations are also becoming more diverse because of increases in migration and differential birth rates within subpopulations, presenting a challenge to those pursuing inclusive design principles (Vanderheiden, 1997). Meta-analytic studies have shown that age is a moderate (r > .3; Cohen & Cohen, 1975) predictor of performance on most laboratory tasks (Verhaeghen & Salthouse, 1997). Age is an important predictor of performance with life management tasks such as driving, use of products (e.g., technology), and success with training materials. Aging reduces adaptive capacity, so achieving a successful person-environment fit (Fozard, 1981a) is more challenging, requiring older adult involvement in general product design (Nayak, 1995) because they can sometimes identify design flaws more successfully than younger adults (e.g., Stephens, Carswell, & Schumacher, 2006). Another promising approach is the application of technology to both prevent and alleviate age-related declines in abilities (gerontechnology: Bouma, Fozard,

Source: Human Factors: The Journal of the Human Factors and Ergonomics Society, 50 (2008): 548–555.

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Human Development

Bouwhuis, & Taipale, 2007). Physical ergonomics (Kelly & Kroemer, 1990) can play an important role in ensuring that today’s young adults reach old age in the best possible health. Although there is little consensus on how to define older adult, a distinction that has proven useful in the general aging literature is to consider chronological age bands such as the young-old (65–74), middle-old (75–84), and old-old (85+). These bands are often associated with normative events such as full pension access (in the United States, it used to be age 65) and tend to differentiate (e.g., from the earlier age band of 65+) and move upward depending on current longevity in the population. Older is a relative term, and some prefer to use the term aging adult. Those in the older bands often have very different needs than their younger counterparts. Improved design necessitates coupling age bands with a range of environments (e.g., housing, work environments, public places) and assessing associated physical and mental demands. Although Human Factors has not served as a primary outlet for research on aging, it has played an influential role. A citation analysis was conducted using the ISI Web of Science (http://scientific.thomson.com/products/wos/) for articles published in Human Factors using the search terms age OR aging for topic, evaluating which articles had the most impact via citations from ISI-covered sources ( journals, books). Citations are only one tool for judging impact and are probably not ideal for assessing impact on product design. As Figure 1 shows, interest in this topic was low until special issues on aging in 1981 and 1990, and particularly the 1991 issue on age and driving, from which point there has been an increasing stream of papers. As Table 1 makes clear, the articles having the most impact (as judged by citations) make up a fairly narrow set: 8 of the top 10 deal with driving, and the other 2 (by Welford, Salthouse) discuss age-related changes in perceptual and cognitive performance. Given the severe consequences of errors in driving 16 14

Number

12 10 8 6 4 2 0 1950

1960

1970

1980 Year

1990

2000

2010

Figure 1: Number of published articles in Human Factors for the topic of age or aging by year through 2007.

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Aging and Human Performance

5

Table 1: Top 10 cited papers in Human Factors dealing with the topic of age or aging Citations through 2007

Citations/Year

Ball, K., & Owsley, C. (1991). Identifying correlates of accident involvement for the older driver. Human Factors, 33, 583–595.

93

5.17

Summala, H., & Mikkola, T. (1994). Fatal accidents among car and truck drivers: Effects of fatigue, age, and alcoholconsumption. Human Factors, 36, 315–326.

70

4.67

Welford, A. T. (1981). Signal, noise, performance, and age. Human Factors, 23, 97–109.

65

2.32

Shinar, D., & Schieber, F. (1991). Visual requirements for safety and mobility of older drivers. Human Factors, 33, 507–519.

55

3.06

Parasuraman, R., & Nestor, P. G. (1991). Attention and driving skills in aging and Alzheimers-disease. Human Factors, 33, 539–557.

50

2.78

Jette, A. M., & Branch, L. G. (1992). A 10-year follow-up of driving patterns among the community-dwelling elderly. Human Factors, 34, 25–31.

44

2.59

Brouwer, W. H., Waterink, W., Vanwolffelaar, P. C., & Rothengatten, T. (1991). Divided attention in experienced young and older drivers: Lane tracking and visual analysis in a dynamic driving simulator. Human Factors, 33, 573–582.

42

2.33

Waller, P. F. (1991). The older driver. Human Factors, 33, 499–505.

41

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Stelmach, G. E., & Nahom, A. (1992). Cognitive-motor abilities of the elderly driver. Human Factors, 34, 53–65.

38

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Salthouse, T. A. (1990). Influence of experience on age-differences in cognitive-functioning. Human Factors, 32, 551–569.

38

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Publication

and the greater risk to older adults from crashes, it is not that surprising that publications on age and driving have received so much attention. Similarly, models of age-related changes in performance, the neural noise model of Welford, and the Salthouse article on the mitigating role of experience have stimulated theoretical (e.g., Mireles & Charness, 2002) and practical (e.g., Web design) advances. The themes represented by these top 10 articles are representative of the development of the field. Much of the progress and many of the contributions over the past 50 years have taken the form of increasingly precise measurements of the impact of aging on human performance (Welford, 1958; see also Charness & Bosman, 1990; Fisk & Rogers, 1997; Fozard, 1981b; Nichols, Rogers, & Fisk, 2006; Pew & Van Hemel, 2004). Welford (1958) stressed the importance of understanding aging for skilled performance from the perspective of the training and retraining needs of older adults in rapidly changing job settings in the United Kingdom (Rabbitt, 1997). Significant contributions to national productivity can be had by improving efficiency in an aging workforce, and recent research has uncovered better ways to

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measure productivity and to train older workers (Callahan, Kiker, & Cross, 2003; Charness & Czaja, 2006; Kubeck, Delp, Haslett, & McDaniel, 1996; Marbach, 1968; Schulz & Adams, 2007; Straka, 1990; Wegman & McGee, 2004). British (e.g., Belbin, 1965; Murrell, Powesland, & Forsaith, 1962) and European investigators (e.g., Marquié, Cau-Bareille, & Volkoff, 1998) have led research into age and work, probably because aging occurred earliest for European populations.

Designing for Present and Future Older Cohorts: Methodological Challenges Aging research is handicapped because chronological age cannot be manipulated, so causal reasoning about aging is difficult, necessitating chains of assumptions and sophisticated statistical modeling techniques (e.g., Hofer & Sliwinski, 2006). Most studies are cross-sectional, quasi-experimental designs that measure age differences in performance as a proxy for aging: within individual changes. Cross-sectional studies typically contrast the performance of younger adults (age ~25) with older adults (age ~65). For human factors practitioners facing a short time horizon, a cross-sectional study, particularly one that uses representative sampling from the target population, is sufficient for understanding how best to design for today’s older adults. Determining sign characteristics that enable older and younger drivers to respond to road conditions in a timely fashion is a good example (icons vs. words: Kline, Ghali, & Kline, 1990). Longitudinal and sequential designs are useful for understanding changing trends in development that may forecast the capabilities of future cohorts of older adults. A critical contribution of early research was the finding that older adults are more heterogeneous in level of functioning (interindividual variability) than younger adults. As has been shown more recently with sophisticated analyses of longitudinal data, older adults also exhibit greater intraindividual or occasion-to-occasion variability (Hultsch, MacDonald, & Dixon, 2002). So, designing for the 95th percentile within a targeted older population may make for very broad design boundary conditions. A recent accomplishment for the field has been the development of reliable, representative data on changes in cognition, perception, and psychomotor skill from cross-sectional studies (e.g., Kroemer, 2005; Steenbekkers & van Beijsterveldt, 1998). Standards bodies are capitalizing on such research to develop guidelines to support older users – for example, the International Organization for Standardization (ISO) developing a document on ergonomic needs of older persons and persons with disabilities (http://www.iso.org/iso/iso_catalogue/ catalogue_tc/catalogue_detail.htm?csnumber=40933) and a developing standard for telehealth (http://portal.etsi.org/stfs/STF_HomePages/STF299/ STF299.asp).

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Theoretical Frameworks Given the difficulty of establishing causation, much of the progress in aging research has resulted from conceptual development. The life span developmental perspective (Baltes, 1979) stresses that biology and culture jointly determine the developmental trajectory of human capabilities, with performance at any point in the life span representing a balance between losses and gains. How to mitigate the losses and promote and capitalize on the gains has been a central focus of human factors practitioners. Another helpful framework is that of differential trajectories for fluid and crystallized abilities. Abilities that are culture laden, known as “crystallized abilities” (Horn, 1982) or “pragmatic abilities” (Baltes, 1993), often measured by tests of word knowledge or information, show modest increases with age until the 50s or 60s. The “fluid abilities” or “mechanics of intelligence,” abstract problem-solving abilities that are more biologically constrained, show consistent cross-sectional declines from the 20s, as does working memory (Dobbs & Rule, 1989). These findings about age-related changes in cognition have influenced design by providing guidelines for how to minimize age differences in performance by drawing on preserved abilities (e.g., Fisk, Rogers, Charness, Czaja, & Sharit, 2004). The general slowing with age framework (Salthouse, 1996) argues that slowing accounts for much of the age-related variance in performance on complex cognitive tasks. Typically, older adults are slower than young ones by between 50% and 100%. Using meta-analytic techniques to identify informationprocessing parameters, Jastrzembski and Charness (2007) found different slowing factors for cognitive (1.7), perceptual (1.8), and motor (2.1) processing. Welford (1981) postulated that aging resulted in increased neural noise with concomitant diminished signal-to-noise ratios for environmental inputs. This neural noise framework leads to straightforward design recommendations to increase signal strength by increasing the size and contrast of text and other perceptual information, as well as by decreasing noise in the display (irrelevant flanking information for vision and external noise for auditory signals). Such theoretical frameworks, coupled with basic research on perception and cognition, have led to checklists for Web design for older adults (www. nlm.nih.gov/pubs/checklist.pdf ) and model sites for health information (http://nihseniorhealth.gov/). See Morrell (2002). Recent advances in neuroscience have identified important brain changes and are suggestive of ways to intervene to change the course of aging. ReuterLorenz, Stanczak, and Miller (1999) and Cabeza (2002) have shown that for cognitive tasks that activate one lateralized brain region in younger adults, older adults also recruit the homologous region in the other hemisphere. This finding suggests that “brain workload” (metabolic demand) may be greater for older adults, even when they are performing at levels similar to the performance of younger ones, and implies that they are at greater risk of

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excess workload for complex procedures. Hence, minimizing complexity, as in the number of steps in a procedure, may be more important for older than younger adults (Fisk et al., 2004). A related framework is cognitive reserve (e.g., Stern et al., 1995). Those engaging in cognitively complex work and leisure activities may build up brain reserves that enable them to weather both normal aging and dementia much longer, although they may show faster decline when the reserve is exhausted. An exciting discovery is that training interventions can modestly remediate normative age-related cognitive decline in some abilities (e.g., Ball et al., 2002; speed training: Ball, Edwards, & Ross, 2007; and exercise: Colcombe et al., 2003). Craik (1986) noted that older adult memory performance depended on the extent to which there was “environmental support” for retrieval operations, with recall generating worse performance than did cued recall and recognition. The principles of providing environmental support for users (environmental cuing) and relying on old habits that demand few processing resources (Craik & Anderson, 1998) are useful guides for designing tools and environments for older users. Such principles are readily seen in the human-computer interaction field (e.g., Sharit, Czaja, Nair, & Lee, 2003) through provision of online help systems and the shift from command line to graphic user interfaces wherein menu items and icons may cue the user to specific functions that are available. However, such shifts do not always provide differential benefit to older adults (Charness, Kelley, Bosman, & Mottram, 2001). Differential benefit has been seen in other domains, such as air traffic control read back (Morrow et al., 2003). Other techniques for minimizing age effects have relied on identifying processes that are least impaired by aging (direct vs. indirect pointing devices for computer systems: Charness, Holley, Feddon, & Jastrzembski, 2004). Simulation and modeling can sometimes substitute for usability testing to allow designers to make choices about different designs. As reliable estimates for basic information-processing parameters become available for older adult populations, cognitive architectures can be modified to predict older adult performance. Influential models such as ACT (Anderson, 1996), EPIC (Meyer & Kieras, 1997), and CHREST (Smith, Gobet, & Lane, 2007) have been modified to predict older adult performance by changing base parameters to older adult variants (Byrne, 1998; Salvucci, Chavez, & Lee, 2004). GOMS modeling (Card, Moran, & Newell, 1983) using older adult parameters has been successful at predicting multiple tasks for different models of mobile phones (Jastrzembski & Charness, 2007).

Application of Human Factors to Aging: Driving Ensuring safe driving for older adults is an important applied issue (see Table 1), given that older occupants of crash vehicles are about three times

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more likely to die from an impact compared with younger ones (Evans, 2004). Driving safety typically follows a U-shaped function, with relatively high crash rates at young and very old ages on a crashes/km driven basis and safest driving performance in the 50s and early 60s (Evans, 2004). Classic human factors approaches – redesigning the tool and environment, as well as training the user – have been adopted in driving research. Ball and colleagues (e.g., Owsley, Ball, Sloane, Roenker, & Bruni, 1991) have developed a product, the useful field-of-view (UFOV) test, assessing the ability to divide attention between central and peripheral target detection. UFOV predicts automobile crashes (retrospectively and prospectively) and could become a screening device for determining fitness to drive. More important, training on speeded perception is possible and durable, and it improves driving simulator performance (Ball et al., 2007). Another approach to improving safety in older drivers is to substitute automated procedures for age-degraded monitoring functions. Despite added workload, a GPS was superior to physical maps for older drivers (Dingus et al., 1997). Kramer, Cassavaugh, Horrey, Becic, and Mayhugh (2007) showed that older drivers benefited to the same or sometimes to a greater extent from a combined auditory and visual collision avoidance warning system in driving simulator tasks. Aging is a relatively young field, and given the roughly 20- to 30-year lag from research to application (Adams, 1972), we would not expect to see many products in the marketplace that take advantage of human factors efforts in aging. However, human factors researchers have contributed valuable data and guidelines to many domains: aircraft piloting (Taylor, O’Hara, Mumenthaler, Rosen, & Yesavage, 2005), workplace performance (Sharit et al., 2004), training principles (Jamieson & Rogers, 2000), medication adherence (Morrell, Park, & Poon, 1990; Morrow, Leirer, Carver, & Tanke, 1998), and technology interaction (Czaja et al., 2006). Another example of commercial product development is Intelehealth’s adoption of a rotary knob as an input device for a product designed to enable informal family caregivers to interact with and support older adults living alone, based on research by Rogers, Fisk, McLaughlin, and Pak (2005; Rogers, personal communication, April 16, 2008).

Needs Going Forward Future generations of older adults are likely to be advantaged compared with current cohorts. Disability has been decreasing (Manton, Stallard, & Corder, 1995); general cognitive capabilities have increased by a standard deviation in a generation (Flynn, 1987); education levels have been increasing, as has societal and personal wealth (Charness, in press, 2008), which permits greater access to assistive technologies. Nonetheless, demands made by complex products, particularly by miniaturized technological artifacts, threaten

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to undermine advances in older adult well-being. Furthermore, rehabilitation and assistive technology research (e.g., Mihailidis & Fernie, 2002) will probably be in increasing demand given lengthened lives and work lives. Sometimes being able to predict speed of performance is adequate to predict errors, as in the case of a crash in a simulator. However, for those older adults no longer working or driving, performing accurately may be more important than performing quickly, for instance, when taking medications or using health care devices. Although a number of frameworks have been constructed for understanding error (Norman, 1981; Reason, 1991; Sharit, 2006), additional development is needed for error prediction in older adults. Design for error-free performance can be an exceptionally valuable goal. The design community tries to optimize productivity, safety, and comfort/ satisfaction. As the citation analysis suggests, there is limited attention to determining how to make products, such as assistive technology, safe and attractive to older users. With the advent of artificially intelligent robotic assistants (nursebots: Matthews, 2002) and pets for older adults (robotic seal: Wada & Shibata, 2007), design for comfort (that includes privacy concerns) and safety will be increasingly important. Much more can be done to improve quality of life for older adults, particularly in the exciting area of interventions to mitigate normative negative age changes, if the design and research community more frequently enlists human factors specialists who have practical implementation experience.

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Morrow, D., Leirer, V., Carver, L. M., & Tanke, E. D. (1998). Older and younger adult memory for health appointment information: Implications for automated telephone messaging design. Journal of Experimental Psychology: Applied, 4, 352–374. Morrow, D. G., Ridolfo, H. E., Menard, W. E., Sanborn, A., Stine-Morrow, E. A. L., Magnor, C., et al. (2003). Environmental support promotes expertise-based mitigation of age differences on pilot communication tasks. Psychology and Aging , 18, 268–284. Murrell, K. F. H., Powesland, P. F., & Forsaith, B. (1962). A study of pillar-drilling in relation to age. Occupational Psychology, 36, 45–52. Nayak, U. S. L. (1995). Elders-led design. Ergonomics in Design, 3, 8–13. Nichols, T. A., Rogers, W. A., & Fisk, A. D. (2006). Design for aging. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (3rd ed., pp. 1418–1445). Hoboken, NJ: Wiley. Norman, D. A. (1981). Categorization of action slips. Psychological Review, 88, 1–15. Owsley, C., Ball, K., Sloane, M. E., Roenker, D. L., & Bruni, J. R. (1991). Visual/cognitive correlates of vehicle accidents in older drivers. Psychology and Aging, 6, 403–415. Pew, R. W., & Van Hemel, S. B. (Eds.). (2004). Technology for adaptive aging. Washington, DC: National Academies Press. Rabbitt, P. (1997). The Alan Welford memorial lecture. Ageing and human skill: A 40th anniversary. Ergonomics, 40, 962–981. Reason, J. T. (1991). Human error. Cambridge, UK: Cambridge University Press. Reuter-Lorenz, P. A., Stanczak, L., & Miller, A. C. (1999). Neural recruitment and cognitive aging: Two hemispheres are better than one, especially as you age. Psychological Science, 10, 494–500. Rogers, W. A., Fisk, A. D., McLaughlin, A. C., & Pak, R. (2005). Touch a screen or turn a knob: Choosing the best device for the job. Human Factors, 47, 271–288. Salthouse, T. A. (1996). The processing-speed theory of adult age differences in cognition. Psychological Review, 103, 403–428. Salvucci, D. D., Chavez, A. K., & Lee, F. J. (2004). Modeling effects of age in complex tasks: A case study in driving. In Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 1197–1202). Mahwah, NJ: Erlbaum. Schulz, K. S., & Adams, G. A. (Eds.). (2007). Aging and work in the 21st century. Mahwah, NJ: Erlbaum. Sharit, J. (2006). Human error. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (3rd ed., pp. 708–760). Hoboken, NJ: Wiley. Sharit, J., Czaja, S. J., Hernandez, M., Yang, Y., Perdomo, D., Lewis, J. E., et al. (2004). An evaluation of performance by older persons on a simulated telecommuting task. Journal of Gerontology: Psychological Sciences, 59B, 305–316. Sharit, J., Czaja, S. J., Nair, S., & Lee, C. C. (2003). Effects of age, speech rate, and environmental support in using telephone voice menu systems. Human Factors, 45, 234–251. Smith, L. I., Gobet, F., & Lane, P. C. R. (2007). An investigation into the effect of ageing on expert memory with CHREST. In Proceedings of the United Kingdom Workshop on Computational Intelligence – UKCI07. Available from http://hdl.handle.net/2438/1064 Steenbekkers, L. P. A., & van Beijsterveldt, C. E. M. (Eds.). (1998). Design-relevant characteristics of ageing users. Delft, the Netherlands: Delft University Press. Stephens, E. C., Carswell, C. M., & Schumacher, M. M. (2006). Evidence for an elder’s advantage in the naive product usability judgments of older and younger adults. Human Factors, 48, 422–133. Stern, Y., Alexander, G. E., Prohovnik, I., Stricks, L., Link, B., Lennon, M. C., et al. (1995). Relationship between lifetime occupation and parietal flow: Implications for a reserve against Alzheimer’s disease pathology. Neurology, 45, 55–60. Straka, G. A. (1990). Training older workers for and in the years after 2000. Journal of Educational Gerontology, 5, 68–78.

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Taylor, J. L., O’Hara, R., Mumenthaler, M. S., Rosen, A. C., & Yesavage, J. A. (2005). Cognitive ability, expertise, and age differences in following air-traffic control instructions. Psychology and Aging, 20, 117–133. Vanderheiden, G. C. (1997). Design for people with functional limitations resulting from disability, aging, or circumstance. In G. Salvendy (Ed.), Handbook of human factors and ergonomics (2nd ed., pp. 2010–2052). New York: Wiley. Verhaeghen, P., & Salthouse, T. A. (1997). Meta-analyses of age-cognition relations in adulthood: Estimates of linear and non-linear age effects and structural models. Psychological Bulletin, 122, 231–249. Wada, K., & Shibata, T. (2007). Social effects of robot therapy in a care house: Change of social network of the residents for two months. In Proceedings of IEEE International Conference on Robotics and Automation (WeD8.4, pp. 1250–1255). Piscataway, NJ: Institute of Electrical and Electronics Engineers, Inc. Wegman, D. H., & McGee, J. P. (2004). Health and safety needs of older workers/Committee on the Health and Safety Needs of Older Workers. Washington, DC: National Academies Press. Welford, A. T. (1958). Ageing and human skill. Oxford, UK: Oxford University Press. Welford, A. T. (1981). Signal, noise, performance and age. Human Factors, 23, 97–109. World population ageing: 1950–2050 (ST/ESA/SER.A/207). (2001). New York: Department of Economic and Social Affairs Population Division, United Nations. Retrieved April 28, 2008, from http://www.un.org/esa/population/publications/worldageing19502050/

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2 Violence and Human Development Elton B. McNeil

B

irth control may ultimately be the only trustworthy way to limit the amount of violence on this planet. Throughout history we have tried to reduce the human potential for violence by killing as many of our fellow men as we could, but we are falling behind in the task. Somewhere, between these extremes of was and never was, mankind still seeks a middle ground on which to stand without fear and trembling. The human condition is this: we can control violence in some of the people all of the time; we can control violence in all of the people some of the time; and we have failed throughout history to control violence in all of the people all of the time. Perhaps violence involves so much primitive joy and raw gratification that the quest for its absolute control is nothing but a fool’s errand. It may well be that only the long-term evolutionary alteration of humankind will produce a level of wisdom and restraint sufficient to banish assault as a means of communicating feelings to one’s fellow man. It is equally possible that man has strayed from basic truth, has been corrupted by civilized living, and can only rediscover peaceful coexistence by examining the ways of our more primitive brethren – the animals.

Violence – Fang and Claw Style An ancient adage states that man is distinguishable from animals primarily by his capacity for making trouble for himself. While this may be a somewhat cynical view of the condition of Homo sapiens, it remains true that in moments

Source: The ANNALS of the American Academy of Political and Social Science, 364 (1966): 149–157.

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of despair about human violence we wistfully search the animal kingdom for moral and ethical guidance. Ever since Rousseau, we have suspected that the human condition is one depraved by the baleful influence of high-rise crowding, megalopolis, breakneck speed, and the unremitting clamor of industrial society. In the earthy simplicity of species less complex than our own, we have sought a sign that there is hope for mankind. In simplicity there may indeed be truth, but it is wildly improbable that the anthill, beehive, or monkey colony has much to teach modern, interplanetary, atomic man straining to burst the bonds of time, place, and person. Yet, there are some basic observations of animal life reported by Scott1 that are worth underscoring. Scott insists, for example, that our classic stereotypes of animal behavior simply do not square with the facts. Wolves, for example, are regularly maligned; yet, the “traditional slinking, slavering, and treacherous animal of fiction corresponds only to the behavior of a wolf that has been recently trapped and is extremely frightened.” Wolf packs, in a natural setting, live peacefully and co-operatively once a means of social control of aggression is established. Among dogs and wolves, the principles of dominance and submission and of territoriality serve to limit the occasions on which violence occurs. Fighting is an instrument used to establish relative position in the social hierarchy, but it disappears once the rank-ordering is accomplished. Combat reappears, primarily, when the maturing young demand, from an aging social order, a new alignment of power and privilege. The establishment of territories for foraging or nesting is another means of preventing conflict with rival groups. At the edges of these territories there are, regularly, the “border incidents” typical of human nations. Conflict, it seems, occurs most often at the point of intersection of one group competing with another. Human bands have long sought to control and regulate violence by establishing territoriality (the nation-state) and by dominance and submission (power politics). Yet, the yield has been no more than continuous assaultive conflict and a history of civilization that is writ excessively large with dramatic accounts of senseless “heroism.” Scott suggests that, at least in puppies, methods of rearing the young can foster gentility in the mature animal. He has demonstrated the truth of this assumption by raising five hundred puppies while only once being bitten. His method was simple: the puppies were never punished; they were hand-carried from place to place from birth; and, whenever they appeared aggressive, he rendered them helpless by hoisting them off the floor with a firm grip under the belly. This method required consistent but gentle restraint of aggressiveness early in life; he reports that it worked, and he suggests that, with some modification, these methods would produce comparable gentility in human beings. While techniques of child-rearing are important contributors to the final shape of adult behavior, they cannot be considered in isolation from some

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measure of the organized-disorganized status of the society to which the individual must adjust. As Scott indicates, in an organized, highly structured, stable society the “social animal” is peaceful and cooperative; in a society that is disorganized and in transition, he is capable of the worst of destructive and violent behavior. Man’s animal nature is a feeble excuse for violence; a more reasonable explanation is that the seemingly senseless violence of humans may be one of the costs of urban living. In the neglected center of our crowded cities the young, unmarried, unemployed male product of a broken home tends to be a prime source of the purposeless assault of one human on another. Yet, when individuals are driven to seek out kindred restive souls and to construct of them groups with a common hostile purpose, we have all the necessary ingredients of violence and defiance of social control. Studies of the animal kingdom have a limited usefulness in expanding our grasp of the human condition. At best, the lives of social animals are only an approximate fit to that period in human life when the young child is without the tool of language and must rely solely on primitive methods for expressing aggression. As the child’s capacity for verbal, abstract, and symbolic responses increases, the comparison of animal and man no longer contains either truth or relevance.

The Creation of Violent Individuals The monster created by the legendary Dr. Frankenstein had to be destroyed because, having been spared the psychological trials and tribulations of childhood, it failed to learn alternative, nonviolent ways of reacting to frustration. In every known culture, history has been mute witness to the unremitting production of generation after generation of Frankensteinlike citizens. The methods vary, but the prime ingredients of this bitter stew of physical destruction of one man by another are recognizable even without a written recipe. How do parents in any culture deliberately fashion a social Frankenstein? It is not a task easily accomplished since there is resiliency to youth that resists even the most horrendous of child-rearing circumstances. Yet, the steps parents must take are fairly direct, if not simple. Step 1: Have no love for the child Love is a mercurial element that can vitiate the best of malicious intentions. Love topples what hate constructs. Love undermines rejection, softens the sting of anger, and dulls the edge of rage. Love fashions a protective cocoon that shelters the individual from the full force of the blows of fate. Love is insidious, and its workings are invisible to the eye. When love is absent, the child becomes an object like any other – an object to be used or misused as the needs of the parents dictate.

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Step 2: Shape the child’s view of the world and people Reward and punishment are the most useful tools for this purpose. The selective reward of some natural responses, coupled with the punishment of others, can underscore particular dimensions of personality at the same moment that it selects others for exclusion. If selective reward and punishment are begun early enough, continued for a sufficient length of time, and meshed subtly with an unmistakable parental example, the child will grow to maturity with a fixed and immutable perception of what constitutes truth and reality. The world view of the inexperienced and only partly comprehending child inevitably contains the image of the dominant parent whose philosophy of life and reaction to mankind get reflected in the basic fabric of the child’s developing psychological life. Parents need only act as an interpretive filter of the real world; the selective experiences to which the child is exposed and the selective interpretation of these by the parents work together to fashion a child with a unique and highly personalized view of world affairs. Step 3: Reinforce preferred behavior while rationalizing it The child must be totally convinced that his reactions to people and the fashion in which he treats them are natural, reasonable, correct, and not monstrous. Essentially, an extremist philosophy must pulse through the veins of the individual if violence is to lose its menacing aspect and become a necessary means to an absolutely essential end. The child must learn that in a jungle only savages survive. Addiction to violence on a personal, smallgroup, national, or international level must either be rewarded more often than it is punished or the punishment must seem undeserved and produce even greater dedication to a lethal life style. Violence needs an end to justify its means, and if the child can come to believe that his aggressive actions have a rational base he will, in times of high anxiety, become predictably violent as a means of solving problems. Western society has always given lip-service to the belief that the end does not justify the means. It is an interesting ethical notion, but in real life mortals rarely live by such an unworkable dictum. Perhaps the production of a Frankensteinlike monster requires simply a conscious reversal of the “means-ends” ethic such that the emerging leader does as people do rather than as people say ought to be done. As far as the power-hungry individual is concerned, any ethic is defensible if it produces success. How, then, do education and methods of child-rearing produce docile, non-aggressive, nonviolent adults? It is done, most often, by taking advantage of the dependent, helpless nature of the growing child. A set of expectations is established for the child, and a model is outlined of the kind of person he must strive to become. Then, the average child is exposed to pain, fear, deprivation, and isolation from others if he behaves in a nonacceptable manner.

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At the same time, he is praised and rewarded for approved behavior. These externally applied punishments produce, in the human animal, psychological reactions of guilt, fear, anxiety, a sense of loss and alienation from others, and feelings of rejection. It is from these internal emotional experiences that the child’s self-image and self-esteem are formed. This simplified schematic view of human development has one very serious limitation. The psychological reaction of the child to the internal and external events and pressures in his life is not always a direct or straight-line arrangement in which stimuli X predictably produces reaction Y. The psyche of the child is not a mechanical system in which a known amount of push is automatically counterbalanced by a fixed amount of pull. Human beings are capable of distorting reality into shapes and forms that have a nightmare quality about them; human beings “process” their reactions through a complex psychological apparatus that allows seemingly incompatible and opposite reactions to issue from what appear to be identical, or at least similar, life circumstances. Hollywood was so enthralled by the discovery of this psychological fact that it produced an appalling series of Grade-B movies in which the central theme was always the same – of a pair of siblings, one became a priest and the other, a gangster chieftain. The dramatic conflict between the two and the puzzling suggestion that both had issued from the same seed and the same squalid environment provided the dramatic denouement of the film. The psychological truth, of course, is that each individual is unique and that the “environment” is an inert substance until it is mixed with the volatile chemicals of a particular and peculiar psychological structure and stirred briskly by fate. Thus, the fiveand-ten-cent variety of psychological generalization about the “mentality” of world leaders is, typically, grossly and frighteningly in error. Producing two psychological peas in a pod is beyond the capability of any known science.

Growing Up Violent Violence on a planetary scale ought to be the most frightening possibility any of us could imagine; yet, our immediate anxieties are most often triggered by reports of teen-agers and young persons rioting across the face of the land. The dynamics of group violence among teen-agers can be instructive of violence in other groups, as the kind and quality of organization of basic impulses, rather than the fact of teen-agedness, is the key to group violence. According to the psychotherapist Rhoda Lorand, groups of young people riot as one means of dealing with a collection of personal and social pressures for which no other workable outlet is provided by the society – pressures such as a lack of confidence in their own masculinity, a need to discharge sexual excitement, or a deep-seated hostility toward parents and the adult model dictated by society. This analysis of the impulses expressed in “group acting-out”

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of basic problems and urges may or may not be accurate, and in no instance could we blithely assume that such an interplay of dynamic forces is typical of all members of a mob. Our concern is less with the personal dynamics of the individual members of a mob than with the chemistry of how these individual patterns of behavior get translated into violent group action. The loosely federated mass of young people at a jazz festival or resort area – each of whom is there because he anticipates that “that’s where the action is” – needs only the addition of alcohol to begin the transformation from mass to mob. It was once said that an individual’s conscience is best defined as that part of the personality soluble in alcohol. As alcohol dissolves inhibitions, those persons in the crowd with the least self-confidence, the least self-control, and the greatest need to “be someone” become visible as they impulsively act out their problems in a primitive, childish, and aggressive fashion. These first daring, violent, or defiant outbursts surge through the milling crowd and strike a responsive chord in a second wave of young persons who, stimulated by seeing in action the inclinations, urges, and impulses they themselves have barely been able to contain, soon join the melee. And they join it with a vigor that outdoes the initiators. The members of this second wave of violence are unaware that the search for an excuse (someone else started it) for the open expression of violence is what has led them to be an “innocent” bystander at the scene of the action. Statistically, these innocent bystanders are most numerous, and they form the bond between a series of isolated incidents and the final mob ugliness. Shortly, the fingers of riot reach out to the remaining onlookers – young people who now and then slip out of character and do foolish things when swept along by the tide of excitement that washes over their usually wellcontrolled impulse systems. It is at this point in time that the thinness of the veneer of civilization becomes most apparent. What we learn from history is that there never existed a time free of cruelty and violence and that any age is capable of becoming the worst that mankind has ever known, once the veneer of self-control is removed.

A Time of Juveniles Eric Hoffer observed that “history is made by men who have the restlessness, impressionability, credulity, capacity for make-believe, ruthlessness, and selfrighteousness of children.”2 He suggests that it is a reasonable assumption – given the average life expectancy of past eras – that the invention of the wheel and the calendar, the chivalry and romanticism of knights in armor, and the savagery of every recorded historical epoch may well be the work of the “juvenile mentality.” Even the ranks of elders may be populated by persons who grew older but never grew up. Perhaps whole societies can come to act and think like juveniles if they are directed by leaders who personally epitomize

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this mentality and capitalize on the promise of unfettered impulse expression for all. The drums, the bugles, the uniforms, and the posturings of humanity – the deadly serious playing-of-soldiers – appears in every age. Hoffer suggests that the juvenile turn of mind can be produced in an otherwise mature adult whenever that adult – be he immigrant, deprived citizen, civilian becoming soldier, or serf becoming free man – finds himself enmeshed in a mode of existence or state of in-betweenness of the adolescent. Perhaps the state of in-betweenness is the devil, and, perhaps, we are witnessing Hoffer’s time of juveniles reborn. A society that must call out the National Guard in order to control its youth is an unappetizing society, indeed. There is a quotation from the psychologist, Shakespeare, that contains the nub of difficulty in our attempt to erase violence. First Servant: Why then we shall have a stirring world again. This peace is nothing, but to rust iron, increase tailors, and breed ballad makers. SECOND SERVANT: Let me have war, say I; it exceeds peace as far as day does night; it’s spritely, awaking, audible, and full of vent. Peace is a very apoplexy, lethargy; mulled, death, sleepy, insensible. . . . SHAKESPEARE, Coriolanus

Managing the Medusa If violence is a function of complicated individual psychic processes, what can be done to manage the various forms in which it will rear its ugly head? It would be unrealistic to hope that child-rearing procedures will ever be systematized and regulated by a controlled educational process designed to eliminate violence in cultures the world over. Societies of every sort will continue to supply the world with potentially violent citizens. The challenge is to manage the expression of aggression in adults who can no longer be controlled by the simple devices of childhood. Violence tends to be a pastime of the young, and no society has succeeded in the search for an adequate substitute for it. William James’ suggestion of a Moral Equivalent of War exactly describes the dimensions of the problem we face: For the young, life needs to be defined in terms of the strenuous, the vivid, the intense. Life is to be conceived in such heroic terms that, in comparison with it, the heroism of war will offer no charms. It is doubtful whether a peaceful way of living will be achieved for modern man in terms of the traditional hymn writers’ conception of peace as a region of lilies in the green pastures beside a murmuring brook. The old, the sick, the tired can be charmed by such visions; the young, the tough, and the resolute cannot. They will have their danger; they will have their struggle against obstacles.3

The preferred means of managing violence – prevention – may also be only a pipe dream. Fritz Redl once said that prevention, in its simplest form,

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means do not poison the soup. Thus, the prevention of violence may require correction of the conditions that produce the frustration that finds its outlet in assault and physical injury. Prevention, at another level, means detecting those among us most subject to uncontrolled violent expression and altering their personal adjustment or life circumstances – keeping the socially sick from becoming even sicker. To date, the best we seem able to accomplish is a kind of fire-brigade psychology in which we get to the conflagration shortly after the barn has burned down. We cannot prevent what we cannot comprehend, and the older generation has yet to understand that times are different – that their musty memories of their own youth are a confused guide to the future. We have become the unwilling victims of the speed with which cultural change is taking place and we have become an Uncomfortable Generation. What has been lost to us is the comfort of slow-motion change that once gave us enough time to adjust, adapt, and come to terms. We have lost sympathy with the needs, anxieties, and frustration of the modern young and have forgotten that, throughout history, violence has been an anguished outcry of the hopeless, the frightened, and the insecure. To prevent is not the same as to stifle or ignore. We must find a means to render less alien this new generation, the placard-bearing, social-protesting, civilly disobedient segment of our social fabric.

The Excuse for Violence Violence has always cloaked itself in the garments of some means of making it legitimate. In defense of violence, man has insisted that he was provoked beyond all human endurance; he has stated that he was not responsible by reason of insanity; he has pointed out that he acted only in self-defense; he has claimed that honor and manhood required violent response; he has maintained that he never intended to produce the outcome that occurred; he has said that what he did was for the ultimate good of society; and he has felt, if not said, that his actions were inescapably necessary given the situation in which he found himself. Theoretically, these reasons are an inadequate apology for human violence; in real life, these explanations are a valuable catalogue of excuses for destruction of one’s fellow man. Every society manages to teach a certain proportion of its members that these reasons for violence are acceptable and sensible explanations for recourse to injury of one’s fellow man. If we teach some of our young that nonviolence is a luxury to be afforded only when conflict is not intense, then violence will never be dropped from the repertoire of human responses because, in certain circumstances, crime does pay, and may even be pleasurable. Physical assault too often produces exactly the outcome for which it was designed. Children bully one another and get away with it; adults threaten one another and achieve their goals; parents encourage violence in

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their children in conscious and unconscious ways; society rewards violence if it is conducted in good taste and is a means to a socially agreed-upon end; and subtle forms of social blackmail have long been an important aspect of man’s interpersonal relations both on an individual and international level.

Violence and Leadership Mob violence, while distressing, and often fatal when it reaches its frenetic, fever pitch, remains a fairly isolated and infrequent event. Our anxiety is misplaced if it dwells for long on mob destruction, because the primary issue to be resolved is that of the violent leader who stimulates to action the impulses of those who would be less violent if not provoked. If the urge to power among the mature is substituted for the beery motivation of the young, we can assemble a fatal equation. Political violence is far more dangerous than the panty-raids of the young. Our larger and more complex cultures demand cultured and sophisticated forms of violence in the service of power; the less “developed” the national unit, the more convenient and comfortable it is to wear the shroud of raw and apparent violence. It is in the setting of an emerging country that the leader makes his most visible contribution to the aggressive course of human affairs, but his influence is no less real in sophisticated cultures. The leaders of people do not issue from the common mold of men; they tend, rather, to be drawn from among those deviates from the average whose personal charisma matches closely the needs and spirit of the times. Leaders with the unique ability to draw the human race willingly down the path to its eventual destruction must – in this view of humanity – have assembled a collection of personal characteristics and ways of behaving that fit the temper of the times and match the age in which they live. The nature of their developing years is a critical factor in understanding their response to the state of the world. Despite the insistence of some theorists that the complex and highly interdependent organization of society acts to emasculate the forceful leader and render him powerless, it must be noted that even the advice and counsel of political associates must finally be shaped into a decision by one man. In truth, leaders rarely surround themselves with followers who are openly critical of their personality, life style, and decision-making techniques; leaders tend, rather, to establish a decision-making environment with a great deal of built-in consensus. The violent leader assures himself at least of sympathy and support and, often, of carte blanche for his actions. In so doing, the leader is less the victim of bad advice from others than he is the manufacturer of final consensus. Thus, group violence – at either the mob or the national level – has a series of preconditions which weld the needs of the group to the personality and psychological structure of the leader. These psychological forces become an

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inseparable part of the current political and national conditions that define the direction that events will take. The conclusion stressed here is that the comprehension of individual or group violence will continue to be a mystery if the form of development of human personality and the form to which human psychic structure can be modeled are treated as nothing more than an annoying gadfly pestering the concept of large-scale violence. The complexity of the human psyche has made it so forbidding an area of exploration that modern theorists have discounted human personality as an important influence in the affairs of mankind. It is, indeed, an alien concoction and one not easily digested by the politico-economic-sociological theorists of this generation. Yet, denying that the psychological nature of man has relevance in understanding human violence has produced only a bankrupt and barren vision of the future of humanity. Man’s psychic nature cannot remain an unknown in the equation of violence or we will find ourselves presiding over the dissolution of the human race. In the course of development of the hostile human being destined for leadership, we see an organism fashioned to perceive a world composed primarily of threatening elements – threatening to him as a person and threatening to his conception of the way things ought to be in the world. The threat so visible to such a person is reacted to rapidly, intensely, and violently. Thus, his violent response happens easily, it happens often, and it needs little provocation. Faced with threat, the aggressive leader has few alternative forms of response at his command and, being incapable of tolerating stress, he falls back rapidly on the only response that has served him faithfully in the past. Cornered, he is incapable of a rational judgment free of the urge to aggrandizement or the impulse to strike out and destroy those he perceives as plaguing him with anxiety. We are rapidly approaching that point in time when the fate of humanity will be cradled in the sweating palm of just such a person. At this fatal juncture in the history of man we may pay sorely if we fail to recognize that violence and human development are twin facets of the same basic process. The dehumanized study of violence is very much like pretending that “things” and “abstract conceptions” of political-economic-social events have an existence all their own, and should be called “living” systems. I think that the historians of 1984 will conclude that “the proper study of man is mankind.” I am convinced that an understanding of the pattern of human development is the key that will one day suggest a workable plan for controlling violence in Homo sapiens.

Notes 1. J. P. Scott, “The Anatomy of Violence,” The Nation (1965), pp. 200, 662–666. 2. Eric Hoffer, “A Time of Juveniles,” Harpers, Vol. 230 (June 1965), p. 238. 3. William James, Memories and Studies (1911).

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3 The Life-course and Human Development: An Ecological Perspective Glen H. Elder, Jr and Richard C. Rockwell

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he ecology of human development relates patterns of development to the enduring and changing environments in which people live (Bronfenbrenner 1977). This enterprise has much in common with prominent analytic concerns of the flourishing early stage of the social sciences in the United States, the 1920s and 1930s. W.I. Thomas, among others, made a compelling case for an historical and comparative study of life patterns in their sociocultural environments (Volkhart 1951). Since then a number of developments in theory and method have separated the study of lives from social context, as implied by the critical title of a recent essay, ‘Bringing society back in’ (Barton 1968). In studies uninformed by the lifecourse and its historical context, the study of development has generated knowledge bearing an uncertain relationship to the actual lives of individuals (Baltes et al. 1977). By ‘bringing context back in’, the ecology of human development has given new vitality to three analytic themes long dormant in research. First, it reasserts the significance of place by attending to the family, neighborhood, and larger community as settings of development. Second, it charts the course of families and lives by focusing attention on age differentiation in the timing and coordination of events. Third, it acknowledges the importance of historical time by a concern with events, crises, and social change. The sociological analysis of age, life-span developmental psychology, life history methodology, and social demography have converged in the past decade in a life-course perspective on human development from birth Source: International Journal of Behavioral Development, 2 (1979): 1–21.

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to old age. This perspective offers a fruitful way to address each of the above themes. We briefly outline this perspective, and provide an example of its application in a study of unwed teenage mothers, and contrast it to other perspectives on life change, careers, and social position, in which research practice falls short of potential. Throughout the essay our concern is with the fundamental role of problem formulation in research, theory development, and method.

The Life-course Perspective: Essential Elements and an Exemplar The life-course perspective locates individuals in age cohorts and thus in historical context, depicts their age-differentiated life patterns in relation to this context, and illumines the continual interplay between the social course of lives and development. The relation of age and time lies at the core of this perspective and is expressed in three temporal meanings: (1) chronological age marks developmental time as a simple index of stage in the inevitable process of growing older; (2) social age identifies age patterns in social roles and timetables; and (3) historical time enters through a concern with birth year as it relates membership in a specific cohort to the experience of history and social change. Each meaning of age informs our study of pathways through the age-differentiated life course and their developmental implications. The life-course refers to these pathways, to social patterns in the timing, duration, spacing and order of events and roles.

Social Age Differentiation in the life-course arises from social meanings of age, as well as from biological facts of birth, sexual maturity, and death. Throughout history and across cultures these social meanings have varied, as evidenced by the shifting meaning of ‘childhood’. Norms, expectations, privileges, and constraints express societal distinctions regarding age. Age strata are socially recognized divisions of the life span which constitute a basis for identity and specify appropriate behavior. In complex societies age structures and timetables are plural; the individual life-course is comprised of interlocking careers, such as those of work, marriage, and parenthood (Elder 1975). The scheduling of events and obligations thus becomes a problem of how resources and pressures are managed. The economic squeeze of early childbearing illustrates the adaptive problems that stem from asynchrony between resources and demands. This perspective assumes that the consequences of events in the life-course vary according to their context and timing. There are cultural definitions of

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appropriate times for schooling, leaving home, marriage, and childbearing. As a rule, individuals are aware of how the timing of their lives fits with cultural timetables and of the consequences associated with off-timed events (Elder 1975). Extreme departure from cultural timetables often entails decisions among undesirable options, and formal and informal sanctions. The plight of the unwed teenage mother illustrates this bind, for she has few desirable options open to her. Adult career progress is judged in terms of life phase. For example, one type of status inconsistency (occupational status well below education) is normatively inconsistent and a source of distress only in middle age, a time of peak earnings and status (House and Harkins 1975). Moreover, a complete understanding of status inconsistency during the middle years requires knowledge of the process by which it occurred. Both midlife demotion and prolonged worklife instability may produce the same inconsistent pattern, but their implications for health and well-being are bound to differ.

Historical Time Birth year locates people in history just as social age locates roles in the social structure. Individuals are exposed to a slice of historical experience in the process of moving through age-graded roles, and they share much of this life experience with other members of their cohort. Cohort membership acquires substantive meaning when we relate cohort experiences and characteristics (such as composition and size – themselves products of historical experience) to historical events and trends. Size differences between the birth cohorts of 1930–34 and 1946–50 reflect the historical experience of the Depression and postwar years. During times of rapid change successive cohorts are likely to differ in life patterns. They encounter the same historical event at different points in their life-course and thus differ in their experience of it. A recent comparative study of two cohorts of men (birthdates 1920–21 and 1928–29) found substantial cohort differences in the effect of Depression hardship on psychological development from childhood to middle age (Elder and Rockwell 1978). Deprivation (relative income loss between 1929 and 1933) imposed a greater burden on members of the younger cohort, for family hardship occurred at an earlier age and spanned a longer period of their lives. On the transition to adult status, Reuben Hill (1970: 322) observes that each cohort in periods of rapid change “encounters at marriage a unique set of historical constraints and incentives which influence the timing of its crucial life decisions, making for marked generational dissimilarities in the life cycle career patterns”. In summary, a life-course perspective directs inquiry toward understanding the process by which lives are lived. As we trace the impact of larger contexts

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and distant events to the world of the child and his family, we find that knowledge of the social course of families and individual lives is fundamental. Through an understanding of the life-course and its consequences for development, we are able to explain the process by which early life events are related to later events. Age relates history and social structure in the human biography, and it is through age differentiation that we find the implications of time and place for development. With these general points in mind, we turn to a specific example of a life course study.

Unwed Teenage Motherhood as a Moral Career Furstenberg (1976) has advanced the study of unwed teenage motherhood by applying the concept of career to a topic formerly viewed in terms of simplistic, atemporal concepts. Prior research generally identified specific kinds of people most likely to have illegitimate births; it viewed unwed motherhood as an event isolated from the life-course. In contrast, Furstenberg showed that a birth out-of-wedlock represents one point in a moral career (see also Rains 1971) and that specific sequences of events lead to an illegitimate birth. At each stage young women have an option: premarital sex or not, contraception or not, abortion or not, marriage or not. Only a few of the possible paths lead to an illegitimate birth, and Furstenberg explored why some girls followed these paths and others did not. After the birth girls encounter further decisions: abandonment of the child, putting the child up for adoption, marriage or single parenthood, more illegitimate births, educational and vocational options, entry into the welfare system or economic independence. The impact of illegitimate births depends on how the career of unwed motherhood meshes with the other careers of marriage, occupation, and education. Each point of decision lies at a different stage in a career, and each requires a different explanation. A full understanding of the sources and results of unwed teenage motherhood involves linking these separate explanations into a broader perspective. Furstenberg’s study of the association between out-of-wedlock births and economic dependency shows the extent of his departure from previous analyses. As background to his study of a longitudinal sample of mostly black adolescents living in low-income areas of Baltimore, Furstenberg reported two studies that obtained conflicting results on the economic effects of premarital births. Cutright (1973) studied women who had borne children, dividing the premaritally pregnant from others. His comparison of these groups indicated that no ill effects of premarital pregnancy occurred if the mother married. In contrast, Coombs and others (1970) observed a longterm economic disadvantage of premarital pregnancy among two samples of married women. Though differing samples restrict comparability, these contradictory findings may reflect an incomplete research question: do

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women who are economically dependent tend to have a history of premarital pregnancy? Answers to this question do not reveal why the effect is observed. Furstenberg poses a different question: “How many women with similar childbearing careers manage to remain economically independent [and] how many with entirely different histories ultimately end up on welfare?” (emphasis author’s 1976: 148). By investigating how “the process of recovery is achieved” and “the critical conditions that determine whether or not the economic consequences of premarital pregnancy will be temporary or persistent” (1976: 149), Furstenberg is able to explain the effects of unwed teenage motherhood. Recovery from economic loss turned upon marriage, education, household composition, and additional children – not upon personal values, for few young mothers desired public welfare. Marriage was a critical decision in economic recovery; women who did not marry had little chance of recovery. Though employment increased prospects for economic recovery, young mothers entered the labor market with a handicap. They were deficient in education and experience, younger than their competitors and often dropouts from school. They suffered labor market discrimination against women, and because most were black, they also faced racial discrimination. The jobs they obtained often did not even cover the expenses of child care and maintenance of a family. If other adults were present in the household, the young mother was often able to use this economic and child-care help to finish school and bring home net income. Finally, if the young mother had additional births, child-care problems often rendered it impossible for her to get a job that provided net income. Persistent economic dependency thus turned not upon the event of unwed pregnancy itself but instead upon which of several pathways through the life-course were followed by the young mother. Furstenberg’s analysis also helps us to understand the effects of illegitimate birth upon children. The young mother’s occupational and marital status after childbearing made the greatest difference in her child’s cognitive and social development. Her status at the time of the birth made relatively little difference. Indeed, children in families with a father present displayed cognitive skills almost equal to those of children not born out-of-wedlock. Thus Furstenberg’s analysis illumines the process by which some unwed teenage mothers were able to repair the damage of an illegitimate birth in their own lives and in the early lives of their children.

Practice and Potential in the Study of Lives In this section we examine selected developmental studies in which the research problem neglects temporal distinctions emphasized in the life-course perspective. Problem formulation – underlying both theory and method – fails to meet the demands of developmental research for an understanding

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of process. Research on the family commonly gives no attention to temporal variations in family life that are related to timing of events, and studies of careers fail to examine pathways that connect events widely separated in time. Research on the psychological effects of life change all too frequently ignores when the change occurs, its nature and relation to other life events. These deficiencies stem from research questions that disregard two principles of life-course analysis: first, the effects of an event depend on its timing and relation to other events; and second, the social and developmental meaning of an event is derived from its context and from life history. On matters of timing Furstenberg focused on the effects of a disturbance in the normative schedule during a woman’s adolescence. Such effects would not be seen if the illegitimate birth had occurred some ten or fifteen years later, after the completion of school and a period of work and accumulation of assets. Likewise, late marriage differs from early and on-time marriage in divergent patterns of disadvantage and advantage: late marriers often have well-established worklives and sometimes advanced education, but they also have a smaller number of potential mates (Elder and Rockwell 1976). Economic gain or loss bears different meaning when household size is expanding than when it is contracting, and when economic demands of children are high or low. The analytic significance of these temporal matters is underscored by unsatisfactory explanations when they are slighted. The second principle distinguishes between the cross-sectional and the longitudinal study of lives. Consider studies of the relation of socialization to family status. Cross-sectional analysis is not sensitive to the socioeconomic history of the family, nor can it attend to consequences of status change for childbearing. A sample of working-class families may include the downwardly mobile, the upwardly mobile from the laboring class, and the stable working class. Although each type of family is ‘working class’ in cross-section, they have substantially different aspirations and provide different resources for children (Elder and Rockwell 1978). A life-course perspective on family status moves beyond correlations or regression coefficients between statuses at different points in time to examine the process or paths that link events at different times. Thus some working-class men who advance into the middle class by mid-life do so through an orderly pattern of worklife progression, while others switch lines of work. The status change may occur early for some and late for others. One would not expect a single explanation of mobility to suffice for each of these patterns. In what follows, we explore the cost of ignoring these two principles for knowledge about lives and briefly suggest modes of inquiry that are informed by a life-course perspective. We begin with a study of status differences in psychological status and indicate the inadequacy of research that fails to view status within specific phases of the life-course. This is followed by a study of ‘careers’ that is not guided by a concept of the life-course and how it is socially patterned. Finally, we identify both of these weaknesses (treating

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status apart from time and apart from the socially patterned life course) as major flaws in analyses of children’s socioeconomic environment and of life change in relation to psychological functioning.

Status Variations in Psychological States Values, attitudes, and psychological functioning reflect the constraints and opportunities of life situations; education, occupation, and income structure this context (see Kohn 1977). But we still know very little about the mechanisms that link social position to life outcomes. Why are differences in occupation relevant for health and child care? A life-course perspective focuses inquiry on these mechanisms, and orients research to potential historical and lifetime variations in the psychological effects of status. The impact of status varies across the life span as status and status change assume different meanings within the normative context of age strata. Age-graded standards give specific meanings to status. Promotion to senior partner in a law firm has different meaning for lawyers at the midpoint and at the end of their worklives. Prospects for advancement diminish in later life. The effects of loss of status are different among older and younger workers; opportunities, obligations, resources all differ by age. Moreover, occupation is not expected to reflect education, or income to reflect occupation, in the early years of worklife. During the middle years job advancement and earnings more nearly approach their lifetime peak. These observations favor analysis which views the psychological correlates of status by life stage or phase. The appropriate analysis is one in which status patterns are linked to psychological states within age strata – such as young adult, the early and later phases of middle age, and old age. But we see no evidence of this recommended method in one of the more ambitious studies conducted on status differences as they affect people’s lives. Curtis and Jackson (1977) sampled men in six American communities (male heads of households, 21 years of age or older) for a study of the sources of educational, occupational, and income inequality, and their psychological consequences (perceptions of the class structure, conservatism, anomia, and punitiveness). We shall only deal with those portions of the study that bear upon age-related lifetime variations in psychological states. Age clearly has relevance for this problem, but the authors use age as a statistical control, not as an index of context [1]. In their regression analyses Curtis and Jackson assume that the apprentice lawyer has the same attitudes whether younger or older; that an increase in earnings of $1000 has the same impact for men starting out and concluding their careers. The simple adjustment for the ‘effects’ of age assumes additivity and linearity where theory underscores the need to examine interaction. They are thus prevented from observing that higher-status jobs reach an economic peak

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later in life than lower-status jobs; that imbalances between supply and demand contribute to the stresses of childbearing in family life; and that income acquires psychological meaning in relation to demand, which varies over the family life-course. Although they do acknowledge potential differences in the relation between status and attitudes by life stage (1977: 156 –157), this expectation is not based on an understanding of the life-course. In our judgment the study’s basic flaw stems from a research problem that is uninformed by the sociology of age and the life-course. The life course perspective calls for a study of the relation between psychological status and status within life stages. This type of study selects a sample stratified by men just entering the labor force, at the peak of their careers, and nearing retirement. Analysis could then examine the multiple sources of status differences (education, occupation, and income) in relation to their various effects within each stratum. Age patterns in norms and career progress support the expectation of systematic variation in status effects within age strata.

Social Status vs. Career The study of careers involves questions that cannot be answered by information on a person’s status at a point in time, or by the relation between statuses at different points in the life-course. Though a person’s first job may predict his last job with fair success, the association does not tell us about his occupational career between these points – stability of a line of work, status change, idleness, and shifts in employer. The concept of career refers to a sequence of activities that are functionally related across settings. In this sense a career is roughly the same as a person’s life history in work, marriage, parenthood, or consumption. Career analysis is oriented toward the process of situational change and its implications. This task is illustrated by a study which found relatively high levels of worklife achievement in men with incongruently low levels of formal education (Elder and Rockwell 1978). These men, born in Berkeley, California, just before the Great Depression, had grown up in deprived families. Whether middle or working class, they obtained substantially less education than did nondeprived men. However, at midlife, there was no difference in average occupational status between deprived and nondeprived men. Contrasts in worklives resolved this incongruity: deprived men generally began their worklives and established a stable line of work at earlier ages. This pattern of accelerated career formation countered the handicap of Depression hardship and limited education. In the Berkeley analysis the research problem stemmed from the convergence of life patterns among men who entered the labor force with significantly different historical and pre-adult experiences. Another type of career question

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starts with people in a common situation and seeks to explain why some are more successful than others. This is the question which Coleman and associates (1972) explored in a sample of white and black men born in the 1930s. Working with men in the same occupational stratum at first job, they sought the “mechanisms which lead to differential levels of success” some ten years later. Additional education emerged from regression analyses as the most significant source of success, especially among white men. Occupational events were second in importance for whites; marital and family events, for blacks. When combined, these factors accounted for a substantial portion of the variation in men’s status after ten years, but they do not explain the process by which specific worklife or family events made a difference in level of success. Moreover, the study does not place such events in the context of temporal phases of careers. For example, number of jobs and employers assume different meanings when part of orderly or disorderly careers (Wilensky 1961). Though event timing and sequencing do not enter Coleman’s analysis, a lifecourse perspective would orient the study to such concerns. When did marriage occur relative to work entry, exit from education, and military service? Timing of the first birth bears directly upon worklife pressures of family needs, but this was not part of the research. The study also does not distinguish between career costs of unemployment at the beginning and end of the ten-year period. Overall, the Coleman study exemplifies research that lacks theory on careers and the mechanisms by which men attain differential success. We find a similar deficiency in Robert Sear’s (1977) longitudinal study of sources of occupational satisfaction among men near the end of their careers (average age 61). The men were members of Terman’s original sample of gifted children in California. Unlike Coleman, Sears did not focus his study on life-course questions. His analytic task entailed prediction of satisfaction, not an exploration of life patterns which have differing implications for the later years. However, a man’s satisfaction with what he has done in his occupational life is a function of who he is, where he started from, how he arrived at his final position, and what he did along the way. It is a product of the life-course. The same degree of satisfaction may have different meaning for men who followed different paths. Sears used a step-wise regression procedure for the selection of predictive factors. Not surprisingly, prior attitudes emerged as the most substantial predictors of occupational satisfaction at age 62. Work satisfaction in 1960, “extent of having lived up to intellectual capacity”, and vitality in 1972 assumed statistical precedence over worklife and the life-course. This suggests that attitudes regarding work are more powerful determinants of work satisfaction than the career itself. Indeed, Sears concluded from his analysis that work does not matter in work satisfaction (an invalid interpretation even on statistical grounds, Duncan 1970): “it looks as if there were some continuing affective quality” rather than “the objective facts of life” that determines work satisfaction.

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It is most unlikely that analysis undertaken from a life-course perspective would support this conclusion (Elder 1974; Kohn 1976). Though Sears’ work may be a perfectly valid description of attitudinal correlates of work satisfaction in later life, it does not help us understand the psychological consequences of the various routes men followed to old age. The study fails to do justice to the diverse realities in men’s lives. A life-course study of sources of work satisfaction would trace early differences, such as class origin, aspirations, and interests, to occupational choices, education, and career formation. Orderly work-lives would be differentiated from disorderly; early career establishment, from later; and upward mobility, from downward mobility and stability. Work satisfaction would be linked to these differences. Certain career lines offer satisfaction through steady progression; others are gratifying because they represent unplanned achievement; and still others yield satisfaction for men with low aspirations. Although some of these variables enter Sears’ analysis, they are not ordered in a life-course account of men’s work satisfaction at age 62.

The Family Economy in Children’s Lives We criticized the Curtis and Jackson study for its failure to examine the psychological meaning of status within the life stages of men. The Coleman study ostensibly focused on the mechanisms of differential achievement, but it did not in fact examine this process from a career perspective. Both of these limitations also appear in traditional concepts of the socioeconomic environment of children. Child development is a temporal process, yet research in this area has generally relied upon atemporal measures of the child’s socioeconomic environment such as parental occupational status and income. In her review of the literature on social class and development, Cynthia Deutsch (1973) emphasized the diversity within general class strata, but she did not acknowledge the limitations of atemporal measures of family position for developmental research on children. The problem of diversity within classes is at least matched by that of variation in socioeconomic career of families. As a panel study documents (Lane and Morgan 1975), poverty is not a stable condition for a substantial number of lower-income American families. Over a six-year period families moved above and below the poverty line. Static measures of economic wellbeing represent a mixture of temporal patterns that obscures their social and psychological significance. Development, social reality, and the life-course perspective all make a persuasive case for temporal concepts of children’s socioeconomic environments, but this is only a first step. It is also necessary to recognize that both family income and composition change over time and that their effects on children cannot be fully understood apart from their relationship. Few studies have actually explored this relationship

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or its consequences for family interaction and child development. The familiar concept of the family life cycle captures change in family composition resulting from the addition and departure of children (Elder 1978). Change may also occur when parents die, divorce, or remarry, or relatives move in or out of the household. Studies have examined marital relations and parenting in relation to family stage, and a critical review of the literature (Clausen and Clausen 1973: 186) cautions that “the meaning and consequences of having a given number of children in the family will vary with each phase of the family cycle”. The meaning of family stage and size also varies by the timing of events. Variations of ten years or more in mother’s age at first birth produce large differences between the age and career position of parents in the childbearing stage. Even within the same occupational stratum, late marriage and childbearing offer a number of socioeconomic advantages when compared to the early timing of these events. The later the events occur, the more both husband and wife are able to accumulate material resources and augment their income. As Freedman and Coombs (1966: 648) point out, couples “who have their children very quickly after marriage find themselves under great economic stress, particularly if they married at an early age …”. These effects are not adequately specified in terms of social status or family stage alone; problems of child care, parental stress, and family management arise from the temporal relation between socioeconomic career and family composition. By focusing on this relationship we obtain a simple model of the family economy that takes into account demands and contributions of all members of the household. Economic consequences of change in household composition stem from the relation between supply and demand, from earning levels and number of earners, and from the number of young and old dependents. Change in the family economy occurs as the household head ages and changes roles; as children arrive, age, and depart; and as productive family members are lost through disability, death, divorce, and new family formation. One of the most significant results of studies based on this concept is that a family’s economic welfare has more to do with household composition than with economic loss or gain among family earners (Lane and Morgan 1975: 50). How do families adapt when resources fall below, match, or exceed demands? Gove and associates (1973) identify three responses to decline: (1) efforts to control and reduce consumption – a reduction in living standards, such as a move to lower-cost housing; (2) reallocation of time and energy resources – more labor intensive operations, employment of the wife, double shift work; and (3) attempts to balance income and outgo through credit, use of savings, and loans from kin. Young and Wilmott (1973: ch. 7) identified three adaptations to economic squeeze in large families: highly paid overtime work, shift work, and moonlighting. Reentry of mothers into the labor force after childbearing may reflect accumulated pressures of debt, aspirations, and pending educational costs. A child’s family environment

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thus changes as families adapt to new relations between household composition and resources. These changes create pressure points in the family’s experience that increase the likelihood of family problems. Sensitivity to the relation of household composition and resources leads us to study temporal variations in a family’s support network and its ties to community agencies and institutions. For many years sociologists have explored the complex meaning of a family’s residential environment in the socio-psychological effects of neighborhoods on children. Families with majority- and minority-status in a neighborhood – such as middle-class families in a working-class neighborhood (Rosenberg 1975) – have been compared on environmental influences. But appropriate attention has not been given to family attributes such as career stage and its relation to income. Middle class families in working class areas may include aspiring, younger couples with grade school children and families that have suffered financial misfortune. Such differences in family history and stage are relevant to assessments of neighborhood composition and effects. Residential choice is one adaptation to the relation between household composition and economics and acquires meaning from its context within the life course.

Life Change and Its Psychological Effects Family responses to change are among the life changes which constitute important foci in developmental research. Family routines, roles, and relationships change as mother enters the labor force, as the father takes on overtime work (and has few hours to spend with children), and as grandparents begin to care for younger children. Change in the family economy entails change in the lives of family members. These changes are structured in part by norms regarding the life course, such as appropriate times for children to leave home and for mothers to enter the labor force. But changes may also be offtimed and conflict with other events and obligations, producing disadvantage and stress. According to a life-course perspective, the stressfulness of a life change depends on three primary considerations: (1) the nature of the change (drastic in alteration of customary habits or not, loss or gain, expected or not); (2) the life history of experience, expectations, and adaptive skills that one brings to the change (Elder 1974: ch. 2); and (3) the temporal context of the change – its position within the life course and relation to other events. On all of these counts, a parent’s death, mother’s employment, and father’s loss of job have different implications for child and family at each family stage. Geographic mobility entails minimal disruption for children in grade school; far greater disruption, if they are locked into school curricula, testing schedules, and peer networks of the high school years. A shift in line of work may be a gain in the early years of the worklife and a loss in later years.

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The nature and stressful impact of a life change cannot be understood apart from knowledge of its temporal context, and the resources and beliefs people bring to it. However, this view of life change and its implications for health bears no relation to that proposed by Holmes and Rahe (1967). Their physiological view argues that life change itself entails risk of illness. This risk is not affected by the temporal context of the change and its relation to other changes, by any characteristics of the change other than a global perceived stressfulness, or by differences in the life histories of individuals and families. Using the method of magnitude estimation of psychophysics, they built a Social Readjustment Rating Scale (SRRS) for psychiatric and research use. The scale includes judgments of 43 changes that range from family relations and economics to social activities. A high level of inter-rater agreement has been achieved across samples of old and young, males and females, and different cultures (cf. Askenasy et al. 1977). Death of spouse is consistently ranked highest in magnitude of life change, followed closely by divorce and separation. At the low end of the scale raters place ‘change in eating habits’, ‘Christmas’, and ‘minor violations of the law’. However, this ranking is not independent of major social change: Janney et al. (1977) report that economic changes took precedence over other personal and family events in an earthquake-stricken Peruvian city. The SRRS has appeared in the work of medical, psychological, and sociological researchers. It (or a variant) is one of the most widely-used instruments for the study of life change in stress (Dohrenwend and Dohrenwend 1974; Gunderson and Rahe 1974; Wildman and Johnson 1977). The scale has earned a fair record in predicting health change, although mainly in questionable retrospective studies. But studies using the SRRS are necessarily divorced from theoretical and empirical knowledge about the life course. As a result we are unlikely to learn precisely what psychosocial processes link life change and health. The SRRS favors what might be termed a ‘trait approach’ to situations, rather than an approach which examines behavior as a function of transactions between person and situation (Eckehammer 1974). Each event receives a single ‘life change’ score. A change in living conditions entails the same degree of social readjustment for a single man and a father of adolescents, for young adults and the elderly, even though research and observation suggest readjustment demands greatly differ. Consider also the meaning of death of spouse, an event which typically occurs late in life according to demographic timetables. Death of a young husband leaves a family with few resources and heavy obligations, and can markedly alter the development experience of children. Although a spouse’s death may qualify as a life change of maximum proportions in all life stages, its effects depend on timing within the life-course. The SRRS makes little effort to specify the direction of life change, though others have assessed the different adaptive outcomes of loss (widowhood, empty nest) and gain (marriage, parenthood) (Lowenthal and Chiriboga

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1973). But the implications of direction depend on timing, and Barbara Dohrenwend’s (1973) failure to consider timing might well account for her conclusion that gains and losses differ little in their effects on anxiety. The SRRS obscures the causal structure of life changes. The same global score indexes any number of causal sequences, each with differing health implications. Some changes, such as taking a mortgage or loan over $10,000, may be adaptations to pressure that, in fact, alleviate stress. Other events are clearly evidence of health change, such as change in sleeping habits. Hetherington and associates (1976) observed effects of a single life change, divorce, that range from changes in psychological functioning and self-perception to economic stress, effects which varied by characteristics of the marriage before divorce and by sex of the child. Family stability may be enhanced by ‘change in work hours or conditions’, ‘change in social activities’, and ‘marital reconciliation’ – all of which are presently scored as stressful (and perhaps destabilizing) events. When all such changes are lumped together into a single score, the researcher cannot specify the precise social and behavioral meaning of the score. The SRRS may predict stress and health decline, but we do not know precisely what it means or what process links life change to stress. Sociologists have taken preliminary steps toward introducing theory into a research form of the SRRS (Mechanic 1975). Hough and associates (1976) developed a revised version which incorporates some distinctions of direction and timing. This form distinguishes certain gains from losses: ‘health of family member become better’ and ‘health … becomes worse’ received ratings some 42 ranks apart. They also introduced limited ordering distinctions: birth of first child ranked 18; birth of second or later child, 34; and ‘gain of new family member other than child’, 22. Wife’s entry into and departure from the labor force produced no reliable difference, but additional precision would be needed to bring out the meaning of these events. The wife’s entry into the labor force after a long period of homemaking is probably more stressful than reentry after the early phase of child rearing. These clarifying steps are essential in making the scale more interpretable, but such steps ultimately document the theoretical limitations of a global measure of life change. Despite glaring deficiencies as a representation of life change, the SRRS’ utility as a predictive device generally affirms that life changes do follow a predictable order. Consensus in judging the magnitude of life change undoubtedly reflects this social order. The evidence of the SRRS suggests that people do have their own catalog of life events which are ranked by required readjustments and that this ranking is based on both normative and factual (biological, demographic) criteria. However, the SRRS’ research use has reversed steps in the process of inquiry: it has placed technique before problem formulation, explanatory theory, and an understanding of the lifecourse. Informed questions on life change and health do not lead in the direction of a global life change measure which ignores the timing and context of events.

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Overview: A Matter of Question and Perspective We have sought to illumine some differences in problem formulation and perspective by application of a life-course perspective to selected problem areas. Our criticism has focused on the kinds of questions posed and on the method which was brought to bear. An ecologist of human development might have addressed any of these questions, and we suggest that a lifecourse perspective would be fruitful. Questions on context and process come readily from this perspective, a point well illustrated by Furstenberg’s research objective: to elucidate the effects of precipitate parenthood in adolescence by “exploring when, how, and why childbearing before the age of 18 jeopardizes the life prospects of the young mother and her child …” (1976: 1). Thomas (Volkhart 1951: 114) once called question formulation the “hunting activity” of the creative social scientist. It is a core task of inquiry, and one where developmental research often falters. The goal is to trace out the linkages which explain processes, not simply to assess the validity of a hypothesis of theory. Depending on the problem, research may utilize case histories, clinical judgments, surveys, field experiments, and varied statistical techniques. Explanatory linkages, once teased out, must be tested for consistency, unambiguity, and invariance, using the full spectrum of statistical tools (see McCall 1977: 336). Theory and method are interwoven with substance and the flow of ideas through the course of explanatory research. In all of this, there is above all “the use of some imagination or mind from point to point”, as Thomas once put it (Volkhart 1951: 84); the analyst “raises the question, at appropriate points, ‘what if’, and prepares a setup to test this query”. These succinct observations define an appropriate strategy for life-course analysis. The life course perspective offers a conceptual means of introducing temporal considerations and explanatory analysis to the study of lives and human development. Through its articulation of age and time, this perspective views persons in age-differentiated careers and phases over the life span. Career stages and their relation specify the meaning and consequences of life events. By locating people in historical context and in the social order, the sociology of age orients research to the process by which historical change is expressed in life experience. In this essay, we have explored some implications of the neglect of such temporal distinctions in a review of research on the psychic effect of status variation, on careers and occupational satisfaction, on children’s socioeconomic environment and the health impact of life change. Though each example addresses topics that are relevant to developmental study, their problem statements do not incorporate temporal principles on life patterns. In each case a lifecourse perspective suggests alternative modes of research of the process of human development.

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Note 1. Curtis and Jackson occasionally introduce the ‘family life cycle’ as a dummy variable in their regression analyses. However, each stage is defined by role change and configurations, not by roles in relation to age patterns. Thus families in a particular stage, such as childbearing, will vary widely in age range and status. Moreover the approach does not specify processes by which families move from point to point in the life-course. Family cycle analysis conveys the erroneous impression that all families move through the stages and all at the same rate (see Elder 1977).

References Askenasy, Alexander R., Bruce P. Dohrenwend and Barbara Snell Dohrenwend, 1977. Some effects of social class and ethnic group membership on judgments of the magnitude of stressful life events: a research note. Journal of Health and Social Behavior 18, 432– 439. Baltes, Paul B., Steven W. Cornelius and John R. Nesselroade, 1977. ‘Cohort effects in developmental psychology: theoretical and methodological perspectives’. In: W.A. Collins (ed.), Minnesota symposium on child psychology, Vol. 11. Minneapolis: University of Minnesota Press. Barton, Allen, 1968. Bringing society back in. American Behavioral Scientist 12, 1–9. Bronfenbrenner, Urie, 1977. Toward an experimental ecology of human development. American Psychologist 32, 513–531. Clausen, John A. and Suzanne R. Clausen, 1973. ‘The effects of family size on parents and children’. In: James T. Fawcett (ed.), Psychological perspectives on population. New York: Basic Books, pp. 185–208. Coleman, James S., Zahava D. Blum, Aage B. Sorensen and Peter H. Rossi, 1972. White and black careers during the first decade of labor force experience. Part I: occupational status. Social Science Research 1, 243–270. Coombs, Lolagene C., R. Freedman, J. Friedman and W. Pratt, 1970. Premarital pregnancy and status before and after marriage. American Journal of Sociology 75, 800–820. Curtis, Richard F. and Elton F. Jackson, 1977. Inequality in American communities. New York: Academic Press. Cutright, Phillips, 1973. Timing and first birth: does it matter? Journal of Marriage and the Family 35, 585–596. Deutsch, Cynthia, 1973. ‘Social class and child development’. In: Bettye M. Caldwell and Henry Ricciuti (eds.), Review of child development research, Volume 3. Chicago: University of Chicago Press, ch. 4. Dohrenwend, Barbara Snell, 1973. Life events as stressors: a methodological inquiry. Journal of Health and Social Behavior 14, 167–175. Dohrenwend, Barbara Snell and Bruce P. Dohrenwend, 1974. Stressful life events: their nature and effects. New York: John Wiley and Sons. Duncan, Otis Dudley, 1970. ‘Partials, partitions, and paths’. In: Edgar F. Borgatta and George W. Bohrnstedt (eds.), Sociological methodology 1970. San Fransisco: Jossey-Bass, pp. 38 – 47. Eckehammer, B., 1974. Interactionism in personality from a historical perspective. Psychological Bulletin 81, 1026–1048. Elder, Glen H. Jr., 1974. Children of the Great Depression: social change in life experience. Chicago: University of Chicago Press. Elder, Glen H. Jr., 1975. ‘Age differentiation and the life course’. In: Alex Inkeles (ed.), Annual review of sociology, Volume 1. Palo Alto: Annual Reviews Inc.

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Elder, Glen H. Jr., 1977. Family history and the life course. Journal of Family History 2 (Winter), 279–304. Elder, Glen H. Jr., 1978. Approaches to social change and the family. Special issue, Sarane Boocock and John Demos (eds.), American Journal of Sociology. Elder, Glen H. Jr., and Richard C. Rockwell, 1976. Marital timing in women’s life patterns. Journal of Family History 1, 34 –53. Elder, Glen H. Jr., and Richard C. Rockwell, 1978. ‘Economic depression and postwar opportunity in men’s lives: a study of life patterns and health’. Forthcoming in: Roberta G. Simmons (ed.), Research in community and mental health: an annual compilation of research. Greenwich, Connecticut: JAI Press. Freedman, Ronald and Lolagene Coombs, 1966. Childspacing and family economic position. American Sociological Review 31, 631–648. Furstenberg, Frank F. Jr., 1976. Unplanned parenthood: the social consequences of teenage childbearing. New York: The Free Press. Gove, Walter, James W. Grimm, Susan C. Motz and James D. Thompson, 1973. The family life cycle: internal dynamics and social consequences. Sociology and Social Research 57, 182–195. Gunderson, E.K. Eric and Richard H. Rahe, 1974. Life stress and illness. Springfield, Illinois: Charles C. Thomas. Hetherington, E. Mavis, Martha Cox and Roger Cox, 1976. ‘The aftermath of divorce’. Paper presented to the meetings of the American Psychological Association, Washington, D.C. Hill, Reuben, 1970. Family development in three generations. Cambridge, Mass.: Schenkman. Holmes, Thomas H. and Richard H. Rahe, 1967. The Social Readjustment Rating Scale. Journal of Psychosomatic Research 11, 213–218. Hough, Richard L., Dianne Timbers Fairbank and Alma M. Garcia, 1976. Problems in the ratio measurement of life stress. Journal of Health and Social Behavior 17, 70–82. House, James S. and Elizabeth Bates Harkins, 1975. Why and when is status inconsistency stressful? American Journal of Sociology 81, 395– 412. Janney, James G., Minoru Masuda and Thomas H. Holmes, 1977. Impact of a natural catastrophe on life events. Journal of Human Stress 3, 22–34. Kohn, Melvin L., 1976. Occupational structure and alienation. American Journal of Sociology, 82, 111–130. Kohn, Melvin L., 1977. Class and conformity. Chicago: University of Chicago Press (orig. pub. 1969). Lane, Jonathan P. and James N. Morgan, 1975. ‘Patterns of change in economic status and family structure’. In: Greg J. Duncan and James N. Morgan (eds.), Five thousand American families – patterns of economic progress. Volume III: Analyses of the first six years of the panel study of income dynamics. Ann Arbor: Institute for Social Research, ch. 1. Lowenthal, M.F. and D. Chiriboga, 1973. ‘Social stress and adaptation: toward a life course perspective’. In: C. Eisdorfer and M.P. Lawton (eds.), The psychology of adult development and aging. New York: American Psychological Association, pp. 281–310. McCall, Robert B., 1977. Challenges to a science of developmental psychology. Child Development 48, 333–344. Mechanic, David, 1975. Some problems in the measurement of stress and social readjustment. Journal of Human Stress 1 (3), 43– 48. Rains, Prudence M., 1971. Becoming an unwed mother. Chicago: Aldine-Atherton. Rosenberg, Morris, 1975. ‘The dissonant context and the adolescent self-concept’. In: Sigmund E. Dragastin and Glen H. Elder, Jr. (eds.), Adolescence in the life cycle. Washington: Hemisphere, ch. 6. Sears, Robert R., 1977. Sources of life satisfactions of the Terman gifted men. American Psychologist 32, 119–128.

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Volkart, Edmund H., 1951. Social behavior and personality: contributions of W.I. Thomas to theory and social research. New York: Social Science Research Council. Wildman, Richard C. and David R. Johnson, 1977. Life change and Langner’s 22-item mental health index: a study and partial replication. Journal of Health and Social Behavior 18, 179–188. Wilensky, Harold L., 1961. Orderly careers and social participation in the middle mass. American Sociological Review 26, 521–539. Young, Michael and Peter Wilmott, 1973. The symmetrical family. New York: Pantheon Books.

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4 The Family Conference: The Social Control of Human Development David R. Buckholdt

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here are alternative ways of approaching the study of human development and the life course. Psychologists have tended to focus on traits of the person such as cognitive ability, memory, perception, and motivation, and have shown how these skills and characteristics change (decline) with age (Birren and Schaie, 1976). There is also a strong interest among psychologists in describing human development as a series of stages through which we all must pass if we are to grow and develop normally or optimally (Piaget, 1932; Erikson, 1950; Kohlberg, 1969; Levinson, 1978). Social historians (Aries, 1962; Shorter, 1975; and Kett, 1977) and anthropologists (Mead and Wolfenstein, 1955; Whiting, 1963) also have contributed through their studies of the significance of age at different historical periods and the wide variation in cultural definitions of age-appropriate behavior. Sociologists have had a part in the study of the life course through their research on matters such as the influence of class position on social opportunity (Blau and Duncan, 1967), the effects of occupation on childrearing practices (Kohn, 1969), the impact of social disasters at childhood or youth on the adult (Elder, 1974), and the consequences for persons who marry early or late or have a few or many children (Aldous, 1978). While the several approaches make an important contribution to our understanding of human development and the life course, they tend to blind us to the concrete social processes through which development or change is experienced in social interaction. It is as if factors external to human consideration and control force us to develop in one way or another. Seemingly, forces behind Source: Journal of Family Issues, 4(4) (1983): 613–631.

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the life cycle are beyond human experience and social construction. We are pawns in the play of stages, culture, historical moment, or class position. Without denying the obvious importance of such conditions or factors, it may be useful to look beyond or through them to see how matters such as human development and the life course are addressed as practical matters of everyday life. In a recent book, Karp and Yoels (1982) suggest such an approach, using the theoretical resources of symbolic interaction. They conceptualize the life course as an emergent product of social interaction, a social creation of the reality-defining and reality-sustaining work of human actors. While insisting on the importance of historical, cultural, and social-locational factors in matters relating to the life course, Karp and Yoels make their most important theoretical contribution by emphasizing the role of social process. They insist that aging and related matters of the life cycle are not merely relevancies imposed on persons by external forces, but are experienced products of immediate social processes whereby human actors engage one another in the business of coming to terms with the meaning of human development and the life course. Although historical, cultural, psychological, and other factors may frame and constrain the issues and provide a stock of acceptable questions and answers, the meaning or significance assigned to matters relating to the present, past, and future are arrived at through a process of symbolic work in concert with others.

Controlling the Definition of Development A social interactionist perspective on the life course attends to the negotiated meanings of age and related matters in everyday contexts, rather than in predefined categories or stages that are outside of human experience and construction (Gubrium and Buckholdt, 1977). Given the significance of social context, individuals may encounter quite different inventories of symbols and experience different aging selves. A person can feel, or be made to feel, hopelessly old in one context and young and vital in another. The experienced truth of the matter is an emergent product of social process, not simply a question to be answered by decontextualized measurement or expert opinion. Yet there is something too fluid about this. Particular images, interests, and audiences serve to shape the social processing of development (Gubrium and Buckholdt, 1982). While the significance of age and the life course may arise as an issue in numerous settings, there are contexts in which these matters are particularly focused and controlled in terms of the formal attention given to them and the consequences of decisionmaking. I am referring to contexts in which one or more participants claim expertise or professional insight. They are often found within organizations or institutions that have a responsibility for assessing life course trajectories and providing

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remedial or therapeutic help for those off-course or otherwise in need of care. Organizations like schools, hospitals, prisons, and residential treatment centers are examples. The process of symbolic or definitional work undertaken in these contexts is similar to what takes place in more informal, everyday settings, but there are also some important differences. Professional or expert judgment tends to be more self-assured and conclusive and less susceptible to alternative, nonprofessional opinion. And the interests of professional disciplines and organizations become entangled with judgments about the person, making the business of deciding on development and the life course much more than simply objective assessment of an individual’s needs and problems (Gubrium and Buckholdt, 1977; Karp and Yoels, 1982). There are important interests and control components to the social processing of the life course. This article deals with the social control of the meaning or relevance of the life course and age, particularly old age, in two organizations: nursing homes and a physical rehabilitation hospital. The particular setting of interest is the family conference, a meeting between professional staff and family members at which there is extended discussion of matters related to the resident’s past, current status, and likely future. The focus of the proceedings is on the resident or family member. Participants discuss the life course of the resident, particularly as this relates to success or failure in treatment and care and prognosis for the future. Although discussion is centered on the resident or family member, it is not the sole concern. The significance of age and the life course also involves consideration of the professional and organizational interests of staff members and attention by family members to their own development.

The Settings Observations of twenty family conferences in a physical rehabilitation hospital were undertaken as part of a larger study of caregiving and professional practice in physical medicine and rehabilitation (Gubrium and Buckholdt, 1982). The hospital, a 92-bed facility, cared for patients in need of specialized services for the physical and psychological problems resulting from strokes, amputations, head trauma, hip fractures, and spinal cord injury. Normal stay in the hospital was 6 to 8 weeks. A family conference was scheduled for most patients, usually several weeks before discharge. Staff members in attendance normally included physical, occupational, activity, and speech therapists; a primary care nurse; a social worker; and occasionally a physician. Representatives of the family varied considerably from only a spouse to several generations of family members, and occasionally a neighbor or friend. The official rationale for the conference, which usually lasted for about one hour, included: to report on progress in treatment, to answer

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questions, to enlist the family’s help with problems, and to prepare the family and the client for a future that would be different from the past. Observations of 25 family conferences also were made in two nursing homes, one a 360-bed facility and the other with 270 beds. While both facilities provided a variety of levels of care, they were primarily skilled care institutions. Some of the residents were considered temporary, being inpatients until they recovered from an illness or an accident, but the majority were not expected to return to independent living or to leave the confines of the nursing home for any extended period. A family conference was scheduled for each resident about one month after admission. Staff and family members met to discuss reasons for and feelings about placement; problems and progress in therapy; what the family could do to ease the transition; particular concerns family members might have with medications, roommates, or staff members; and related matters. As in the physical rehabilitation hospital, a wide range of family members and others attended. Those who came regularly from the staff included a social worker, a nurse, and a representative from the activity department. Dietitians and physical and occupational therapists attended irregularly, but they usually did supply written progress notes that the social worker read to family members.

Charting a New Stage of Life Once family members are seated around a conference table, a social worker typically explains that he or she has called the meeting in order for staff to meet the family, answer questions about the resident’s condition, and report on progress in treatment and care. Various staff members then report individually on their work with the resident and evaluate responsiveness or progress. Staff ask the family to apprise itself of life and care in the institution so that they can assist in motivating the resident and in making life there more comfortable and meaningful, the typical message being that if the resident is cooperative in treatment and therapy, he will fare better than he would without professional help. Staff members focus their presentations on disabilities, treatments, and possible futures for the resident. Family members are not often content to limit discussion in this way, however. They speak of the patient’s past, presenting him or her in various roles, how he or she was a successful professional, a loyal worker, or a generous and inexhaustible mother and wife. The stories often end on a note of remorse that the last phase of life must end this way, with a serious disability or being confined to an institution. Staff members listen to the negative imagery but are not content to allow discussion to dwell there too long. They have a message of hope, that the future need not be so discontinuous with the past if certain things are done. First, the resident must cooperate with their treatment plans. If their

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advice is followed, at least partial recovery from the disability may be possible or life in a nursing home can be fulfilling. Second, the family must assist the professional staff. They need to understand that life for their kin and for them has changed. The family must assume a new role as “linker” or “facilitator” between the professional staff and the resident. They are still responsible but the burden must now be shared. It will not do to think of this as a hospital where responsibility for the care of a family member is given up only temporarily. A positive future will depend in part on how open they are to professionals. However this message is delivered, discussion typically returns to the resident’s past – but there is a new focus. Instead of portraying a life course which has been sadly diverted or redirected, attention is given to personal history, to characteristics or dispositions that may help to explain the resident’s responsiveness to treatment and care in the institution. For example, an elderly woman is evaluated as having made remarkable progress in her treatment for an amputated leg. Her son and daughter explain that, because of her “independent spirit” and “fighting instincts,” she has overcome numerous adversities in her life. All predict a positive future for her. An aged man is said to be adapting well to the nursing home. A son explains that his father has always been an “adaptive” and “cooperative” person, a man who does whatever he has to do. They decide he will enjoy his last days in a nursing home or at least he will not be too unhappy. There are also negative cases to explain. Staff complain that a 60-year-old male resident refuses to follow an exercise program to strengthen his arms and legs, which have been weakened by a stroke. His wife notes that she is sure that her husband appreciates their efforts, but that he is an “ornery” and “independent” person who was taught by his parents to do things for himself and has always done so. Once he gets out of the hospital and is more on his own, she is certain that he will follow some of their suggestions. Several staff members predict poor results and thus less independence in the future unless he follows their directions immediately. An elderly woman in a nursing home is said to constantly complain about her care and food. Her sister explains that she was spoiled as a child and that it has become a habit for her to complain about everything. In cases where longstanding dispositional characteristics can explain problems with responsiveness to treatment or adaptation to an institutional environment, staff remain hopeful for positive change. They explain to the family that sometimes the shock of facing a major life transition will produce new attitudes and behavior. Also, they are trained to deal with behavioral and attitudinal problems. They will structure new expectations and new demands that will make it difficult to maintain former behavioral patterns and dispositions that limit adjustment and the effectiveness of treatment. Of course they will need the help of the family if, together, they are to confront the resident with a consistent approach.

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When responsiveness to care and treatment can be understood with reference to the resident’s past, staff and family members are usually content with their hunches about why things are going well or poorly. In some cases, however, the resident’s attitudes or behaviors seem “out of character” to the family. A husband is shocked to learn that his wife is swearing and striking out at her nurses and their aides. A wife cannot believe that her husband is making sexual advances toward elderly women on his floor. A daughter is overwhelmed when she hears that her father, who has always been strongwilled and optimistic, refused his medications and other treatments and claims that he wants to die. Staff members ordinarily explain responses that are out of character as consequences of the shock or trauma of disability or relocation to an institution. The person’s life has been altered dramatically and it will take time to adjust to new and different circumstances. The family can help with the process of adjustment. They can visit regularly and call when they cannot come in person. Occasional visits to the home or favorite restaurant may help. They can bring cherished items from home to the institution, such as a chair or photographs, and inform the staff about favorite foods and activities. Staff members do not accept the occasional suggestion that a person’s life is now so different and so unacceptable that he or she should simply be left alone by staff and allowed to spend the remaining years without externally imposed goals or demands. Their role is to serve and they will not be turned away. They were shocked when the son of a nursing home resident made the following plea: Look, Dad has always been a kind a generous person but you’re pushing him too far. He’s always prided himself on his independence but now you people have to do almost everything for him. He hates his roommate and that night nurse who wakes him up and shoots him in the ass. He may come out of it and he may not. Just leave him a little space for himself and understand why he’s not glad to see you. If he wants to go his own way, that’s okay.

Some families willingly accept the staff’s explanation that “out of character” behavior is a normal response to a dramatic change in physical condition or living arrangements. They are confident that, given time and therapeutic intervention, the person will return to his or her old and real self. However, some families see a different source. They suggest that at least some of the problems may be traced to professional treatment or institutional routine and organization. In this view, institutions and their caregivers may be interfering with positive development and creating barriers for those seeking a return to health and happiness. The family conference is, in part, an occasion for reviewing the life course and charting the future for the aged and disabled. While family members may be allowed to engage in wide-ranging discussion and speculation for a time, the staff sooner or later focus attention on matters that are relevant to

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treatment and care. Details of a person’s past are useful if they help to explain adjustment to the institution and responsiveness to therapy. The future will depend on how a resident and his or her family cooperate with professional caregivers.

Professional Care and the Future Most family members arrive at a conference with vague expectations of what is to be accomplished there. At best, they plan to find out how their loved one is responding, learn what they can do to help, thank staff for their work, and possibly present one or more specific complaints. Staff members, however, usually prepare a specific “game-plan” for the conference. Sometimes the plan is simply to congratulate the family on the progress or cooperation of the resident and to thank them for their own cooperation. In other instances their purposes are more elaborate and complex. The family needs to be won over to the goals of the institution and its caregivers. Continued inaction or obstruction on the part of the resident and family will result in a poorer future than could have been achieved by proper responsiveness to professional care. Many families are seen by staff as being “unrealistic” about the future. They have not yet adjusted to the changes in family and personal lifestyles that are clearly demanded. Some families of disabled persons expect their relative to return home “good as new.” As a result, they have not made important physical alterations in the home or made arrangements for homecare or out-patient therapy. Staff members take the opportunity provided by the conference to describe the likely condition of the resident upon release and to insist on better planning. Some families readily accept the staff’s forecast of the future and agree to be more realistic about what they will be facing. Others object, either by questioning the staff’s predictions or by suggesting that it is the responsibility of the institution to do more. Such disagreements sometimes lead to heated debates that usually end with staff alluding to their training and expertise in such matters but admitting that sometimes they are wrong. Another – and for staff a more serious – form of unreality comes in the form of families who expect their relative to return home some day when staff feel certain that permanent residence in an institution is highly probable. This is a particularly annoying situation when the staff believe that false expectations are interfering with the resident’s responsiveness to professional care and adaptation to institutional living. Residents who falsely think they will be going home soon often cause problems. They may refuse to participate fully in therapy and activities and do not conform to institutional routine. Staff members sometimes suggest that it would be best for all involved if the relative believes that he or she will reside in this or another institution permanently,

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even if there is some small chance of returning some day. The staff ’s work will certainly be easier and more successful. Families often counter with the argument that their relative might lose all hope and interest in life if he or she thinks that institutional residence is permanent. Some families admit to encouraging their relative to think about, and work toward, the day of return, even when they are uncertain that this is possible. Staff discourage this practice in the name of realism, honesty, and pragmatism. They ask family members to change their approach and to enlist the help of others, particularly family physicians, who are sometimes suspected of encouraging false expectations. Some families also are asked to take additional concrete steps in order to insure the resident’s realistic view of the future which, they feel, will lead to better cooperation with institutional care and routine. Some common suggestions are to sell the resident’s house, rent an apartment to someone else, and divide up household items among family members. Of course, legal custody will be needed first. Most families agree to cooperate with the staff, particularly after they hear about the problems the staff are having with their relative and the improvements that will come only with proper responsiveness to professional care and treatment. In some instances, the new strategy is put into effect immediately by bringing the resident to the family conference and announcing that he or she should plan on residing in this or another institution for a long, long time. The family is comforted with the assurance that tears and accusations of betrayal are a small price to pay for a more realistic outlook. Sometimes the recommended strategy to increase cooperation and adjustment is exactly the opposite to the one described above. Family members are asked to lure their relative into a more positive stance toward care and treatment through a promise of eventual return to the outside world. Staff members do not hesitate to suggest other ways that the family can assist the staff as well as their relative beyond issues related to length of stay. Some residents are said to be too demanding, expecting too much from staff. Their families are asked to explain that individual needs cannot always be taken care of on demand. The resident must understand that a nursing home or physical rehabilitation center is not a regular hospital, meaning that the level of care is not as intense or recovery not as immediate. Other problems mentioned frequently include disagreements with roommates, nasty remarks to “colored” aides and residents of different ethnic or religious backgrounds, and complaints about the food. Staff members ask the family to help with these problems for, in so doing, they can assist their relative to adjust to the environment and take advantage of the services offered. Most families agree to help, although some express concern that the problem caused by daily living in an institution might cancel out the advantages to be had from the professional care and treatment available there. One additional issue that is forced on family members, particularly in the physical rehabilitation hospital but also on occasion in the nursing homes,

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is the matter of progress in therapy. Staff call on family members to visit therapy sessions and to encourage their relative to follow directions and practice what they are taught. In part these requests are based on a professional model of treatment that recognizes the positive contribution that the family can make to treatment. There is also a self-serving motive for both staff and families. Third-party insurers will not pay for physical, occupational, or speech therapy beyond a few weeks if progress cannot be demonstrated. Staff members are often concerned that the institution might not be reimbursed for services rendered. In that case, they will try to bill the resident or the family and further services will certainly have to be on a private basis. Staff do not hesitate to remind family members that regular attendance at therapy sessions and follow-up practice and exercise are in everyone’s interest. Many family members passively accept both the long-term prognosis of the professional staff concerning the future of their relative, and the latter’s views on particular issues and problems concerning life in the institution. Some questions may be asked about the probability of alternative futures and how much the will of God and personal determination might help matters, but there are ordinarily no direct challenges to the expertise of the caregivers. On occasion, however, one or more family members suggests that the institution and its staff are in fact hastening or contributing to the decline of which they were reporting. A wife suggests that her husband’s apparent confusion and lack of motivation are caused by being around so many others who are disoriented. She feels that the only way to save him from further decline in his mental functioning is to take him home. A son believes that his father’s growing apathy can be traced to his dependence on institutional personnel and routine. He has always been fiercely independent and now has no reason to live. He has lost his pride. A former social worker argues that the role expectations of the institution encourage her mother to utilize less ability and independence than she actually possesses. “She thinks you are supposed to feel sick and complain a lot in a nursing home,” her daughter argues. Another common explanation is that medications are making the resident lethargic, confused, or belligerent. The possibility that an imbalance in medications or a reaction to them might be causing problems is usually given some credence by staff, at least to the extent that they promise to check with a physician. Any suggestion, however, that the nursing home or hospital is contributing to the very problem staff are reporting on is greeted with some combination of rejection, disbelief, or anger. Sometimes staff consult their charts to demonstrate that mental or physical problems were evident well before the resident came to the present institution, or they question the memory of family members concerning when the problems actually appeared. Another common strategy for avoiding blame is to remind the family of the excellent care and services that they are providing, and to suggest how much more severe the problem might be if these were not available.

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In spite of their denial of any personal or institutional responsibility for problems, staff admit in other ways that institutions are not always therapeutic or even benign to health and development. Sometimes they trace problems of a patient to a hospital or other nursing home that they claim has a bad reputation. They often counsel family members on how to be proper advocates for their relative. This usually means how to approach a nurse or aide with a complaint so that they will not get angry and take it out on the patient or label him or her as a “squealer” or “complainer.” Finally, during light moments in a conference or after the family departs, staff members admit occasionally that an institution can have negative as well as positive effects. As one nurse put it, “If I have to go to a home someday, I sure hope it’s like this one. But even this place will make you nuts. When you’re around senility all day, you catch it.” Whether inside or outside of an institution, the future of a resident, as portrayed by staff, is contingent on a person’s cooperation with institutional routine and professional expectations. The family can and should assist with their member’s adaptation. Discussion focuses on the interests of residents and their families, which are assumed to be similar if not identical, and the work of the staff on behalf of the resident. Any suggestion that institutional living or treatment may be irrelevant to the person’s future, or even harmful to it, is officially denied.

Family Life Courses Family members have a stake in whatever the future is to hold for their relative beyond his or her personal well-being or development. This becomes clear in numerous conferences as family members discuss, often with great emotion, how their relative’s problems have affected their own lives and how the future of the person being considered in the conference will have an impact on their own life course. Sometimes the primary concern is money. Family members worry that their personal savings or other assets will be depleted if private or social insurers will not cover all or a significant part of the stay in the institution or subsequent out-patient treatment. If family resources are to be used in this unanticipated way, future plans of the family may have to be altered. A daughter with young children will need a full-time job, teenage children will have to delay their plans for college, a middle-aged woman might have to give up her job in order to attend to an older sister since professional help is beyond their means, or plans for a new home may be shelved. The physical and emotional toll of caring for an aged or disabled spouse or parent are presented often in discussion. A common theme, particularly in the nursing homes, is a spouse or child who has cared for a loved one for several years. For many this has been a full-time job, around the clock.

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The decision to seek nursing home placement has been promoted not from any dramatic change in the person needing care but from the exhaustion of the caregiver. An elderly woman tells how she has cared for her husband for ten years, usually getting up at night every two hours to tend him. Her own health has suffered as a result and she feels that if she is to have any satisfaction in her remaining years, she needs relief from this daily grind. A middle-aged woman, whose husband died several years before, reports that she had two full-time jobs. She puts in eight hours each day at a hospital as a nurse and then spends most of the remainder of the day caring for her aged father. She has little time or energy left for her two younger children or for herself. The decision to put her father in the nursing home has been difficult, but she feels that she has no choice if she and her children are to have any chance for a satisfactory family life. Similar concerns are expressed by family members in conferences at the rehabilitation hospital. They worry about the impact of their loved one’s disability on their own lives. For some, the effects are regrettable but will be managed since this is part of being a family. A daughter will give up her job in order to take care of her stricken father rather than send him to a nursing home. A wife will resign from her beloved service activities so that she can transport her husband daily to outpatient therapy. In other cases, the needs and interest of one or more members of the family take precedence in decision-making. A young woman whose husband was paralyzed in an automobile accident tells the staff that his future is in a nursing home. She refuses to learn how to take care of him at home. As soon as his condition stabilizes she plans to seek a divorce. The husband of a stroke victim likewise refuses to learn the techniques of therapy or to involve other members of his family. If they can afford to pay for care, his wife can stay at home. Otherwise, she will go to a nursing home or elsewhere. Their future cannot be restricted by this unfortunate accident to one of them. The symbolic significance of decisions concerning the future of a family member is as important, or more important, to some families as the concrete impact on their own lives. Some families take pride in living up to what they see as cultural, ethnic, or religious beliefs concerning illness or old age. A loved one will return home even though the physical and emotional costs to the family will be enormous. These costs are justifiable given the personal debts to be repaid to a loving parent and the satisfaction stemming from beliefs upheld. The daughter of a woman who immigrated from Romania put it this way: “We do things differently here but in Romania you don’t go to a nursing home. My mother would never forgive me and I don’t think I could live with myself.” On the other hand, family members can suffer when they are unable to live up to their ideals of family responsibility. Some family members in conferences at both the physical rehabilitation hospital and the nursing home express a firm belief that one should never pay someone for services or care that the family can provide. A variety of circumstances, however, will prevent them from living

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up to this principle. While the expenses for outside service will seriously damage the family budget, the real hurt comes from a loss of pride and dignity. There is also pain from their inability to repay personal debts and obligations. One young woman illustrates this well as she tearfully agrees with the staff’s suggestion that her mother remain permanently in the nursing home: I really have no choice with my kids and my job. You’re probably right, mom will be happy here and that’s what counts. But it won’t be good for me. When I was young I was sick a lot and she took care of me and now it’s my turn to repay her. I can’t and it’s going to be hard to live with.

Sometimes a spouse or parent will add to the difficulty by reminding the family of debts owed and suggest betrayal. He or she will depict the nursing home or hospital as a prison or tell of his or her misery at being kept away from home. Some residents who attend a family conference will directly contradict the reports by staff members on how well they are being cared for. At other times, however, there is more of a sense of resignation or even satisfaction with one’s circumstances. The resident thanks the staff for their helpfulness and care and the family expresses their appreciation. Even so, there is often an underlying tone of discontent, a wish that things could be different. A son whose father is a permanent resident in a nursing home and who is pleased with the institution nevertheless has some lingering concerns that he expresses this way: This is a real nice place and I’m glad for Dad. But what are we doing to ourselves? Everyone has his own things to do, a job or what else. If you can’t care for your parents and only visit them once in a while and have them over for Thanksgiving and Christmas, are you really still a family?

Issues relating to interlocking life courses complicate the decisionmaking done in family conferences considerably beyond the concerns of staff for the well-being of the resident. Staff members are ordinarily sympathetic to these matters and willing to listen but their interests are limited. Their primary concern is with the resident’s responsiveness to institutional care and routine and they, sooner or later, return to this topic. They envision a future for their client that depends to a great extent on how he or she cooperates with their efforts. The family can help with this. The fact that the resident’s future may have a significant effect on the futures of other family members is of concern to them, but is not seen as something they can or should deal with in the context of a family conference.

Conclusion This study of family conferences contributes to the growing social interactionist literature on the processing of human development by focusing on its social

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control component. It illustrates the role of professional and organizational contexts in which matters of human development and the life course are increasingly being addressed in our society. Contexts similar to the family conference can be found in schools, hospitals, prisons, and numerous other settings. While the skills, deficiencies, and problems of individual clients are officially at issue in these contexts, judgments are made not merely in terms of ideal standards or life in a social vacuum but with reference to the purposes and goals of particular professional groups and the interests of particular organizations. Human development then becomes a matter of how well or poorly a client meets the expectations and needs of professional caregivers and their host institutions. An emphasis on the importance of social context and control makes it reasonable to assume that a person may experience a variety of developing and aging selves. Thus, a person may be cast as difficult or objectionable in one setting, and strong and inspirational in another, depending on contextually-variable interests in the person whose life course is being addressed. The study documents the intermingling of life courses, in this case in families. Interpretations of problems, needs, and desirable future for loved ones include concern for one’s own development as well as the interests of other members of the family. This is not to suggest that self-serving interests dominate. Often they do not. However, how a family interprets the developmental needs and rights of a relative can have a significant impact on their own finances, opportunities, commitments, and feelings of self-worth. Decisions made about the life course of a parent or spouse will constrain or expand developmental opportunities and trajectories of family members. The family conference is one among many similar forums in which the life course is not only assessed and understood, but also shaped and directed. While attention is on the resident, patient, or family member, and the official purpose is to diagnose and correct concrete problems, a variety of other concerns enter the decisionmaking process. Institutions and their professional caregivers have interests in particular life courses that verify their interpretations, facilitate their work, and testify to their effectiveness. The needs and interests of families also become intertwined with the futures of relatives and the work of caregivers. An appreciation of this makes it reasonable to claim that the life course is not only experienced but also controlled.

References Aldous, J., 1978. Family Careers. New York: John Wiley. Aries, P., 1962. Centuries of Childhood. New York: Vintage. Birren, J. and K. Schaie, 1976. Handbook of the Psychology of Aging. New York: Van Nostrand, Reinhold. Blau, P. and O. D. Duncan, 1967. The American Occupational Structure. New York: John Wiley. Elder, G. H., 1974. Children of the Great Depression. Chicago: Univ. of Chicago Press.

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Erikson, E., 1950. Childhood and Society. New York: Norton. Gubrium, J. F. and D. R. Buckholdt, 1977. Toward Maturity: The Social Processing of Human Development. San Francisco: Jossey-Bass. ———(1982). Describing Care: Image and Practice in Rehabilitation. Cambridge, MS: Oelgeschlager, Gunn, and Hain. Karp, D. A. and W. C. Yoels, 1982. Experienceing the Life Cycle. Springfield, IL: Charles C. Thomas. Kett, J., 1977. Rites of Passage. New York: Basic Books. Kohlberg, L., 1969. “Stage and sequence: the cognitive-developmental approach to socialization,” in D. A. Goslin (ed.) Handbook of Socialization Theory and Research. Chicago: Rand McNally. Kohn, M. L., 1969. Class and Conformity: Study of Values, Homewood, IL: Dorsey Press. Levinson, P., 1978. The Seasons of a Man’s Life. New York: Knopf. Mead, M. and M. Wolfenstein (eds.), 1955. Childhood in Contemporary Cultures. Chicago: Univ. of Chicago Press. Piaget, J., 1932. The Moral Judgment of the Child. New York: Harcourt Brace Jovanovich. Shorter, E., 1975. The Making of the Modern Family. New York: Basic Books. Whiting, B. (ed.), 1963. Six Cultures: Studies of Child Rearing. New York: John Wiley.

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5 From Childhood to the Later Years: Pathways of Human Development Robert Crosnoe and Glen H. Elder Jr

A

ccording to life course theory, human development and aging are lifelong processes (Elder and Johnson 2002). Experiences in childhood have long-term consequences that filter into later stages of the life course, whereas patterns of adjustment and functioning in the later years arise from trajectories through preceding life stages (Block 1993; Settersten 1999). Thus, although the data requirements of studying long-term development are great, linking multiple life stages is an important goal in the study of the full breadth of aging. Linking developmental processes from childhood to the later years is one such example that, for very practical reasons, has not been fully examined. This study attempts such a linkage by exploring whether, how, and why men who grew up in different types of family environments demonstrate different patterns of adjustment and functioning later in life. It draws on the longestrunning study in the United States, the Terman study of talented California children that began in 1922. This data source, a mixture of survey and qualitative data spanning seven decades, is highly specialized, but it offers a unique opportunity to trace the lives of men from their earliest experiences to their final days and across eras of sweeping historical change. As such, it is a valuable resource for life course studies. In pursuing this research, we draw on two key features of an earlier study of the Terman men that we conducted. First, in this earlier study, we created holistic profiles of aging based on men’s life satisfaction, vitality, family engagement, occupational success, and civic involvement. In the present study, we replicate Source: Research on Aging, 26(6) (2004): 623–654.

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these profiles in order to capture, more broadly, the adjustment and functioning of men in their later years. Second, in the earlier study, we formulated a basic conceptual framework that posited two pathways from adult to later experiences: the mediational in which adult experiences predicted aging profiles by shaping life circumstances in the later years and the supplemental in which adult experiences and current circumstances were independently related to aging profiles. The evidence supported the latter, suggesting that knowledge of the journey added to knowledge about the destination. The present study applies this framework to the potential carryover of early family experiences. In doing so, we address a challenging question of developmental research: how much does childhood really matter?

Family Experiences in Childhood and Adolescence Scholars and laypersons agree that the family has a profound impact on how children and adolescents “turn out” (Dornbusch 1989). After all, the child development literature, one of the richest in the social and behavioral sciences, has documented the numerous ways that families influence the growth and adjustment of young people (Maccoby 2000). The underlying assumption of this literature is that these effects are long-lasting. Certainly, family experiences early in life predict many adult outcomes, but the potential of early family experiences to shape the full life course has been largely untested. The Terman data, however, give us an opportunity to test this basic assumption for a special group of men. Conceptualizing the family of origin as a context of lifelong development could conceivably entail a laundry list of early family factors as predictors of later outcomes. To avoid such an ad hoc endeavor, we chose to focus on three meaningful categories of family status that have been studied extensively in the past, have been linked to adulthood (if not the later years), and map onto distinct mechanisms. These characteristics of early family life are socioeconomic status, parental divorce, and the affective tone of parentchild relationships. First, socioeconomic status has long been a focal point of social research. Among children, it encompasses numerous circumstances, including the income and occupational status of parents. In this study, we focus on another key dimension of socioeconomic status: parent education (Sewell and Hauser 1980). Being raised by well-educated parents influences child development (e.g., less antisocial behavior, better health, higher achievement in school) and, more important, this influence has been found to extend to adult experiences, in the form of educational and occupational attainment (Crockett and Petersen 1993; McLoyd 1998; Schneider and Coleman 1993). The potential for the socioeconomic status of the family of origin to have implications for later life is based on its power to stabilize the multiple trajectories of development in and out of childhood. The educational attainment of

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parents taps many things, including financial capital, access to opportunities, stable careers and living situations, and the well-documented tendency for education to cultivate psychological resources (e.g., personal control) that allow individuals to better manage their lives (Cameron and Heckman 1993; Mirowsky and Ross 2003). What ties these disparate things together? In effect, well-educated parents are better able to construct a stable home life, open up social and educational opportunities for children, and model effective strategies for life management and social interaction, all of which will smooth entry into young adulthood; this, in turn, provides a firmer foundation for subsequent life stages (Clausen 1991; Shanahan, Hofer, and Miech 2002). In other words, well-educated parents ensure a measure of stability and life opportunity that likely has far-reaching benefits. Second, divorce is a decision about a relationship between adults, but children’s lives are linked to this decision, affected by how it influences their parents’ well-being, their living circumstances, and their relationships with their parents and other family members. In other words, divorce transforms the family context, and the extent of this transformation may have lasting effects (Hetherington, Bridges, and Insabella 1998). For these reasons, parental divorce has long been of interest to developmental research. Indeed, children who experience a parental divorce have been found to have greater problems with school, relationships, emotional health, and conduct, even controlling for the conflict and economic changes that come with this event (Amato and Keith 1991; Cherlin 1992), and more recent, longitudinal evidence suggests that some of these consequences linger into adulthood (Amato, Loomis, and Booth 1995; Chase-Lansdale, Cherlin, and Kiernan 1995; Hetherington and Kelly 2001; Wallerstein, Lewis, and Blakeslee 2000). Divorce can alter many aspects of life that, independently, shape individual outcomes in the short term and long term. Together, these myriad consequences likely influence the later stages of the life course because of their potential to create an early disruption in development. Thus, in many ways, divorce acts as the counterpoint to socioeconomic advantages. Children who experience a parental divorce are likely to witness a certain degree of conflict in their homes, undergo at least a temporary period of upheaval (e.g., moves, school changes, economic adjustments), and experience a change in time spent with adults. Moreover, boys often have greater trouble with parental divorce because it generally signifies a separation from their same-sex parent (Hetherington et al. 1998). Many children recover nicely from parental divorce, but this experience raises the prospect that young people will have “shocks” to their schooling careers, relationship models, and social life that, in turn, interfere with stable transitions into adulthood. In other words, unlike socioeconomic advantages, parental divorce early in life could initiate a pattern of instability in the life course that filters into later life. Third, central to the study of child development are the affective bonds between parents and children. Warm, supportive relationships with parents are a support system at the most crucial stages of development, when children and

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adolescents are learning how to engage the world (Grotevant 1998; Steinberg 2001). Scholars from different disciplines have conceptualized the role of these bonds in different ways, as seen in the social capital literature in sociology and attachment theory in developmental psychology, but the basic theme, and empirical evidence, is the same. Young people do better when they have these supportive bonds to fall back on as they grow up, even after they have left home (Bowlby 1988; Call and Mortimer 2001; Furstenberg et al. 1999). How parent-child attachment influences early development is more or less clear, but why would its apparent benefits persist even after the child has left home, even if the parent-child relationship changes dramatically with time or even after the relationship ends through volition or death? Several specific explanations suggest why this would occur. To encompass them, we focus on the ability of parent-child attachments to create a secure base for the navigation of the world (Furstenberg et al. 1999). Axiomatic in the developmental literature is the notion that children need a foundation of trust and commitment from parents before they can try new things, enjoy novel situations, test boundaries, and risk failure, all of which are necessary to create social networks and pursue new opportunities (Grotevant 1998). Once the young person has developed this model of social engagement, it tends to be applied through self-direction in later life stages, sustaining itself independent of changing family relations. These factors are important in their own right. They also come together to form a basic picture of family life, one that is significant for early development and its potential to serve as a foundation for lifelong development.

Holistic Profiles of Aging This study considers the linkage between early family experiences and later patterns of adjustment and functioning. Having introduced these family factors, we turn now to later life. To gauge adjustment and functioning in the later years, we use the holistic profiles of aging that we created for an earlier study (Crosnoe and Elder 2002) to reflect emerging themes in developmental science that view individuals as multidimensional but indivisible wholes. This holistic (or person-centered) approach combines multiple attributes together in a single profile rather than focusing on any one aspect (Magnusson and Cairns 1996). For example, psychological adjustment by itself likely categorizes a different set of individuals as doing well or poorly than when psychological adjustment, interpersonal functioning, and social involvement are considered together. The key is to consider how different aspects of life come together in one person (Bergman 2001). Using this holistic approach, we identified four basic aging profiles that encompassed multiple dimensions of adjustment and functioning, such as health, role enactment, social engagement, and life review that are prioritized by different fields of aging research (Crosnoe and Elder 2002). Specifically,

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1

0.5

0

−0.5 Life Satisfaction Vitality

−1

Family Engagement Occupational Success

−1.5

Civic Involvement −2

Less Adjusted (N = 52)

Career-Focused but Socially Disengaged (N = 121)

Family-Focused (N = 126)

Well-Rounded (N = 113)

Aging Profiles

Figure 1: Four aging profiles Source: Profiles originally derived by Crosnoe and Elder (2002).

we performed a cluster analysis on five factors measured in men’s later years: family engagement, perceived occupational success, civic involvement, life satisfaction, and vitality (Antonucci and Akiyama 1995; Menaghan 1989; Moen, Dempster-McClain, and Williams 1992; Neugarten 1969; Thoits 1992). A lengthier discussion of the creation of these profiles is included in the Methods section. The analysis produced four “styles of aging” (see Figure 1). The less adjusted men scored low on all factors, career-focused but socially disengaged men scored low on all factors but perceived occupational success, family-focused men scored low on all factors but family engagement, and the well-rounded men scored high on all factors. By integrating well-documented significant dimensions of aging into general profiles, this holistic approach addressed the within-person heterogeneity of people’s lives in the later years as well as the between-person diversity of the aging process (Baltes and Baltes 1990; Shanahan et al. 2002). These aging profiles serve as the reference by which we evaluate the potential long-term implications of early family experiences.

Linking Childhood to the Later Years Up to this point, we have detailed the reasons why certain early family factors might contribute to development after young people have left the family of origin and the holistic perspective taken to gauge adjustment and functioning in the later years. To consider how these life stages may be linked, we again

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Mediational Pathway Adult Experiences Early Family Factors

Aging Profiles Current Circumstances

Supplemental Pathway

Adult Experiences Aging Profiles

Early Family Factors Current Circumstances

Figure 2: Mediational and supplemental pathways between early and later life childhood and aging

draw on the model of later life aging that we developed in an earlier study (Crosnoe and Elder 2002) to examine and explain how later life experiences are embedded in the life stages that preceded them. Two basic pathways of this framework – the mediational and supplemental – are useful for thinking about how the significance of early experiences filters across the life course. In the mediational pathway, early family experiences shape later adjustment and functioning indirectly through their influence on intervening life stages (Figure 2). For example, if the socioeconomic status of the family of origin is a stabilizing and goal-orienting force for the life course, early socioeconomic advantages predict more stable and rewarding trajectories through young adulthood and middle age that translate into socioeconomic, health, and interpersonal advantages later in life; these, in turn, become significant determinants of adjustment and functioning in the later years. In other words, holistic profiles of aging are most closely related to proximate circumstances (e.g., marriage or health in current period), but early family experiences retain importance for the later years by serving as a foundation for these proximate circumstances. Aspects of the current situation and adult trajectories, therefore, mediate the linkage between early family experiences and aging profiles (Baron and Kenny 1986). In the supplemental pathway, early family experiences have consequences for adjustment and functioning that are not completely channeled through current circumstances (Figure 2). Instead, they have an independent association with aging profiles because, along with adult experiences and current circumstances, they detail the journey that men take to the later years. For example, one child may experience a parental divorce, whereas another does not.

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According to the mediational pathway, these boys will one day have different aging profiles because this early family experience charts their young adult and adult trajectories in different ways. Yet, consider the possibility that, despite this early difference, the basic circumstances of their life eventually converge by the time that they enter their later years. If so, we still might speculate that their aging profiles (which encompass social and psychological functioning) might be different, as they likely arrived at these current circumstances in different ways. If so, taking into account experiences at each stage of life taps a life history that best predicts aging profiles. In other words, the supplemental approach injects an additive value of life history into proximate models of aging (Shanahan et al. 2002). These two pathways provide different ways of thinking about life course trajectories, but they are not mutually exclusive. Indeed, early family experiences may be significant for adjustment and functioning in the later years because they lead to current circumstances and because, at the same time, they have lingering effects in their own right. To explore these pathways, we will examine whether aspects of the current situation (e.g., retirement, marital, and health status in the later years) and key adult trajectories (e.g., educational attainment, stable marital history, persistent alcoholism in adulthood) mediate the associations between early family experiences and aging profiles or whether early family experiences, adult trajectories, and current circumstances are each independently associated with aging profiles.

Method Sample This research was based on data from the Terman study, a longitudinal study of intellectually talented children that was started by Lewis Terman, a psychologist at Stanford University. In 1922, a sample of children who had scored in the talented range on the newly created Stanford-Binet IQ test was selected from large public schools across the state of California. This original sample included 857 boys and 671 girls who were born in the first two decades of the twentieth century. The primary motivation for this study was to identify the most able young people as a means to enable society to ensure the flow of talent into important leadership positions. With this motivation in mind, the study tracked the children as they grew up. The original sample was surveyed nearly every five years until 1960 and then again beginning in 1972. These survey data can be supplemented with qualitative information (e.g., letters, newspaper stories, written responses to open-ended questions) organized by Elder and colleagues. For those unfamiliar with this data set, a thorough discussion of its genesis is found in Terman and Oden (1959); a detailed description of its basic structure and recasting is available in Elder, Pavalko, and

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Clipp (1993); and an excellent example of the longitudinal research possible with it is provided by Vaillant (1983). The Terman children were a highly select group. Not only did they score high on one of the first IQ exams; they were generally White, came from middle-class homes, and – not surprisingly – had high educational attainment. The generalizability of results from these data, therefore, is always a question. This problem must be acknowledged but is also mitigated by some important factors. For example, comparison of the Terman sample with the general population reveals few differences in marriage, divorce, and other family experiences (Pavalko and Elder 1990). At the same time, no representative, individual-based data are available that cover such a broad swath of time for long-term longitudinal analyses. For these reasons, we argue that this specialized sample is a valuable resource for life course research if it is presented with the appropriate, fully described warnings. For the purposes of this study, we selected a subgroup of the Terman sample. Because we use key measures and approaches from our earlier study (Crosnoe and Elder 2002), we were bound to follow the same sample selection process. This process had three steps, discussed in detail below, and resulted in a study sample of 424 respondents. First, the study sample included men only. Given the extreme gender differences in opportunity and experience in the early twentieth century, the creation of measures of adjustment and functioning, as well as the identification of key life-course markers, for men and women as a single group would be inappropriate. The study of men, therefore, is a first effort that will then be followed with equal attention to the women. Second, the selection process narrowed the study sample to the men born between 1905 and 1914. Past research has often divided the Terman sample into pre-1910 and post-1910 cohorts (Shanahan, Elder, and Miech 1997). To avoid conducting parallel analyses of two cohorts, other studies have elected to focus on the sample members born between 1905 and 1914, the years clustered around the cohort breakpoint that also contained the largest concentration of sample members, approximately 80% (Crosnoe and Elder 2002). This selection minimizes age variation; simplifies analyses; and, compared to studying only one cohort by itself, retains a larger portion of the original sample. Ancillary analyses revealed that the basic results of this study, and the earlier work on which it was based, replicated across the original cohort divisions. Third, the final filter excluded those who did not remain in the study until the 1970s, the end point of our analyses. Of the 688 men still eligible after the application of the first two filters, 264 did not make the study sample. Of these, 60% had died; the rest dropped out, were lost, or died without notifying the study. Considering the broad time span of the Terman study, this attrition is hardly surprising, but it could be problematic.

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Comparison of the men who remained in the sample until the 1970s (the study sample) and those who remained until the 1940s but dropped out by the 1970s (who remained long enough to measure key life circumstances for comparison) revealed that the first group had higher educational attainment and marriage rates than the second but that the two groups did not differ on other factors (e.g., age, cohort, family socioeconomic status). Thus, attrition biased the study sample toward greater social adjustment, but this bias was less extreme than it could have been. These comparisons echo past studies that report a minimal attrition bias in the Terman sample (e.g., Shanahan et al. 1997).

Measures We have already presented, in Figure 1, the four aging profiles, created for our earlier study, that serve as the foundation of the present study. We first describe our replication of these aging profiles and then turn to the measures of early family experiences, adult experiences, and current circumstances that serve as primary independent variables. The complete descriptions of, and descriptive statistics for, all study variables are included in Table 1. Table 1: Descriptions and descriptive statistics for study variables Variable

M

SD

Description

108.28

33.68

Vitality in 1972

4.50

1.12

The sum of respondents’ self-rated energy and happiness (1 = low to 3 = high).

Family engagement in 1977

1.50

1.06

The sum of three measures: communication with offspring (1 point for speaking to at least one child once a week), communication with siblings (1 point for speaking to at least one sibling once a week), and time with relatives (1 point for spending frequent time with relatives).

Occupational success in 1972

3.95

0.95

Respondents’ agreement with whether they had achieved their occupational goals in life (1 = low to 5 = high), essentially a review of the respondents’ careers as they wind down.

Civic involvement in 1977

1.05

1.14

The sum of nine binary items from 1977: participation in service, community, professional, religious, political, social, educational, recreational, and miscellaneous organizations (1 = yes).

Aging profile factors Life satisfaction in 1972

The mean of four composites: satisfaction with family, friendships, cultural life, and social service. To create each, we took the respondents’ rating of the salience of that domain during adulthood (1 = low to 5 = high) and their satisfaction with goal achievement in each domain (1 = low to 5 = high) and then calculated (Salience × Achievement) – (Salience – Achievement).

(Continued )

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Table 1: (Continued ) Variable

M

SD

Description

Socioeconomic status

2.73

1.49

The highest level of education reached for two parents or for one parent in a single-parent family (1 = no high school graduation, 2 = high school graduate, 3 = some college, 4 = bachelor’s degree, 5 = postgraduate degree). Based on parents’ self-reports in 1922.

Parental divorce

0.10

0.30

Retrospective family history given by Terman participants in 1936 identified those who had experienced a parental divorce (1 = yes, 0 = no) before turning 18.

Parent-child attachment

3.57

0.76

The mean of level of attachment to each parent as a child (1 = none, 2 = very little, 3 = moderate, 4 = a good deal, 5 = very close), reported in 1936 survey. We recognized the qualitative difference of attachment to mother and father, and so we compared the two measures (as well as the combined measure) in all analyses and found no meaningful differences.

Education level

4.37

1.26

Education obtained by 1940: 1 (no high school graduation), 2 (high school graduate), 3 (some college), 4 (bachelor’s degree), 5 (master’s degree), 6 (Ph.D., MD, JD)

Long-term intact marriage

0.65

0.48

1 (1972 marital record shows one marital partner, still living, in life), 0 (no or multiple partners, widower)

Persistent alcoholism

0.11

0.30

1 (self-reported alcohol problem in 1940, 1950, 1960), 0 (no problem in any or only one time period)

62.07

2.45

Self-reported age in 1940 (+32) (M = 62.07, SD = 2.45)

.26

0.44

1 (retired and not seeking employment), 0 (employed or seeking employment)

22.57

19.58

Early family factors

Adult experiences

Current circumstances Age in 1972 Retirement status in 1972 Income level in 1972

Self-reported total earnings from work in 1972: 0 (none) – 91 (over $90,000)

Physical health in 1972

3.24

0.81

Self-reported physical health, 1 (poor) to 4 (very good)

Emotional health in 1972

4.61

0.74

Self-reported emotional health, 1 (poor) to 5 (excellent)

Marital status in 1972

0.91

0.28

1 = currently married, 0 = unmarried.

Two measurement issues deserve comment. First, all measures are based on self-reports. Because exclusive use of self-reported survey data has drawbacks (e.g., problems of recall, the tension between perception and reality, shared method variance), this study is best thought of as an examination of the life-course pathways as seen and perceived by those who lived them. Second, because our study sample has a broad age range, each year of data collection falls at slightly different stages of life for respondents of different ages. Table 2 contains an age-by-year breakdown to facilitate the age-related interpretation of measures and results.

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Table 2: Age range of study sample for each stage of data collection Age range 1922 1928 1936 1940 1945 1950 1955 1960 1972 1977

8–17 14–23 22–31 26–35 31–40 36–45 41–50 46–55 58–67 63–72

Aging profiles. The four aging profiles were created through cluster-analytic techniques, described below, of four factors measured in either 1972 or 1977: life satisfaction, vitality, family engagement, perceived occupational success, and civic involvement. The rationale for the selection of these factors is explained in greater detail in our earlier article (Crosnoe and Elder 2002), and we will direct the reader to that source for the information that we cannot provide here. Essentially, these factors together provide an overall assessment of adjustment and functioning in the later years in both social and psychological domains. As opposed to physiological markers, these factors, in total, tapped how men felt about themselves and their lives and how engaged they were in the social world. As seen in Table 1, the measure of life satisfaction differs from traditional treatments in that it essentially taps the achievement of life goals rather than an overall assessment of life circumstances. We argue that this difference is useful because it gives men a standard – earlier goals and dreams – by which to evaluate their lives. These five factors were then entered into a cluster analysis with Sleipner 2.0 (Bergman and El-Khouri 1998). This analysis grouped together similar cases, as determined by the squared Euclidean distance between them, using Ward’s hierarchical method to optimize the variance within clusters (Aldenderfer and Blashfield 1984). As previously noted, we replicated the solution from our earlier study, resulting in four clusters that explained 41% of the variance in the five factors. A total of 412 men were assigned to a cluster. Seven cases were eliminated because of missing data and five because Sleipner identified them as outliers. The four clusters produced by this analysis correspond to the four aging profiles presented in Figure 1. These profiles were the dependent variables in multivariate analyses. Early family factors. The three measures of early family experiences were socioeconomic status of the family of origin (measured by the highest level of parent education), parental divorce, and parentchild attachment. We should

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note that, although Terman data collection began when most respondents were children or adolescents, we did not always use the surveys from respondents’ child and adolescent periods to measure early family experiences. Socioeconomic status was based on the reports of parents in 1922, when the respondents in the study sample were minors, but the other two family measures were indexed by data from the 1936 survey, when most respondents in the study sample were young adults. Whether parents divorced during a respondent’s childhood or adolescence could only be assessed after the end of this long period. Furthermore, the Terman study was designed to focus on psychological and intellectual issues rather than contexts of development, but, over time, more contextual items began to appear on the survey. Consequently, items about relationships with parents, surprisingly, were not asked in abundance until the respondents had grown older. Our focus on socioeconomic status of the family of origin may seem curious, considering that the Terman sample was largely middle-class. Socioeconomic status, however, encompasses more than money, and, indeed, our early discussion about the need to study socioeconomic status tapped more social psychological mechanisms than financial capital. Our focus on the educational side of this status is important because, in the early twentieth century, education was not as much of a natural byproduct of class (e.g., college was not necessarily the normative experience for high-status youths as it is today). Educational attainment of parents, therefore, had more variation than might be expected. Adult experiences. The heart of our earlier study of aging profiles (Crosnoe and Elder 2002) was the linkage of these profiles to adult trajectories, which were constructed by combining and collapsing data across multiple time points from the 1930s to the 1960s. As noted earlier, we chose to focus on three that proved to be consequential for adjustment and functioning in the later years. The first two – long-term intact marriage and lifetime educational attainment – represent social pathways, the life-course continua made up of social role sequences. Because of the looser organization of life-course sequences in the early twentieth century and the interruption of major historical events (e.g., Great Depression, World War II), the educational careers of the men in this sample were not as limited to young adulthood as they might be today. The third – persistent alcoholism – represents another type of life-course continua, developmental trajectories, which refer to patterns of continuity and change in psychological, physiological, or health-related factors. Alcohol use is a health behavior that, when extreme, has serious repercussions for health and other domains. Current circumstances. The aging profiles were created based on data from 1972 and 1977, and so current circumstances refer to some basic aspects of life during this time: age, retirement status, income, physical health, emotional health, and marital status.

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Plan of Analyses In all multivariate analyses, the four-category aging profile measure served as the dependent variable in multinomial logistic regression. In this type of regression, independent variables (measures of early family experiences, adult experiences, and current circumstances) predicted the odds of being in one category of the dependent variable versus the reference category. We estimated multinomial models with each category of the dependent variable as the reference. These analyses produced voluminous results that would be difficult to present in total. Consequently, we present the results for the models with the two “anchor” profiles – less adjusted and well-rounded – as the references but discuss other models when necessary. The modeling strategy for this study was intended to capture the associations between early family experiences and the aging profiles and to examine whether these associations best fit the mediational or supplemental pathway. Support for the mediational pathway would be strongest if significant associations between early family experiences and the aging profiles were reduced substantially or eliminated by taking into account adult experiences and current circumstances. Support for the supplemental pathway would be strongest if these associations persisted even after taking into account these later experiences. To compare these pathways, we first estimated two separate models for the aging profiles – one that included only the early family measures as predictors, one that included only the adult and current measures as predictors – and then a comprehensive model that included all predictors together. Examining the magnitude and significance level of the associations between the early family measures and the aging profiles before and after the inclusion of the measures of adult experiences and current circumstances demonstrated how much these associations were attenuated by the adult and current measures, which provided evidence of the extent to which these factors from three stages of the life course were part of one sequential pathway or were largely independent. Additional analyses, in which the adult/ current measures were regressed on the early family measures, provided further evidence on the extent to which these three sets of factors were mediational or supplemental predictors of the aging profiles.

Results Early Family Experiences and Aging Profiles in the Later Years The general purpose of this study was to examine the associations between family experiences in childhood and adolescence and patterns of adjustment and functioning many decades later. Before turning to multivariate analyses

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Table 3: Mean differences in profile factors and early family experiences, by aging profile Career focused but less adjusted

Socially disengaged

89.70c

25.57

99.80b

35.11

97.81bc

24.23

137.23a

25.96

Family focused

Well-rounded

Profile factors Life satisfaction in 1972 Vitality in 1977

3.58c

1.00

4.09b

1.11

4.18b

0.97

5.40a

0.63

Family engagement in 1977

0.96c

0.88

0.37d

0.49

2.35a

0.54

1.87b

0.87

Occupational success in 1972

2.22d

0.65

4.19b

0.58

3.95c

0.71

4.54a

0.56

Civic involvement in 1977

0.83b

0.97

0.36c

0.52

0.84b

0.98

1.76a

1.25

2.64

1.52

2.82

1.55

2.56

1.44

2.86

1.48

Early family experiences Family socioeconomic status Parental divorce

0.08

0.27

0.12

0.33

0.08

0.27

0.12

0.32

Attachment to parents

3.55ab

0.77

3.38b

0.79

3.57ab

0.68

3.75a

0.80

n

52

121

126

113

Note: Means with different subscripts differ significantly ( p < .05) according to one-way analyses of variance; a indicates the highest mean, with b, c, and so on indicating means in descending order from the highest.

of these associations, we present a basic descriptive picture of the men in our sample during each of these stages of life (see Table 3). Table 3 is broken down into the four aging profiles that were replicated in this study to capture, more holistically, adjustment and functioning in the later years. To clarify what these four profiles represent, we have presented the means, for each profile, of the five factors used to create them. Two of the profiles (well-rounded, less adjusted) were internally consistent – the men in these profiles tended to be consistently high or low on the five factors used to create the profiles. The other two (career focused, family engaged) were internally inconsistent – the men in these profiles scored low on all but one factor. Age was not included in Table 3, but we should note that the profiles did not differ by age (the mean age for all was approximately 62 years) even though they were measured from two time points (1972, 1977) and covered a broad age range (59–72). To explore the possibility that the men in these profiles had different family experiences early in life, we also present the within-profile means of the three family factors. The four profiles exhibit few differences in their early family experiences, except that the men in the well-rounded profile reported the strongest attachment to their parents as children and the men in the socially disengaged profile reported the weakest. These bivariate statistics did not take into account other life circumstances from any stage

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of the life course and, consequently, may have masked real differences among the aging profiles in early family experiences.

Early, Adult, and Current Experiences and Aging Profiles Are early family experiences associated with adjustment and functioning in the later years? To answer this question, Table 4 presents results from our first set of multinomial logistic regressions. Model 1 in Table 4 contains the odds ratios for the model in which the three family factors were the only predictors of the aging profiles. According to Table 4, one family factor differentiated the less adjusted men from the men in the other profiles, and two factors differentiated the well-rounded men from men in the other profiles. The ancillary analyses – in which the career- and family-focused profiles served as reference categories – revealed additional associations between early family factors and the aging profiles, and so we will include those results, not shown in Table 4, in this discussion. All three family factors predicted aging profiles, although in different ways. The first, socioeconomic status of family of origin, differentiated men in the family-focused profile from those in the well-rounded profile. Specifically, a one-unit change in socioeconomic status in childhood (e.g., parent with high school degree vs. parent with college experience) Table 4: Results of multinomial logistic analyses predicting aging profiles by early family experiences, adult experiences, and current circumstances in separate models (N = 309) Less adjusted as reference

Well-rounded as reference

Career focused

Family focused

Wellrounded

Less adjusted

Career focused

Family focused

1.21 1.38 0.75

0.97 0.90 1.14

1.20 1.95 1.51†

0.84 0.51 0.66

1.01 0.50 0.71***

0.81* 0.46† 0.75

Adult experiences Educational attainment Long-term intact marriage Persistent alcoholism

0.85 0.48 1.85

0.71† 1.21 1.05

1.55* 1.35 0.78

0.64* 0.74 1.28

0.55*** 0.36* 2.37

0.46*** 0.90 1.35

Current circumstances Age Retirement status in 1972 Income level in 1972 Physical health in 1972 Emotional health in 1972 Marital status in 1972

1.05 1.45 1.05** 1.20 1.21 0.98

1.02 0.87 1.06* 1.26 1.64† 0.77

0.94 2.12 1.06* 4.07*** 1.62 0.83

1.07 0.47 0.94** 0.25*** 0.62 1.20

1.13 0.69 0.99 0.30*** 0.75 1.17

1.09 0.41* 0.99 0.21*** 1.01 0.93

Variable Model 1 Early family experiences Family socioeconomic status Parental divorce Attachment to parents Model 2

Note: Coefficients are odds ratios. Model 1 also controlled for age. †p < .10. *p < .05. **p < .01. ***p < .001.

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decreased the odds of being in the family-focused profile later in life, compared to being in the well-rounded profile, by 19% (1 – odds ratio = 1.00 – .81 = .19). The exact same pattern was seen for the family-focused profile when compared to the career-focused profile (not shown). The second family factor, parental divorce, differentiated the men in the familyfocused profile from those in the well-rounded profile – experiencing a parental divorce as a child or adolescent decreased the odds of being in the family-focused profile decades later, compared to the well-rounded, by 54%. The third, parent-child attachment, differentiated the well-rounded men from the less adjusted and career-focused men (51% and 29% changes, respectively). It also differentiated the family-focused men from the career-focused men (not shown), with a one-unit increase in attachment associated with a 51% decrease in the odds of being career focused rather than family focused. To offer a baseline comparison for the associations between more proximate factors and aging profiles, Model 2 in Table 4 presents the results from multinomial regressions that contained only the measures of adult experiences and current circumstances as predictors of the aging profiles. Again, we supplement the results presented in Table 4 with results from regressions in which the other two aging profiles served as the reference categories. The two major aspects of current circumstances were physical health and income. Compared with all other aging profiles, the men in the wellrounded profile reported better physical health. Compared with all other aging profiles, the men in the less adjusted profile reported lower annual earnings later in life. A third current factor, emotional health, was also related to the aging profile. Emotional health predicted membership in the family-focused aging profile compared with the less-adjusted profile. Turning to the three longitudinal measures representing adult experiences, educational attainment predicted membership in the well-rounded profile compared with all other profiles, long-term intact marriage predicted membership in the family-focused and well-rounded profiles compared with the career-focused profile, and persistent alcohol problems did not predict aging profiles at all. The nonsignificant main effect of alcohol problems differed from our earlier study (Crosnoe and Elder 2002), which reported that such problems differentiated the career-focused men from others. This difference could be due to slight differences in the variables included in the model.

Mediational and Supplemental Pathways from Early to Later Life The results just described catalog the associations between factors from various stages of the life course and aging profiles measured in the later years, but they do not really speak to how these associations are connected. In other

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words, do the early family factors identified as important in the previous analyses matter because they influence the associations between adult/current experiences that were also identified as important, or were all of these factors important in their own right? Table 5 presents results from a comprehensive set of multinomial regressions aimed at answering this question. In reference to Figure 2, the mediational pathway suggests that early family factors predict adult experiences and current circumstances, which in turn predict aging profiles. If the mediational pathway best captured the role of early family experiences, then we would expect that any significant associations between the early family factors and the profiles would be attenuated by the inclusion of measures of adult experiences and current circumstances. The supplemental pathway suggests that early family experiences predict aging profiles above and beyond experiences in the intervening stages of the life course. If the supplemental pathway best captured the role of early family experiences, then we would expect that any significant associations between the early family factors and the profiles to persist even after the inclusion of the measures of adult experiences and current circumstances. To assess the two pathways, we focus, in turn, on each association between early family factors and the aging profiles. The significant association between family socioeconomic status and membership in the well-rounded (versus the family-focused) profile was almost completely eliminated by the measures of adult experiences and current circumstances (odds ratio = .81, p < .05 in Table 4, .98, ns in Table 5).

Table 5: Results of multinomial logistic analyses predicting aging profiles by early family experiences, adult experiences, and current circumstances in comprehensive model (N = 309) Less adjusted as reference

Variable

Career focused

Well-rounded as reference

Family focused

Wellrounded

Less adjusted

Career focused

Family focused

Early family experiences Family socioeconomic status Parental divorce Attachment to parents

1.14 1.49 0.88

0.99 0.86 1.36

1.01 2.51 1.65

0.98 0.28 0.60

1.13 0.43 0.53**

0.98 0.24* 0.82

Adult experiences Educational attainment Long-term intact marriage Persistent alcoholism

0.83 0.48 1.89

0.69† 1.16 0.97

1.50† 1.28 0.72

0.67† 0.78 1.38

0.56** 0.38* 2.61

0.46*** 0.91 1.35

Current circumstances Age Retirement status in 1972 Income level in 1972 Physical health in 1972 Emotional health in 1972 Marital status in 1972

1.04 0.84 1.05* 1.18 1.12 0.57

0.98 1.05 1.05** 1.27 1.60† 0.93

0.98 1.40 1.06** 3.48*** 1.47 0.95

1.06 0.60 0.94** 0.23*** 0.68 0.96

1.11 0.70 0.99 0.28*** 0.81 1.02

1.09 0.39* 0.99 0.30*** 1.09 0.87

Note: Coefficients are odds ratios. †p < .10. *p < .05. **p < .01. ***p < .001.

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This attenuation suggests a mediating process, but what was the mediator? Table 6 contains the results of a series of ordinary least squares and logistic regressions in which the early family factors predicted the adult experiences and current circumstances. In tandem with the results from Table 4 on the associations between the adult/current factors and the aging profiles, these results allow us to chart potential pathways from family factors to adult/ current factors to the aging profiles that might explain this mediation. Only educational attainment has a significant association with family socioeconomic status (as an outcome) and a well-rounded versus family-focused profile (as a predictor). Thus, the tendency for children from more advantaged profiles to have more well-rounded, as opposed to family-focused, profiles in their later years was largely explained by their greater success in the educational system. The significant association between family socioeconomic status and a career-focused (versus family-focused) profile changed only slightly after the inclusion of the adult and current factors. Moreover, no adult or current factor was linked to both family socioeconomic status early in life and to greater odds of being career focused versus family focused later in life (seen by reviewing Tables 4 and 6). This role of early socioeconomic status appeared to better fit the supplemental pathway. It was largely independent of other life course experiences. Turning to the second early family factor, the apparent effect of experiencing a parental divorce as a child or adolescent on the likelihood of a wellrounded versus family-focused profile actually strengthened significantly once adult experiences and current circumstances were taken into account (odds ratio = .46, p < .05 in Table 4, .24, p < .05 in Table 5). This change in TABLE 6: Results from ordinary least squares (OLS) and logistic regressions predicting current circumstances and adult experiences by early family experiences OLS models

Adult-experiences models Family socioeconomic status Parental divorce Attachment to parents

Education

Marriage

Alcohol

.22*** −.12 .17†

−.12 .34 .19

.15 .67 .23

Retirement

Marriage

Income Current-circumstances models Family socioeconomic status Parental divorce Attachment to parents

Logistic models

.44 −.38 −1.19

Physical health .02 −.37* .03

Emotional health .01 .14 .09†

−.10 −.22 .23

−.07 −.44 −.30

Note: All coefficients are unstandardized b coefficients – the logistic coefficients are not odds ratios. All models controlled for age. †p < .10. *p < .05. ***p < .001.

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the odds ratio for this early family factor suggests the opposite of mediation: a suppression effect. Physical health offers a clue as to why this suppression might occur. Physical health in the later years was the only factor associated with both early parental divorce and greater odds of being well-rounded versus family focused. Thus, young people raised by married parents were more likely to have well-rounded lives in their later years than to be more focused on family life relative to other domains of life. They also, however, had better health, which was itself related to aging profiles. Taking into account this added advantage revealed that the apparent advantage of being raised in such a home was even greater than it first seemed. As for the third early family factor, greater attachment to parents early in life predicted being well-rounded versus career focused or less adjusted. In both cases, the odds ratio for attachment increased slightly upon including the current and family factors. These changes reveal an apparent suppression effect, mostly because of educational attainment. This effect, however, was weak in magnitude. The other role of early attachment (differentiating career focused and family focused) fits better the supplemental pathway – the coefficient did not differ substantially across models, and no current/ adult factor was related to attachment and to the difference between the career- and family-focused profiles.

Discussion Research on human development has been largely stage specific – focusing on developmental processes within rather than across different stages of the life course. This traditional focus has arisen from several factors, such as differing emphases across disciplines, the complexities of linking life stages both empirically and theoretically, and the lack of long-term longitudinal data. Unfortunately, this tradition has likely constrained our understanding of the full breadth of human development and aging. Recent trends in psychology, sociology, and related disciplines, however, have begun to break down the barriers among different stages of the life course. As longitudinal samples age from childhood into subsequent life stages and as studies gather retrospective data to inform aging models, these contemporary trends can be expanded significantly to capture developmental processes across the full life course. This study represents a preliminary step in such life course research. It builds on past research with one such data set that had aged long enough to facilitate long-term longitudinal analysis. This past research was interested in how styles of aging, holistically defined profiles of social psychological adjustment and functioning, were embedded in earlier experiences. The present study reversed this objective, exploring the carryover of early experiences into later life. We used the same basic modeling strategy and data source as the earlier research but addressed different goals. Essentially, we were interested

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in leveraging longitudinal data, life-course perspectives, and conceptual models from aging research to address a central issue that has concerned research on child development and family relations for decades: whether experiences earlier in life influence developmental trajectories into adulthood and the later years. Specifically, this study asked whether, and how, key aspects of the family environment in childhood and adolescence were related to patterns of adjustment and functioning in the later years. We reported that the socioeconomic status of the family of origin predicted being career focused decades later. Men who came from higher status back-grounds may have had more advantages in life but also appeared to be at risk for more problematic functioning (e.g., concentrating on careers at the expense of other domains). We also reported that early family disruption had little long-term impact on individual adjustment in this sample, except that the men who were defined by the well-rounded profile were more likely to have experienced a parental divorce in the early life course than those with a family-focused profile. Thus, men who had early family problems appeared to be more likely to apply themselves equally across life domains rather than to specialize in family life. Finally, we reported that early attachment to parents generally differentiated the two profiles highest in family engagement in later life from the two profiles lowest in such engagement. Our multinomial analyses, therefore, established links between early and later stages of life, but why did these links occur? We attempted to discover whether these associations were mediated by experiences in adulthood and the basic circumstances of later life (mediational pathway) or whether they existed independently of, or were additive to, these later experiences (supplemental pathway). We expected that these two pathways were not mutually exclusive, and our analyses bore out this possibility. The evidence did not uniformly support either pathway. Our analyses revealed one clear example of the mediational pathway. An observed association between socioeconomic advantages early in life and a well-rounded aging profile later in life was almost completely a function of the greater educational attainment of young people who grew up in such families, in line with the basic predictions of the Wisconsin Model of Status Attainment (Sewell and Hauser 1980). We also found one clear example of a suppression effect, which might be considered in the same class of pathways as mediation. The observed association between experiencing a divorce in the early life course and having a well-rounded aging profile (compared to focusing on the family at the expense of other domains) actually masked the added benefits of good physical health for later patterns of adjustment and functioning. Taking this into account revealed an even stronger tendency for those with this early family experience to be more well-rounded later in life. The other associations reported above all most closely fit the supplemental pathway. We should acknowledge, however, the possibility that more mediational pathways did exist in these men’s lives but that we were unable, with what we have done here, to identify the mechanisms.

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Comparison of mediational and supplemental pathways demonstrated the myriad ways that early experiences might matter in the long term. Fitting traditional perspectives on the linkages among life stages, these early experiences are important because of what they lead to over time, as in a cumulative process where later stages build on earlier ones. We also saw evidence of another form of linkages among life stages that has been reported from earlier research on the Terman sample and that has received greater attention in recent years. In this form, each stage of the life course has additive value in explaining adjustment and functioning. Circumstances at one stage of the life course do not merely subsume early experiences and statuses. Instead, these early experiences provide information about how two people in the same position arrived at that position in different ways and whether these differences have implications for overall adjustment and functioning. Put another way, our findings suggest that early experiences can have long-term consequences that are not necessarily cumulative in nature. In both the mediational and supplemental pathways, elements of earlier life stages, including family experiences, provide information about the routes that individuals take through life. They are chapters in a biography, both related to and independent of each other. Drawing on life histories to link multiple stages of the life course, as we have done here, represent key tools in life-course studies of human development and aging. The Terman study facilitates this endeavor with data encompassing most of the twentieth century and multiple domains of life. Use of the Terman data, however, also comes with trade-offs, the most important of which are their limited generalizability. The Terman men – highly intelligent, from generally comfortable backgrounds – are certainly not representative of American men as a whole, not even those from the same cohort. Yet, in many other ways, these men are quite “average.” Past comparisons of the Terman sample with more representative data, for example, have consistently revealed more similarities than differences (Pavalko and Elder 1990; Shanahan et al. 1997). Our results are specialized, but we have two key reasons for arguing that they are not as specialized as the sample on which they were based. First, Neugarten (1969) contended that social psychological aspects of well-being are valuable for life-course research because of their power to transcend many population-level differences, and our research does have a focus on a social psychological phenomenon – how men feel about themselves and their worlds. Second, the men whose lives are captured in the results of the present study came of age in a social context that might have minimized their differences with other Americans. They lived through the Great Depression and two world wars, period economic booms and busts, the rise of the service sector, and the exponential growth of higher education. Such sweeping historical change likely minimized a good deal of the standardization of the life course and increased the opportunities for diverse developmental trajectories in the process (Shanahan 2000). Finally, this study of more advantaged men might be viewed as a companion piece to focused studies of men from other

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race and class backgrounds, such as Blauner’s (1964) study of working-class men or Newman’s (2003) study of minority aging. Triangulating such studies, and looking for commonalities or differences among them, offers a strategy for sorting out the life course of Americans in the twentieth century. The role of history, just mentioned, also brings up another important issue for long-term longitudinal research. In any study covering multiple stages of the life course, the sample members will have lived most of their lives in a time much different from the present, and their life courses will have spanned diverse historical epochs. The Terman sample is a prime example of this issue – the results of this study are embedded in a larger historical context that gives special meaning to them. The men were born in a time of relative peace and prosperity, things disrupted by World War I. They were typically out of high school and in college during the Great Depression and were young adults during World War II, in which many of them served both in the United States and abroad. They were adults during the sheltered postwar 1950s, middleaged (at least) as the rapid social changes (e.g., civil rights, feminism) of the 1960s took place, and at or near retirement age in the economically stagnant 1970s. Consequently, the socioeconomic advantages many enjoyed early in life were likely partially blunted by massive historical events; the higher education that they pursued in great numbers was certainly not as normative as it might be for similar men today but also part of a larger wave of men who entered college on the G.I. Bill, and the parental divorces that some experienced certainly had a stronger social stigma to them than today. Regardless of historical era, research on children and early family life often concludes that early experiences matter, but does the extent to which they matter “wear off” with time and with age? At the same time, studies have identified many social and psychological factors that play key roles in the aging process, but how and from where do these factors arise? These two questions point to an increasingly important and recognized connection between different fields of research on widely separated phases of the life course. This study, and others based on the Terman data, provide at least a step toward the ultimate goal of life-course research: to capture how lives are lived within context from birth to death.

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Baltes, Paul B. and Margaret M. Baltes. 1990. “Psychological Perspectives on Successful Aging: The Model of Selective Optimization With Compensation.” Pp. 1–34 in Successful Aging: Perspectives From the Behavioral Sciences, edited by Paul B. Baltes and Margaret M. Baltes. New York: Cambridge University Press. Baron, Reuben and David Kenny. 1986. “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations.” Journal of Personality and Social Psychology 51:1173–82. Bergman, Lars R. 2001. “A Person Approach in Research on Adolescence: Some Methodological Challenges.” Journal of Adolescent Research 16:28–53. Bergman, Lars R. and Bassam M. El-Khouri. 1998. Sleipner: A Statistical Package for PatternOriented Analyses. Stockholm, Sweden: Stockholm University. Blauner, Bob. 1964. Alienation and Freedom: The Factory Worker and His Industry. Chicago: University of Chicago Press. Block, Jack. 1993. “Studying Personality the Long Way.” Pp. 9–41 in Studying Lives Through Time: Personality and Development, edited by David C. Funder, Ross D. Parke, Carol Tomlinson-Keasey, and Keith Widaman. Washington, DC: American Psychological Association. Bowlby, John. 1988. A Secure Base: Parent-Child Attachment and Healthy Human Development. New York: Basic Books. Call, Kathleen T. and Jeylan T. Mortimer. 2001. Arenas of Comfort in Adolescence: A Study of Adjustment in Context. Mahwah, NJ: Lawrence Erlbaum. Cameron, Stephen V. and James J. Heckman. 1993. “The Nonequivalence of High School Equivalences.” Journal of Labor Economics 11:1–47. Chase-Lansdale, P. Lindsey, Andrew Cherlin, and Kathleen E. Kiernan. 1995. “The LongTerm Effects of Parental Divorce on the Mental Health of Young Adults: A Developmental Perspective.” Child Development 66:1614–34. Cherlin, Andrew. 1992. Marriage, Divorce, and Remarriage: Social Trends in the U.S. Cambridge, MA: Harvard University Press. Clausen, John A. 1991. “Adolescent Competence and the Shaping of the Life Course.” American Journal of Sociology 96:805–42. Crockett, Lisa J. and Anne C. Petersen. 1993. “Adolescent Development: Health Risks and Opportunities for Health Promotion.” Pp. 13–37 in Promoting the Health of Adolescents: New Directions for the 21st Century, edited by Susan G. Millstein, Anne C. Petersen, and Elena O. Nightingale. New York: Oxford University Press. Crosnoe, Robert and Glen H. Elder Jr. 2002. “Successful Adaptation in the Later Years: A Life Course Approach to Aging.” Social Psychology Quarterly 65:309–28. Dornbusch, Sanford M. 1989. “The Sociology of Adolescence.” Annual Review of Sociology 15:233–59. Elder, Glen H., Jr. and Monica Kirkpatrick Johnson. 2002. “The Life Course and Human Development: Challenges, Lessons, and New Directions.” Pp. 49–81 in Invitation to the Life Course: Toward New Understandings of Later Life, edited by Richard A. Settersten. Amityville, NY: Baywood. Elder, Glen H., Jr., Eliza K. Pavalko, and Elizabeth C. Clipp. 1993. “Introduction.” Pp. 1–23 in Working With Archival Data: Studying Lives, by Glen H. Elder Jr., Eliza K. Pavalko, and Elizabeth C. Clipp. Newbury Park, CA: Sage. Furstenberg, Frank, Thomas Cook, Jacquelynne Eccles, Glen Elder, and Arnold Sameroff. 1999. Managing to Make It: Urban Families and Adolescent Success. Chicago: University of Chicago Press. Grotevant, Harold D. 1998. “Adolescent Development in Family Contexts.” Pp. 1097–1147 in Handbook of Child Psychology, edited by William Damon. New York: John Wiley. Hetherington, E. Mavis and John Kelly. 2001. For Better or Worse: Divorce Reconsidered. New York: Norton.

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Hetherington, E. Mavis, Margaret Bridges, and Glendessa M. Insabella. 1998. “What Matters? What Does Not? Five Perspectives on the Association Between Marital Transitions and Children’s Adjustment.” American Psychologist 53:167–84. Maccoby, Eleanor. 2000. “Parenting and Its Effects on Children: On Reading and Misreading Behavior Genetics.” Annual Review of Psychology 51:1–27. Magnusson, David and Robert B. Cairns. 1996. “Developmental Science: Toward a Unified Framework.” Pp. 7–30 in Developmental Science, edited by Robert B. Cairns, Glen H. Elder Jr., and E. J. Costello. New York: Cambridge University Press. McLoyd, Vonnie. 1998. “Socioeconomic Disadvantage and Child Development.” American Psychologist 53:185–204. Menaghan, Elizabeth G. 1989. “Role Changes and Psychological Well-Being: Variations in Effects by Gender and Role Repertoire.” Social Forces 67:693–14. Mirowsky, John and Catherine E. Ross. 2003. Education, Social Status, and Health. New York: Aldine. Moen, Phyllis, Donna Dempster-McClain, and Robin M. Williams Jr. 1992. “Successful Aging: A Life-Course Perspective on Women’s Multiple Roles and Health.” American Journal of Sociology 97:1612–38. Neugarten, Bernice L. 1969. “Continuities and Discontinuities of Psychological Issues Into Adult Life.” Human Development 12:121–30. Newman, Kathleen. 2003. A Different Shade of Gray. New York: New Policy Press. Pavalko, Eliza K. and Glen H. Elder Jr. 1990. “World War II and Divorce: A Life Course Perspective.” American Journal of Sociology 95:1213–34. Schneider, Barbara and James S. Coleman. 1993. Parents, Their Children, and Schools. Boulder, CO: Westview. Settersten, Richard A. 1999. Lives in Time and Place: The Problems and Promises of Developmental Science. Amityville, NY: Baywood. Sewell, William H. and Robert Hauser. 1980. “The Wisconsin Longitudinal Study of Social and Psychological Factors in Aspirations and Achievements.” Pp. 59–100 in Research in the Sociology of Education and Socialization, edited by Alan C. Kerckhoff. Greenwich, CT: JAI. Shanahan, Michael J. 2000. “Pathways to Adulthood in Changing Societies: Variability and Mechanisms in Life Course Perspective.” Annual Review of Sociology 26:667–92. Shanahan, Michael J., Glen H. Elder Jr., and Richard A. Miech. 1997. “History and Agency in Men’s Lives: Pathways to Achievement in Cohort Perspective.” Sociology of Education 70:54–67. Shanahan, Michael J., Scott M. Hofer, and Richard A. Miech. 2002. “Planful Competence, the Life Course, and Aging: Retrospect and Prospect.” Pp. 189–211 in Personal Control in Social and Life Contexts, by Steven Zarit, Leonard Pearlin, and K. Warner Schaie. New York: Springer. Steinberg, Laurence. 2001. “We Know Some Things: Parent-Adolescent Relationships in Retrospect and Prospect.” Journal of Research on Adolescence 11:1–20. Terman, Lewis M. and Melita H. Oden. 1959. Genetic Studies of Genius, Volume 5: The Gifted Group at Mid-Life: Thirty-Five Years of Follow-Up of the Superior Child. Stanford, CA: Stanford University Press. Thoits, Peggy A. 1992. “Identity Structures and Psychological Well-Being: Gender and Marital Status Comparisons.” Social Psychology Quarterly 55:236–56. Vaillant, George E. 1983. The Natural History of Alcoholism. Causes, Patterns, and Paths to Recovery. Cambridge, MA: Harvard University Press. Wallerstein, Judith S., Julia Lewis, and Susan Blakeslee. 2000. The Unexpected Legacy of Divorce: A Twenty-Five Year Landmark Study. New York : Hyperion.

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6 The Developmental Niche: A Conceptualization at the Interface of Child and Culture Charles M. Super and Sara Harkness

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esearch on human development has been shaped by two contrasting images. The first is of a single individual in a carefully controlled setting, demonstrating behaviors characteristic of a certain level or kind of functioning. Questions asked in this setting include: how do people like this perceive, think, or react? What is the structure of their intellectual abilities or the style of their affective regulation? How does this change from one age to another? The metaphor of development here is growth, an unfolding or emergence of structures and functions, a sequence of transformations that belongs to our species and the laws of which can be discovered by detailed probing under laboratory conditions. The second image is of a person richly attired in ceremonial garments and surrounded by friends and kin, behaving in a way unique to that particular setting and to the larger culture which creates it. The questions here are: why is this person doing this thing, and how did he or she learn to do it? How does the behavior fit with other aspects of the culture? What does it mean to the persons involved, and how did it come to mean that? The metaphor of development evoked by these questions is the molding by culture of human potential to the particular patterns of behavior that are adaptive in that context. Each of these images has been associated with a field of academic inquiry. The ‘universal’ individual observed under special conditions has been the Source: International Journal of Behavioral Development, 9 (1986): 545–569.

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object of psychological research, from Wundt’s brass-instrumented laboratory in Leipzig to Piaget’s methode clinique and American experiments in cognitive development. The behavior of people in exotic cultures, on the other hand, has been the domain of anthropological study. Unlike the vertical theories of developmental psychology, anthropological theories have presented a horizontal panorama of human variation. To be sure, anthropological studies have drawn on psychological theory in attempts to formulate the links between culture and the individual, and psychological researchers have recently acknowledged that the laboratory is a cultural artifact embedded in socially regulated meanings (see Jahoda 1982; Harkness and Super in press). Nevertheless, the contrasting images continue to function in the creation and presentation of research on human development, and the integration of these metaphors is a continuing challenge. In this essay, we briefly review some earlier formulations of the interface between culture and individual, and we then introduce the ‘developmental niche’, a set of concepts that is proving useful in research on culture and child development. The physical and social settings of everyday life, the customs of child care, and the psychology of caretakers are seen as three integrated subsystems of the niche, each with its own set of relations to the larger environment. Although not a formal theory in the classical sense, the developmental niche provides a framework for examining the effects of cultural features on child rearing in interaction with general developmental parameters. These ideas have been developed in the context of our work in East Africa, and we will draw on it to illustrate the major points.

Anthropological Perspectives on Human Development Culture, according to one major perspective in anthropology, resides in the individual mind; a theory of culture must therefore include how it gets there and how it functions there. From the time of Margaret Mead and Ruth Benedict, anthropologists have attempted to draw the relationships between cultural environments and the behavior of individuals within them. The most fully elaborated of these attempts is John Whiting’s ‘model for psychocultural research’, which postulated that: (1) Features in the history of any society and in the natural environment in which it is situated influence (2) the customary methods by which infants (and children) are cared for in that society, which have (3) enduring psychological and physiological effects on the members of that society, which are manifested in (4) the cultural projective-expressive systems of the society and the physiques of its members’ (J. Whiting 1981: 155).

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As drawn in schematic form (e.g., J. Whiting 1977: 30), history and environment jointly influence the ‘maintenance systems’ of a society, which include the settlement patterns, economic base, division of labor, and household structure. From the maintenance system flow elements of the child’s ‘learning environment’, the whole of socialization that leads to variation in adult psychological functioning. Aspects of adult personality as culturally formed can be inferred, finally, from the ways that they are culturally expressed or ‘projected’ in rituals or belief systems. Although Whiting explicitly acknowledges that other arrows of causality might reasonably be drawn (for example, expressive systems might influence child rearing techniques), research using this model generally follows the hypothesized causal lines, with much of it centered on describing elements of the learning environments of children in different cultures (see Munroe, Munroe and B. Whiting 1981). The Whiting model was built on prevailing psychological theories of the 1940s and 1950s, as well as on the premise in functionalist anthropology that different domains of a culture are systematically and usefully related to each other. From psychology came the idea of personality as a set of enduring dispositions whose roots could be traced to early experience. Both Freudian theory and social learning theory were used in formulating the links from individual experience to adult behavior as represented by rituals and beliefs. In addition, the model assumed that both methods of child rearing and projective systems were patterns of behavior and thought shared by many if not all members of the culture. This was, in anthropology, the ‘culture and personality’ approach to understanding the ‘typical’ or ‘modal’ personality of members of a culture. More recently, some of the theoretical underpinnings of the Whiting model have been challenged. The usefulness of ‘personality’ as a construct, its roots in interpersonal experience, and the assumption of continuity over the life-span have all been strongly questioned by empirical research (Fiske 1974; Mischel 1968; Kagan and Klein 1973; Shweder 1979). At the same time, studies of individual people in different cultures have shown that, as Whiting himself has said, culture is orthogonal to personality, and the constructs that are useful for describing behavior at the group level do not seem to apply very well to the explanation of individual behavior. In addition, the linear assumptions of cause and effect, borrowed from social learning theory’s experimental model, have been recognized as inadequate. LeVine (1970: 596–597) comments: ‘Customs like child-rearing practices and the variety of cultural behavior patterns with which they have been hypothetically linked tend to be associated with many other customs, and these multiple associations lend themselves to a variety of interpretations, some of them sociological or ecological rather than psychological. In the welter of multiple connections ... it is all to easy to find support of simple causal hypotheses by limiting one’s investigation to a few variables rather than looking at the larger structure of relations in which they are embedded.’

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The Whiting model stimulated, and over the years synthesized, a full generation of anthropological research on children and their caretakers. One of its fruits has been renewed thinking about the interface between individual development and its cultural context (see Harkness and Super in press). As anthropological understanding of this interface evolved from ‘child training’ (Whiting and Child 1953) to ‘learning environments’ (Whiting and Whiting 1975) to the ‘acquisition of culture’ (Schwartz 1981), developmental psychology was also revising its appreciation of the relationship, and we turn now to that history.

Psychology and the Environment The notion that development is influenced by the environment is about as old as the idea of development itself; in a trivial sense, environment in the form of ‘stimuli’ or even ‘experience’ has been a cornerstone of psychology since its earliest philosophical beginnings. But as the formal discipline of psychology was created to apply ‘the scientific method’ to understanding the human mind, the environment as an object of study was excluded. The new science of the mind sought universal laws, free of context, in the isolation of the laboratory. The child study movement, as it grew from collateral roots in the early part of this century (see Siegel and White 1982), was nutured by humanist, educational, interdisciplinary, and policy-oriented concerns as well as scientific ones. When it came to be firmly and broadly established in the acadamy in the two decades after World War II, however, it was transformed as ‘developmental psychology’, a logical-positivist, laboratory-based enterprise. It had fully incorporated psychology’s dedication to the individual as the object of study (Cairns 1983; McCandless 1970; Super 1982). Although the experimental paradigm has dominated the field of child development for several decades, a small but persistent tradition has always been concerned with the limitations of studying human behavior only in the laboratory. In its interdisciplinary origins it has connections to the work of Mead and other anthropologists, but it also includes observational work by psychologists: Dennis’s (1940) research in a Hopi village, for example, and studies of the psychological ecology of growing up in the Midwest by Barker and Wright (1949). The latter was particularly inspired by Lewin’s (1936) ‘field theory’ of behavior which incorporated both experimentation and non-laboratory locales. Based to some degree on this tradition, there occurred in the mid-1970s a major shift within the field of developmental psychology concerning the role of the environment in development. Many exemplars of this shift could be cited, but we will review three major statements to indicate the breadth of theoretical reorientation. McCall (1977), in one of the more widely applauded critiques of what was then modal work in developmental psychology, focused on inherent

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problems in the laboratory paradigm. ‘Few studies’, he wrote, ‘are concerned with development as it transpires in naturalistic environments’, and he attributed the triviality of much research to excessive devotion to an experimental model that came ‘to dictate rather than serve research questions’ (1977: 333). Because it is neither practical nor ethical to manipulate essential aspects of human development, McCall concluded, laboratory research can never answer questions essential to the discipline. A similar concern prompted Bronfenbrenner’s (1979) frequently quoted statement that ‘much of developmental psychology, as it now exists, is the science of the strange behavior of children in strange situations with strange adults for the briefest possible periods of time’ (1979: 19). The major thrust of his ecological approach to child development is to expand both the methods and the vision of psychology beyond the individual as the exclusive focus of analysis. A child’s environment can not be reduced to a single immediate setting containing the subject, Bronfenbrenner argued, for ‘environmental events and conditions outside any immediate setting containing the person can have a profound influence on behavior and development within that setting … for example (by) defining the meaning of the immediate situation to that person’ (1979: 18). Bronfenbrenner’s (1979) scheme for dividing the child’s environment into micro-, meso-, exo-, and macro-systems has proven widely influential as a framework for examining the ‘environment’ in a new way. One aspect of his approach especially relevant here is an emphasis on ‘the progressive accommodation throughout the life span, between the growing human organism and the changing environments in which it lives and grows’ (1977: 513). Kessen’s (1979) essay on ‘the American child and other cultural inventions’ and its subsequent elaborations (Kessel and Siegel 1983) present a philosophical and historical argument that complements McCall’s critique of methodology and Bronfenbrenner’s statement of theory. Our understandings of the nature of the child are too varying over time and too related to contemporary intellectual ambiance to permit any confident conclusions about ‘the child’. In Kessen’s words: ‘If we were truly to recognize that the study of children is not exclusively or even mainly a scientific enterprise in the narrow sense [he means “experimental”], but stretches out toward philosophy and history and demography, if we were to recognize such an expanded definition of child study, we might anticipate a new (science) whose object of study is not the true child or my piece of the true child but the changing diversity of children’ (Kessen 1983: 37–38). In short, Kessen’s claim, like McCall’s and Bronfenbrenner’s, is that child study of the previous decades did not use an adequate model of development and did not provide adequate tools for arriving at one. The appropriate object of study, he argues, is not the child but the child-in-context. As the theoretical ferment of the 1970s centered on the nature and role of the developmental environment, it is not surprising to find also at that time a reconsideration of the models for the environment. Bronfenbrenner

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and Crouter (1983) have presented an analysis of the theories of the environment hidden in the major theories of development, and correspondingly the ‘latent paradigm shifts’ concerning the environment that accompanied more overt changes in developmental theory. Until recently ‘hidden’ theories of the environment were the only kind available in psychology because of the personological bias in the discipline (Harkness 1980) and, in fact, in Western culture generally (Shweder and Bourne 1982). Bronfenbrenner’s (1979) ecological model of the environment and its network of influences has already been briefly mentioned as one product of the new look at the context of development. A second, increasingly common approach is to see continuous, inductive aspects of the environment as an ‘epigenetic landscape’ (e.g., Fishbein 1976; McCall 1981, Scarr-Salapatek 1976), borrowing the metaphor from Waddington’s (1957) genetics and Spemann’s (1938) embryology. Life-span and life-course approaches (e.g., Elder and Rockwell, 1979; Baltes 1979) represent a third rethinking of the environment, while the application of general systems theory to human development combines some features of all these models (see Sander, Stechler, Burns and Lee 1979; Sameroff 1983; Sameroff and Chandler 1975). In sum, developmental psychology underwent a fundamental change in its appreciation of the context of development in the 1970s. The limitations of a purely analytic, laboratory discipline were argued by a number of prominent authors, the validity of a developmental model based exclusively on the individual child was questioned, and fresh theories blossomed quickly from a variety of historical roots in order to represent psychology’s new insights.

The Developmental Niche The concept of the developmental niche lies at the juncture of the theoretical concerns in psychology and anthropology outlined above, and it attempts to capture important features from both disciplines. The recent models of the environment for development, however, do not generally acknowledge its cultural structuring, even though this may be the most important aspect of human ecology. On the other hand, anthropological approaches to culture and human development have been excessively oriented to the ‘final product’ in adulthood rather than focusing on developmental processes throughout the life-span. The developmental niche, in response, is a theoretical framework for studying cultural regulation of the micro-environment of the child, and it attempts to describe this environment from the point of view of the child in order to understand processes of development and acquisition of culture. The developmental niche has three major subsystems which operate together as a larger system and each of which operates conditionally with other features of the culture. The three components are: (1) the physical and social

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settings in which the child lives; (2) culturally regulated customs of child care and child rearing; and (3) the psychology of the caretakers. These three subsystems share the common function of mediating the individual’s developmental experience within the larger culture. Regularities in the subsystems, as well as thematic continuities from one culturally defined developmental stage to the next, provide material from which the child abstracts the social, affective, and cognitive rules of the culture, much as the rules of grammar are abstracted from the regularities of the speech environment. The three components of the developmental niche form the cultural context of child development.

Physical and Social Settings B. Whiting (1980) has pointed out that one of the most powerful ways culture influences child development is through providing the settings of daily life. The people who frequent the settings are seen as especially formative of social behaviors because they determine the kind of interactions children have the opportunity and the need to practice. Infants, for example, universally elicit nurturant acts from caretakers and others around them. Societal institutions such as formal schooling have a major effect on the age and sex of children’s daily companions, and thus on the types of social interactions experienced. B. Whiting, Edwards, and their collaborators (1986) have recently compiled observational data from a number of communities around the world to explore this function of culture. In our research in Kokwet, a rural Kipsigis community of Kenya, we have examined relationships between the settings of children’s everyday lives and various aspects of child development. Some of these studies reveal differences from Western norms in aspects of development that have been considered universal. For example, differences in sleep patterns between infants in Kokwet and in an urban American sample were related to differences in settings: whereas the Kokwet babies slept with their mothers and were never left alone during the day, the American babies generally slept in their own beds, often in a separate room, and they slept in separate, quiet places during the day as well. One result of these differences in physical settings and daily routines was that the Kokwet babies slept less, overall, than the American ones; they also continued to wake every few hours at night months after most American babies had begun to sleep for long periods (Super and Harkness 1982). Similarly, the percent of time an infant spent sitting (e.g., in a caretaker’s lap) as opposed to lying down was found to be a factor in the speed at which the universal skill of sitting alone is acquired (Super 1976, 1981). The physical environment of mats, cribs, and/or chairs, combined with the social environment of caretakers and companions, structure the infant’s opportunities for developing emerging behavioral potentials.

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Another example of the power of settings in determining the development of apparently universal behaviors is in the domain of gender segregation in children’s peer groups. Recent American research has established the tendency of boys and girls to associate preferentially with members of their own sex, and some effort has been oriented to documenting the exact onset of this behavior in the preschool years. In Kokwet, however, children from late infancy through middle childhood spent most of their time in mixed-age, mixed-sex groups of children from the same or neighboring households. The tendency for boys and girls to associate more with same-sex peers did not emerge until after the age of six, when they were considered old enough to leave their own homesteads to seek companions. Thus, it appears that the question of developmental trends in children’s choice of companions cannot be addressed independently of the settings of their daily lives (Harkness and Super 1985b). A salient aspect of child life in rural East African communities, as in many other cultures, is the extent to which children participate in the work of the household. Age trends in work activities, contrasted to play or rest, illustrate this aspect of the physical and social settings of the children in Kokwet. Using several hundred ‘family spot observations’ which noted the activities and locations of all members of a household at different times of day, we assembled a composite picture of the main activities of children from infancy to age nine years in Kokwet. The category of ‘work’ included a long list of chores such as processing food, cooking and tending the fire, collecting firewood and bringing water, taking care of animals (mainly cows or sheep and goats), and caring for babies. ‘Play’ included both individual and social play, while ‘rest’ included sleeping, lying down, and sitting quietly alone or with others. Together, these three categories accounted for approximately 80 percent of the children’s observations, with eating and school filling in most of the remainder (most children in Kokwet under the age of 10 years, however, did not attend school). Analysis of the activities of children of different ages shows that at age two years, play occupied almost half the children’s time while rest accounted for another 25 percent. However, the proportions of time spent in these different activities began to change rapidly toward participation in the household economy. By age four, children were observed almost equally often in play, rest, and work. By six or seven years, children were spending half their time in work activities while play and rest came to occupy minimal proportions of their days. The structuring of settings in terms of activities set the parameters for the kinds of social interactions which could take place within them, in much the same way as the cast of characters present also sets limits. In the case of Kokwet, playful interactions might occur within the context of carrying out household tasks such as watching the cows or caring for a younger sibling; but these play sequences were frequently punctuated by the demands of work (Harkness and Super 1986). In contrast to the middle class

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Western emphasis on play as central to young children’s development, work was clearly the main task of childhood in Kokwet.

Customs of Child Care Physical aspects of the setting can shape the growing child’s experience, at the most basic level, through infectious pathogens and parasites that slow, alter, or terminate the processes of biological growth. Similarly the physical availability of adequate nutrients is critical. Virtually all aspects of the physical setting, however, are mediated by cultural adaptations in child care practices. The presence, for example, of dangerous objects such as cooking fires, deep water, staircases, and large or poisonous animals will prompt accommodations in techniques of care, including closeness of supervision. Given the human and technological resources available, parents and other caretakers adapt the customs of child care to the ecological and cultural settings in which they live. Customs as discussed here are sequences of behavior so commonly used by members of the community, and so thoroughly integrated into the larger culture, that they do not need individual rationalization and are not necessarily given conscious thought. Although at the group level they can be seen as adaptations to the larger environment or ways of coping with developmental issues, they are more likely to be regarded by members of a culture as the ‘reasonable’ or ‘natural’ thing to do. As such, these features of child rearing are not so much the immediate product of individual choice or personal disposition as they are community-wide solutions to recurrent issues in child rearing. Customs in this sense include not only routine tools for everyday living, such as where to put the baby, but also infrequent, complex, and institutionalized mechanisms such as adolescent circumcision rituals and sending children to school. From the point of view of the researcher, customs of child care can be seen as behavioral strategies for dealing with children of particular ages, in the context of particular environmental constraints. Carrying an infant on the back, tied with a shawl or piece of cloth, is a customary method of infant care in many societies. Our spot observations in Kokwet show that backcarrying was rare in the first month of life but thereafter during the first year was used for 17 percent of the infant’s daytime care. Initially much of the carrying was done by the mother, but by three months of age a sibling caretaker (typically a 7-year-old sister) had assumed more than 25 percent of the immediate handling of the baby. Reasons for carrying given by Kipsigis mothers and child caretakers when asked were to soothe the baby (through contact and rocking) and to keep him or her out of trouble. In addition, the infants were riding on the caretaker’s hip or being held vertically in her arms for an additional 12 percent of the day.

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There are a number of possible consequences for the infant, including the pattern of visual experience, social interaction, and physical exercise through bodily adjustments to the caretaker’s movement (see Super 1981). In the latter case, experimental research has identified lasting effects. Porter (1972) and Clark, Kreutzberg and Chee (1977), for example, introduced passive limb exercise and vestibular stimulation to normal American infants and demonstrated significantly increased physical growth and reflexive and gross motor development. Their limited interventions appear to be less than the routine difference between rural Kipsigis and urban American customs of care. Further, though perhaps of less significance, the increased time being held results in less time available for practicing prone and supine behaviors. Infants in Kokwet were observed to be lying down about 10 percent of their waking time compared to 30 percent in an urban American sample. This difference in the patterning of physical exercise is thought to contribute to the later emergence of crawling in Kokwet, just as greater experience with sitting and walking behaviors contributes to the Kipsigis infants’ earlier accomplishment of these milestones (Super 1976, 1981). Corresponding to the physical care that results in differential exercise, parents in Kokwet customarily and deliberately ‘taught’ their infants to sit and walk (but not to crawl). There were specific behavioral routines, with specific words to refer to them, that parents and siblings all knew and practiced on a nearly daily basis months before the skills were fully acquired by the baby.

Psychology of the Caretakers Although most child-rearing customs are accepted without critical examination, they are often accompanied by specific beliefs concerning their significance. Kipsigis parents believed that without specific teaching, infants’ sitting and walking would be delayed or impaired (Super 1976, 1981); the belief did not extend to crawling. There are many beliefs and values that are regulated by the culture and that in turn regulate development of the child; we separate them, as the psychology of the caretakers, to be the third systematic feature of the developmental niche. The psychology of the caretakers includes ethnotheories of child behavior and development as well as the commonly learned affective orientations which parents bring to their experience of parenting. Most important among the ethnotheories are beliefs concerning the nature and needs of children, parental and community goals for rearing, and caretaker beliefs about effective rearing techniques. Within constraints created by the physical environment, available technology, customs of child care, and the demands of parents’ own activities, the psychology of the caretakers organizes parental strategies of child rearing in both the immediate and the more long-term

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sense. For example, parents’ assignments of their children to different settings expresses beliefs about the capabilities of children at different ages as well as parental goals for their children’s development. The responses of parents and other caretakers to children’s emotional displays also are directed by ideas, often implicit, about the development of the self in the context of the particular culture. Caretaker psychology provides immediate structure to children’s development through the meaning it invests in universal behaviors and processes. Even to the earliest behavior of newborns adults apply culturally relevant schemas of interpretation. We asked mothers in Kokwet and Boston to rate the similarity of various neonatal bahaviors included on the Neonatal Behavioral Assessment Scale (Brazelton 1973), and the results indicate that while mothers in both cultures used similar dimensions in making their responses, their emphasis differed (Super 1986b). A jerky sweep of the hand in response to an examiner’s touch on the face, for example, was seen positively by Kipsigis mothers as reflecting responsive motor integrity (Dimension II), a sign of health and strength. A mother in Boston, in contrast, was more likely to weight her perception of the motion with concern over the disorganization implicit in the jerkiness, for controlled states of arousal (Dimension I) were the dominant organizing feature of American perceptions of the newborn. More generally, deVries and Super (1979) concluded on the basis of conducting neonatal examinations in the home that some cultures (Masai, Kikuyu, and Kipsigis in their study) assume infants to be ‘fragile creatures, easily threatened by rough handling or overstimulation … In contrast, the Digo appear to think of their babies as relatively hardy and not in need of special protection from physical distress’ (1979: 95). Mothers’ beliefs were also evident in their approaches to child language socialization. In interviews about how children learn to talk, the Kokwet mothers generally expressed the view that children learned to talk more from each other than from the mothers themselves. Some of the mothers claimed they did nothing to enourage their children’s language development, and among those who did, commands (which generally do not require a verbal response) were the most frequently mentioned type of language input. Naturalistic observations confirm the mothers’ reports: by comparison with American studies, the frequency of the Kokwet mothers’ speech to their twoto three-year-old children was remarkably low. We have suggested that this approach to child language socialization in Kokwet reflects Kipsigis parental goals of training for obedience and responsibility rather than for verbally expressive individuality (Harkness and Super 1982). The centrality of obedience and responsibility in Kipsigis parental theories was also demonstrated in our explorations of mothers’ ideas of intelligence and personality in Kokwet (Super 1983). Discussions with a group of mothers in the community yielded a group of words and phrases that were commonly used in talking about children. Concepts referring to a child’s helpfulness

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and obedience were the largest group among these. Another term, translated as ‘intelligence’ (ng’om), also carried a strong component of ‘responsibility’. One informant illustrated the meaning as follows: ‘For a girl who is ng’om, after eating she sweeps the house because she knows it should be done. Then she washes the dishes, looks for vegetables, and takes good care of the baby. When you come home, you feel pleased and say, “This child is ng’om.” Another girl may not even clean her own dishes, but just go out and play, leaving the baby to cry. For a boy, if he is ng’om, he will watch the cows, and take them to the river without being told. He knows to separate the calves from the cows and he will fix the thorn fence when it is broken. The other boy will let the cows into the maize field and will be found playing while they eat the maize.’

Further investigations showed that while ‘intelligence’ was recognized as a verbal, social quality in the abstract, its most salient expression was in the domain of carrying out one’s responsibilities at home (cf. Dasen, Barthelemy, Kan, Kouame, Daouda, Adjei and Assande 1985). In this context the ability to be helpful without being reminded by an adult emerged as an important marker of intelligence. Likewise, mothers in Kokwet stated that they felt they could make judgments of a child’s personality at about the same age (five or six years) that they expected to be able to assign the child to run an errand to a nearby homestead or store (Super and Harkness 1983). Culturally constructed theories such as these were important in parents’ definitions of their children’s developmental stage; and such definitions, in turn, were translated into parental assignments of their children to different physical and social settings. The concepts of obedience and responsibility were important not only for parents’ judgments of their children’s enduring qualities, but also in parental decisions about whether a child was ‘old enough’ to carry out culturally salient tasks, e.g., old enough to send on errands.

Three Corollaries We have borrowed the term ‘niche’ from biological ecology, where it is used to refer to an organism’s place or function in a biosystem (the etymological origin is the same as ‘nest’). There are three corollaries to be borrowed as well. (1) The three components of the niche operate in a coordinated manner. (2) Each component interacts differentially with other features of the larger ecology. (3) The organism and the niche are mutually adapted. It is noteworthy that these ideas are also represented to varying degrees in culture theory; that cultural components act as a coordinated system, in particular, has been a central concept in anthropological theory almost from its beginnings. The niche as a system. The three components of the developmental niche operate as a system with homeostatic mechanisms that promote consonance

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among them. This is particularly evident in the examples of motor and language development described above. The settings, customs, and caretaker psychology each dispose toward the same acquisition and socialization. It is through such reinforcing patterns that culture has its most powerful immediate influence. Coordination in subsystems of the niche is also evident at times of successful transition in the child’s culturally defined developmental status, for example the shift from infancy to early childhood. Like many other sub-Saharan peoples, the Kipsigis believed that having a younger sibling was an important element in the socialization of children. Last-born children, because they were never replaced by a new baby as the center of the family’s indulgent attentions, tended to be ‘spoiled’ and difficult throughout life, it was thought, lacking in those qualities of obedience and responsibility which we have described above. For this reason, the arrival of a new baby was seen as the opportunity to implement a change of status for the second-to-youngest, which was expressed through changes in the settings and customs related to the child. While as an infant the child had slept at the mother’s breast, he or she would now be moved to sleep at her back or perhaps with the other young children in a separate bed. This child would also no longer be carried by the mother, and would be considered old enough to be the junior member of a household play or chore group rather than being assigned to a child caretaker. We have documented the changes in the amount and nature of adult attention which children received as a function of this culturally denned developmental change, as well as the changes in their daily activities (Harkness and Super 1983, 1986). Subsystems of the niche and external systems. Each of the three subsystems of the niche is also embedded, in different ways, in other aspects of the human ecology; the niche is an ‘open system’ in the formal sense (von Bertalanffy 1968). We have discussed some immediate effects of the physical setting above, but there are larger effects of the physical environment on various aspects of the niche. For example, the differences in infant carrying between Kokwet and Boston appear, in wider perspective, to be strongly influenced by climate. J. Whiting (1981: 175–176) concluded on the basis of a cross-cultural survey: ‘The manner in which infants are cared for is to a considerable extent constrained by the physical environment, the temperature of the coldest month of the year being the most important factor. In cold climates infants tend to be carried in a cradle, swaddled, and put in a cradle to sleep. In warm climates they are usually carried in a sling or shawl, often nap on their caretaker’s back, sleep next to their mothers at night, and are clothed lightly or not at all.’ Similarly, the subsistence base of a society (agricultural vs hunting and gathering) has been related to the goals and techniques of socialization for independence and obedience (Barry, Child and Bacon 1958), that is particularly, in our terms, to the psychology of the caretakers and the parent–child interactions that derive therefrom. The concept

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of the development niche is designed, in part, to facilitate identification of the specific mechanisms that lie behind such large-scale, cross-cultural findings, and in so doing it reveals that the three components are differentially responsive to features of the larger culture and environment. The connections are most evident under conditions of change, for any component of the niche can be a route of innovation and disequilibrium. In Kokwet, the introduction of free, government-sponsored schooling has affected the settings of daily life for school-age children and the younger siblings who have been their charges. The custom of adolescent circumcision has been affected by the strictures of Christian missionary churches in the area, and more recently by a Presidential order that female circumcision was to be disallowed altogether. Parental beliefs about parent–child relations have been affected by teachings of the churches and other sources of ‘modern’ thinking, with wide-ranging effects that include language socialization (Harkness 1977) and family intimacy. In order to understand local adaptations to these changes introduced from the outside, it is useful to refer back to the first corollary of homeostatic mechanisms promoting cultural consistency. When change is introduced through one of the subsystems of the developmental niche, the initial cultural response is likely to be ‘conservative’ in that attempts are made to preserve as many elements as possible of the subsystem affected, and the other two subsystems may not change at all. Thus in the example of schooling, child caretakers continued to be used for infant care in Kokwet, even though in theory there should have been fewer of them available. Mothers have overcome this potential shortage through using younger than ideal siblings as caretakers, enrolling some children in school later, or hiring children from other families. The parental theories of obedience and responsibility, central for traditional roles of children in Kipsigis society but probably less adaptive for success in school, continued to define children’s developmental status and social identities. Eventually, however, if consequences of changes grow and ripple through the system, the same forces of homeostasis that minimize the initial response will now bring the three subsystems of the niche into a new consonance. In the case of schooling in Kokwet, parents began to perceive the importance of education as a way to send children into the salaried economy and reduce pressure on the farm land. This fostered changes in the settings parents assigned their children to and their customary child care practices. Daily homework and year-end exam preparation have come to replace some chores and other traditional features of family life. The concept of ng’om has been elaborated to ng’om en ga (‘intelligent at home’) and ng’om en sukul (‘intelligent at school’), child characteristics which were generally agreed to be uncorrelated. The frequent appearance of ‘all or nothing’ forms of culture change for children may be the joint result of the homeostatic and the differential linkage features of the developmental niche.

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Mutual adaptation. Popular conceptions of adaptation have the organism adapting to the environment. Evolutionary biologists have found the relationship more problematic. Lewontin (1978), for example, agrees that as antelope and other hooved species migrate to new grasslands, selection may indeed, over time, effect their adaptation to the niche. On the other hand, he points out, the animals also alter the grasses through the physical action of their feet, the biochemical action of their droppings, and of course their selective actions of consumption and seed dispersal. The niche adapts too, and the ‘final’ result, if there is one, is a mutual adaptation of organism and niche, a co-evolution of the individual–environment system. The same mutuality occurs in the developmental niche. Certainly children ‘adapt’ to their environment; that is the basis of a full literature concerning environmental effects on child development. But there is also a complementary environmental adaptation, or more accurately, a co-evolution. At the level of individuals, this has received wide attention in the study of temperament and ‘child effects on parental behavior’ (Bell 1968; Thomas and Chess 1977). It is also evident in attempts to conceptualize the individual and environment as a formal system (Sander et al. 1979; Sameroff 1983). More generally, however, species-wide characteristics of growth act to constrain the kinds of niches that work. Rogoff, Sellers, Pirrota, Fox, and White (1975) have drawn inferences about universal stages in development from similarities across cultures in the ages at which certain tasks and responsibilities are assigned to children. In a more limited study, we have found an age-related structure to children’s social environments in Kokwet that is familiar to the Western eye. Despite some unique features, it seems to reflect environmental accommodation to the universal needs and abilities of different aged children (Harkness and Super 1983). There is a growing body of evidence on maturationally controlled shifts in children’s cognitive and emotional characteristics (Kagan 1976, 1984; Konner 1982; Super 1972, in press), and these changes appear to be a critical element in the expectations and demands placed on children by parents and the community. Because of the multiple interconnectedness of elements of the niche with each other and with the larger environment, however, there are constraints on the ability of niches to adapt. For example, the daily schedules of American parents and their values regarding independence and autonomy make particularly troublesome an infant who is irregular in sleeping habits. This aspect of individual temperament is one factor in the classic ‘difficult child syndrome’ of Thomas and Chess (1977). In Kokwet, however, sleeping arrangements and the absence of institutionalized work schedules virtually eliminated sleep as a source of difficulty in caring for infants. On the other hand, the Kipsigis niche was not easily able to deal with the baby who did not like being carried on the back, or who objected to being cared for by someone other than the mother. These two common features of infant care in Kokwet were too tightly connected to the mothers’ work and the larger organization

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of family life to be very flexible in the absence of major reorganization (Super and Harkness 1981; Super 1986a).

Niches in Development The developmental niche of a child does not remain constant for long. In large part this is environmental accommodation to the growing individual, but the quality and timing of shifts in the niche bear the imprint of culture. Most importantly, there is a synergy to the sequence of niches that creates the most powerful long-term effects of culture on development. Western theories of development, aside from the most extreme behaviorist position, locate discrete stages in psychological growth, a hierarchical, goaloriented anologue to ‘punctuated equilibrium’ in evolutionary theory (Gould and Eldredge 1977, 1986). At the core of each stage is a common, age-related task, be it understanding object permanency (Piaget 1970), establishing basic trust (Erikson 1950), or resolving Oedipal issues (Freud 1956). There are important truths represented in such theories, but they overlook culturally specific themes that run across stages. One consequence of these larger themes is a subtle restatement of the task for any one stage in light of the transcendent issues. In Kokwet, the values of obedience and responsibility provided a central theme of continuity in successive developmental niches of infancy and childhood. The sharing of infant care, the close proximity of infants to others, and the consequent necessity for the infant to adapt to the exigencies of other people’s daily lives composed this lesson: you are part of a social group whose needs will shape your life from moment to moment, just as it will accommodate to your needs. The universal transition to early childhood took place with local goals and methods. The child was distanced from the mother’s breast, back, and bed, stronger ties were developed with peers and older sibling caretakers, and the child began in the third year of life involvement in the household economy. The child learned about respect for elders and responsibilities to the household. By six or seven years the child spent the majority of waking hours in productive and largely prosocial activities, but forged a new and generally positive relationship with the parents as a reliable helper in the tasks of the household. The acquisition of social responsibility was the criterion for adequate development, and its growth defined the beginnings, ends, and internal structuring of Kipsigis developmental stages. This agenda for social behavioral development is intimately related to the themes of affective development. Unlike most middle-class American parents, parents in Kokwet did not customarily engage in negotiations with their infants or children over the regulation of emotion, sleep–wake patterns, or eating. Initially, infant care practices consisted of management by others of the infant’s state. Signals of hunger, tiredness, or fussiness were responded

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to promptly for the restoration of equilibrium. Although this pattern of care is sometimes labeled indulgent, as might be appropriate for a European or American who used it, the local meaning was probably quite different: others, not the baby, are in charge of dealing with variations in the baby’s physical and emotional state. The decrease in outside regulation as a Kipsigis child progressed to the early childhood niche could be difficult, but one theme remained constant: emotional perturbations were met with canning and distraction, not communication and elaboration with others. By middle childhood the focus was on what needs to be done, not on what the child felt like doing. The management of state in the individual became an accessory to the management of the social group as a whole. Short of physical symptoms of distress, variations in emotional state were not a focus of major concern to either caretakers or the child. The ‘affective–cognitive structures’ (Izard 1978) developed by the child who moves through these niches necessarily reflects the meanings abstracted from them. As revealed by symoblic interpretations of line drawings (Harkness and Super 1985a), Kipsigis children have, by middle childhood, learned to experience a relatively calm state as positive. They are cautious with regard to a more ‘agitated, excited’ stimulus, universally labeled ‘happy’ by American adults and older children. As one Kipsigis explained, ‘Being happy is when nothing is bothering you.’ Even when responding to identical physical stimuli and using common words with broadly similar denotation, Kipsigis and Americans have constructed different systems of meaning, different affective – cognitive structures, from the scripts learned and relearned during childhood. Our discussion so far has dealt with similarities in the content of themes across niches, but it is important to realize that the sequence of niches also regulates transitions. Thus what may appear to be a sudden break in societal demands may actually be a familiar and rehearsed transformation– metaphorically, an intra-dimensional rather than extra-dimensional shift (Kendler and Kendler 1967). The ‘indulgent’ niche of infancy and the strict prohibitions on crying at the time of adolescent circumcision are not inconsistent. Rather the surgical ceremony marks quite dramatically the transition from childhood to adulthood, a transition that has been prepared by all the previous niches. The change is a sharp one from an outside view, but for the Kipsigis child it is an important culmination of experience, tying together the central symbols of childhood and transforming one to an adult, a Kipsigis adult.

Summary and Conclusion The developmental niche is a conceptualization at the interface of child and culture. It can serve as a framework for relating findings in the separate disciplines of psychology and anthropology, and for examining the mechanisms involved in the cultural regulation of child development. The three components

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of the developmental niche involved in this mediation are: (1) the physical and social settings in which the child lives; (2) the customs of child care and child rearing; and (3) the psychology of the caretakers. These three subsystems function with different relationships to other features of the larger culture and environment and thus they constitute somewhat independent routes of disequilibrium and innovation in the rearing of different cohorts of children. Nevertheless, homeostatic mechanisms tend to keep the three subsystems in harmony with each other and appropriate to the developmental level of the child. The settings, customs, and caretaker psychology share a common function in organizing the individual’s developmental experience. Regularities within and among the subsystems, and thematic continuities and progressions across the niches of childhood provide material from which the child abstracts the social, affective, and cognitive rules of the culture. Research on human development has been shaped by two central but contrasting metaphors. In psychology, human development has been viewed as a process of growth, of stage-like unfolding species-specific abilities. In anthropology, development has been viewed primarily as learning, even as a process of molding from rather general potentials the culturally particular patterns of behavior and thought. The concept of the developmental niche represents an attempt to synthesize these two opposing metaphors, and it has drawn from several disciplines recent theories of the relationships between individual growth and its environmental context. The development niche is thus also a metaphor, in which the child and the culture are seen as mutually interactive systems. The usefulness of this metaphor for research lies in its delineation of aspects of the child’s environment that have gone often unrecognized in psychology, while focusing on the processes of growth that are at the heart of developmental theory.

Note The original research summarized here was supported in part by grants from the Carnegie Corporation of New York, W.T. Grant Foundation, the National Institute of Mental Health (grant no. 33281), and the Spencer Foundation. All statements made and opinions expressed are the sole responsibility of the authors.

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7 Conceptualizing Adult Development Calvin F. Settlage, John Curtis, Marjorie Lozoff, Milton Lozoff, George Silberschatz and Earl J. Simburg

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ur study of adult development rests on the premise that human psychological development is a lifelong process. Originally development was regarded as a childhood phenomenon ending with the attainment of adult sexual capabilities during puberty and adolescence. Freud’s (1905) theory of psychosexual development was anchored in the biologically predetermined maturational progression through the oral, anal, oedipal, latency, and adolescent stages of psychosexual development. Psychoanalytic thinking later defined development as being initiated not only by biological, but also by psychological factors; development was extended to include adulthood. For example, Erikson (1950) elaborated Freud’s psycho-sexual theory to include three post-adolescent stages characterized by the successive attainment of a capacity for intimacy, for generativity, and for ego integrity. The concept of adult stages of development was also employed by Benedek (1959) in her discussion of parenthood as a developmental phase. Similarly, Bibring et al. (1961) viewed pregnancy as a part of a woman’s development and as initiating a developmental process leading to the special and relatively unambivalent attitude of the grandmother toward the grandchild. Freud’s theory of psychosexual development has been significantly extended and complemented by separation-individuation theory, a psychoanalytic developmental schema conceptualized by Mahler (Mahler et al., 1975). Although focused on the first three years of life, separation-individuation theory has life-course applicability (Panel, 1973).

Source: Journal of the American Psychoanalytic Association, 36 (1988): 347–369.

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In recent years, new studies in “lifespan psychology” have generated data showing that development continues in an active way throughout life, and that structural change does not stop with adolescence (Emde, 1985, pp. 59–60). A number of writers have applied stage theory to adulthood. Citing evidence from his own and other studies, Gould (1972) has described adulthood as a time of active and systematic change embodying a series of distinct stages. Gould’s stages reflect changes in the sense of self as influenced particularly by the passage of time. Jacques (1981) demarcated adult developmental stages consisting of early adulthood; the midlife crisis, with transition into mature adulthood; and the late adult crisis, with transition into late adulthood. He has reserved judgement about the possibility of a still later stage at about 80 years of age. Dewald (1981) related adult stages to developmental tasks and crises: choice of occupation, marriage, parenthood, personal limitations and disappointments, illness and disability, retirement, aging and death. Moving away from stage theory, Colarusso and Nemiroff (1981) divided adulthood into early, middle, and late periods, stating as their objective the definition of the dynamic adult developmental tasks which occur roughly within these arbitrary chronological demarcations, rather than the definition of phases as such. They noted the absence of a comprehensive theory of adult development and offered a basis for such a theory. Similarly, Neugarten (1979) feels that psychological remodeling occurs throughout adult life, but that it is inaccurate to describe adulthood as a series of discrete and neatly bound stages. In his discussion of adult development based on the mourningliberation process, Pollock (1981) speaks of life-course expectancies and of adult developmental sequences as fields of development encompassing many variables. He, too, does not delineate discrete developmental stages. Our initial explorations led to the recognition that the stage model is not entirely satisfactory for adult development. Whereas it does provide a coherent and consistently applicable framework for the progression of individual child and adolescent development, the stage model has less coherence and a more limited application in individual adult development. In childhood development, each stage is initiated by a biologically predetermined maturational change. The childhood stages are therefore universal, occurring in all individuals. They follow an invariant sequence, and the ages for the onset and accomplishment of the stages are quite uniform, usually varying narrowly among individuals. In contrast, adult development is not initiated by biological maturational change, and the adult stages are not universal. For example, not everyone marries or becomes a parent, and all women do not become pregnant. Consequently, the stages are not invariant. The age of onset and accomplishment of an adult stage, such as parenthood, can vary widely among individuals. While the adult stage model provides a useful outline of the overall adult developmental progression and offers valuable developmental insights, it does not provide sufficiently specific criteria for adult development. We therefore

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perceived the need to delineate a new model for development that would have applicability in adulthood and in childhood as well. In this initial conceptualization, we propose and will attempt to illustrate that such criteria can be derived from the concept of developmental process. Our pursuit of this objective will involve: (1) examination of the nature of development, (2) delineation of developmental process, (3) conceptualization of a process model of development, and (4) application of the proposed model to individual life-course development.

The Nature of Development The following statements from various authorities reflect close agreement on definitions of development1: 1. A gradual advance or growth and differentiation through progressive stages. 2. The whole process of growth and differentiation by which potentialities are realized. 3. A progressive development from lower and simpler to higher or more complex forms of organization. 4. A sequence of continuous change in a system extending over a considerable time leading to progressive change to a higher degree of differentiation and complexity (English and English, 1958, p.. 148). 5. Development proceeds from a state of relative globality and lack of differentiation to a stage of increasing differentiation, articulation, and hierarchic integration (H. Werner, in Wolff, 1960, p. 29). 6. A progression of stages wherein the transition from one stage to the next is defined by phase-specific and qualitatively new interactions between the individual and his environment; the transition constitutes a total restructuring of already present schemata under a new total organization in which more global (earlier) forms of behavior as well as more differentiated forms are available to the more mature individual (J. Piaget, in Wolff, 1960, p. 34). 7. Development is usually considered to include growth, learning, and the changes of biological maturation (English and English, 1958, p. 34). Psychoanalytic theory conceives human development to be determined by individual biological endowment and the influence of the parental, familial, sociocultural, and physical environment. The term maturation refers to: (1) the emergence of hereditary potential, such as talent and intelligence; (2) the biologically predetermined progression through the psychosexual and separation-individuation stages; and (3) the parallel unfolding of ego apparatus, as for locomotion, language, and procreation. Development, in both

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children and adults, refers to progressive growth resulting from the interaction of endowment factors and environmental factors. Drive manifestations and the ego apparatus, although biologically provided, are shaped in this interaction. Those ego functions and structures which are not biologically rooted are developed through human interactions and the processes of internalization and identification (see Hartmann, 1939, pp. 50, 103–105, and Hartmann and Kris, 1945, pp. 24–26, regarding the concepts maturation and development). In basic agreement with the above-stated definitions, we define development as a process of growth, differentiation, and integration that progresses from lower and simpler to higher and more complex forms of organization and function. We propose further that the functions and structures resulting from development constitute additions to or advances in the self-regulatory and adaptive capacities. Although in general agreement with Pollock’s (1981) conceptualization of adult development, our definition of development differs from his. Pollock states (p. 552): Aging is development throughout the life course. Development, obviously, is not the same as growth and can include progression, regression, new contributions, remodeling, and, in some ways, decline. Aging, beginning with conception and ending with death, is to be distinguished from aged, a period of late adult life (usually after 75) where changes that lead to ultimate failures become evident. Decline may or may not be regressive.

In Pollock’s view, aging as a life-course process involves development as well as decline. We do not believe that decline or loss of function, or regression can be regarded as development. Temporary regression is part of the ebb and flow of developmental process, but the eventual outcome of development is forwart and new. In contrast to the biological changes of childhood and adolescence which are intrinsic to the development of new and higher functions, the biological changes of adulthood commonly result in decline and loss of function. Such change is nevertheless instrumental in adult development. Particularly in late adulthood, it can be a major stimulus for new development to compensate for loss.

Developmental Process Developmental process is the function- and structure-forming process that parallels and derives from developmental interaction. Our discussion of developmental process begins with childhood where its basis is most readily perceived. In its original form, it rests on the mother-child interaction. The mutual interactive regulation of the child’s emotional state begins at birth, and is the predominant regulatory mode during the first year of life.

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During the first year and more intensely during the second year, developmental process accounts for the formation of the child’s self-regulatory capability. Within the mother-child interaction, the mother serves initially as an external auxiliary ego for the child. Through identification with the mother, her regulatory interventions and the attitudes governing them are internalized and become part of the child’s own regulatory functions. These functions gain increasing autonomy as they gradually become independent of their source in the parent. Concurrently, they become integrated, organized, and grouped within the developing ego and superego structures as they progress from preliminary to more definitive structuring. A developmental interaction also takes place with the father and with other closely involved persons, such as older siblings, grandparents, and parent surrogates. In early development, the influence of the broader sociocultural and physical environment is mediated through these primary dyadic relationships. The attainment of a new function by the child requires a corresponding relinquishment by the child of the mother’s no longer needed participation as an external auxiliary ego, and the relinquishment by the mother of the new function to the child. Such relinquishment is essential to the full internalization of functions in the progression toward integration and relative autonomy. Self-regulation means that a function, although still related to its source, is operative without immediate external support. Successful developmental process thus leads to a diminishing developmental need for the human object (see, e.g. Settlage 1980). The mother-child relationship embodies a developmental potential or gradient determined by the difference between the functional and structural level of the mental apparatus of the mother and that of the child. Because of this gradient, the developmental interaction “lifts” the forming structure of the child to successively higher levels of function and organization (Loewald, 1960, pp. 20–21; Loewald, 1978, p. 498; Settlage, 1980, pp. 152–153). The gradient concept can be applied to any two-person relationship wherein one individual can learn arid develop in interaction with the other. Developmental process in the adult can involve an overt, close relationship, as between mentor and student, or a more subtle and distant relationship, as between lecturer and listener or even author and reader. The developmental positions of the involved pair and the gradient can shift back arid forth as each develops in relation to the other. This phenomenon can be observed in study arid discussion groups. It is conceivable that developmental process, in its most refined form, can take place solely intrapsychically in imagined interactions with internalized representations of others or oneself, as in creative process. In the progression from infancy through adulthood, the interactive process tends to become less interpersonally close, more refined, and more internalized. Developmental process usually becomes engaged and is carried out at a conscious or preconscious level of awareness. But it can take place, in part or in whole, at an unconscious level.

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Loss and Development Historically, the role of loss in psychic structure formation was set forth by Freud (1917) in his account of the mourning process (pp. 237–258). Through mourning and internalization, the tie to the lost object is replaced by psychic structure in the form of an identification. Freud (1923) subsequently applied the concept of mourning to development, stating that an attenuated process similar to that of mourning is inherent in development generally. In his words, “the character of the ego is the precipitate of abandoned object cathexes” (p. 29). Of interest in this regard is Pollock’s (1977) concept of the mourningliberation process. Seeing mourning as a transformational process that provides for the adaptation to change (p. 14), Pollock emphasized gain, not loss (p. 11): I have found the focus on the mourning-liberation process to be of great importance. The basic insight is that parts of the self that once were, or that one hoped might be, are no longer possible. With the working out of the mourning of the changed self, lost others, unfulfilled aspirations, as well as feelings about reality losses and changes, there is an increasing ability to face reality as it is and as it can be. “Liberation” from the past allows the unattainable to occur [Pollock, 1981, p. 576].

Mahler (1972), in her conceptualization of the separation-individuation process, observed that a minimal threat of object loss is inherent in every new step of independent functioning. Separation is obligatory in normal development, and the threat of object loss is an indispensable developmental catalyst (p. 333). Thus, loss in the course of development is associated with advances in ego and superego development resulting from internalization and identification. The full structuring of an identification-derived function entails the attenuation and eventual relinquishment of the function-associated involvement with and tie to the love object. Such a “letting go of” entails both pleasure in the child’s autonomous functioning and varying degrees of sense of loss. The sense of loss ranges from absent or minimal, to strong, for both the child and the mother. Facing the loss and letting go involve feelings of sadness and grief in a process similar to mourning (Mahler, 1961, p. 162). However, the forward-moving impact of the maturational thrust and the anticipatory excitement of the desire to develop can supercede the sense of loss. The threat of passively experienced loss can also be overridden defensively by an active relinquishment of object ties and earlier adaptive modes. In both childhood and adulthood, normative loss or threat of loss within the developmental progression and adventitious loss from life’s unexpected experiences can stimulate and mobilize developmental process. Adventitious loss can include the following: loss of a loved one; loss of a function; loss of a body part; loss of self-esteem; loss of a pet; and even loss of a valued inanimate object.

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Loss of a relationship and the often involved tacit dependency commonly confront the individual with the need to develop new capacities. Despite the prevalence of loss experiences in old age, it is noteworthy that psychic integrity and the sense of identity can be maintained, often through further development.

The Process Model of Development The stimulus for development is disturbance of the previously adequate selfregulatory and adaptive functioning. Such disturbance is caused by different kinds of stimuli: (1) biological maturation; (2) environmental expectation and demand; (3) a loss or other traumatic experience; and (4) a perceived possibility of achieving a better adaptation resulting in a self-initiated desire to develop. The disturbance of the previously satisfactory functioning creates an unsettled state or disequilibrium with varying degrees of mental and emotional stress. The state can be ego-syntonic, as in the case of the self-initiated desire to develop, or it can be ego-dystonic, as in the case of the thrust-upon, intrusive, traumatic experience. Its emotional concomitants can range from pleasant anticipatory feelings, even eagerness, to intensely unpleasant, anxious or depressed feelings. Regardless of whether the state is self-initiated or thrust upon the individual, there is a conscious or unconscious sense of dissatisfaction with one’s situation that calls for solution and change. In response to the dissatisfaction, the individual may develop, may attempt to maintain the status quo, may regress to earlier levels and modes of self-regulation and adaptation, or may employ defensive moves that can lead to the formation of psychopathology.

A Sequence of Developmental Process A developmental response to the dissatisfaction of the unsettled state activates a sequence of developmental process. This sequence includes the following elements: 1. Developmental Challenge. Either consciously or unconsciously, the individual perceives and accepts a developmental challenge. Examples of developmental challenges are the need for new skills, new modes of regulating feelings and impulses, and new attitudes and values. Acceptance of a specific developmental challenge engages developmental process and transforms the unsettled state into an organized, goal-directed state. 2. Developmental Tension. Within this goal-directed state, the gap between where the individual is and where the individual now wants or needs to

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be creates a developmental tension. This positive tension, which replaces the negatively experienced disequilibrium of the unsettled state, serves as a motivating and development sustaining force. 3. Developmental Conflict. The engagement of developmental process also generates developmental conflict. The acceptance of a developmental challenge transforms the internally generated or environmentally presented expectations and demands, and the resulting unsettled state, into an internal developmental conflict. The desire to change, as it includes the wish for approval and fear of disapproval, commonly evokes fear of loss of the security experienced in the status quo, fear of failure and discouragement at seeming lack of progress, and anxieties about imagined negative consequences of success. Optimally, developmental conflict causes only transient or no symptomatic behavior and is resolved through development (see Nagera, 1966, pp. 39–47). 4. Resolution of Developmental Conflict. Resolution of developmental conflict leads to self-regulatory or adaptive structure formation. It proceeds hand in hand with the mastery, internalization, and integration of the new function. 5. Change in the Self-representation. Finally, the development and integration of a new function or structure is marked by a change in the selfrepresentation and in the individual’s overall sense of identity. A sequence of developmental process results in one or more of the following accomplishments: (a) formation of a new function; (b) elaboration or refinement of an existing function; (c) further integration of an existing function toward greater autonomy and structural stability; (d) reorganization of psychic structure to a higher level of function. To reiterate and elaborate, a developmental challenge can come from within the individual, through self-initiation or as a result of maturational processes, or from the environment, through the presentation of a new expectation, a new opportunity, or a new problem. Examples of developmental challenges are: the maturational unfolding of the language capacity in childhood; adult experiences such as marriage, parenthood, or the death of spouse; desirable opportunities such as learning a new skill or assuming new responsibilities; and threatening experiences such as illness of oneself or an important love object, loss of a job, loss of a friend, or loss of function due to aging. Engagement of developmental process can result from a conscious decision or from unconscious processes. The developmental tension resulting from the engagement of developmental process is positively experienced and serves to motivate and propel the individual toward the acquisition, mastery, and integration of the new function. Developmental process includes developmental conflict. The striving to learn and master a new function can be complicated by the fear of failure, by

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frustration over lack of progress, and by fear of success. Movement toward a new and higher level of functioning also engenders conflict. It characteristically involves letting go of emotional ties and giving up familiar, well-practiced and therefore “safe” modes of functioning in which there also has been a significant emotional investment. Conflict also can be generated by conscious and unconscious fantasies and concerns about the effect of one’s developmental advance on others. Examples of such concerns are: guilt over winning in actual or imagined competitions; guilt over surviving and enjoying success at the expense of another; and guilt over “abandoning” the parent or the spouse in moving ahead of, and thus away, from them. Under normal conditions, resolution of developmental conflict is a usual and natural occurrence. It requires tolerance of frustration and anxiety while resolving the conflict and relinquishing failed or outmoded functions and dependencies. Resolution of conflict is paralleled by the acquisition, mastery, and integration of the new function; pleasure and satisfaction are associated with these achievements. Although resolution and mastery characteristically involve progressive and regressive alternations, development sometimes proceeds smoothly, quickly, and relatively free of conflict. Failure to resolve developmental conflict tends to occur under pathological conditions and results in temporary or indefinite arrest of development in given areas. Resumption of development sometimes takes place spontaneously at a later time, due to more favorable conditions (Goodman, 1977, pp. 56–60) or as a consequence of successful treatment.

Examples The following examples are taken from the clinical psychoanalytic situation and from everyday life. In keeping with the focus of this paper, the illustrations are mainly of adult development. One example of child development and one of adolescent development are included to demonstrate the lifecourse applicability of the model. The immediate antecedents and the proposed steps in the accomplishment of developmental process are denoted and underscored in the child example and are discernable and noted, in some measure, in the other examples. By these examples, we are not seeking to explicate all the factors in therapeutic and developmental process that result in the initiation or accomplishment of a developmental advance. Rather, our purpose is to demonstrate the application of the major dimensions of our process model of development. The child example involves a grandmother and her two grandsons, ages five and ten, whom she regularly took on outings. Customarily, the younger boy would go with his grandmother to the ladies’ room while the older boy went to the men’s room. On an outing at the time the younger boy had begun kindergarten, the three of them headed for the ladies’ and men’s rooms.

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As they approached them, conflict was unmistakably present in the younger boy’s facial and bodily expressions: He had a very warm relationship with his grandmother. With her, was security and the familiar. With the older brother, who tended to be impatient and indifferent, lay change and uncertainty. Yet, in the service of growth, the choice of the five-year-old seemed inevitable. Pulled by his gender identity, he tagged along behind his brother. As he marched into the men’s room, his expression of hesitancy and conflict turned into determination and swagger. The five-year-old’s previous comfort in going to the ladies’ room with his grandmother was disrupted by his maturationally induced ability to fend for himself, by his growing identity as a male, and by the environmental expectation that he use the men’s room and not the ladies’ room. The resulting unsettled state, which he also had been experiencing in school and in other contexts with his parents, eventuated in his solution of the problem through development. He embraced the developmental challenge. The attendant developmental tension and developmental conflict were manifested in his briefly expressed facial and bodily gestures. Resolution of conflict was evinced in the action of using the men’s room. In this example, the emotional experience of loss and relinquishment was not manifest, but was suggested by the hesitancy and the defensive stance of determination. The pleasure and pride of accomplishment were evident in the swaggering gait. In his developmental advance, the boy employed existing ego functions to regulate his feelings and adaptively fend for himself. The adaptive value of behaving like a man was affirmed; the resulting change in the self-representation strengthened and elaborated his identity as a male. An example of adolescent development was observed during the fourth year of analysis of an adolescent girl. When she began treatment at age 16, she suffered from a chronic, intense separation anxiety which appeared to stem mainly from conflicts between her parents. They had separated for some months when the patient was three years old and were divorced when she was 10. She had lived in an excessively dependent relationship with her mother from 10 to 17, and then with her father while attending college. Her neurotic conflicts hampered her learning capacity and her ability to form relationships with male peers. At the time of the observation, the patient was successfully working toward transfer to a better college and was enjoying relationships with males. The therapeutic resolution of the separation anxiety and the associated freeing up of her independent strivings upset the patient’s heretofore comfortable parental dependency. Her disrupted adaptation led her to move out of her father’s home into the nearby home of the father’s fiancée and her 18-year-old son. She thus accepted the developmental challenge of becoming more independent while still living close to her father. For the first week after the move, she felt an exhilarating sense of freedom and self-sufficiency. She also thoroughly enjoyed the relationship with the

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18-year-old. She cooked for him, did his laundry, and made his bed. During the same week, her father bought her a car. He had promised to provide it when she went to the new college, where she would be living in a dormitory. She felt he was generally nicer to her than when she was living with him. The following week the patient came to treatment feeling “down” for no apparent reason. As she talked, she became aware of mixed feelings about the car. She felt that the premature gift of the car was a reaction to her move toward independence and reflected her father’s desire to hold on to her and keep her dependent. It was confusing, though, because she also felt he was demonstrating that it was alright to leave and grow up. The patient felt sad and cried as she realized that she was indeed growing up. She recognized that her “down” mood was due to the sense of loss experienced in moving away from her father, physically and psychologically. As would be expected, previously analyzed oedipal conflicts were reawakened, reinterpreted, and further worked through in this new context. The resumption of the arrested separation-individuation process of early childhood continued for some months, intertwined with the age-appropriate individuation of adolescence (Blos, 1967). There were repeated regressive shifts in her overall forward progression. The patient gradually worked through the feelings of loss associated with the relinquishment of dependency ties to her parents. As her developmental conflicts became resolved, she took increasing pride in her new image as a self-sufficient, responsible person. An example of adult development, taken from the analysis of a woman in her late forties, illustrates a developmental conflict involving the parenting and post-parenting phases of the stage model. For better than a year during the latter part of her analysis, this woman had been moving toward full adult functioning and free exercise of her considerable, formerly inhibited abilities, talents, and creativity. Her conflict about successful performance had been analyzed mainly in terms of her guilt about outdoing her parents, particularly her mother, and outdistancing and therefore figuratively abandoning them. Her guilt about achieving an adult level of independence was accentuated by the initially unconscious implication of figurative parricide in no longer needing the parent (Loewald, 1979). This woman’s developing identity as an independent adult, facilitated by the resolution of her pathological conflicts, then came into conflict with her long-standing identity as the mother of three children. She had intermittently been aware of the conflict between her new interests and new functioning and her still-practiced role of a mother. The conflict became an unsettled state when her grown children, in part in reaction to the healthy changes in her, forcefully began to assert their autonomy and independence. This disrupted the relationship with each of them and led to temporary withdrawal on both sides. In reaction to the disruption of her maternal adaptation, the woman experienced intense feelings of bitterness, loss, and sadness alongside

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ambivalent anticipation of freedom from the responsibilities of being a mother. In the process, she vacillated between feeling “lousy,” worthless, and depressed, and having suicidal thoughts. In her blackest mood, she felt it was too late for her, too late to get anywhere. She wondered whether the opposite of benign motherhood was mean, ugly misanthropy. After a period of struggle, she reembraced the challenge to develop to her full potential. Through the mourning process, she gradually worked through the feelings of loss associated with giving up her identity as an active parent. The initial incompatibility between her new self and her parenting self was resolved. The developing new functions serving her independence became more fully structured and integrated into her personality. A second example of adult development also derives from clinical observation. The life situation of a 54-year-old woman was disrupted by her husband’s heart attack and subsequent continuing disability. The initial reaction of shock was followed by a period of distress and anxiety and a sense of helplessness and vulnerability. A major determinant of this state was ignorance about her and her husband’s financial resources and their management. This had been her husband’s province. With a view to helping her husband in this area and, if necessary, managing it herself, she embraced the challenge of learning about their finances and the investment and business world. Excited by the challenge and motivated by the developmental tension, she also experienced doubt and conflict: “Am I capable of doing it?” “Do I really want to take care of myself?” “Will my husband approve?” Her conflict waxed and waned as she progressed in her learning and took pleasure in her growing competence. In the midst of these changes, she pulled a ligament in her arm while playing racquet ball. She both joked and cried during the analytic session in which she reported this incident. Mindful of previously acquired insight about her fear of her aggression and her mixed feelings about competition and success, she quipped that she had ruined her “killing arm.” Connecting the pulled ligament with her success in learning about the finances, she wondered why she was crying. “I should be triumphant! It is a very satisfying thing to feel I can handle our financial affairs.” She then burlesqued the tragedy of “Poor Me.” “I have to take care of myself. All by myself. I have to be – I can be? – both the mother to myself and the child.” In response to the observation that she was crying over and mourning the loss of being taken care of, she said, “That did flash through my mind. I’ve lost my innocence, my dependency, a comfortable way of being that I counted on.” This woman resolved her conflict and continued her learning. She developed not only investment skills, but also business skills, and eventually undertook her own business venture. The third example of adult development comes from the case of a 69-year-old man, a retired professional who was first analyzed in his early forties. Through that analysis, he had improved his professional functioning, gained insight into his troubled relationship with his wife, and experienced

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a marked diminution of anxiety and depressive reactions. He sought treatment again four years after the dissolution of his marriage. He was then living with a woman who, though physically handicapped, was professionally active. She managed her colostomy and the consequent limitations with imagination, fortitude, and good spirit. A concern in this new relationship was the man’s long-standing attitude of not wanting to be dependent on anyone, even if he were sick and disabled. Therefore, he did not want to marry again and felt he would prefer suicide to being a burden. His ladyfriend could not understand his willingness to take care of her, but his unwillingness to let her take care of him, should the need arise. The treatment helped the man deal with a major obstacle to his acceptance of dependency. He became aware that his considerable hostility toward his adult adopted son was behind his own fear of dependency. The son had never been self-sufficient, and the patient had supported him for years, financially and emotionally. The patient feared that his own dependency would, in turn, evoke intense hostility toward himself. This insight permitted him to reconsider his attitude about dependency and to accept the challenge of developing a healthier perspective. Acceptance of this challenge mobilized anxieties and conflicts which the patient needed to resolve in developing an appropriate attitude about dependency. Was he a good enough parent and a good enough person to deserve care? Would he be exploited? Would he exploit? Could he trust himself not to exploit? Would he suffer shame in losing control and strength? Would he be respected for the strengths he could maintain, or would he be belittled, disregarded, and treated as less advanced and less wise because of physical disability? Could he maintain his personal integrity? With his ladyfriend as a prime example, he became aware that a degree of dependency not only can be an essential part of successful adaptation in the later years but, paradoxically, can serve to support the maintenance of overall personal integrity. An example of unconscious engagement of developmental process, observed outside of the clinical situation, is represented by the sudden realization of an elderly woman writer that she had inexplicably acquired a new critical faculty toward her own writing. This function appeared some months after she had grieved the death of her loved, but resented, older sister. The writer had experienced her sister as hypercritical of her generally, and of her writing in particular. It can be inferred that the combination of mourning the loss of the sister, the consequent identification with the sister and her criticism, and the no-longer present external criticism freed the writer to develop and use her own critical functions in relation to her writing. The final example of adult development, also from outside the clinical situation, is that of a recently widowed man in his early seventies. Throughout his long and happy marriage, his wife had done all of the grocery shopping and the cooking, and had done all of the preparations for entertaining friends at dinner. In the months following his wife’s death, friends frequently invited him to their homes for dinner. As his mourning process eased, he felt ready to

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initiate social relationships and wanted to invite friends to dinner at his home. But he found himself completely lacking in culinary knowledge and cooking skills, and was not sure he could even set a proper table. He decided to acquire the necessary knowledge and skills, proceeded to do so, and shortly was having friends over for a dinner he had prepared. Significantly, these accomplishments represent development in two respects. He acquired new skills serving self-regulation and adaptation. At the same time, he altered his self-representation, not only in these regards but through identification with his deceased wife and her abilities. This identification enhanced his sense of closeness to his wife as he remembered her, and thus helped him deal with his loss.

Discussion As observed in the psychoanalytic situation, development during adulthood involves adult-level development and the resumption of childhood development. Adult-level development includes (1) development in response to life events characteristic of adulthood, such as marriage, parenthood, retirement, biological decline, or the loss of loved ones through death; and (2) development in response to the perceived possibility of achieving a better level of function and adaptation. Although taking place during adulthood, the resumption of childhood development, such as the consolidation of the superego enabled by resolution of the oedipal conflict, is not adult-level development. We wish to reiterate that the loss of function due to biological decline and adaptation through regression to earlier, previously developed modes of functioning docs not fit our definition of development. Development, as we define it, involves new functions and structures, and new and higher levels of organization. However, the acceptance of a necessary dependency can represent development insofar as it reflects a change in attitude and self-perception without loss of the sense of integrity. Our conceptualization of a process model of development involving a sequence beginning with a developmental challenge and ending with a change in the self-representation is not meant to suggest that development of the involved function is thereby closed off or completed once and for all. On the contrary, development is normally an open system, in contrast to the closed system mental apparatus resulting from pathological structure formation (Emde, 1980, pp. 218–220). Furthermore, psychic structure is not fixed or rigid (Loewald, 1978; Settlage, 1980, p. 160). Under conditions of emotional stress, psychic structure is subject to regression in degree of integration and level of organization and function. At the same time, it is open to revision and further structuring and organization through further developmental process. Despite integrating plateaus and epigenetic leaps and discontinuities, overall development is coherent and continuous (A. Freud, 1963).

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Appreciation of the fact of development gives rise to several related questions: 1. What is the relation between therapeutic process and developmental process? From one perspective, they are different and separate, in that therapeutic process merely enables the resumption of developmental process through the undoing of the arrest-causing psychopathology. From another perspective, therapeutic process can be conceived to embody developmental process. This perspective rests on an analogy between processes in the parent-child relationship and the therapist-patient relationship (Loewald, 1960; Settlage, 1980, pp. 159–166). 2. In what ways does our understanding of adult development bear on clinical understanding and psychoanalytic technique? 3. What are the determinants of the strikingly different individual responses to trauma and loss? Why do some adults, including very elderly adults, respond with growth and development, and why do others succumb to lasting regression and increasing withdrawal from life? In conclusion, we hope that the conceptualized process model of development will stimulate and aid the search for answers to these important questions.

Note 1. The first three definitions derive from Webster’s Third New International Dictionary.

References Benedek, T. (1959). Parenthood as a developmental phase: a contribution to libido theory. J. Amer. Psychoanal. Assn., 7:389–417. Bibring, G. L., Dwyer, T. F., Huntington, D. S. & Valenstein, A. F. (1961). A study of the psychological processes in the pregnancy and earliest mother-child relationship. Psychoanal. Study Child, 16:9–72. Blos, P. (1967). The second individuation process of adolescence. Psychoanal. Study Child, 22:162–186. Colarusso, C. A. & Nemiroff, R. A. (1981). Adult Development: A new Dimension in Psychodynamic Theory and Practice. New York: Plenum. Dewald, P. A. (1981). Adult phases of the life cycle. In: The Course of Life: Psychoanalytic Contributions Toward Understanding Personality Development. Vol. 3. Washington, D.C.: U.S. Gov’t. Printing Office, pp. 35–53. Emde, R. N. (1980). Ways of thinking about new knowledge and further research from a developmental orientation. Psychoanal. Contemp. Thought, 3:213–235. ———(1985). From adolescence to midlife: remodeling the structure of adult development. J. Amer. Psychoanal. Assn., 33 (Suppl.):59–112. English, H. B. & English, A. C. (1958). A Comprehensive Dictionary of Psychological and Psychoanalytic Terms. New York: Longmans, Green.

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Erikson, E. H. (1950). Childhood and Society. New York: Norton. Freud, A. (1963). The concept of developmental lines. Psychoanal. Study Child, 8:245–265. Freud, S. (1905). Three essays on the theory of sexuality. S. E., 7. ———(1917). Mourning and melancholia. S. E., 14. ———(1923). The ego and the id. S. E., 19. Goodman, S., Ed. (1977). Psychoanalytic Education and Research: Current Situation and Future Possibilities. New York: Int. Univ. Press. Gould, R. L. (1972). The phases of adult life: a study in developmental psychology. Amer. J. Psychial., 129:521–531. Hartmann, H. (1939). Ego Psychology and the Problem of Adaptation. New York: Int. Univ. Press, 1958. ———& Kris, E. (1945). The genetic approach in psychoanalysis. Psychoanal Study Child, 1:11–30. Jacques, E. (1981). The midlife crisis. In The Course of Life: Psychoanalytic Contributions Toward Understanding Personality Development, Vol. 3. Washington, D.C.: U.S. Gov’t Printing Office, pp. 1–23. Loewald, H. (1960). On the therapeutic action of psychoanalysis. Int. J. Psychoanal., 41:16–33. ———(1978). Instinct theory, object relations, and psychic structure formation. J. Amer. Psychoanal. Assn., 26:493–506. ———(1979). The waning of the Oedipus complex. J. Amer. Psychoanal. Assn., 27:751–755. Mahler, M. S. (1961). On sadness and grief in infancy and childhood: Loss and restoration of the symbiotic object. Psychoanal. Study Child, 16:332–351. ———(1972). The rapprochement subphase of the separation-individuation process. Psychoanal. Q., 41:487–506. ———Pine, F., & Bergman, A. (1975). The Psychological Birth of the Human infant: Symbiosis and Individuation New York: Basic Books. Nagera, H. (1966). Early Childhood Disturbances, the Infantile Neurosis, and the Adulthood Disturbances: Problems of a Developmental Psychology. New York: Int. Univ. Press. Neugarten, B. (1979). Time, age, and the life cycle. Amer. J. Psychiat., 136:887–894. Panel, (1973). The experience of separation-individuation in infancy and its reverberations through the course of life (3 parts). M. C. Winestine, I. M. Marcus & I. Sternschein, reporters (consecutively). J. Amer. Psychoanal. Assn., 21:135–167, 633–645. Pollock, G. H. (1977). The mourning process and creative organizational change. J. Amer. Psychoanal. Assn., 25:3–34. ———(1981). Aging and aged: development on pathology. In The Course of Life: Psychoanalytic Contributions Toward Understanding Personality Development, Vol. 3. Washington, D.C.: U.S. Gov’t. Printing Office, pp. 549–585. Settlage, C. F. (1980). Psychoanalytic developmental thinking in current and historical perspective. Psychoanal. Contemp. Thought, 3:139–170. Wolff, P. H. (1960). The Developmental Psychologies of Jean Piaget and Psychoanalysis. New York: Int. Univ. Press.

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8 Early Child Care and Children’s Development Prior to School Entry: Results from the NICHD Study of Early Child Care NICHD Early Child Care Research Network

H

istorical changes in the economy of the United States, as well as changes in women’s concepts of their roles in society and the family, have together led to substantive changes in the rearing of infants and young children (Scarr, 1998). Early child care, beginning a few months after birth, has become a normative experience for American children (Bachu, 1995). In 1997, for example, 79% of children under the age of 3 years regularly spent time in nonparental care, with 39% of these children in care for 35 or more hours per week (Capizzano & Adams, 2000). In the kindergarten class of 1998–1999, 81% of the children had child-care experience prior to school entry (West, Denton, & Germino-Hausken, 1999). This child-rearing landscape contrasts with that of some other countries where parents (most often, mothers) provide most of the care for their young children. The placement of infants and young children in child care challenges deeply held beliefs and scientific theories that stress the importance of maternal care (Bowlby, 1973; Brazelton, 1986). Research on the effects of early child care on children’s development has proved highly controversial (Fox & Fein, 1990), with researchers drawing vastly different conclusions about the direction of those effects. Some have contended that child care is a source of enrichment that promotes academic and social development (e.g., Clarke-Stewart,

Source: American Educational Research Journal, 39(1) (2002): 133–164.

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Gruber, & Fitzgerald, 1994; Lamb, 1998), whereas others have expressed concerns about the developmental risks associated with early child care (e.g., Belsky, 1999, in press). A third group asserts that reports of both negative and positive consequences of child care are vastly exaggerated because discerned effects are negligible and do not endure over time (e.g., Blau, 1999; Scarr, 1998). Increasingly, as nations move to raise educational standards for children’s performance in school (National Education Goals Panel, 1997), experiences in child-care settings are looked to as sources of variability in children’s readiness for school (Pianta & Cox, 1999). Because the debate about the effects of child care on school readiness has implications for social and educational policy, clarification of the nature and extent of child care as a source of variability in children’s developmental status is a pressing scientific concern. One reason for the ongoing controversy about the developmental consequences of child care is that different child-care parameters – quantity, quality, and type of setting – typically have been examined in isolation or in only limited contexts (Vandell, Gallagher, & Dadisman, 2000). Consider, for example, child-care quantity. Studies have reported significant associations between substantial amounts of nonmaternal care during infancy and poorer parentchild relationships (Belsky, 1999; Clark, Hyde, Essex, & Klein, 1997), elevated rates of insecure infant-parent attachments (Belsky & Rovine, 1988; BraungartRieker, Courtney, & Garwood, 1999), heightened behavior problems (Baydar & Brooks-Gunn, 1991; Park & Honig, 1991), and problematic peer relationships (Bates et al., 1994; Hoffman & Youngblade, 1999; Vandell & Corasaniti, 1990). Unfortunately, research examining the effects of large amounts of care has rarely included assessments of child-care quality, making it impossible to determine if the seemingly adverse effects of substantial hours in child care are a function of poor-quality care or particular types of care. Other researchers have documented positive relations between child-care quality and children’s linguistic, cognitive, and social functioning (e.g., Burchinal et al., 2000; Goelman & Pence, 1987; Howes & Stewart, 1987; McCartney, 1984; Vernon-Feagans, Emmanuel, & Blood, 1997). In their studies, stimulating and emotionally supportive care was associated with enhanced development. Indeed, the link between quality of child care and children’s development is probably the most consistent finding to emerge during the past two decades (cf. Lamb, 1998). Most of this research, however, did not consider the quality of care in different types of settings (but see Clarke-Stewart, Gruber, & Fitzgerald, 1994; Melhuish, Lloyd, Martin, & Mooney, 1990, for notable exceptions) or the child’s history of care, including the amount of care since birth. Child-care research also has been limited in that it has tended to focus on centers, even though the care of infants and very young children often occurs in the child’s own home or the home of a relative or unrelated caregiver (Hofferth, Shauman, Henke, & West, 1998). Positive effects of center-based

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programs on children’s cognitive and language skills have been demonstrated in experimental interventions for infants and children from low-income families (Burchinal et al., 2000; Ramey et al., in press). It has not been established whether there are similar beneficial effects of center-type experiences for children from affluent families, or beneficial effects when the quality of care is not exemplary or when the care is used for brief periods. An initial series of reports from the current project highlighted both positive and negative child-care effects up to 3 years of age. A lot of time in child care was associated with less harmonious mother-child interactions during the first 3 years (NICHD Early Child Care Research Network, 1999a) and heightened behavior problems according to caregivers at 2 years (NICHD Early Child Care Research Network, 1998). Higher-quality care and more experience in centers predicted better linguistic, cognitive, and pre-academic functioning (NICHD Early Child Care Research Network, 2000b) and fewer behavior problems (NICHD Early Child Care Research Network, 1998). These initial findings must be regarded as preliminary, however, because children’s developmental trajectories are quite fluid during the first 3 years of life. In the current study, all three child-care parameters – quantity, quality, and type of care – are considered in relation to children’s cognitive, language, and social functioning at 4½ years of age. That age is an important one to study because it is just prior to children’s entry into formal schooling. A recent meta-analytic review of 60 studies (Laparo & Pianta, in press) determined that cognitive and social skills measured in the late preschool years were predictive of performance in the same domains during the early school years. For cognitive or academic predictors of similar outcomes, effect size was moderate (.49). For social or behavioral predictors of similar outcomes, effect size was small (.27). Unfortunately, many children begin formal schooling with deficiencies in these areas. In one recent national survey (Rimm-Kaufman, Pianta, & Cox, in press), kindergarten teachers reported that 15% of their pupils had “serious problems” and that another 30% had “some problems” in adjusting to school. Even more telling was the finding that when asked about specific difficulties in readiness skills, half of the kindergarten teachers said that the majority of the children in their current class lacked competencies in pre-academic skills, following directions, and peer relations. Determining whether early childcare experiences contribute to individual differences in the kinds of skills valued and assessed by kindergarten teachers is a pressing scientific concern that has widespread implications for educational and social policies. Consequently, in this report we consider two basic questions: (1) Are early child-care experiences positively or negatively related to child functioning prior to school entry? And if so, (2) are statistical effects sufficiently large to be meaningful? In addressing these questions, we seek to move beyond a global characterization of early child care as good or bad for children and to examine specific aspects of care that may foster or undermine children’s

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development, by focusing on the cumulative amount or quantity of care from birth onward, the quality of the care received throughout the early years, and the types of care experienced (e.g., center-based versus home-based care). Investigators have different views about how best to test for the effects of child care. Some (Blau, 1999) have argued that previous studies have inflated effects because selection factors were not adequately controlled. Others (Burchinal & Nelson, in press; Vandell & Wolfe, 2000) have contended that child-care effects are underestimated when too many family selection factors are included because some controls, such as parenting, that influence selection of child care also are influenced by children’s child-care experiences. To address the issue of appropriate levels of selection controls, we compare the results of analyses involving many covariates with results of those involving fewer covariates. We also consider if child-care effects are large enough to be of practical importance. Only recently have investigators begun to report effect sizes, and the effects have varied from large to small: d = 1.0 in a clinical trial in which high-quality child care was randomly assigned to low-income AfricanAmerican children (Campbell, Pungello, Miller-Johnson, Burchinal, & Ramey, 2001); d = .75 for cognitive and language outcomes among predominantly low-income African-American children (Burchinal et al., 2000); d = .5 for 4-year vocabulary, and d = .3 for 4-year math in a large four-site study of center child care (Peisner-Feinberg et al., in press). Social outcomes were examined only in the latter study and were not significantly related to observed quality of care. Effect sizes in naturalistic studies are typically small because they are measured in the context of many other influences (Cohen, 1988). Comparisons with other effects judged to be meaningful can be used as a gauge of social significance (McCartney & Rosenthal, 2000). In this report we compare the effect sizes associated with quantity, quality, and type of care with effect sizes associated with two other well-recognized developmental contexts. One, quality of parenting, is defined at the level of family process. The second, family poverty, represents a socioeconomic context. Parenting is a major predictor of children’s cognitive and social development because of the centrality of the family in children’s early lives and because it includes possible genetic as well as environmental influences on the child (Collins, Maccoby, Steinberg, Hetherington, & Bornstein, 2000). The negative relations of poverty to children’s cognitive, social, and physical development also are well documented (McLoyd, 1998). Children from economically poor homes begin school at a disadvantage that has been judged to be large enough to warrant public expenditures for Head Start, Title I early education programs, and other services. Hence parenting quality and family poverty are socially significant contexts with which to evaluate child-care effects. Studying effects of child care in the United States is methodologically challenging because children typically experience multiple arrangements

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(Hofferth et al., 1998; NICHD Early Child Care Research Network, 1997a). It cannot be assumed that reliance on a single assessment at one point during the first 5 years is an adequate representation of a child’s experience in child care. Consequently, in the NICHD Study we have collected information about amounts and types of care every 3 to 4 months. Observations of the quality of primary arrangements were obtained at five points, when children were 6, 15, 24, 36, and 54 months of age. Previous research (e.g., Chin-Quee & Scarr, 1994; Deater-Deckard, Pinkerton, & Scarr, 1996; Peisner-Feinberg & Burchinal, 1997) that examined long-term effects typically has relied on assessments of quality at a single age.

Method Participants Families were recruited through hospital visits shortly after the birth of a child in 1991 at ten locations in the United States (Little Rock, AR; Irvine, CA; Lawrence, KS; Boston; Philadelphia; Pittsburgh; Charlottesville, VA; Morganton, NC; Seattle, WA; Madison, WI). During selected 24-hour intervals, all women giving birth were screened for eligibility and willingness to be contacted again. Of the 8,986 mothers who gave birth during the sampling period, 5,416 (60%) met the eligibility requirements (mother was more than 18 years of age; mother spoke English; mother was healthy; baby was not from multiple birth or released for adoption; mother and child lived within an hour of research site; no move from the area was planned in the next 3 years; the neighborhood was not too dangerous to visit, as verified by police; and mother agreed to be telephoned in 2 weeks. Of that group, a conditionally random sample of 3,015 was selected (56%) for the telephone call. The conditioning assured adequate representation (at least 10%) of single mothers, mothers without a high school degree, and ethnic minority mothers. At the 2-week call, families were excluded if the baby had been hospitalized for more than 7 days, if the family expected to move in the next year, or if the family could not be reached after three attempts at contact. A total of 1,525 eligible families agreed to an interview. Of these, 1,364 completed a home interview when the infant was 1 month old and became study participants. The resulting sample was diverse, including 24% children of color, 11% mothers who had not completed high school, and 14% single mothers. Mothers had an average of 14.4 years of education. Average family income was 3.6 times the poverty threshold. Seventy-nine percent of the children were white and non-Hispanic. The participating families were very similar to the eligible hospital sample in terms of maternal education, percentage in different ethnic groups, and presence of a husband or partner. The participants differed from the 281 children who were recruited but were lost to follow-up.

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Mothers of participants had significantly ( p < .05) more education (M = 14.4 years and SD = 2.5, as opposed to M = 13.6 years and SD = 2.6), higher family incomes (income–poverty ratio: M = 3.6 and SD = 2.8, as opposed to M = 3.2 and SD = 3.1), and were more likely to have a husband or partner in the household (85% as opposed to 76%). The children were less likely to be African American (11% as opposed to 19%).

Overview of Data Collection Children were followed from birth to 4½ years of age. Mothers were interviewed in person when infants were 1 month old. Detailed measures of home and family environments were obtained by means of interviews and observations when children were 6, 15, 24, 36, and 54 months old. Primary child-care settings were observed at the same ages for all children who were in nonmaternal care on a regular basis for 10 or more hours per week. Mothers were telephoned regularly to update reports on child-care usage. Children’s cognitive skills and social behavior were assessed at 4½ years. Means and standard deviations for all measures included in the analyses are presented in Table 1.

Child-care Measures During telephone interviews conducted at 3-month intervals through 36 months and at 4-month intervals thereafter, mothers reported types and hours of nonmaternal care that were being used.

Type of Care For each 3– 4 month interval (16 epochs, or intervals, in all), the child’s primary care arrangement was classified as center, child-care home (any home-based care outside the child’s own home except care by grandparents), in-home (any caregiver in the child’s own home except father or grandparent), grandparent, or father. Epochs in which children were in nonmaternal care for less than 10 hours per week were coded as exclusive maternal. The proportion of epochs in which the child received care in a center and the proportion of epochs in a child-care home were determined and included as type of care predictors in analyses.

Child-care Quantity Parents were asked about the hours of routine nonmaternal care during the telephone and personal interviews. The hours spent in all settings were summed for each of the 16 epochs.

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9.4 (5.5) 0.2 (0.7)

.03 (.64) −.01 (.07)

M (SD) M (SD)

M (SD) M (SD) M (SD) M (SD)

M (SD) M (SD) n M (SD) M (SD)

Child-care characteristics Quantity: hrs/week Mean level Rate changea Quality: Positive caregiving rating Mean level Rate changea 26.3 (1.9) 1.9 (6.4) 985 2.8 (0.2) −.03 (.07)

11.2 5.6 78.9 4.3 85.2 14.4 (2.5) 3.7 (2.8)

50.4

Overall (n = 1,083)

% % % % % M (SD) M (SD)

%

% or M

Family characteristics Ethnicity African American Hispanic White Other Single parent Maternal education Income-to-needs ratio Maternal depression Mean level Slope Parenting: HOME & maternal sensitivity composite Mean level Rate change HOME total Maternal sensitivity

Child characteristics Gender (male)

Characteristic

Table 1: Descriptive statistics for all measures

500 3.0 (0.6)

26.0 (17.0)

36.8 (4.5) 9.3 (1.8)

9.1 (8.4)

3.8 (3.1)

87.5

6 months (n = 1,073)

561 2.9 (0.6)

23.3 (21.4)

37.6 (4.4) 9.4 (1.6)

9.0 (8.1)

3.7 (3.2)

86.5

15 months (n = 1,069)

597 2.8 (0.6)

25.1 (21.6)

9.4 (1.7)

9.5 (8.7)

3.8 (3.0)

86.3

24 months (n = 1,066)

641 2.8 (0.5)

26.1 (21.3)

41.7 (7.3) 17.3 (2.7)

9.2 (8.3)

3.7 (3.1)

84.0

36 months (n = 1,073)

Early Child Care

(Continued )

850 3.0 (0.6)

28.1 (21.1)

46.1 (5.3) 17.0 (2.9)

9.8 (8.6)

3.6 (3.2)

83.5

54 months (n = 1,075)

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% % % % % %

% or M

Average rate of change per month across study period.

a

Child outcomes Cognitive (n = 1,043) Pre-academic composite Letter-word indentification Applied problems Memory for sentences Preschool language scale Mother report, social behavior (n = 1,044) Social competence Behavior problems Caregiver report, social behavior (n = 699) Social competence scale Behavior problems

Type of care (10 + hrs/wk) Exclusively mother Father care Grandparent In-home Child-care home Center Number of months child with caregiver

Characteristic

Table 1: (Continued )

25.2 16.4 10.0 7.3 19.8 22.1 n = 787 13.4 (15.4)

Overall (n = 1,083)

34.8 13.5 11.6 8.3 21.8 8.2

6 months (n = 1,073)

30.3 17.3 9.6 8.8 23.2 11.1

15 months (n = 1,069)

27.1 14.9 9.2 7.7 24.3 17.3

24 months (n = 1,066)

20.7 14.5 9.6 7.2 20.9 28.4

36 months (n = 1,073)

104.9 (13.5) 50.4 (10.3)

98.3 (13.5) 50.8 (89.5)

98.9 (11.6) 99.3 (13.6) 103.4 (15.8) 93.1 (18.6) 99.8 (10.3)

10.8 17.7 9.7 5.9 12.3 53.8

54 months (n = 1,075)

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Child-care Quality Observational assessments of quality were obtained for primary nonmaternal arrangements that were used for 10 or more hours per week at 6, 15, 24, 36, and 54 months. Observations were conducted during two half-day visits scheduled within a 2-week interval at 6 –36 months and one half-day visit at 54 months. At each half-day visit, observers completed two 44-minute cycles of the Observational Record of the Caregiving Environment (ORCE). The ORCE format consists of 44-minute cycles, each broken into four 10-minute observation periods. In each 10-minute period, observers alternate between 30-s observe and record frames. During the observe intervals, observers focus on the study child’s behavior, activities, and interaction with the caregiver or with other people. During the record intervals, observers complete the frequency checklist. At the end of the 10-minute period the observer makes brief notes and tentative qualitative ratings of the caregiver’s behavior and the child’s behavior for 2 minutes. This process is repeated for three 10-minute periods. In the final 10-minute period the observer makes observations exclusively for the qualitative ratings. At the end of the 44 minutes the observer makes final qualitative ratings for up to three caregivers using 4-point scales that range from not at all characteristic to highly characteristic, based on all four 10-minute periods. On average, four ORCE cycles were completed for children from 6 to 36 months, and two ORCE cycles were completed at 54 months. ORCE quality ratings were obtained for at least one age period for 91% of the sample (985 of 1,083) and for at least two age periods for 779 children. Thirty-four children were never in nonmaternal care on a regular basis; thus it was not possible to observe them in a child-care arrangement. Specific items that constitute the ORCE behavioral scales and qualitative ratings are listed in the Appendix. The behavior scales provided a record of the occurrence or quantity of specific acts, whereas the qualitative scales took into account the quality (and nuances) of the caregiver’s behavior in relation to the child’s behavior. Positive caregiving composites were calculated for each age level. At 6, 15, and 24 months, positive caregiving composite scores were the mean of five 4-point qualitative ratings (sensitivity to child’s nondistress signals, stimulation of cognitive development, positive regard for child, emotional detachment [reflected], and flatness of affect [reflected]). Cronbach alphas for the composite were .89 at 6 months, .88 at 15 months, and .87 at 24 months. At 36 months, these five ratings and two additional ratings, fosters child’s exploration and intrusive (reflected), were included in the composite (Cronbach α = .83). At 54 months the positive caregiving composite was the mean of 4-point ratings of caregivers’ sensitivity or responsivity, stimulation of cognitive development, intrusiveness (reflected), and detachment (reflected) (Cronbach α = .72). The behavioral composite, which was highly correlated with the qualitative composite at each age, was not used in the current analyses.

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Before conducting observations at each age, observers studied extensive manuals, which detailed age-appropriate expectations for caregivers at 6, 15, 24, 36, and 54 months. At each age, observers from the 10 sites also attended a centralized training at which they viewed master-coded videotapes of appropriately aged children and their caregivers, conducted live observations at centers and home-based child-care settings, completed written tests, and participated in question-and-answer sessions. Further training and practice were conducted at each site using videotaped and live examples and instruction. To ensure cross-site reliability before data collection was initiated at each age, observers coded six tapes, each containing one 44-minute ORCE cycle that focused on a specified child who was the same age as the study children for a given assessment. The tapes represented all types of care and captured a range of quality. To be certified as a data collector, observers had to achieve exact agreement with the master codes of the behavior scales at 70% or better and with the qualitative ratings at 60% or better. To prevent observer drift, reliability at each age was checked with two further tests, each consisting of six new master-coded 44-minute ORCE cycles. A criterion of 60% exact agreement of the qualitative ratings and 70% on the behavioral frequencies was required for continued data collection. In addition, observer agreement was assessed during live on-site observations. At each site, all possible pairs of observers visited both home-based care and centers. Interobserver agreement for the positive caregiving composite score was computed for the master-coded videotapes and the live observations, using Pearson correlations and the repeated measures ANOVA formulation described in Winer (1971, p. 287). Inter-observer agreement exceeded .90 at 6 months, .86 at 15 months, .81 at 24 months, .80 at 36 months, and .90 at 54 months. Detailed descriptions of the infant version of the ORCE assessments can be found in NICHD Early Child Care Research Network, 1996. Details about the toddler versions and preschool versions can be found in NICHD Early Child Care Research Network, 2000a. Complete observation manuals can be found at http://public.rti.org/secc/.

Maternal, Child, and Family Controls Measures of maternal, child, and family characteristics were collected and used as controls for selection effects.

Demographic Variables During home interviews at 1 month, mothers reported their own education (in years) and the study children’s race and ethnicity (non-Hispanic African American, non-Hispanic European American, Hispanic, or other)

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and sex. The presence of a husband or partner in the home was reported in telephone interviews spaced every 3 to 4 months. Partner status was the proportion of 3-to-4-month intervals during which the mother reported that a husband or partner was present. Mothers reported family income at 6, 15, 24, 36, and 54 months. An income-to-needs ratio was calculated at each age from U.S. Census Bureau tables based on family income relative to household size and number of children under 18. In the current analyses, these ratios were averaged.

Maternal Depressive Symptoms Maternal depressive symptoms were assessed at 6, 15, 24, 36, and 54 months, using the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977), a self-report measure that assesses depressive symptomatology in the general population. Cronbach’s alpha coefficients ranged from .88 to .91 in the present sample. The intercept and linear slope were included as factors in the current analyses.

Mother-child Interactions Mother-child interactions were videotaped in semi-structured 15-minute observations at 6, 15, 24, 36, and 54 months. The tasks provided a context for assessing age-appropriate qualities of maternal behavior. The observation task at the 6-month visit had two components. In the first 7 minutes, mothers were asked to play with their infants and were told that they could use any toy or object available in the home or none at all. For the remaining 8 minutes, mothers were given a standard set of toys that they could use in play. At 15, 24, and 36 months, the observation procedures followed a three-boxes procedure in which mothers were asked to show their children age-appropriate toys in three containers in a set order (see Vandell, 1979). For example, at 15 and 24 months, a storybook was in the first container (different books were used at 15 and 24 months); a toy stove and related objects were in the second; and a toy house with various moving parts, a person, a dog, and a car were in the third. At 36 months, washable markers, stencils, and paper were in the first container; dress-up clothes and a cash register were in the second; and Duplo blocks with a picture of a model were in the third. The mother was asked to have her child play with the toys in each of the three containers and to do so in the order specified. Data were collected by research assistants who had attended centralized training sessions. Each data collector passed certification procedures based on a common certifier’s review of videotapes of the data collector administering the procedures. The certification procedures were designed to ensure that standard data collection procedures were used across the sites.

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Videotapes of the mother-child interactions were shipped to a central location for coding by raters who were blind to other information about the families. Inter-coder reliability was determined by assigning two coders to 19–20% of the tapes randomly drawn at each assessment period. Coders did not know which tapes were assigned to double coding, and reliability assessments were made throughout the period of coding. Inter-coder reliability was calculated as the intra-class correlation coefficient. Reliability for the composite scores used in the current report exceeded .83 at every age. At 6, 15, and 24 months, composite maternal sensitivity scores were created from the sums of three 4-point ratings (maternal sensitivity to child nondistress, intrusiveness [reversed], and positive regard). At 36 and 54 months, the maternal sensitivity composite was the sum of the three 7-point ratings of supportive presence, hostility (reversed), and respect for autonomy. Cronbach alphas exceeded .70 at every age. The maternal sensitivity composite rating was a significant predictor of children’s attachment security at 15-months (NICHD Early Child Care Research Network, 1997b) and 36-months (NICHD Early Child Care Research Network, in press–a) and peer competencies at 36 months (NICHD Early Child Care Research Network, in press–b). It also was a significant mediator and moderator of relations between maternal depression and children’s expressive language and cooperation (NICHD Early Child Care Research Network, 1999b). The Home Observation for Measurement of the Environment (HOME; Caldwell & Bradley, 1984) was administered during home visits at 6, 15, 36, and 54 months. The focus is on the child in the environment, the child as a recipient of inputs from objects, events, and transactions occurring in connection with the family surroundings. The Infant/Toddler version of the Inventory (IT-HOME) is aimed for use during infancy (birth to age 3). It is composed of 45 items clustered into six subscales: (a) Parental Responsivity, (b) Acceptance of Child, (c) Organization of the Environment, (d) Learning Materials, (e) Parental Involvement, and (f ) Variety in Experience. Each item is scored in binary fashion ( yes/no). Information used to score the items is obtained during the course of the home visit by means of observation and the semi-structured interview. The Early Childhood version of the Inventory (EC-HOME) is aimed for use during early childhood (age 3 to 6 years). It is composed of 55 items clustered into eight subscales: (a) Learning Materials, (b) Language Stimulation, (c) Physical Environment, (d) Responsivity, (e) Academic Stimulation, (f ) Modeling, (g) Variety, and (h) Acceptance. Each item is scored in binary fashion ( yes/no). Information at this age also is scored during the course of the home visit by means of observation and the semi-structured interview. Both forms of the HOME are correlated with intellectual or academic performance and adaptive social behavior in the expected direction. A centrally located system of training was used for data collectors at each age. Every 4 months, each observer coded videotaped visits, and the coding

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was compared with gold standard codes. All observers were required to maintain a criterion of scoring like the master coder on 90% of the items. Cronbach alphas for the total score at each age exceeded .77. The HOME and maternal sensitivity ratings were standardized at each age and then averaged at each age to create a composite score. Together, these combined scores reflect parenting in two contexts: in the home and during semi-structured play. In previous research (NICHD Early Child Care Research Network, 1998, in press–b), we have found this composite parenting rating to be a strong and consistent predictor of children’s cognitive and social competencies at 24 and 36 months. Two indices of parenting quality (the intercept and slope) were created from the mean of the standardized scores at each age using HLM.

Expanded List of Child and Family Covariates Additional child and family variables were included as covariates in some analyses. These variables were maternal rating of child temperament obtained at 6 months measured by the 55-item Revised Infant Temperament Questionnaire (Carey & McDevitt, 1978), maternal psychological adjustment measured by three subscales (Agreeableness, Neuroticism, Extraversion) of the NEO Personality Inventory (Costa & McCrae, 1985) collected at the 6-month home visit, maternal report of social support using 11 items that were rated with 6-point Likert scales collected at all visits (Marshall & Barnett, 1993), maternal report of separation anxiety using 21 items that were rated with 5-point Likert scales averaged from the 1-to-24-month visits (DeMeis, Hock, & McBride, 1986), and maternal beliefs about the benefits of maternal employment for children using 11 items collected at the 1-month visit (Greenberger, Goldberg, Crawford, & Granger, 1988). These additional covariates were based on established measures with excellent psychometric properties.

Child Functioning at 4½ Years Measures of child functioning were obtained during a laboratory visit, home visit, and child-care visit at 4½ years.

Pre-academic Skills The score for pre-academic skills is a composite score from two subtests of the Woodcock Johnson Achievement and Cognitive Batteries (1990). The Letter-Word Identification test measures skills at identifying letters and words. Standard scores range from 63 to 180, with values above 100 indicating

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that the raw score was above the mean score of children on whom the test was standardized. The Applied Problems test measures skill in analyzing and solving practical problems in mathematics. Standard scores range from 41 to 157, with values above 100 indicating that the raw score was above the mean score of the standardization sample. Internal consistencies for 4-year-olds are .92 and .91 for the two scales, respectively. Their correlation with each other was .51 in the standardization sample and .57 in our sample. Cronbach alphas for the Letter-Word Identification and Applied Problems tests were .86 and .85 in the current study. The composite score was formed by averaging the standardized scores on the two subtests.

Short-term Memory Short-term memory was assessed using the Woodcock Johnson Cognitive Memory for Sentences subtest. Standardized scores ranged from 17 to 150 (M = 93, SD = 18.57), with values above 100 indicating that the raw score was above the mean score for the standardization sample. Cronbach alpha was .84 for this measure in the current sample.

Language Competence Language competence was assessed using the Preschool Language Scale (PLS-3; Zimmerman, Steiner, & Pond, 1979). It measures a range of language behaviors, including vocabulary, morphology, syntax, and integrative thinking, which are grouped into two subscales: Auditory Comprehension and Expressive Language (Cronbach α = .89 and .92, respectively, in the current study). These scales were highly correlated (r = .70, p < .001, in our sample). The test is standardized to have a mean of 100 and a standard deviation of 15. In our sample, scores ranged from 50 to 133 (M = 99.39, SD = 18.43). The PLS-3 correctly identified 4-year-olds with language disorders 80% of the time and was correlated with other language measures (rs = .66 to .82).

Social Competence Social competence was measured by having mothers complete the Social Skills Rating System (SSRS, Gresham & Elliott, 1990) for their children. This instrument is composed of 38 items describing child behavior. Mothers responded on a 3-point scale reflecting how often their child exhibited each behavior. Items are grouped into four areas: cooperation (e.g., keeps room neat and clean without being reminded), assertion (e.g., makes friends easily), responsibility (e.g., asks permission before using a family member’s property), and self-control (controls temper when arguing with other children). The total score is the sum of all 38 items, with higher scores reflecting higher levels of perceived social competence. The SSRS was normed on a

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diverse, national sample of children in the 3-to-5-year age range and shows high levels of internal consistency (median = .90) and test-retest reliability (.75 to .88). Cronbach alpha in the current sample was .88. For children who were in child care at least 10 hours per week at age 54 months (n = 833), caregivers completed the California Preschool Social Competency Scale (Levine, Elzey & Lewis, 1969), a 30-item instrument assessing a range of social competencies especially relevant in child-care settings (e.g., safe use of equipment, using names of others, greeting new child, initiating group activities). Four items were added to index specific features of peer play (cooperation, following rules in games, empathy, and aggression). Items were rated on 4-point scales. Items scored as not applicable were set as missing. The Total Social Competency score was the sum of the 34 items, with higher scores denoting greater social competence. Scores ranged from 46 to 135 (M = 104.88, SD = 13.6, α = .88).

Behavior Problems Behavior problems were assessed by having mothers and caregivers complete the appropriate versions of the Child Behavior Checklist (Achenbach, 1991). The parent version lists 113 problem behaviors. The parent rates each as not true (0), somewhat true (1), or very true (2) of her child. Caregivers (n = 768) in children’s child-care settings completed the 100-item caregiver teacher version developed for children ages 2-5 years. Both parent and teacher versions contain two subscales: Internalizing Problems (e.g., too fearful and anxious) and Externalizing Problems (e.g., argues a lot). Achenbach reports test-retest reliability of .89, inter-parent agreement of .70, and stability of scale of .71 over 2 years. Cronbach alphas for the mother version in the current sample were .81 for internalizing and .88 for externalizing. For the teacher version, Cronbach alphas were .90 for internalizing and .95 for externalizing in the current sample. For both subscales as well as for the Total Problem score, raw scores were converted into standard T scores, based on normative data for children of the same age. Details about all data collection procedures are documented in Manuals of Operation of the study, which can be found at http://public.rti.org /secc/.

Results Longitudinal Analyses of Child Care and Family Characteristics Preliminary analyses summarized our longitudinal assessment of the children’s child-care experiences and family context. Mothers were asked every 3 to 4 months about the number of hours spent in routine nonmaternal

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care and the type of setting. Two quantity indices were created from these maternal reports of hours per week in all nonmaternal care arrangements using Hierarchical Linear Model (HLM) analyses (Bryk & Raudenbush, 1987). The HLM approach offered parsimonious, interpretable, and continuous summary scores describing quantity of child care over the first 4½ years of life by estimating individual measures reflecting overall amount of care and rate of change over time. Unconditional quartic individual growth curves were estimated, with age centered at 24 months. The HLM analyses revealed significant individual differences in the quantity intercept (z = 23.77, p < .001) and the quantity linear slope (z = 19.29, p < .001). Overall, at 24 months children experienced almost 25 hours of care per week (M = 26.25 hours/week, SD = 17.4) and showed modest increases in child-care hours over time (M = 1.90 hours/week, SD = 4.5). Two individual growth-curve parameters were retained for subsequent analysis: the intercept (general tendency) of hours per week that nonmaternal care was used during the 16 intervals from 1 month through 4½ years and the linear slope of reported hours per week over time. In addition, two analysis variables representing the type of care were computed: the proportion of epochs that the child was in center care (% center care) and the proportion of epochs that the child was in a child-care home (% cc home). Observations of caregiver sensitivity also were summarized using HLM to describe longitudinal patterns of change. Unconditional linear growth curves were fit, and individual intercepts and slopes were estimated. Two cumulative quality indices were formed using scores for all time periods in which a particular child’s care settings were observed and rated. HLM analyses yielded significant individual differences in the positive caregiving quality intercept (z = 10.21, p < .001) and positive caregiving linear slope (z = 5.68, p < .001). On average, children experienced moderately good quality care (M = 2.82, SD = .23), with child-care providers showing slightly more sensitivity to children when they were younger (M = −.029, SD = .013). In addition, HLM analyses were used to summarize longitudinal assessments of maternal depression and parenting. An unconditional linear growth curve was fit to the repeated assessments of maternal depression. There were systematic individual differences in the intercept (z = 20.9, p < .001) and linear change over time (z = 6.8, p < .001). On average, mothers reported few symptoms overall (intercept M = 9.35, se = .18, p < .0001) and very modest gains over time (M = .19 symptoms per year, se = .06, p < .003). An unconditional linear model also was fit to the repeated assessments of parenting. Significant individual differences emerged in both the overall level (z = 22.9, p < .001) and linear change over time (z = 8.2, p < .001). The parenting variable was created as the mean of standardized variables, so it is not surprising that the group growth curve was characterized by intercepts and slopes that did not significantly vary from zero. Nevertheless, the substantial individual differences in the intercepts and slopes made these summary measures interesting as covariates in subsequent analyses. Finally, income

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was summarized as the mean income-to-needs ratio and partner status was summarized as the proportion of time the mother reported a partner in the household from the 6–54 assessments.

Is Child Functioning Associated with Child-care Quantity, Quality, and Type? The primary analyses involved multivariate linear regression models that tested if child functioning at 4½ years varied as a function of child-care quantity, quality, and type. Two quantity indicators (individual intercept and slope of reported hours/week in care from 3 months to 4½ years), two quality indicators (individual intercept and slope of positive caregiving ratings), and two type indicators (proportion of 3-to-4-month epochs in which children attended centers and proportion of epochs in which children attended child-care homes) were tested along with the following control variables: child sex (1 = male), child ethnicity (coded African American, European American, Hispanic American, and other), proportion of epochs in which a husband or partner was in the household, maternal education, average ratio of income to needs, maternal depression intercept and slope, and parenting-quality intercept and slope. Interactions between the child-care parameters and each of the controls were tested to determine if child-care effects were moderated by family characteristics. Interactions between the three child-care parameters were tested to determine if those factors acted synergistically. The tests of interactions also served as tests of homogeneity of regression. Because none of the interactions was significant, they are not presented or discussed further. The results of the primary analyses are shown in Tables 2 and 3. The second, third, and fourth rows in Table 2 present the explained variance (R2) for the models as a whole, the block of child-care predictors, and the block of variables composed of the child and family controls. Also presented in Table 2 are the multivariate test statistics for the child-care and control blocks. The next six rows list the test statistics for the multivariate test and the standardized regression coefficients for each child-care predictor; the final rows list the standardized regression coefficients for each one-degree-of-freedom covariate and the p-value level for multiple-degree-of-freedom covariates. Table 3 shows a complementary measure of association, the structural coefficients (Courville & Thompson, 2001). This measure reflects the relative predictive power of each predictor included in the analysis model without adjusting for shared variance among the predictors. The structural coefficient is computed as the zero-order correlation between a predictor and an outcome measure divided by the multiple correlation. We identified these coefficients within the context of a given model (i.e., within each column in Table 3) by identifying the coefficients that are largest as the best unconditional predictors if the overall model provides significant prediction of the outcome. Examination of both the structural and the standardized

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Table 2: Prediction of child functioning at 4½ years from child-care quantity, quality, and type: Model tests and standardized coefficients ( β) Social outcome Caregiver report (n = 533)

Cognitive outcome (n = 737) Predictor

MANOVA Acad

Model fit F Overall model Child care 1.84* block Covariate 11.39*** block Predictors Child care Quantity intercept Quantity slope Quality intercept Quality slope % Child-care home % Centers Covariates Site Male Ethnicity Maternal education Partnered Income Parenting intercept Parenting slope Depression intercept Depression slope

2

Lang 2

Mem

MANOVA Skills

2

2

Mother report (n = 748)

Prob 2

MANOVA

R .39*** .02*

R .44*** .01*

R F .22*** .01 2.95***

R R .13*** .14*** .02 .04***

.23***

.25***

.11*** 3.02***

.07*** .07*** 10.34***

F

β

β

1.48

.03

−.02

−.05

5.78**

0.53

.03

−.01

−.00

4.16**

.16***

.10*

R .18*** .00

R2 .17*** .00

.15***

.16***

β

F

−.07

.16**

.05

−.01

.01

2.40

−.08

.09*

.02

−.00

.01

.08

2.73

.11

−.00

.44

.01

.04

−.03 .05

1.64 0.28

.09 .04

−.01 −.02

.79 .09

.03 −.02

.04 .01

3.45*

.02

.11

.02

.00

−.01

.01

F

β

.20

Prob

2

β

β

3.04* 1.71

.10* −.01

3.98**

.05

.11**

.11*

−.06*

−.11***

.03

−.11**

.09*

.06

.04

−.10

−.05

.03 −.00 .16*

−.08 .07 −.16*

−.10* .02 .01 .04 .28*** −.09

.06

.08

−.14**

.16***

.05 .06

β

F

Skills

−.08* −.06 −.08* .01 .07 .06 .40*** .37*** .28*** .12***

.15***

.04

.00

−.06

−.06

−.03

−.07*

−.08*

−.07

.04

−.06

.17*** −.03

.06

−.02

−.15***

−.20***

.33***

−.02

.08*

Note: Acad = pre-academic skills, Lang = language competence, Mem = memory, Skills = social skills, Prob = behavior problems. *p < .05, **p < .01, ***p < .001.

coefficients provides information about the degree to which a predictor is associated with the outcome and offers unique prediction.

Cognitive Outcomes Three cognitive outcomes (pre-academic skills, language, and short-term memory) were considered. As shown in the first four columns of Table 2, the multivariate analysis indicated that cognitive functioning was significantly

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Table 3: Prediction of child functioning at 4½ years from child-care quantity, quality, and type: Structural coefficients (rs) Caregiver report on social Outcome

Cognitive outcome Predictor

Mother report on social outcome

Acad

Lang

Mem

Skills

Prob

Skills

Prob

rs

rs

rs

rs

rs

rs

rs

Child Care Quantity intercept Quantity slope Quality intercept Quality slope % Child-care home % Centers

.02 −.13 .43 −.13 −.03 .18

.01 −.18 .36 −.11 .02 .22

−.05 −.14 .45 −.27 −.04 .23

−.19 −.30 .39 −.05 .03 −.09

.47 .27 −.32 .09 −.04 .37

−.06 −.05 .18 .00 −.06 .08

−.02 .02 −.08 .02 .00 −.03

Covariatesa Male Maternal education Partnered Income Parenting intercept Parenting slope Depression intercept Depression slope

−.14 .69 .25 .51 .86 .29 −.27 −.16

−.20 .63 .32 .55 .84 .31 −.30 −.17

.04 .60 .24 .54 .80 .23 −.38 −.25

−.31 .48 .35 .38 .66 .36 −.39 −.24

.00 −.44 −.40 −.19 −.56 −.43 .32 .12

.40 .35 .19 .33 .71 .25 −.66 −.05

−.06 −.35 −.21 −.23 −.50 −.42 .86 .24

Note: rs = rX Y /R, where rX 1

1Y

is the correlation coefficient for predictor X1 and outcome Y; R is the square

root of R2 of the model. Acad = pre-academic skills, Lang = language competence, Mem = memory, Skills = social skills, Prob = behavior problems. a

Site and ethnicity were also included in the model but are not listed because they are categorical predictors.

associated with child care, F(18, 2003) = 1.84, p = .02, and specifically with the quality intercept, F(3, 708) = 4.6, p = .006, the quality slope, F(3, 708) = 3.04, p = .03, and the proportion of center-care epochs, F(3, 708) = 3.98, p =.008. Children who attended higher-quality child care scored higher on tests of pre-academic skills and language than did children who attended lower-quality child care. Children whose child care increased in quality over time had better pre-academic skills, whereas pre-academic skills were lower when child care decreased in quality over time. Children who had more center experience displayed better language skills and better performance on the memory test than did children with less center-type experience. The structural coefficients in Table 3 show a similar pattern of results. These unconditional measures of association indicate that family characteristics such as parenting, maternal education, and income show the strongest association with the cognitive outcomes, but that overall quality of child care (quality intercept) was a moderately strong predictor of these outcomes. In contrast, amount of center care was a stronger predictor in the regression model than when considered alone.

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Social Outcomes We considered four aspects of social functioning (social skills and behavior problems reported by mothers, social skills and behavior problems reported by caregivers) in relation to child-care quantity, quality, and type by using multivariate hierarchical regression models that paralleled those used to predict cognitive functioning. Separate analyses were conducted for reports by mothers and by caregivers because those reports were only minimally related. The correlation between maternal and caregiver reports of behavior problems was r = .23, p < .001; and the correlation between maternal and caregiver reports of social skills was r = .21, p < .001. As shown in Table 2, caregiver reports of social behavior were significantly related to child care, F(12, 1010) = 2.95, p < .001, and specifically to overall quantity of care (individual intercept) from 3 months to 4½ years, F(2, 505) = 5.78, p = .003, and proportion of epochs of center care, F(2, 505) = 3.45, p = .03. The structural coefficients shown in Table 3 reveal a similar pattern of associations, although the various family measures, not surprisingly, show stronger associations with the structural coefficients than was the case for standardized coefficients because of their shared variance. Both sets of coefficients indicated that children with more child-care hours per week (quantity intercept) had more problem behaviors according to their caregivers than did children with fewer child-care hours. Although the multivariate test indicated that proportion of center-care epochs was significantly related to caregiver reports of social outcomes and although the structural coefficients identified proportion of center care as a moderately strong predictor of behavior problems, the individual betas associated with center care were not significant for either social skills or behavior problems. Similarly, quality of care shows a moderate association with the unadjusted structural coefficients but not with the adjusted standardized coefficients. Thus, among the child-care variables, only quantity of care provides significant prediction when all covariates are considered; but both type and quality of care are associated before adjusting for the extensive set of family characteristics. To address the concern that the difference in caregiver reports was an artifact of differential familiarity with the study child, the length of time that the caregiver provided care to the child was added in a follow-up analysis of the caregiver’s ratings of social behaviors. Caregiver ratings of problem behaviors continued to be significantly related to overall amount of time the child spent in nonmaternal care (B = .13, p < .05) and became significantly related to proportion of center care that the child experienced (B = .14, p < .05).

Analyses with Additional Covariates To address the concern that selection factors were not adequately controlled for, analyses were reconducted with the expanded list of covariates, consisting

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of the maternal rating of child temperament, maternal psychological adjustment, maternal report of social support, maternal separation anxiety, and maternal beliefs about the benefits of employment. These covariates were added to the nine child and family predictors in the previous model. The same significant child-care findings were obtained with the expanded list of covariates, suggesting that the obtained findings were not an artifact of inadequate controls for family characteristics.

Analyses of the “Whole” Sample Additional multivariate regressions were then conducted for all of the children in the sample, including children without any nonmaternal care. In these additional analyses, two quantity indicators (hours intercept and slope) and two type indicators (proportion of epochs of center care and proportion of epochs of child-care homes) were used as predictors. Childcare quality was not included as a predictor in these analyses, because quality could not be assessed for children who were not in child care. Findings regarding quantity and type of child care very similar to the findings described above were obtained in these follow-up analyses. Consequently, the quantity and type findings, excluding quality controls, are not presented or discussed further. They are available from the authors upon request.

How Large Are the Effects of Child-care Quantity, Quality, and Type? Follow-up analyses were then conducted to evaluate the magnitude of the statistically significant child-care effects reported above. Following the recommendation of McCartney and Rosenthal (2000), the obtained effects were evaluated in relation to two other well-established predictors of child outcomes – parenting quality and poverty. Effect sizes were computed as the difference between the adjusted means for high and low groups divided by the pooled standard deviation. For these analyses, continuous variables were transformed to categorical ones so that differences between the mean scores for high- and low-risk groups could be compared. Child-care quantity was categorized as 1.96) effects were set to zero. The resulting model was evaluated in terms of its goodness of fit. The analyses were run separately for each cohort. The importance of the cross-lagged effects was evaluated by comparing this model with a more restricted model that allowed for stability paths only (see Bentler & Bonnet, 1980).

Results The correlations, means, and standard deviations of the variables used in analyzing the data are given in Table 2. Coefficients above the diagonal refer to the early adolescent cohort, below the diagonal to the middle adolescent cohort. As revealed in the bottom part of Table 2, the older adolescents reported significantly less self-derogation independent of time of measurement. This is consistent with research on change in self-esteem across adolescence (O’Malley & Bachman, 1983). Older adolescents also felt significantly less rejected by their school peers. The significant differences in transgression proneness and contacts with deviant peers at second measurement were mainly due to the younger adolescents’ much lower scores than those of the previous year. As many of them changed schools, this decrease may actually indicate a change in the baseline of their ratings. The structural equation model for the early adolescent cohort is depicted in Figure 1. Paths, structural residuals, and covariances are shown. Only significant effects are given. As indicated by χ 2 = 32.91 with df = 41 (ratio of .80, much smaller than the recommended limit of 2), and a goodness-of-fit index of .91 (larger than .90 is suggested), the fit of the model was appropriate. As shown, only a fraction of the possible effects was significant. This model shows a significantly better fit than an alternative model assuming no cross-lagged effects. The difference amounts to χ 2 = 4.92 with df = 1 ( p < .05). Thus, the path from self-derogation to contacts with deviant peers is relevant for interpretation.

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Table 2: Correlations, means, and standard deviations for year 1 and year 2 measurements of variables in structural equation models Process Variables

Background Variables

SD1

TP1

CDP1

SD2

TP2

CDP2

− .28 .18 .55 .15 .11 −.06 .01 −.14 .42

.13 − .42 .23 .64 .43 −.02 −.12 −.24 .18

.42 .22 − .18 .35 .68 .17 .03 −.25 .30

.58 −.05 .39 − .24 .10 .00 .00 −.21 .27

−.03 .49 .17 .13 − .59 .09 −.01 −.20 .34

Early Adolescents – X 5.15 SD 2.62

4.65 2.75

3.27 2.20

5.19 2.68

Middle Adolescents – X 4.39 SD 2.18

4.49 2.80

3.38 2.11

.39

−.35

SD1 TP1 CDP1 SD2 TP2 CDP2 Q1 S1 PS1 PR1

Difference t

2.27*

Q1

S1

PS1

PR1

.39 .25 .48 .39 .31 − .05 −.02 −.19 .25

.02 .06 .06 −.04 −.06 .05 _ .72 −.07 .11

.23 .02 .15 .05 −.11 −.01 .70 − −.01 .04

−.13 −.18 −.30 −.02 −.02 −.26 −.03 −.10 − −.26

.45 .12 .28 .19 .07 .17 .09 .26 −.18 −

3.95 2.43

2.76 1.96

−.44 .74

−.31 .88

7.92 2.18

2.32 2.08

4.25 2.36

4.80 3.07

3.37 2.09

−.52 .77

−.55 .73

8.01 2.12

1.85 1.76

2.64*

−1.99*

−.20

.72

2.14*

−.29

1.75

Note: SD = self-derogation, TP = transgression proneness, CDP = contacts with deviant peers, Q = quick, S = slow, PS = parental support, PR = peer rejection. Measurement at year 1 (1), at year 2 (2). Coefficients above the diagonal refer to the early adolescent cohort (n = 62, r > .21/.30 are significant at the .05/.01 level), coefficients below the diagonal to the middle adolescent cohort (n = 193, r > .12/.17 are significant at the .05/.01 level). In comparing means across cohorts, the critical t for p < .05 is 1.65. *indicates significant ts.

The model for the middle adolescent cohort is given in Figure 2. Again, paths, structural residuals, and covariances are shown. Only significant effects are depicted. The goodness of fit is adequate: χ2 = 21.69, df = 28, resulting in a ratio of .77; goodness-of-fit index = .98. Again, the fit of this model was better than the fit of a model with no cross-lagged effects at all (difference χ2 = 40.81, df = 4, p < .001). Obviously the number and variety of significant effects in the middle adolescent cohort was much more extensive than was found with younger adolescents, especially for the background variables. Multi-group comparisons were used in order to confirm the structural differences between the cohorts. While it was possible to fit the structure of the older cohort to both groups (χ2 = 84.15, df = 83, p = .44), it was not possible to fit the structure of the younger cohort to both groups (χ2 = 190.63, df = 93, p < .001). Thus, processes and background influences shown for early adolescence seem to undergo a qualitative and quantitative change in the course of further development.5 In the following, further results will be presented by hypotheses. Only significant coefficients (z > 1.96) are mentioned. See Figures 1 and 2 for reference.

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Maturational Timing

Year 1

Year 2

Quick .70

.58

Peer Rejection

.79

1.0

Parental Support −.18

Self Derogation

.45

Transgression .26

.66

.80 Self Derogation

Slow

321

.46

Transgression .20

.28 −.24

.24

.26 Contact Deviant Peers

.80

.36

Contact Deviant Peers

.73

Chi–Sq. = 32.91 df = 41

Early Adolescence N = 62

Figure 1: Structural equation model for the early adolescent cohort. Data on these girls were gathered twice, with an interval of 12 months. At first measurement, the mean age was 11.5 years. Parental support, peer rejection, self-derogation, contact with deviant peers, and transgression proneness are represented by the sum of the items per construct. Quick and slow indicate adolescents whose self-reported maturational timing was faster (early maturers) or slower (late maturers) relative to same-aged peers. Only significant paths, covariances among the exogeneous variables, structural residuals, and covariances among residuals are depicted (z > 1.96). Path coefficients are set in boldface. Chi-square statistics refer to the model as depicted.

Hypothesis 1 In both cohorts, covariances with peer rejection are relevant in assessing the role of maturational timing.6 Among the early adolescents, later maturation had a positive relation with peer rejection (β = .26). That is, late maturers reported more problems concerning acceptance by peers in school than did the other adolescents. The rejection may be based on their prepubertal status and its psychosocial correlates. In considering effects of the background variables on the processes depicted in the model, early maturation was predicted to increase self-derogation and contacts with deviant peers. In the early adolescent cohort, there were no effects of maturational timing at all. In the middle adolescent cohort, however, some effects were found. In support of the hypothesis, early maturers reported more contacts with deviant peers (β = .15). As the negative effect of early maturation on self-derogation shows (β = −.21), they were also more satisfied with themselves. This is quite the opposite of what was expected. The slight positive effect of late maturation on self-derogation (β = .16, ns) was consistent with the surprising result. Although analysis of variance revealed no significant differences among

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Year 1 Quick

Year 2

−.21

.68

.82 Self Derogation

.72 .16

Self Derogation

.53

−.14

.42

Slow

.18

.11

−.11

.55

.95

.11

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Transgression Parental Support

.26

−.23

−.19 Peer Rejection

.20

.26

.33

.15

−.26

Transgression

Contact Deviant Peers

.87

.18 .60

Contact Deviant Peers

.52

Chi–Sq. = 21.69 df = 28

Middle Adolescence N = 193

Figure 2: Structural equation model for the middle adolescent cohort (mean age 14.7 years). Significant paths are depicted (z > 1.96); for the path from slow to self-derogation, z = 1.7. See Figure 1 for further explanation.

timing groups in either cohort, the means for self-derogation declined from slower to faster in both cohorts (6.35, 4.83, 4.11 and 4.67, 4.44, and 4.00, for younger and older cohorts, respectively). In sum, early maturation increased the risk of contacts with deviant peers in middle adolescence, as hypothesized. However, early maturation also corresponded to a more positive self-evaluation, contrary to hypothesis.

Hypothesis 2 Low social competence as a consequence of low parental support appeared to be responsible for the negative relation of support and peer rejection in both cohorts (β = −.18 and −.26, respectively). Higher support resulted in fewer contacts with deviant peers in the younger (β = −.24) as well as in the older cohort (β = −.19). Thus, there was a protective effect as stated in the hypothesis. Higher support was protective against transgression proneness, however, for middle adolescence only (β = −.23). The older cohort showed another peculiarity. Whereas there was no significant effect of parental support on self-derogation at first measurement ( β = −.04, ns), more support corresponded to less self-derogation in the following year ( β = −.14). Thus, the impact of parental support on selfderogation became more pronounced from age 14 to age 15 years. To test whether the model was similar for different levels of parental support, the scores for the older cohort were split at the median for parental

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support and this variable was removed from the model; the resulting model was tested with high and low parental support subsamples. Although the low parental support subsample was significantly higher on transgression (time 1), deviant peers (time 1), peer rejection (time 1), and self-derogation (time 2), there were few differences in which standardized coefficients were significant in the model. Two cross-lagged paths were significant for only one group: The path from self-derogation to transgression was significant only among the low-support group (β = −.18, high: β = .02) and the path from transgression to deviant peers was significant only among the high-support group (β = .29, low: β = .11). Only one difference appeared among the exogenous effects: The path from late maturation to self-derogation became significant among the high-support group (.30). Other effects were similar in size to the original effects, although occasionally they did not attain significance because of small ns.

Hypothesis 3 The influences of peer rejection were almost invariant across cohorts. Higher peer rejection resulted in more contacts with deviant peers in the younger (β = .24) and older groups (β = .20). Similarly, higher peer rejection corresponded to higher self-derogation in both cohorts (β = .45 and .42, respectively). This relation between peer rejection and self-derogation was generally the strongest effect in the model. An effect restricted to the middle adolescent cohort was also found: Higher peer rejection in the first year was related to higher transgression proneness in the following year (β = .26). Again, the contemporaneous relationship between these two variables was less pronounced (β = .13, ns) than a time-lagged relationship. The time-lagged effects of peer rejection and parental support could be spurious because second measurements on these variables were not part of the model (see Rogosa, 1979). Therefore, additional two-wave cross-lagged analyses were run for peer rejection and transgression proneness and for parental support and self-derogation. In both cases, the lagged effects shown in Figure 2 were not attenuated by the reverse lagged effects on peer rejection and parental support, respectively; the latter were almost nonexistent (β = .00 and (β = .05). Thus, both background variables became more important over the one-year period.

Hypothesis 4 Not expectedly, the dominant effects among the process variables were their stabilities across time. Although all were significant, the size of the coefficients varied considerably between cohorts. Both transgression proneness

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(β = .46 vs. .63) and contacts with deviant peers (β = .36 vs. .60) became more stable during the adolescent transition. The hypothesis posited an influence of self-derogation on later transgression proneness and contacts with deviant peers. The latter was confirmed with the early adolescent cohort (β = .26). In the older cohort, however, the effect on transgression proneness was reversed in sign. Adolescent girls who were not satisfied with themselves tended to show less transgression proneness, not more as expected (β = −.11). It is open to speculation whether they invest instead in positive alternatives such as academic performance. This result was contrary to what was hypothesized. With the middle adolescent group, higher scores in prior transgression proneness corresponded to more contacts with deviant peers (β = .18), as hypothesized. Taking both cross-lagged effects together, transgression proneness seemed to play a mediating role between self-derogation and deviant peer contexts.

Discussion The antecedents of and links between self-derogation and contacts with deviant peers differed in the two cohorts. In early adolescence, the levels of both variables varied mainly as a function of peer rejection. Adolescent girls who felt rejected by schoolmates had a less favorable self-perception and also tended to affiliate with deviant peers. More important, selfderogation directly increased the risk of future contacts with deviant peers, while transgression proneness was not related to this process. In middle adolescence, however, self-derogation and contacts with deviant peers were targets of multiple influences in addition to peer rejection. Among the background variables, maturational timing played a prominent role. Adolescent girls who developed faster than their agemates had more contacts with deviant peers but also showed less self-derogation. In contrast to its irrelevance in early adolescence, in middle adolescence transgression proneness seemed to mediate between self-derogation and contacts with deviant peers. Finally, a tendency was observed for parental support and peer rejection to increase their impact on self-evaluation and willingness to transgress by middle adolescence. Consistent with the focus of this paper, the discussion will concentrate on maturational timing. Generally speaking, maturational timing played a role in the development of problem behavior, as expected. More important, however, some of the effects seemed to contradict results of earlier studies. This was especially true for relations with the self. Girls who matured earlier than their agemates in middle adolescence reported less self-derogation.

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The results on the link between maturation and self were different from what was expected from the literature. Certainly, the discrepancy could simply derive from differences in the measurement of maturational timing. However, Petersen (unpublished data) used similar self-report assessments and found comparable relationships with height and weight.7 Although there was no independent, more objective measurement, the assessment of maturational timing seems valid. The findings on contacts with deviant peers are consistent with earlier research. Early-maturing girls reported more such contacts; that is, they agreed more with statements that characterize their friends as having trouble with adult norms. Magnusson et al. (1986) interpreted the higher risk of problem behavior among early-maturing girls as a consequence of age-inappropriate friendships with older males. Unfortunately, no data on the ages of their boyfriends were available. However, early-maturing girls were more likely to have close friendships with males (r = .24; p < .001). Thus, the results may relate at least in part to the more grown-up social affiliates of the girls. How can this result be brought together with the higher self-esteem of early maturers in this study? Simmons et al. (1983) reported lower selfesteem among early-maturing girls. In the present study the reverse was found: higher self-esteem among early maturers. The processes producing these different results require further examination. However, the apparent advantage to early maturers could be temporary. There are a number of problems with the present study that demand further research. The results require cross-validation on independent samples. Additional waves of measurement are required in order to determine whether the reported differences between cohorts indeed represent qualitative change in the role of maturational timing. A more extended time-span would help to distinguish relatively stable effects from short-term variations. We also wish to further illuminate the interplay between maturation and social development by adding other target behaviors to the model. More specifically, it would be highly interesting to compare effects on problem behaviors with those on more positive alternatives such as prosocial action. A final word concerning the positive relation between early maturation and self-evaluation: Most other studies on this issue deal with samples from the United States. Although a definite resolution of whether the present finding may be generalized requires systematic comparisons of matched national groups,8 we risk a premature answer at present. It may well be the case that a true cultural difference exists. Concerning sex education, for instance, adolescents in West Germany receive much more information in regular school curricula than their American gemates. The remarkably low rate of adolescent pregnancy as compared to the United States (Statistisches Bundesamt,1984) is but one of the presumable consequences.

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Notes 1. It is noted that usually even 14-year-olds are called early adolescents. However, distinctive names were needed for the younger and the older cohort. 2. The principal aim of the BYLS (for short technical reference see Verdonik & Sherrod, 1984) is the analysis of the role of problem behavior in normal adolescent development. Risk and protective factors within the individual, and within family, work, and leisure contexts, are investigated in Berlin, West Germany. By 1989, one of the cohorts will have been followed up once every year from ages 11 to 18. A parallel study was started in Warsaw, Poland, in 1985. 3. Adolescents whose maturational timing is consistent with their peers receive a score of −1 on both dummy variables. Adolescents who develop faster get a 1 on quick, and a 0 on slow; conversely, those who develop slower get a score of 0 on quick, and a 1 on slow. These raw dummy variables represent a contrast between the group indicated by 1 and −1, respectively. However, when both are used simultaneously in the same regression analysis, due to effects of partialling-out, each of them represents a contrast with the remaining groups. Thus, a positive effect of quick on contacts with deviant peers would indicate more such contacts among early maturers than in the two other categories of maturational timing. 4. Other attributes such as the development of breast and pubic hair were not measured. Our interpretation is supported by data from the second measurement. One year later, the only predictor is maturational status (β = .29, p < .05; r = .35). Although of similar size, the coefficient for height fails to achieve significance due to large variance. Judged from a study on a Swiss sample in which the age at peak height velocity is 12.2 years (Gasser et al., 1984; Largo & Prader, 1983), the age at peak height velocity for girls is close to 12 years in Germany. 5. Note that all computations were done using the sum of the respective item scores in order to represent the constructs. Additional analyses were based on multiple indicator models. For the older cohort the results were confirmed (χ2 = 609.35, df = 433, GFI = .84). Unfortunately, for the younger cohort the multiple indicator analyses revealed a rather bad fit (χ2 = 668.82, df = 442, GFI = .66). This seems to result from the low stability of contacts with deviant peers (see Figure 1 for comparison). Thus, results on the younger cohort should be taken with some caution. Furthermore, random fluctuations in this small sample lead to unstable estimates (which is especially serious when running multiple indicator models). 6. The covariance between quick and slow deserves no attention as it is simply required by the method of dummy-coding chosen. 7. For example, among eighth graders (13 to 14 years) in a United States sample, “quick” had very low correlations with height and weight, “slow” was significantly negatively related to both variables (−.37 and −.35, respectively), and perceived timing was positively related (.32 and .51 with height and weight, respectively). 8. In collaborative research we have just begun to design cross-national secondary analyses on adolescents living in Berlin and Warsaw (Berlin Youth Longitudinal Study), Chicago (Developmental Study of Adolescent Mental Health), and Pennsylvania (Rural Adolescent Project).

References Baumrind, D. (1968). Authoritarian versus authoritative parental control. Adolescence, 3, 255–272. Bentler, P. M., & Bonnet, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin, 88, 588–606.

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Cohen, J., & Cohen, P. (1975). Applied multiple regression/correlation analysis for the behavioral sciences (pp. 188–195). Hilisdale, NJ: Erlbaum. Crockett, L. J., & Petersen, A. C. (1987). Pubertal status and psychosocial development: Findings from the Early Adolescence Study. In R. M. Lerner & T. T. Foch (Eds.), Biological-psychosocial interactions in early adolescence: A life-span perspective (pp. 173 –188). Hillsdale, NJ: Erlbaum. Dornbusch, S., Carlsmith, J., Bushwall, S., Ritter, P., Leiderman, H., & Hastorf, A. H.(1985). Single parents, extended households, and the control of adolescents. Child Development, 56, 326 –341. Dunphy, D. (1963). The social structure of urban adolescent peer groups. Sociometry, 26, 230–246. Ewert, O. M. (1985, July). Differential rates of maturation and psychological functioning. Paper presented to the 8th Biennial Meetings of the International Society for the Study of Behavioral Development, Tours. Faust, M. (1960). Developmental maturity as a determinant of prestige in adolescent girls. Child Developmental, 31, 173 –184. Galambos, N. L., & Silbereisen, R. K. (1987). Income change, parental life outlook, and adolescent expectation for job success. Journal of Marriage and the Family, 49, 141–149. Gasser, T., Kohler, W., Muller, H. G., Kneip, A., Largo, R., Molinari, L., & Prader, A. (1984). Velocity and acceleration of height growth using kernel estimation. Annals of Human Biology, 11, 397– 411. Greif, E. B., & Ulman, K. J. (1982). The psychological impact of menarche on early adolescent females: A review of the literature. Child Development, 53, 1413–1430. Helmke, A., & Väth-Szusdziara, R. (1980). Familienklima, Leistungsangst und Selbstakzeptierung bei Jugendlichen [Family climate, fear of failure, and self-acceptance in adolescents]. In H. Lukesch, M. Perrez, & K. A. Schneewind (Eds.), Familiäre Sozialisation und Intervention [Familial Socialization and Intervention]. Bern: Huber. Holmes, B. (Ed.). (1983). International handbook of educational systems: Vol. 1. Europe and Canada. New York: Wiley. Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychosocial development. A longitudinal study of youth. New York: Academic Press. Jöreskog, K. G., & Sörbom, D. (1986). Lisrel VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least square methods. Mooresville, IN: Scientific Software. Kaplan, H. B. (1978). Social class, self-derogation and deviant response. Social Psychiatry, 13, 19–28. Kaplan, H. B. (1980). Deviant behavior in defense of self. New York: Academic Press. Kaplan, H. B., Martin, S. S., & Robbins, C. (1984). Pathways to adolescent drug use: Selfderogation, peer influence, weakening of social controls, and early substance use. Journal of Health and Social Behavior, 25, 270–289. Kracke, B. (1988). Problemverhalten und Pubertät [Problem behavior and puberty]. Unpublished master’s thesis, Technical University of Berlin, West Berlin. Largo, R. H., & Prader, A. (1983). Pubertal development in Swiss girls. Helvetica Baediatrica Acta, 38, 229–243. Magnusson, D., Stattin, H., & Allen, V. L. (1986). Differential maturation among girls and its relation to social adjustment: A longitudinal perspective. In D. L. Featherman & R. M. Lerner (Eds.), Life-span development and behavior ( Vol. 7, pp. 135–172). New York: Academic Press. Marshall, W. A., & Tanner, J. M. (1969). Variations in the pattern of pubertal changes in girls. Archive of Disorders in Childhood, 44, 291–303. Moos, R. H. (1974). Family environment scale (FES) (Preliminary manual). Palo Alto CA: Stanford University, Social Ecology Laboratory, Department of Psychiatry.

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O’Malley, P. M., & Bachman, J. G. (1983). Self-esteem: Change and stability between ages 13 and 23. Development Psychology, 19, 257–268. Petersen, A. C. (1988). Adolescent development. Annual Review of Psychology, 39, 583–607. Petersen, A. C. (1989). [The correlation of height and weight with the timing of maturation]. Unpublished raw data. Petersen, A. C, & Crockett, L. J. (1985). Pubertal timing and grade effects on adjustment. Journal of Youth and Adolescense, 14, 191–206. Petersen, A. C, Crockett, L. J., Richards, M., & Boxer, A. M. (1988). A self-report measure of pubertal status: Reliability, validity, and initial norms. Journal of Youth and Adolescence, 17, 117–133. Petersen, A. C, & Taylor, B. (1980). The biological approach to adolescence: Biological change and psychological adaptation. In J. Adelson (Ed.), Handbook of Adolescent Psychology (pp. 117–155). New York: Wiley. Rogosa, D. (1979). Causal models in longitudinal research: Rationale, formulation, and interpretation. In J. R. Nesselroade & P. B. Baltcs (Eds.), Longitudinal research in the study of behavior and development (pp. 263–302). New York: Academic Press. Silbereisen, R. K., & Kastner, P. (1987). Jugend und Problemverhalten: Entwicklungspsychologische Perspektiven [Youth and problem behavior: Developmental perspectives]. In R. Oerter & L. Montada (Eds.), Entwicklungspsychologie [Developmental Psychology] (pp. 882–919). Munchen, FRG: Psychologie Verlags Union. Silbereisen, R. K., Reitzle, M., & Zank, S. (1986). Stability and change of self-concept in adolescence: Self-knowledge and self-strategies. In F. Klix & H. Hagendorf (Eds.), Human memory and cognitive capabilities: Mechanisms and performances. Symposium in memoriam Hermann Ebbinghaus, (pp. 449–457). Amsterdam: Elsevier Science Publishers. Simmons, R. G., Blyth, D. A., & McKinney, K. L. (1983). The social and psychological effects of puberty on white females. In J. Brooks-Gunn & A. C. Petersen (Eds.), Girls at puberty: Biological and psychosocial perpectives (pp. 229–272). New York: Plenum. Simmons, R. G., Blyth, D. A., Van Cleave, E., & Bush, D. (1979). Entry into early adolescence: The impact of school stucture, puberty, and early dating on self-esteem. American Sociological Review, 44, 948–967. Snyder, J., & Patterson, G. R. (1987). Family interaction and delinquent behavior. In H. C. Quay (Ed.), Handbook of juvenile delinquency (pp. 216–243). Somerset, NJ: Wiley. Statistisches Bundesamt (1984). Zur Situation der Jugend in der Bundesrepublik Deutsch-land [The situation of youth in the Federal Republic of Germany]. Mainz: Kohlhammer. Sullivan, H. S. (1953). The interpersonal theory of psychiatry. New York: Norton. Tobin-Richards, M. H., Boxer, A. M., & Petersen, A. C. (1983). Early adolescents’ perceptions of their physical development. In J. Brooks-Gunn & A. C. Petersen (Eds.), Girls at puberty, biological and psychosocial perspectives (pp. 127–154). New York: Plenum. Verdonik, F., & Sherrod, L. R. (1984). An inventory of longitudinal research on childhood and adolescence. New York: Social Science Research Council. Youniss, J. (1980). Parents and peers in social development: A Sullivan-Piaget perspective. Chicago: University of Chicago Press.

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SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY

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SAGE LIBRARY OF EDUCATIONAL THOUGHT AND PRACTICE

SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY VOLUME II

Edited by

Neil J. Salkind

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Introduction and editorial arrangement © Neil J. Salkind 2011 First published 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Every effort has been made to trace and acknowledge all the copyright owners of the material reprinted herein. However, if any copyright owners have not been located and contacted at the time of publication, the publishers will be pleased to make the necessary arrangements at the first opportunity. SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1, Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 33 Pekin Street #02-01 Far East Square Singapore 048763 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN: 978-0-85702-178-6 (set of five volumes) Library of Congress Control Number: 2010923776 Typeset by Mukesh Technologies Pvt. Ltd., Pondicherry, India. Printed on paper from sustainable resources Printed by MPG Books Group, Bodmin Cornwall

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Contents Volume II Section I: Human Development (Continued) 19. 20. 21.

Motor Development as Foundation and Future of Developmental Psychology Esther Thelen Physical Growth Kai Jensen Mental Development during the Preadolescent and Adolescent Periods Gordon Hendrickson

3 31 79

Section II: Curriculum, Instruction and Learning 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32.

Making Sense of Curriculum Evaluation: Continuities and Discontinuities in an Educational Idea David Hamilton Psychology of Learning Environments: Behavioral, Structural, or Perceptual? Herbert J. Walberg Thought and Two Languages: The Impact of Bilingualism on Cognitive Development Rafael M. Diaz Components of a Psychology of Instruction: Toward a Science of Design Robert Glaser The Emergence of Cognitive Psychology Robert R. Holt The Advancement of Learning Ann L. Brown Paradigms of Knowledge and Instruction S. Farnham-Diggory Health Promotion by Social Cognitive Means Albert Bandura Models of the Learner Jerome Bruner Child’s Talk: Learning to Use Language Jerome Bruner The Reflexivity of Cognitive Science: The Scientist as Model of Human Nature Jamie Cohen-Cole

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33.

History, Culture, Learning, and Development Patricia M. Greenfield, Ashley E. Maynard and Carla P. Childs 34. Biology and Cognition Jean Piaget (Translated by Martin Faigel) 35. Neural Bases of Intelligence and Training Mark R. Rosenzweig

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Section I: Human Development (Continued )

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19 Motor Development as Foundation and Future of Developmental Psychology Esther Thelen

H

uman infants are born with very little control over their bodies. Yet within a year or so, they are able to sit, stand, walk, reach, manipulate objects, feed themselves, gesture, and even speak a few works. A year later, toddlers are adept at running, climbing, scribbling, riding a tricycle, and talking in simple sentences. For parents, these new motor skills are the most dramatic and visible changes in the first few years of life, frequently noted and commented on. For those interested in studying developmental processes, this sequential unfolding of motor milestones is like an open window on change. Whereas children’s mental lives must be measured indirectly, their movements are continuously observable. Each new pattern is there to see and describe. There are no hidden processes between the control of the movement and its actual execution.

Motor Development: The Golden Age It is no surprise, therefore, that the emergence of motor skills has figured so prominently in the first scientific studies of human development, truly laying the foundation of the field. Rich description of infant movement dates to the last century with Darwin’s (1877) well-known “biography” of his own child and the pioneering work of the German physiologist Preyer (1888), and the tradition was continued into the 20th century with the narratives

Source: International Journal of Behavioral Development, 24(4) (2000): 385–397.

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of Millicent Shinn (1900). The full flowering of the descriptive work in motor development began in the 1920s with the publication of the first of Arnold Gesell’s pathbreaking research and popular monographs (e.g., Gesell, 1928; Gesell & Thompson, 1934). The golden age continued through the 1930s with Mary Shirley’s (1931) exquisite longitudinal descriptions of 25 infants, Myrtle McGraw’s (1935) well-known and still contentious study of the twins Jimmy and Johnny, and the Nancy Bayley Berkeley Growth Study (1935). In 1946, Carmichael’s Manual of Child Psychology contained two seminal articles by Gesell (1946) and McGraw (1946), attesting to the theoretical status of motor studies in the field. There were three important and related legacies from this golden age of motor development research. The first legacy from these pioneers is their theoretical contributions, and especially their strong grounding of human development in biology. The second is empirical through the introduction of detailed and rich description, novel methods for capturing human movement, and clever natural experiments. The third, and perhaps the most enduring, was the establishment of developmental norms. I discuss each of these legacies in turn.

The Legacy of Development Grounded in Biology The two most important theorists of this era were Arnold Gesell and Myrtle McGraw. Both provided us with massive descriptions of early motor development. But for both scientists, the motor catalogues were not the ends in themselves. Rather, both Gesell and McGraw saw these descriptions as a way to understanding the most general, and profound, developmental principles. Gesell and the dynamic principles of growth. Gesell left us the most wellarticulated comprehensive developmental theory, one that is still insightful and attractive (for many details, see Thelen & Adolph, 1992). From the start of his career, Gesell claimed deep roots in the science of biology. He viewed development as a unitary process, requiring description at many levels encompassing evolution, embryology, comparative psychology, neurophysiology, and anthropology. He was especially influenced by Charles Darwin, whom he considered the founder of the scientific study of the child and by G.E. Coghill, an early behavioural embryologist. Gesell credited Darwin with making humans legitimate subjects of scientific study. Before Darwin, Gesell maintained, infants and children were understood primarily in theological and philosophical terms. Because Darwin showed that the human mind was continuous with all other living things, he gave scientists the freedom to study human nature in the same scientific way they investigated the rest of the natural world. Gesell attributed to Darwin his core belief that mental life is continuous with, and impelled by, the same

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Thelen

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processes that drive all organic growth. Moreover, Gesell admired Darwin’s naturalistic methods, and he saw himself, too, as a naturalist, “tirelessly” seeking “ideological order” through relentless observation and comparison (Gesell, 1948, pp. 36–37). Gesell’s other hero was Coghill, who was his contemporary and a leader in the new field of behavioural embryology. Coghill’s major work was on the behavioural embryology of the salamander Amblystoma. Coghill’s contribution was to show a correlation between the development of movement patterns in these animals and corresponding changes in their nervous systems. To do this, Coghill took motion pictures of locomoting salamanders at different stages of development and then painstakingly traced the patterns of their limbs and their bodies. He discovered that a particular form of locomotion, for example a “C” body shape or an “S” body shape was coincident with the growth of specific neural connections (Coghill, 1969). Gesell learned several lessons from Coghill. First, he saw that development was a morphological process, that is, a change in the form of behaviour. Gesell believed that just as movements and postures provided a read-out of the nervous system, so even mental development had this morphological character, that is, it could be understood though observable behaviour. Second, Gesell was convinced that principles of growth and development illustrated by Coghill’s studies of the salamander were the same for all species, including humans. “We believe that the growth processes which mold the body and behavior of the human infant are in essence comparable with those which are being successfully analyzed by experimental embryology”, he claimed (Gesell & Thompson, 1938, p. v). This led to Gesell’s third legacy from Coghill, the view that behavioural changes followed biologically driven neural maturation, and not the other way around. Gesell’s maturationist views stemmed directly from Coghill’s discoveries that in the salamander, changes in movement patterns emerged from neural events that happened before sensory connections were made.1 Movements, therefore, were a product of autonomous neural changes, not from the sensory input. “Patterns of behavior in all species”, Gesell wrote, “tend to follow an orderly genetic sequence in their emergence. This genetic sequence is itself an expression of elaborate pattern – a pattern whose basic outline is itself the product of evolution and is under the influence of maturational factors” (Gesell, 1933, p. 217). Just as Coghill unlocked the mysteries of salamander development through filming postures and movements, so Gesell believed he could discover the secrets of human development by filming and describing the morphology of movement. For example, Gesell learned from embryology that new forms and tissues evolved from chemical gradients and polarities in the fertilised egg and early embryo. In his principle of developmental direction, he argued that just as the direction of early embryonic growth is determined by a longitudinal gradient in the mesoderm, so the same direction of change appears in the fetus and the newborn infant. That is, development proceeds in a head-to-tail fashion,

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in succeeding waves, throughout the early years. Superimposed on this gradient is the proximal-to-distal sequence of maturation, with control emerging first in the trunk and head, and later in the distal digits. This theme of patterns arising from gradients and polarities has re-emerged in contemporary neuroembryology. Edelman (1988), for instance, shows how the complexity of form develops, not through precise specification from the genes, but through local, physical, and chemical interactions at the surfaces of the cells. He echoes Gesell’s views of development as a morphological or “topobiological” process, where collectives of cells or neural elements self-organise into waves of pattern change. The practical importance of Gesell’s theory was to act as a counterweight to the popular notions of behaviourism that were fashionable at that time. To parents who were told that infants were totally shaped by their environment, Gesell offered a different view, one of an autonomous unfolding of potential. The role of the environment was to support this unfolding, but it did not engender it. Thus, there was no point of training or teaching children until they were developmentally ready. McGraw and the biology of development. Myrtle McGraw was also a developmentalist of great sophistication and subtlety who used human motor skill development as her principle empirical data. Like Gesell, she questioned the relevance of behaviourism, the dominant paradigm in experimental psychology, as an explanation for developmental change. Rather, she, too, took inspiration from a generation of experimental biologists to consider the processes of growth, which she understood as a continuous, contingent, holistic but nonlinear organic process (McGraw, 1935). She was also influenced by Coghill for his emphasis on development as patterns of total form (rather than the concatenation of individual reflexes, a more behaviourist view) and for his studies relating nervous structure to behavioural function. Although McGraw believed strongly in the ultimate inseparability of structure and experience, she expressly designed her study of the twins Jimmy and Johnny (1935) to ask which of their early motor skills could be trained and which were more fixed by developmental design. One twin, Johnny, was exercised daily in a variety of skills, both universal (sitting, walking) and culturally specific (swimming, roller-skating). She discovered that although training had some effects on the quality and initial performance of Johnny’s movements, in the long run, intensive training did not make a big difference. These results were taken up by the popular press as refuting associationism and supporting the primacy of maturation. Bergenn, Dalton, and Lipsitt (1992) claim that McGraw’s legacy as a maturationist oversimplifies her more sophisticated view of development. They are likely correct, but the twin study, plus her subsequent work on the development of locomotion, put the role of maturation into the forefront. McGraw’s last major work was a monograph on the development of locomotion, The Neuromuscular Maturation of the Human Infant (1945),

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a model of astute interpretation. Again, inspired by Coghill, she described the phases of prone and upright locomotion as a series of whole body forms. At first, she claimed, infants movements are involuntary and under control of subcortical centres. Successive phases of new forms emerged as behaviour becomes increasingly encephalised. In addressing the role of learning versus maturation in this study. McGraw reiterated that the two influences are impossible to parse apart. Nonetheless, she stated that for locomotion, “Improvement of a function through practice or exercise appears to coincide with cortical participation in the activity” (McGraw, 1945/1972, p. 127), in essence agreeing with Gesell that an amount of readiness is necessary, and that there may be critical periods for advancing function.

The Legacy of Methodology From our technology-rich vantage point, we can only stand in awe of the detailed data so cleverly and painstakingly collected by the early movement pioneers. In addition to photographs and movies, analysed in great detail, they also recorded movement directly. For instance, Burnside (1927) and Shirley (1931) recorded infants’ and toddlers’ footprints by oiling the children’s feet and allowing them to walk on paper. Many critical parameters of gait can be measured by this simple technique, and indeed it is still being used today (Adolph, 1997). Another methodological advance, important in the establishment of developmental norms, was repeated testing of the same children in a large number of standardised tasks. Gesell took this testing to high art, with highly standardised equipment, structured interviews, and detailed instructions to the testers (Gesell & Thompson, 1938). The point, of course, was to chart developmental changes in a systematic way. Piaget’s (1952) methods, in contrast, were very flexible, as he continually adapted the tasks to fit the child. Contemporary researchers have again raised the issue of whether a standardised task really reveals the child’s full abilities, suggesting task materials may need to be scaled to children’s growth (Newell, Scully, McDonald, & Baillargeon, 1989) or measured in a supportive social situation (Vygotsky, 1978). Finally, we must credit both Gesell (Gesell & Thompson, 1929) and McGraw (1935) for the intensive study of twins, a method still used intensively today to parse apart the contributions of genetics and environment.

The Legacy of Developmental Norms The legacies of theory and measurement are closely tied to the third, and perhaps most lasting influence, that of developmental norms. Although mental testing of children dates back to the early part of the 20th century, largely through the work of Alfred Binet, it was Gesell who brought the concept of developmental norms for infants and young children both into

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the mainstream of developmental psychology, into the popular psyche, and into the homes of millions of parents. The test battery that Gesell and his staff perfected in the 1920s and 1930s still forms the basis of the most widely used infant tests today, the Bayley Scales of Infant Development and the Denver Developmental Screening test. Today such tests are so universally accepted that their origins are little discussed. But it was Gesell’s theoretical insights and methodological rigour that led to the vast catalogue of motor milestones on which these tests were based. Gesell, like Coghill, saw in the postural and movement forms a direct reflection of the internal, lawful processes of growth. The link to collecting normative data on infant movement was a direct one: . . . the underlying concepts of the normative study may be summed up as follows: Behavior grows. Growth expresses itself in ordered patterns. Behavior growth, like physical growth, is a morphosis. It is a process which produces a progressive organization of behavior forms. This morphogenesis can be investigated by morphographic methods and especially by analytic cinematography. By these methods we can ascertain the lawful sequences and norms of psychological growth for the purpose of genetic research. These norms may also be used as standards of reference for the analytic appraisal of development status (Gesell & Thompson, 1938, p. 4).

Thus, our contemporary reliance on movement for early developmental diagnosis may have had its origins in Gesell’s theoretical interest in the more general issues of the nature of development. In particular, Gesell believed that movement was the most direct expression of the forms of organic growth. By studying postures and movements of infants and children. Gesell both illustrated the more general principles of development and provided an enormous set of normative data. In the late 1920s and early 1930s, Gesell and his colleagues observed over 500 infants in a carefully structured and highly detailed series of tasks to establish age norms for a long list of motor behaviours. For example, Gesell and Thompson (1934, 1938) reported age norms for 41 stages of siting behaviour, 58 stages of pellet behaviour, and 50 stages of standing and walking behaviour. Many of these behaviours were filmed using precise cinematic methods (Gesell & Thompson, 1934). Gesell claimed that his interest in such extensive norms was not to establish a single model of performance for everyone, but to have a standard by which the “abounding variety of individual differences” (Gesell & Thompson, 1938, p. 4) could be detected and understood. In reality, Gesell’s books for both professionals and parents were much more concerned with the behaviour of the “typical” child at particular ages than in this variability. Indeed, Gesell chose his sample to be highly homogeneous: children of European descent, economically stable, intact families, 99% of whom attended church.

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The idea that by a particular age, an infant or toddler “should” have achieved a particular motor milestone has not only become a standard developmental diagnosis, but it has also become completely entrenched in our cultural beliefs about child-raising. For instance, I own a used copy of Gesell’s The First Five Years of Life (1940) which was originally a gift from “Mother Dingle” to the “Dunlaps” with the loving inscription, “Please read from cover to cover”. This book is one of many written by Gesell which sets normative standards of behaviour based on his observations, and we can imagine the Dunlaps scrutinising their child at each age for “typical behaviour” in each “typical day”. In sum, the golden age of motor development left us with a rich heritage: a deep understanding of growth and form, an appreciation for the interweaving and nonlinear course of development, a sense of our biological continuity, and exact scientific methods. What happened to that legacy?

Motor Development: The Dormant Times After more than two decades of extraordinary theoretical and empirical contributions to our understanding of development, the study of motor systems had declined by 1950 and then lay dormant for nearly 30 years afterward. This dramatic reversal of fortunes can be attributed to both the state of the field itself and to changes occurring in psychology as a whole. As I have suggested previously, it may be that the successes of the early pioneers also contributed to the decline of the field (Thelen, 1995). They produced vast and widely published catalogues of motor milestones and richly detailed descriptive studies. The norms were incorporated into developmental tests and became family lore, at least in middle class North American families. There seemed to be little left to do. Moreover, both Gesell’s and McGraw’s theoretical positions appeared to lead to dead-ends in terms of further empirical studies, but for different reasons. Once Gesell showed, through the descriptive topologies of behaviour, that human development obeyed universal principles, the case was effectively closed. Change dictated by the principles of developmental direction, reciprocal interweaving or individuating maturation was a biological imperative. Although Gesell acknowledged (as did McGraw) the interaction of experience and maturation, he did not inquire further about those mechanisms, nor did he encourage others to do so. For McGraw, the situation was more complex. She admitted in her 1962 Foreword to The Neuromuscular Maturation of the Human Infant that she did not achieve her goal of relating the development of function to the maturation of structure. Part of the problem was that methods for studying changes in the brain were inadequate. But she also recognised that the earlier theoretical formulations were too simplistic, especially the division of behaviour into cortical and subcortical influences, and the neat separation of

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“instincts” versus “acquired traits” or “maturation” opposed to “learning” (McGraw, 1945/1973). Throughout her career, McGraw continued to elaborate a biologically sophisticated theory of development (Bergenn et al., 1992), but she did not translate her insights into new empirical work. And she, too, failed to convince others to continue to use motor development as an entry for understanding processes of change. The decline of motor development must also be understood in the light of the other forces in the field during the 1950s, 1960s, and 1970s. During this time, learning theorists and experimental psychologists dominated academic departments of psychology. Descriptive and normative data, no matter how detailed, could not be compared in apparent “scientific rigour” to the tightly controlled experimental methods practised by these disciplines. Moreover, neither learning theory nor mainstream experimental psychology has traditionally considered how people control their bodies a question of major interest. Movement is often treated as only a by-product of the more psychologically interesting processes, or an arbitrary response modality such as pressing a lever. At the same time, the focus in developmental psychology shifted dramatically. The work of Jean Piaget became increasingly well known and highly influential, igniting fervent interest in the inner mental life of children (as opposed to the form of their overt behaviour), which continues to this day. (It is perhaps ironic that although Piaget’s theory of mental development was entirely grounded in perception-action, later interpreters were more interested in the contents of mind than its sensorimotor origins.) Piaget offered descriptive stages, but he also inspired brilliant experimentation, something that the motor development theorists did not do. The other dominant force on the field came from the ethological and psychodynamic theories of John Bowlby on the nature of attachment. Here again, I believe that Bowlby’s richly descriptive work made such a long-lasting impact because it was implemented experimentally by Mary Ainsworth. The lesson may be that even the most elegant theory survives only as it generates new and copious empirical research.

Motor Development: Two Decades of Rebirth Beginning in about 1980, the tide began to turn again, and interest in movement gradually gained momentum. Just as multiple factors contributed to its decline, the field’s revitalisation has come from a number of converging influences. These included several theoretical advances: new ideas in movement science and biomechanics, insights from ecological psychology, and the import of dynamic systems theory. But the field has also benefited greatly from a new understanding of the plasticity of the brain, and from technological advances in recording movement and brain activity.

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The Importance of N. Bernstein However subtle and interactionist their own positions, the legacy from the neuroembryologists, through Gesell and McGraw, was that movement was a direct read-out of the maturational status of the nervous system. The reverse was scarcely considered – that the developmental course of the nervous system may be moulded by the nature of the body and how it moves. The same “top-down” view was also dominant in the psychology of adult movement, where most effort was devoted to understanding the nature of the executive “motor programme”. We can credit the Soviet movement physiologist, N. Bernstein (1967), for inspiring a real revolution in the conceptualisation of movement. Although he too worked during the 1930s, Bernstein’s ideas only became known in the West after his book was translated in 1967. Bernstein’s seminal insight was to pose the control issue differently. He started with the body, which, he noted, had hundreds of bones and joints and millions of muscle fibres. Yet every movement is a coherent, coordinated event. How can the brain accomplish this feat of coordination, given so many possible combinations, or degrees of freedom? The answer, according to Bernstein, was that movements were organised in synergies, that is, a functional linking together of muscles into ensembles that worked together. What the brain recruited, according to Bernstein, was not individual muscles, but an appropriate pattern to accomplish a functional task. This vastly simplified the control problem. Indeed, Bernstein showed that movement was functionspecific and not muscle-specific: His classic example is that you can sign your name using a pen on paper or using a broomstick on a blackboard, but the signature remains the same. Moreover, as the motor system is assembled for functional action, it actually exploits the mechanical properties of the limbs and body. For example, limbs have springlike properties because of the elastic qualities of the muscles and the anatomical configuration of the joints. When an ordinary spring is stretched and let go, it oscillates in a regular trajectory. The movement pattern need not be explicitly configured because it arises from the natural properties of the spring. Similarly, many aspects of human movements need not be detailed in the nervous system because they arise by themselves from the natural properties of the body. One good example is walking: As people step on one leg and then shift their weight forward over it, the back leg is stretched and stores energy, like a spring. As that leg swings forward, it uses little active muscle contraction, but rather relies on the potential energy gained by stretching the muscles. In his writing on the development of movement, Bernstein turned the old theories on their heads. It was not, he claimed, so much the nervous system instructing the muscles, as the dynamics of the movement instructing the nervous system. Children must learn the biodynamics of their bodies, the changing forces that produce and accompany each movement in each situation.

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To learn to walk, for instance, infants must deal with the complex interactions between the movements of the legs, the centre of gravity of the body, and the support surface. These cannot be instructed beforehand, but must be individually assembled through experience. Eventually, infants learn not just to control their movements, but to make them efficient by exploiting their biomechanics, that is, using what the system can provide “for free”. The impact of Bernstein on the study of motor development was to shift the focus from thinking exclusively of the central nervous system as the sole contributor to the emergence of new skills to considering the contributions of the biomechanics of the moving limbs. In my early work on the coordination and control of spontaneous leg movements in infants, for example, I came across two puzzling results. The first involved the kinematic (time-space) organisation of the common kicking movements seen throughout the first six months. These movements are well coordinated, displaying a nearly simultaneous flexion and extension of the hips, knees, and ankles. Moreover, the durations of the parts of the movements were not random, but showed structure as well (Thelen & Fisher, 1983). How did this structure arise? Surprisingly, when my colleagues and I looked at the patterns of muscle activity that produced kicking movements, we found that the underlying electromyographic activity was far less patterned than the resulting movement. Indeed, infants appeared to just contract all the flexors and extensors at the beginning of the flexion phase of the kick and use very little muscle activation thereafter. Extension was largely passive. This meant that the extension was not directly programmed by the central nervous system, but rather emerged as a consequence of the elastic properties of the legs and their stored energy when flexed. In short, the precise patterns of these movements came about from the interplay between the neural command and the peripheral properties of the body. The second puzzle involved the well known newborn “stepping” reflex. Newborn infants will perform alternating step-like movements when held upright with their feet on a surface. Within a few months, such movements can no longer be elicited. Since the time of Preyer (1888), the common explanation has been that the patterns are a primitive reflex, which becomes inhibited as the cortex, and voluntary movement, matures. However, while stepping when upright does disappear, kinematically identical kicking movements performed while infants are supine, do not, and indeed increase in frequency. To account for this strange disparity – movements that are a function of body posture – we looked again to the peripheral contribution. Here again, we discovered that there was a relation between infants’ abilities to step and the mass of their legs. As their legs became heavier through the deposition of subcutaneous fat in the first few months, their ability to lift them in the biomechanically demanding upright posture decreased. Simply laying infants down, or reducing the effective mass by holding them in a tank of water, restored the movement (Thelen & Fisher, 1982). Again, performance depended on all elements of the moving system.

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Because body masses, lengths, centres of inertia and so on are continually changing as infants grow, and because new postures bring on new biomechanical challenges, skill acquisition is a continually interactive process. Infants must discover how to produce the appropriate coordinative pattern and modulate it to fit the task. Furthermore, the addition of each new skill opens different opportunities for these interactions to occur. Development progresses through each new achievement, setting the next set of challenges. Thus, Bernstein’s impact was to reintroduce the child as an active movement problem-solver, much as Piaget had replaced the child as passive stimulusresponder with one who actively seeks stimuli in the world.

The Importance of Ecological Psychology Bernstein’s message of the child as an active explorer meshed beautifully with the second powerful influence on the renaissance of motor development research. This was the theoretical approach loosely known as ecological psychology, based largely on the work of psychologists Eleanor Gibson (E.J. Gibson, 1969, 1988) and James Gibson (J.J. Gibson, 1966). The basic assumption of Gibsonian psychology is that people, and other animals, are able to directly perceive structured information in the environment that enables them to functionally act within it. (This is in contrast, of course, to the view that the environment does not have meaning until it is reconstructed within the brain.) The goal of development, in the Gibsonian view, therefore, is for infants and children to progressively discover the affordances for action in the environment, a process of matching the abilities of the actors with the opportunities in the world around them. Eleanor Gibson has been especially eloquent in championing the child as an active explorer in this process, where both perception and action are mutually coupled together. According to her, infants, from the beginning, are continually coordinating their movements with concurrent perceptual information to learn how to maintain balance, reach for appropriate objects, and locomote across various surfaces and terrains (Gibson, 1988). Research from this perspective is not primarily concerned with the form of the movement or its neurological control, but how children come to recognise the match between their abilities and the qualities of the task environment. One of E. Gibson’s classic studies, for example, showed that crawling infants crossed both a rigid surface and a squishy waterbed without hesitation. Toddlers, however, hesitated and explored the surface of the waterbed, and then shifted to crawling rather than risk falling on the squishy surface (Gibson et al., 1987). Ecological psychology has shaped the field of contemporary motor development studies in several important ways. First and foremost is the notion that perception and action are inseparable in the formation of skills. Perception is essential for movement, but movement also informs perception. Movements of the head and eyes, for instance, enable the perceiver to

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sample the visual array. Movements of the arms, hands, and fingers are necessary for haptic exploration of new objects and surfaces. Locomotion is essential for understanding the quality of surfaces and the layout of the spatial surroundings. Indeed, we can even cast movement as a form of perception, a way of knowing the world by moving in it. Second, research in the ecological tradition has made researchers aware that perception in the service of action is always multimodal, likely right from birth. For example, a study by Rochat and Morgan (1995) has shown that very young infants are aware of the correspondences between their visual perception of their moving limbs and their proprioceptive and haptic senses of them. In these studies, infants were shown two side-by-side televised displays of their own legs, clothed in distinctively striped stockings. On one display, the video image was concordant with the infants’ views of their legs as they sat in the infant seat. On the second, the legs were reversed. Infants showed by their preferential looking that they could distinguish the two displays. Clearly, they must have, through experience of looking and moving, mapped the correspondences between what they saw and what they felt. The third lasting contribution from ecological psychology to current motor development research is the emphasis on exploration as an important force for developmental change. E.J. Gibson (1988), for instance, describes three overlapping phases of exploration in the first year. At first, infants explore events with vision and hearing. Next, they explore objects with reaching, grasping, and mouthing, and later, they explore the large layout with self-produced locomotion. Changing motor skills contribute to infants’ exploratory behaviour, but the behaviour itself also leads to new motor skills.

The Introduction of Dynamic Systems Both Bernstein and the Gibsons were concerned with a similar issue: How to avoid the “homunculus” problem – some entity in the head of the actor that represents the world and makes decisions to act in it. For Gibson, the solution was direct perception. For Bernstein, the solution was reducing the executive decisions by the synergistic organisation of the brain and body. In the 1980s, a group of young movement theorists centred around the Haskins Laboratories in New Haven, Connecticut merged these two theoretical traditions with recent advances in the physics and thermodynamics of complex systems to produce a radical change in the theory of motor control and development. In two landmark papers, Peter Kugler, Scott Kelso, and Michael Turvey (Kelso, Holt, Kugler, & Turvey, 1980; Kugler, Kelso, & Turvey, 1980) considered Bernstein’s synergies in a new light, as self-organising systems, and described by the same dynamic principles that governed complex, so-called dissipative structures, in physics and chemistry. (Dissipative systems absorb energy to maintain themselves in organised states far from

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thermodynamic equilibrium. All biological systems are dissipative, but so are some other natural systems such as cloud formations or fluid flows.) The key insight here was that when a person assembles a motor synergy to do some task, the participating components cohere and produce patterns that have temporal and spatial organisation that is not the result solely of the detailed instructions from the nervous system. These investigators, and subsequently many others, demonstrated that simple, cyclical movements of the limbs in humans could be described by the mathematics used for coupled oscillators in general. In particular, they discovered that when people move their body parts in a rhythmical fashion, they have preferred coordination modes: Spatial and temporal patterns that are comfortable and easily performed. Other possible patterns are unstable, such that the preferred mode seems to suck them in, or “attract” them. Under certain conditions, people may spontaneously shift coordination modes from one preferred regime to another. Again, using locomotion as an example, quadrupeds use different gaits depending on the speeds of their movements. The gaits performed are those that are the most energy efficient for the particular speed (see Kelso, 1995). There were several important implications of dynamic systems ideas for conceptualising motor development, and for developmental theory in general. First is the restatement of Bernstein’s ideas that every movement is a system-wide ensemble of all participating components, assembled in the context of a particular task at hand. Kugler and Turvey (1987) used the terms “soft-assembly” to describe this flexibility: Behaviour patterns are not prescribed, although some may be preferred. This meant that behaviour was not “hard-wired” into the brain, but emerges “online” in the light of the person’s available structure, energetic resources, and the nature of the task to be done. Second, dynamic systems theory emphasised not only patterns in space, but also that behaviour has a pattern over time as well. This is important because it changes the focus from taking a “snapshot” of behaviour at a particular age or skill level, to more serious considerations of how change occurs over time. Here, the timescale could be seconds, or minutes, or weeks, or months. Indeed, from a dynamic perspective, it makes little sense to consider changes at different time scales as different processes. What happens at an action timescale cascades into changes over the timescales of learning and development. Finally, there is the idea that the coordinative state of a stable movement pattern – for instance, walking or reaching – behaves like a dynamic attractor with varying levels of stability. For a pattern to change, something must disrupt the stability of the old pattern, so that the components can coalesce into new forms. In terms of development, this meant characterising behaviour in terms of its stability to identify the transitions into new forms and test the mechanisms that engender them (see Thelen & Smith, 1994; Thelen & Ulrich, 1991).

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Systems ideas are not new in developmental psychology. They have been proposed by Werner, Lewin, Piaget, and many others. For example, Piaget (1952) believed that new levels of mental equilibration could only be achieved through disequilibration of current stages, similar to the phase shifts described by contemporary dynamicists, and he was profoundly concerned with processes of change. Current dynamic systems theories have gone beyond the old formulations, however, in two important ways. First, we have achieved some level of success in refining the precision of our ideas through formal mathematical models (see Newell & Molenaar, 1998; Thelen, Schöner, Scheier, & Smith, in press). But equally important, dynamic thinking has inspired a renewed interest in empirical work that is closer to Piaget’s original agenda of looking at processes of change. (In the postPiagetian era, studies were more concerned with describing age-defined performance stages.) Process accounts often involve detailed longitudinal studies to identify times of transition combined with microgenetic methods that test possible mechanisms that move the child into new developmental phases. Finally, dynamic systems have also inspired studies that incorporate multiple levels of analysis from the cognitive to the biomechanical. For instance, I have used dynamic systems principles in my own work to uncover the multiple influences on change in locomotor-movement (Thelen & Ulrich, 1991) and reaching (Thelen et al., 1993, 1996).

Plasticity in the Central Nervous System There have been two other influences on research in motor development that bear mention. First, just as our predecessors over 50 years ago were inspired by the current findings in neurophysiology, so too has contemporary thinking been shaped by remarkable discoveries in neuroscience. First is a growing understanding of the systems-wide properties of the brain. Although much research is dedicated to pinpointing local areas that subserve different functions, it has also been discovered that no area works in isolation. Indeed, neural networks supporting perceptual, motor, and cognitive processes are widely and densely interconnected (see, for instance, Edelman, 1987). For example, neurones responding to both spatial localisation of visual targets and to intended movements are found in many areas of the cortex. At the same time, a single neurone may be activated by the visual, planning, memory, and movement aspects of a task (reviewed in Thelen et al., in press). These findings reinforce the idea that perception and action, and its cognitive counterparts, are part of the same continuous and coupled process. Second, neuroscientists have discovered remarkable, dynamic plasticity in even the adult brain. By careful brain mapping in monkeys, they have established that experiences in the world both establish and maintain the functional connectivity of both cortical and subcortical areas. Old ideas

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about the fixity of the adult brain have been overthrown (see, for example, Merzenich, Allard, & Jenkins, 1990). The implication for development is profound: Experience moulds the brain. What may have previously been considered as autonomous maturational changes in brain function may indeed be driven by children’s everyday actions in the world. But the loop is still closed: Just as experience reorganises the brain, so also the resulting improvements in perceptual discrimination, memory, and motor control provide children with new opportunities for experience to further remap the brain. A final major influence in the field has been the important theoretical work of Gerald Edelman, which is a synthesis of current neuroembryology, neurophysiology, and behavioural development consistent with Bernstein, Gibson, and dynamic systems. In his Theory of Neuronal Group Selection, Edelman (1987) proposes that adaptive behaviour emerges as the recurrent perceiving and acting in the world strengthens particular neural networks such that patterns are progressively selected from many wider possibilities. In his view, genetic and neuroembryonic processes provide the rough outline of the neural anatomy. The functional mapping of the brain is subsequently experiencedependent, especially through perceptual-motor exploration.

Technological Contributions to Motor Development When Halverson (1931) published his very detailed studies of the development of prehension, he collected his kinematic data by filming infants reaching over a table with a grid drawn on it. From the film, he traced the path of the hand, frame-by-frame, and then extracted the quantitative data from the measurements on the grid. Seventy years later, the fundamental technique for measuring movement is the same: Sample the position of the body part in space many times per second and reconstruct the pathway of the moving segment. But we now have equipment to do this much more rapidly and accurately. Video has replaced expensive movie film, which required special lighting and development. Devices that automatically track markers on the limbs with great precision are commercially available. Computers that can handle the very large datasets generated by movement analysis are also inexpensive and accessible to all. In addition to measuring movement, we can now also track forces through the use of force platforms and patterns of muscle activation with electromyography. Although it requires a great deal of skill and patience to use these sophisticated techniques with infants and children, there are a number of laboratories around the world that have successfully conquered the challenges. There is no question that these technological advances have contributed greatly to advances in our understanding of motor development, perhaps more than in any other area of developmental psychology. It may be that our visions of what questions can be asked are limited by the means we have to answer them.

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Major Themes in Contemporary Motor Development In this section, I review the current major thrusts in the study of motor development in the light of the historical precedence of the field. Again, this review is far from exhaustive. In the final section, I point to some new directions for the future.

Descriptions of the Development of Motor Skill: Understanding Coordination and Control We still carry on the heritage of McGraw and Gesell by using detailed, longitudinal studies as the foundation for understanding motor skill development. Like our predecessors, we also use these studies to infer developmental changes in the underlying mechanisms. Moreover, from the point of view of dynamic systems, such studies are essential for providing the “landscape” of behavioural patterns: When they are stable and when they change. This is the first step for identifying points of transition, where the system may be probed by experiments. Thus, longitudinal studies are further supplemented by experiments to test hypotheses about coordination and control. Notable examples of contemporary descriptive work are the pioneering studies of Claes von Hofsten on infant reaching (1979, 1982, 1984, 1991). Von Hofsten was the first to reintroduce detailed kinematic measures of infant movement, after Halverson’s efforts 40 years earlier. Especially compelling were von Hofsten’s (1980) demonstrations of young infants catching moving objects, a seemingly remarkable precocious ability. Von Hofsten’s work has been extended by other longitudinal studies of reaching using even more dense sampling techniques and adding kinetic and electromyographic measures (Spencer & Thelen, 2000; Thelen et al., 1993; Thelen, Corbetta, & Spencer, 1996), and following infants until they were several years old (Konczak, Borutta, & Dichgans, 1997; Konczak, Borutta, Topka, & Dichgans, 1995). Lower limb movements have also received attention. For instance, my colleagues and I were the first to describe infants’ spontaneous leg movements using kinematic techniques. Previously, infants’ leg movements, because they were not apparently goal-directed, were thought to be disorganised or random. However, we demonstrated a high degree of coordination both within and between limbs and a pattern of developmental changes in that organisation. We followed this by a more in-depth look at the kinetics of infant leg movements, that is, how infants managed the forces that produce movements (Jensen, Ulrich, Thelen, Schneider, & Zernicke, 1994; Schneider, Zernicke, Ulrich, Jensen, & Thelen, 1990). As in motor development’s “golden-age”, the development of upright locomotion has been a primary focus in contemporary studies. Learning to walk is a dramatic developmental milestone, the transition from infancy to

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childhood. Moreover, there has been increasing recognition of the complexity of the task, and thus, in the question of how infants solve the problems involved (Thelen, 1984). For example, Sutherland (1984) reported gait measures on children up to seven years of age, documenting kinematic changes in step parameters and joint excursions, as well as changes in muscle patterns. Bril and Breniere (1992; Breniere, Bril, & Fontaine, 1989) focused more directly on changes in newly walking infants, using a large force plate to provide detailed descriptions of weight shifts and propulsive forces. Clark and Phillips (1988) looked primarily at changes in interlimb coordination over a similar time period. Moreover, there have been two recent longitudinal studies of interlimb patterns in crawling, another topic well-researched by earlier investigators (Adolph, Vereijken, & Denny, 1998; Freedland & Berntental, 1994). Finally, I mention several longitudinal descriptive studies undertaken from an explicit dynamic systems perspective. Thelen and Ulrich (1991) reported changes over the first year in infant treadmill stepping, emphasising the mechanisms promoting transitions to better performance. Angulo-Kinzler, Ulrich, Chapman, & Thelen (2000) followed this by tracing the continuity between treadmill stepping and later supported and unsupported upright locomotion, using multiple measures of kinematics, kinetics (forces), and electromyography. The longitudinal reaching study I mentioned earlier (Thelen et al., 1993, 1996) was also designed to follow dynamic principles, not only in the dense longitudinal design, but also in the multiple measures used to capture the multiple influences on the task (Spencer & Thelen, 2000; Spencer, Vereijken, Diedrich, & Thelen, 2000). Finally, in a most clever study, Goldfield, Kay, and Warren (1993) used a dynamic analysis to understand how infants learned a novel movement task, bouncing in a Jolly-Jumper. Here, I do not dwell on the detailed results of these investigations. Rather, I suggest that taken together, these contemporary studies of reaching and walking have gone beyond the classic studies in two important ways, reflecting the influence of Bernstein, Edelman, and dynamic systems. First, there is more explicit consideration of the biomechanical aspects of early movement. Theoretically, this means thinking about movement as a biomechanical problem to be solved by the nervous system: We cannot think about neural control distanced from what is controlled. For instance, learning to walk requires keeping the centre of mass of the body over a permissible base of support and controlling the “fall” as the infant steps forward. Crawling requires the correct limb combinations to maintain a dynamic base of support. Reaching involves stabilising one arm segment against the forces generated by the other moving segments. In this view, the patterns of muscle activation may well be the result of the biomechanical demands of the movement, not the cause of the movement. For example, Angulo-Kinzler et al. (2000) discovered that, as Bernstein predicted, the patterns of muscle activation underlying treadmill stepping, supported, and independent walking in infants were much more variable than the patterns of forces that moved the legs.

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And second, contemporary researchers are considering new ideas about variation and individual differences. From the start, researchers in motor development have noticed, measured, and discussed individual differences. Shirley (1931), for instance, documented the differences in onsets of various motor skills in her 25 babies and related these differences to the infants’ physical growth, muscle tone, and “willingness to expend energy” (p. 125). Physical dimensions and movement “styles’’ are still seen as an important part of the story (Adolph, 1997; Thelen et al., 1993). Additionally, today’s researchers are less concerned with variability around age norms than in earlier times. Indeed, in many studies, children are compared on the basis of their skill levels rather than their ages. There are two ways in which variation and individual differences have taken on new theoretical status. First, there is increasing recognition that individual differences in body dimensions, muscle qualities, and inherent energy levels, provide children with different kinds of movement problems that they must solve in order to gain skills. Low energy children with large limbs may have to learn different adaptive strategies than small, wiry, highly energetic ones. This emphasises the problem-solving nature of motor skill development, that there cannot be a rigid, phylogenetic blueprint because individuals must fit their own bodies to their own tasks. Second, we have come to recognise variation itself as the source of developmental change, a heritage from both dynamic systems theory and Edelman’s selectionism. If children do not have multiple options, they will be stuck in only a few solutions. Thus, some investigators have reported not just their dependent variables, but also the structure of variability as indicators of when skills are stable and when they change (e.g., Thelen & Ulrich, 1991). Experimental studies of early skills. One important way to uncover processes of change is to experimentally manipulate variables to which the system is sensitive. For example, I mentioned previously my hypothesis that infants’ limb masses were important in the disappearance of the stepping reflex. To test this, we changed the limb mass by submerging the infants’ legs in water or adding weights (Thelen, Fisher, & Ridley-Johnson, 1984). More recently, the effects of biomechanics and movement have been evaluated by changing infants’ postures (Jensen et al., 1994; Savelsbergh & van der Kamp, 1993) or by adding weights to their limbs and torsos (Adolph & Avolio, 2000; Thelen, Skala, & Kelso, 1987). To study the development of postural control, for example, researchers have commonly intervened experimentally by placing infants and children on a platform that perturbs the infants’ balance. By this means, researchers can test the limits of children’s postural stability at various ages, and investigate the underlying neuromotor mechanisms that produce the response (Woollacott & Sveistrup, 1992). I should note here that several studies have combined the longitudinal, descriptive method with experiments by repeating an experimental manipulation in the same infants over different ages. A most elegant example of

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this is Adolph’s (1997) study of infants climbing up and down slopes, where she followed babies from the onset of crawling through stable independent walking. At each visit, Adolph assessed their abilities to judge climbable slopes with a psychophysical measurement on slopes of different grades. Similarly, Angulo-Kinzler et al. (2000) tested infants in three locomotor contexts varying in support from eight months until they were walking well. Studies in perception-action coupling. Although all motor development studies are perception-action studies, experiments explicitly in the Gibsonian tradition continue to dominate the field. Here the issue is not so much motor control per se, but how action is modulated by perception, and in turn, how action informs perception. For example, early work by Lee and Aronson (1974) and Butterworth and Hicks (1977) established the dominance of vision in toddlers’ postural control by using the famous “moving room” paradigm. This situation was later extended and refined by Bertenthal and Bai (1989) with younger infants and also used by Stoffregen, Schmuckler, and Gibson (1987) to show that infants were sensitive to peripheral visual flow. Since then, there has been considerable debate over the relative importance of vision and proprioception in the control of posture. In their review, Bertenthal and Clifton (1998) concluded that the balance between multiple sources of information may be contextually determined, much like the soft-assembly of action I described earlier. The role of perception in prehension has also been a topic of intense study. Early researchers, including Piaget (1952), Bruner (1973), and White, Castle, and Held (1964) stressed the necessity of infants gradually learning to match the sight of their hands with the sight of the objects to be reached. Questions about this gradual visual-visual matching arose with von Hofsten’s (1982) report of infants’ directed reaches during the newborn period, before a long period of learning commenced. Recently, Clifton, Muir, Ashmead, & Clarkson (1993) further challenged the visual-matching idea by showing that at first, infants reached as well in the dark to a lighted or sounding object as when they could see their hands. This work underscored the importance of learning the “feel” as well as the sight of the arm and hand. Over the first year, infants are not only better able to reach a target, they become increasingly skilled in anticipating the location and size of objects. Notable work in this area includes von Hofsten and Ronnqvist’s (1988) descriptions of the development of anticipatory hand shaping and Ashmead, McCarty, Lucas, & Belvedere’s (1993) demonstration of infants’ abilities to adjust movements “online”. McCarty, Clifton, and Collard (1999) investigated infants’ discovery of the correct way to grip a spoon. In a particularly clever experiment, they presented the spoon in varying orientations to see when and how infants could anticipate and perform the appropriate grip. The studies I have mentioned so far have looked at how infants use perception to adjust movements. But the converse is also true: Children use actions

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to inform perception, primarily through the use of exploratory movements (Gibson, 1988). For instance, when confronted with an unfamiliar surface, infants, unsure of the suitability of the surface for locomotion, will use their hands to touch and pat (Adolph, 1997; Gibson et al., 1987). Bushnell and Boudreau (1993) provide an excellent illustration. They show how infants’ perceptual detection of the properties of objects such as weight, texture, or sounding abilities, develops only as their motor abilities are sufficient to manipulate the objects appropriately. In sum, we have learned a great deal about development in general through the experimental study of perception and action. In particular, this work has emphasised the continual active role of children in exploring their environments, and the context-dependent, problem-solving process that constitutes developmental change. Many of the old ideas about developmental timetables can now be recast in different terms. Instead of a phylogenetically determined sequence of stages, development is better conceptualised as a changing landscape of patterns, whose stability depends not only on the organic status of the child, but also on their experiential history, and how those interact with the particular task at hand. A final example makes the point well. In his classic work, Gesell described a series of grip configurations infants used to grasp a cube, ranging from a simple scoop to the fine pincer grasp. Newell and colleagues (1989) looked at the grasping task in a new way. They reasoned that for a young infant, a one-inch cube presented a different task, based on the infant’s hand size, than to an older infant or an adult. Indeed, they discovered that infants were much more adaptable, depending on the cube size, with young infants using more appropriate grasping patterns than previously thought possible. In short, the baby could “soft-assemble” a solution matching their own skill level to the demands of the task. Cognition and motor skill development. A final area gaining in prominence is the intersection between perception-action and cognition. Views on the relationship between these two domains have been complex. For many years, motor skill and cognition were believed to be unrelated because early studies showed only modest correlation, if any, between children’s motor and intellectual development (e.g., Shirley, 1931). (Of course, this has always been a contentious issue, because, especially in infancy “mental” test items have an enormous motor component, e.g., Bayley, 1936.) On the other hand, Gesell believed that both domains were governed by the same developmental principles. For Piaget, cognition was built from perception and action, and Piaget’s descriptions of how early motor skills, such as reaching and sucking, are used in the service of developing cognition are still among our most insightful. Today, there is little interest in using motor development to predict later mental status, but there is increasing agreement with Piaget of the tight linkage between movement and cognitive development. The work of Bertenthal and Campos and their colleagues has been especially influential in this regard

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(e.g., Bertenthal & Campos, 1990; Berthental, Campos, & Barrett, 1984). These scholars argue that one setting event – the onset of crawling – initiates a developmental cascade that has consequences for changes in spatial cognition and emotional development. The mechanisms, by which being able to move about changes the ways that babies think, are not fully understood, but may involve their increased attention to perceptual information as they move about. In other words, movement helps children sample the world more completely. There is, perhaps, even a more basic way in which movement and cognition are tightly linked, a way close to what Piaget envisioned (Thelen, 2000). Infants, children, and adults are perceiving and moving all of their waking hours. Movement itself is a form of perception because the proprioceptive and haptic senses are continuously receiving information, information that is perfectly coupled with information from the external senses such as vision and hearing. Thus, movement is an integral part of the ensemble of all our experience, including the times when we are just looking at something, because looking involves movements of the eyes, head, and neck. If, in the Piagetian sense, higher cognition is built from sensorimotor experiences, then the movement that occurs with those experiences is remembered and recalled to the same degree as information from the other perceptual senses. Even as mental events become more abstracted from the immediacy of the senses with development, they never become fully disassociated from the sensorimotor events that produced them (Edelman, 1987; Thelen, 2000). Indeed the hallmark of a skilled person is the ability to process efficiently both “online” and “offline” and to be able to switch between these modes as the situation demands. I have argued (Thelen, 2000; Thelen et al., in press) that this flexibility demands that action and mental events be encoded in the same dynamic language so that they can be tightly intermeshed, and that this encoding is there from the start.

An Agenda for the Future: It is not Just Motor any Longer Today, motor development is a robust field, with strong theoretical bases and empirical work of great sophistication. The continued strength of the field in the future. I believe, lies both in our abilities to pursue important issues within the field and, at the same time, to tie motor development with advances in other, related disciplines. In this concluding section, I offer some thoughts about these future directions. Multimodal perception and action. We have made great strides in understanding the role of visual perception in the development of reaching, posture, and locomotion. But, as I discussed above, experience is continually multimodal, including the perception of movement. Much less is known about how infants and children use correlated information from multiple sources, especially in decisions to act (but see, for instance, Streri & Pêcheux, 1986).

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Formal models and robotics. The processes involved in motor development are excellent candidates for a variety of types of computational and dynamic models, as well as implementation in robots. Modelling of any type offers the opportunity to think more precisely about the phenomenon in question, and to generate testable hypotheses about the processes involved. There is already considerable progress towards this end (see, for instance, Newell & Molenaar, 1998). Examples inspired explicitly by dynamic systems theory include Robertson’s (1993; Robertson, Cohen, & Mayer-Kress, 1993) dynamic analysis of the time structure of fetal and infant movements, Bertenthal, Boker, & Rose’s (1995) dynamic analysis of infant postural control, Goldfield et al.’s (1993) work on infants learning the Jolly-Jumper, Fitzpatrick, Schmidt, and Lockman’s (1996) analysis of children learning to clap, and Thelen et al.’s (in press) dynamic field model of infant perseverative reaching. Other notable efforts are Taga’s (1995) model of the development of locomotion, and Berthier’s (1996) simulations of infants learning to reach. The models of Sporns and Edelman (1993; Almassy, Edelman, & Sporns, 1998) are implemented both on the computer and in an autonomous mobile robot. Indeed, there is considerable interest in the “developmental” aspects of such robots, and especially how perception and action work together to produce emergent adaptive behaviour (Pfeifer & Scheier, 1999). Embodied cognition. There is a great need for studying the role of movement in so-called “higher” cognition – memory, decision making, categorisation, and language. For example, Thelen, Smith, and colleagues have demonstrated the role of body memory in a classic Piagetian task, the A-not-B error (Smith, Thelen, Titzer, & McLin, 1998; Thelen et al., in press). They showed that when infants reach several times to one of two targets, they build up a location memory of the target that also includes the feel of the arms and infants’ postural set, and that these memories influence further decisions to reach. This is a clear demonstration that movement is not separate from remembering and deciding, which are traditionally considered “cognitive” processes. These authors suggest that movement must be considered as part of every task: What aspects of movements that accompany everyday actions are remembered and encoded as part of the task ensemble? A very promising entry into this question lies in the area of speech and gesture. Infants produce interpretable gestures many months before they speak, and of course, gestures are universal in older children and adults. Until recently, gestures were considered as by-products of speech, or augmented communication. Now the motor aspects of both gesture and speech are being reconsidered, especially the deep coupling between hand gestures and cognition. There is also considerable evidence that control of hand and mouth are both phylogenetically and ontogenetically linked, and that indeed language acts are profoundly embodied (Iverson & Thelen, 1999).

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Neural bases of motor skill development. The field of “developmental cognitive neuroscience” is just coming into its own (e.g., Nelson, in press). Current neuroimaging and direct recording techniques are not well adapted to studying large movements in normal young human subjects. However, scientists have used other approaches to better understand brain correlates of skill development. For example, several research programmes are following groups of infants, largely prematurely born, who have suffered well-characterised, perinatal brain lesions. Many show considerable recovery of function while still others do not attain fully functional outcomes (see review in Elman et al., 1996). Such studies raise profound issues both about early plasticity and the effects of experience, but also about the old ideas of localisation of function, because it is apparent that when there is injury to one part of the brain, other areas can assume needed functions. It is hoped that as collaborations between neuroscientists and developmentalists increase, and with inevitable technical advances, this exciting area of brain-behaviour interface will grow at a rapid pace. Learning and plasticity. The explosion of interest in learning and plasticity in the neurosciences has already reverberated in our field. There is already a large and detailed literature devoted to motor learning and plasticity in human adults and nonhuman primates. Unfortunately, as yet, few of these findings have been extended to infants and children, although they are highly relevant. For example, Shadmehr and colleagues (e.g., Shadmehr & Mussa-Ivaldi, 1994) have conducted elegant studies examining how adults learn novel motor tasks, what is remembered from these newly learned tasks, and then how the newly learned task competes with previously learned ones. This is a good model for learning new skills as infants and children, yet much work needs to be done (but see Adolph, 1997, for a good example of competing skills.) Cultural and individual differences. Individual and cultural differences in learning motor skills are as important as the commonalities that children share. Such differences inform us about the plasticity of developmental pathways, and the influences and limits of daily experience in shaping them. All cultures and all intact individuals learn to sit, walk, reach and manipulate, and speak, but what are the diverse means of attaining similar ends? For example, work by Bril and colleagues (e.g., Bril & Sabatier, 1986) has documented child care practices and beliefs in Mali, which differ markedly from Western cultures, with the suggestion that they may contribute to differences in motor skill development. Campos and colleagues (2000) have undertaken a large project in China investigating the consequences of cultural restrictions on crawling, especially on spatial cognition. This remains an understudied area of motor development, likely due to the difficulties of collecting cross-cultural data, but one with great potential for helping us understand deep developmental issues.

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Conclusion The study of motor development is alive and vigorous at the turn of the century, perhaps reviving and reliving its old golden age. It has again returned to the mainstream of developmental psychology, but the field also has extensions, as I have shown, into new fields, including neuroscience, cognitive science, and motor science. Moreover, the theoretical and empirical work of the last two decades is reshaping clinical practice dealing with perceptual-motor disorders in infants and children. I believe, as in other fields of psychology, real progress will be made in the next millennium, only as we continue to combine our traditional naturalistic and experimental approaches with insights from other disciplines. This will work in two ways. First, we will learn more about the development of perception and action as we bring in information and techniques from neuroscience, cognitive science, clinical practice, and the like. But I also think we will take leadership in the 21st century as we continue to show the centrality of movement in other domains of psychological interest. Finally, we offer models of how development can truly be studied as a time-dependent process.

Note 1. Bergenn et al. (1992) argue that Gesell’s interpretation of Coghill is more ‘‘maturationist’’ than Coghill himself espoused.

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McCarty, M.E., Clifton, R.K., & Collard, R.R. (1999). Problem solving in infancy: The emergence of an action plan. Developmental Psychology, 35, 1091–1101. McGraw, M.B. (1935). Growth: A study of Johnny and Jimmy. New York: AppletonCentury-Crofts. McGraw, M.B. (1945). The neuromuscular maturation of the human infant. New York: Hafner. (Reprinted, 1972.) McGraw, M.B. (1946). Maturation of behavior. In L. Carmichael (Ed.), Manual of child psychology (pp. 332–369). New York: Wiley. Merzenich, M.M., Allard, T.T., & Jenkins, W.M. (1990). Neural ontogeny of higher brain functions: Implications of some recent neurological findings. In O. Franzen & P. Westman (Eds.), Information processing in the somatosensory system (pp. 293–311). London: Macmillan. Nelson, C. (Ed.) (in press). Handbook of developmental cognitive neuroscience. Cambridge, MA: MIT Press. Newell, K.M., & Molenaar, P.C.M. (Eds.) (1998). Applications of nonlinear dynamics to developmental process modeling. Mahwah, NJ: Erlbaum. Newell, K.M., Scully, D.M., McDonald, P.V., & Baillargeon, R. (1989). Task constraints and infant grip configurations. Developmental Psychobiology, 22, 817–832. Pfeifer, R., & Scheier, C. (1999). Understanding intelligence. Cambridge, MA: MIT Press. Piaget, J. (1952). The origins of intelligence in children. New York: International Universities Press. Preyer, W. (1888). The mind of the child: Part I. The senses and the will (H.W. Brown, Trans.). New York: Appleton. Robertson, S.S. (1993). Oscillation and complexity in early infant behavior. Child Development, 64, 1022–1035. Robertson, S.S., Cohen, A.H., & Mayer-Kress, G. (1993). Behavioral chaos: Beyond the metaphor. In L. Smith & E. Thelen (Eds.), A dynamic systems approach to development (pp. 119–150). Cambridge, MA: MIT Press. Rochat, P., & Morgan, R. (1995). Spatial determinants in the perception of self-produced leg movements by 3- to 5-month-old infants. Developmental Psychology, 31, 626–636. Savelsbergh, G.J.P., & Kamp, J.v.d. (1993). The coordination of infants’ reaching, grasping, catching and posture: A natural physical approach. In G.J.P. Savelsbergh (Ed.), The development of coordination in infancy (pp. 289–317). Amsterdam: Elsevier. Schneider, K., Zernicke, R., Ulrich, B., Jensen, J., & Thelen, E. (1990). Understanding movement control in infants through the analysis of limb intersegmental dynamics. Journal of Motor Behavior, 22, 493–520. Shadmehr, R., & Mussa-Ivaldi, F.A. (1994). Adaptive representation of dynamics during learning of a motor task. Journal of Neuroscience, 14, 3208–3224. Shin, M.W. (1900). The biography of a baby. New York: Houghton Mifflin. Shirley, M.M. (1931). The first two years, a study of twenty-five babies: I. Postural and locomotor development. Minneapolis, MN: University of Minnesota Press. Smith, L.B., Thelen, E., Titzer, R., & McLin, D. (1999). Knowing in the context of acting: The task dynamics of the A-not-B error. Psychological Review, 106, 235–260. Spencer, J.P., & Thelen, E. (2000). Spatially specific changes in infants’ muscle co-activity as they learn to reach. Infancy, 1, 275–302. Spencer, J.P., Vereijken, B., Diedrich, F.J., & Thelen, E. (2000). Posture and the emergence of manual skills. Developmental Science, 3, 216–233. Sporns, O., & Edelman, G.M. (1993). Solving Bernstein’s problem: A proposal for the development of coordinated movement by selection. Child Development, 64, 960–981. Stoffregen, T.A., Schmuckler, M.A., & Gibson, E.J. (1987). Use of central and peripheral optical flow in stance and locomotion in young walkers. Perception,16, 113–119. Streri, A., & Pêcheux, M. (1986). Vision-to-touch and touch-to-vision transfer of form in 5-month-old infants. British Journal of Developmental Psychology, 4, 161–167.

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Sutherland, D.H. (1984). Gait disorders in childhood and adolescence. Baltimore, MA: Williams & Wilkins. Taga, G. (1995). A model of the neuro-muscular-skeletal system for human locomotion: I. Emergence of basic gait. Biological Cybernetics, 73, 97–111. Thelen, E. (1984). Learning to walk: Ecologial demands and phylogenetic constraints. In L.P. Lipsitt (Ed.), Advances in infancy research (Vol. II, pp. 213–250). Norwood, NJ: Ablex. Thelen, E. (1995). Motor development: A new synthesis. American Psychologist, 50, 79–95. Thelen, E. (2000). Grounded in the world: Developmental origins of the embodied mind. Infancy, 1, 3–28. Thelen, E., & Adolph, K.E. (1992). Arnold L. Gesell: The paradox of nature and nuture. Developmental Psychology, 28, 368–380. Thelen, E., Corbetta, D., Kamm, K., Spencer, J.P., Schneider, K., & Zernicke, R.F. (1993). The transition to reaching: Mapping intention and intrinsic dynamics. Child Development, 64, 1058–1098. Thelen, E., Corbetta, D., & Spencer, J.P. (1996). The development of reaching during the first year: The role of movement speed. Journal of Experimental Psychology: Human Perception and Performance, 22, 1059–1076. Thelen, E., & Fisher, D.M. (1982). Newborn stepping: An explanation for a “disappearing reflex”. Developmental Psychology, 18, 760–775. Thelen, E., & Fisher, D.M. (1983). The organization of spontaneous leg movements in newborn infants. Journal of Motor Behavior, 15, 353–377. Thelen, E., Fisher, D.M., & Ridley-Johnson, R. (1984). The relationship between physical growth and a newborn reflex. Infant Behavior and Development, 7, 479–493. Thelen, E., Schöner, G., Scheier, C., & Smith, L.B. (in press). The dynamics of embodiment: A field theory of infant perseverative reaching. Behavioral and Brain Sciences. Thelen, E., Skala, K., & Kelso, J.A.S. (1987). The dynamic nature of early coordination: Evidence from bilateral leg movements in young infants. Developmental Psychology, 23, 179–186. Thelen, E., & Smith, L.B. (1994). A dynamic systems approach to the development of cognition and action. Cambridge, MA: MIT Press. Thelen, E., & Ulrich, B. (1991). Hidden skills: A dynamic systems analysis of treadmill stepping during the first year. Monographs of the Society for Research in Child Develompent, 56(1, Serial No. 223). von Hofsten, C. (1979). Development of visually guided reaching: The approach phase. Journal of Human Movement Studies, 5, 160–178. von Hofsten, C. (1980). Predictive reaching for moving objects by human infants. Journal of Experimental Child Psychology, 30, 369–382. von Hofsten, C. (1982). Eye-hand coordination in the newborn. Developmental Psychology, 18, 450–461. von Hofsten, C. (1984). Developmental changes in the organization of prereaching movements. Developmental Psychology, 20, 378–388. von Hofsten, C. (1991). Structuring of early reaching movements: A longitudinal study. Journal of Motor Behavior, 23, 280–292. von Hofsten, C., & Ronnqvist, L. (1988). Preparation for grasping an object: A developmental study. Journal of Experimental Psychology: Human Perception and Performance, 14, 610–621. Vygotsky, L.S. (1978). Mind in society: The development of higher psychological processes Cambridge, MA: Harvard University Press. White, B., Castle, P., & Held, R. (1964). Observations on the development of visuallydirected reaching. Child Development, 35, 349–364. Woollacott, M., & Sveistrup, H. (1992). Changes in the sequencing and timing of muscle response coordination associated with developmental transitions in balance abilities. Human Movement Science, 11, 23–36.

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20 Physical Growth Kai Jensen

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hysical Growth is a biological process which involves rates, directions, and patterns of change and development affected by a variety of diverse and complex external and internal factors and causes. It encompasses a diversity of detectable and measurable changes in size, shape, or function occurring in living organisms with the passage of time. A multiplicity of scientific disciplines study physical growth from a variety of angles at many different levels with increasingly refined and ingenious methods and technics. Genetic origins and backgrounds; reproduction; cell multiplication; protein synthesis; the role of chemical excitors and inhibitors; cell migration; prenatal development; birth phenomena; developmental history of special tissues, organs, and intact organisms; increases in body measurements and changes in shape; comparative growth of groups; interindividual and intra-individual growth; and environmental conditioners and impacts have all proved intriguing and often rewarding fields for study. Because of the many problems involved, the great variety of disciplines interested, the high stakes, and the many levels and avenues of approach, the literature is vast – far too great to be adequately covered in either the time available or the space at hand. What follows is at best only representative of the work being done in this area.

General Publications and Reviews of the Literature A recent symposium on the dynamics of growth processes (35) covered such topics as chemical growth in animals, the relation between skeletal Source: Review of Educational Research, XXV(5) (1955): 369– 414.

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“status” based on carpal X rays and bodily maturity, growth curves, and hereditary mechanisms in animal growth. Several general books in the field of human development which contain special sections on physical growth (39, 49, 61, 67, 114, 157, 158, 160, 167, 269, 311, 343) recently were written or revised. The most recent edition of Holt and McIntosh’s Pediatrics (150) organized several chapters around body organs and in an appendix included growth tables, norms for body measurements of boys and girls, and nomograms for estimating surface area of infants and children. A recent volume by Dunham (87) dealt entirely with premature infants. Gestation was the subject of a conference (99) held at Princeton, New Jersey, in March 1954. Baker (18), Cruickshank (65), Heck (139), and Jenks (166), published volumes on the exceptional child which include many data on the physical growth of various kinds of atypical children. Data on the functioning of the endocrine-controlled systems which maintain homeostasis during the course of various diseases will be found in a volume on functional endocrinology from birth thru adolescence (306). This work also contains tables and nomographs for estimating surface area from body weight and height, and other data of value to those interested in growth and development. The U.S. Department of Health, Education, and Welfare continued to issue bulletins on research in human development currently under way (320, 321, 322, 323). Research listed under the headings, “Pregnancy and the Newborn,” “Growth and Development (Physical and Motor Development),” and “Physical Health and Disease,” will be of particular value for those interested in physical growth. Sontag and Garn (289), in a review of growth phenomena restricted to the field of human growth, discussed the various factors which may modify the genetically determined potentiality for normal growth. Among these factors were defective implantation, deficiencies in maternal diet, infections, intoxications, endocrine disturbances, trauma, penetrating radiations, and fetal anoxia. Soffer and Gabrilove’s review (288) of the literature on endocrinology for the year 1954 included 433 references. Two hundred and eighty titles dealing with primary organizer phenomena, gastrular metabolism, protein and amino acids, the serological approach, enzymes and adaptive enzymes, nuclear embryology, experiments with isotopes, affinities and disaggregations, developmental block, and dissection of morphogenesis by metabolic processes were reviewed under the title, “Developmental Physiology,” by Needham (236). Much pertinent material was reviewed in the annual reviews of physiology (127, 128, 129), medicine (68, 69, 70), and biochemistry (206, 207, 208), and the Year Book of Endocrinology (117). Harris (136) discussed the need for an interdisciplinary society for research in child development.

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Antecedents and Origins of Growth The biogenetic origin of growth and the complicated environmental circumstances modifying development have challenged and intrigued many observers. Growth may be patterned, ordered, and integrated, or it may be unintegrated, invasive, or destructive. Changes occur with the passage of time, but these time relationships may be altered. Speeding of development may occur to the point where the generation time span is markedly shortened. Slowing may occur to the point where immortality, in a particular tissue at least, is achieved. Reversal occurs where or when rejuvenation is demonstrated. Just when has the biogenetic die, which determines the future of a given individual, or individuals in the case of monovular origin, been cast? What factors influence this favorably or unfavorably? To what extent may germ cells be evaluated and selected in advance of conception? How is the progressively changing organization of the fertilized germ cell or reproductive unit controlled? By what biochemical processes are differentiation and orderly growth assured? Spratt (291) reviewed the literature dealing with such fundamental problems as the formation and organization of the ovarian egg; activation of the developmental system; progressive changes in organizations; changes in metabolism during development, regulation, and regeneration; and control of development. Marshall (222) devoted an entire volume to the physiology of reproduction, and Nelson (239) reviewed 223 titles dealing with the subject. Mammalian Germ Cells (338) dealt with a variety of factors influencing fertilization, embryonic death, ovulation, recovery and transplantation of ova, the preservation of spermatozoa, warmth-induced aspermia, semen quality, factors influencing longevity of sperm in vitro, human spermatozoan production in health and disease, and proteolytic enzymes in human semen. Moricard (231) reported on fertilization studies of mammalian ova in vivo and in vitro. Chang (54) concluded that the presence of large numbers of spermatozoa in the female tract was not as important as their physiological integrity, which is contrary to common belief. Doyle (83) used the culdoscope to observe and describe ovulation in women and also reported successful conception in 14 out of 17 infertile women, considered to have tubospasm, following denervation of the tubes and uterus. Asdell (13) studied the effect of several methods of controlling ovulation time upon the fertility of the mammalian egg. Casida’s approach (50) to the study of infertility consisted of attempting to induce fertility in animals representing different developmental or functional states, or different genetic or environmental backgrounds, and then analyzing the results in terms of both fertility failure and embryonic death. He tried to separate environmental causes from inherent defects in the gamete. Terner (309) developed a test for grading semen of good motility of otherwise normal healthy bulls. Factors influencing the longevity of bull sperm

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cells in vitro were studied by Kok (180). MacLeod (215) reported that he and his co-workers were unable to find any relationship whatsoever between abnormal sperm morphology and fetal abnormalities or failure of conception in humans. Bunge and Sherman (46) reported that it has been shown repeatedly that preserved sperm can fertilize ova in the case of the bull and that recently the same was shown for man. Krohn and Zuckerman (182) reviewed the detailed observations that have been made on living gametes before, during, and after fertilization by means of phase-contrast microscopy. They also reported that there were differences in the viability of male and female zygotes, and that the differences probably did not remain constant thruout pregnancy. Wolff and others (337) reported the first successful steps in cultivating embryonic tissues in synthetic mediums (total nitrogen).

Fate of Transplants Autogenous grafts have been made successfully for a long time whereas successful homogenous grafts have proved exceedingly difficult to secure. Some autogenous grafts are impossible to get (for example, corneas), and the real future of grafts lies in the development of technics and procedures which will insure the successful and widespread use of homogenous grafts. Man rarely meets his end as did the one-horse shay, and so the rejuvenation of tissues, and perhaps even of individuals, can never be accomplished by autogenous grafts. This important problem of preservation and transplantation of normal tissues was the subject of a recently published volume (339). Medawar (225) expressed the conviction that the clinical problem of homografts is definitely soluble. He pointed out that gaps in the immunological defenses of the body that were unthought of even six years ago, have recently been discovered. Deanesly (74) demonstrated that prepuberal male and female gonadal tissue may be frozen and later successfully used as homografts with subsequent production of ova and spermatozoa. Smith and Parkes (286) on the basis of their experimentation in storing and homografting endocrine tissues reported that such tissues seemed potentially immortal under the conditions which they described. Precocious sexual development as a result of the use of gonadotropic treatment has been reported for many animals. The resultant ova have been successfully fertilized, but such superovulation and superfecundation have led to very small litters or inability to maintain the pregnancy if normal numbers of ova were involved. This raised the question of whether or not such failures were due to any inherent weakness in the ova. Adams (6) answered this question in the negative by successfully recovering and transplanting fertilized ova from immature animals to mature “recipients” and having normal births ensue. By this technic he also made available a new and brilliant method for markedly shortening the generation interval.

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Prenatal Development The functional role of the placenta, problems of sugar transport, and the comparative anatomy and histology of the placental barrier were dealt with in the transactions of the first conference on gestation, held in 1954 (99). In the second edition of Patten’s Human Embryology (258) even the descriptions of the earliest stages of development were based on human material. Blood pressure effects under various conditions of stress were recorded, samples of blood taken, and injections made in the same monkey fetus in utero in various stages of pregnancy (272). Abdominal and vaginal methods of demonstrating the electroencephalogram of the human fetus in utero were developed (30, 37). High voltage slow-wave activity with superimposed small fast waves was found in fetuses less than three months of age. By use of the electroencephalograph, fetal electrocardiograms were also successfully obtained in 50 cases (29). A three-channel tocograph, using fluid pressure and a metal bellows, was developed by Embrey (92) for use in the study of prenatal development. Adams (7) used intra-uterine roentgenography as an aid in determining fetal age by checking on the appearance of ossification in the distal epiphysis of the femur which is usually present by the thirty-fourth to thirty-seventh week of pregnancy. Armitage (12) administered varying dosages of sodium pentobarbital and sodium barbital to rats during pregnancy and then studied the subsequent learning performance of the offspring. The prenatal administration of the barbiturates produced significant impairment of performance in comparison with control groups.

Special Factors Influencing Prenatal Growth and Development Muller and Rugh stressed the possible damage to posterity that may result from irradiation of the gonads. Rugh (276) urged the use of extreme caution in the use of diagnostic and therapeutic radiation or even of extensive fluoroscopy in the pelvic region on the ground that such X-irradiation-induced changes at the gene or chromosome level are irrevocable, and dosages are cumulative without regard for time. Rugh did not predict that monstrosities would necessarily appear in the first generation, but rather argued that for a dose of 80 roentgens of exposure of gonadal tissue there will probably be a doubling of the normal rate of mutations, a majority of which will be harmful, and that such mutations are hereditary. Muller (234) contended that there is no dose so small as to produce no mutations at all and that even small mutational changes may eventually play a role in the extinction of the line of descent. Patt (257) reviewed 307 articles dealing with radiation effects on mammals. Greulich, Crismon, and Turner (120), Plummer (265), and Yamazaki, Wright, and Wright (341) studied the effects of the atomic bombing of Hiroshima and

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Nagasaki on women and children. Greulich and his co-workers used skeletal ages assessed by X-ray photography and matched control groups. They concluded that growth and development were adversely affected by the atomic bombing and that some of the effects were still evident five years later. Plummer reported that 205 pregnant women who were exposed to the irradiation at Hiroshima produced 28 abnormal infants and that the number of malformed offspring increased with closeness to the hypocenter. Yamazaki, Wright, and Wright reported that the mean height and head circumference of children born to mothers with major signs of radiation, such as epilation, oropharyngeal lesions, purpura, or petechiae, were significantly smaller than in those children born to mothers in the control group. Among 30 such mothers there were seven fetal deaths, six neonatal and infant deaths, and four instances of mental retardation among 16 surviving children. The over-all morbidity and mortality was approximately 60 percent in this group of mothers as compared with 6 percent in a control group. Kaplan (173) and Rubin (275) reported studies involving third-generation follow-up in women receiving pelvic irradiation. Rubin reported that no acquired lethal effects on the genes were observed in hundreds of babies whose mothers were subjected to X-ray irradiation of the ovaries for menstrual dysfunction and sterility. He also reported no harmful effects in five grandchildren. Kaplan in a similar study irradiated the ovaries of 660 infertile women of whom at least 270 became pregnant and produced 347 children, all of whom were normal. Similar evaluation was made of 14 third-generation offspring in this group. Collins (62) raised a theoretical issue of general biological significance. She pointed to the fact that during periods of rapid somatic growth there is little development in integrative activity and conversely that integrative growth increases as somatic growth decreases. From these facts she hypothesized a reciprocal relationship between somatic or cellular growth and integrative or functional growth. She further suggested the existence of a closed system of available energies for which the somatic and integrative growth must compete. Dickerson (77) pointed out that studies of selection and of genetic correlation have both shown that genes influencing rate of growth in body weight had proportionately larger effects on fattening than on skeletal growth and least effect on appendages. Such genes also tended to improve viability, increase efficiency in food utilization, cause earlier sexual maturation, cause larger numbers of ovulations, and cause superior intra-uterine nutrition of the young, coupled with slightly inferior lactation.

Congenital Malformations and Experimental Teratology In the recently published proceedings of the Association for the Aid of Crippled Children (151) the section on congenital malformations considered the background of this subject, the principle underlying experimentally induced

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anomalies, cortisone induced cleft palate in rats, the effect of amino acid analogues on development of the explanted chick embryo, clinical classifications of malformations during prenatal life, traumatic abortion and prenatal death of the embryo, and statistical approaches to the study of congenital malformations. Harris and Steinberg (137) reported and classified the abnormalities found during the first six days of life in a sample of 8716 live-born infants. Their classification was according to systems such as gastrointestinal, respiratory, nervous, genito-urinary, skeleto-muscular, blood and cardiovascular, and skin. Stodard (294) studied the relationships between time of insult and the type of malformation. He classified the environmental factors which may produce anomalies as nutritional, chemical, endocrine, radiation, infectious, and mechanical. Marberger and Nelson (220) used skin biopsies in cases of ovarian agenesis to distinguish chromosomal sex and found the majority of such individuals to be genetic males. Collins (63) reviewed the literature dealing with the incidence of congenital abnormalities following maternal rubella and found that it varied with the stage of pregnancy at which the rubella was contracted. If contracted during the first four months of the pregnancy, the chances of malformations in the offspring were from 70 to 80 percent. After the fourth month, chances of malformation of the fetus were not great. Several people reported that congenital malformations may occur as a result of defective nutrition during pregnancy. Hogan (149), Nelson, Lyons, and Evans (238), and Workany (340) published comprehensive reviews of the literature on the effects of dietary deficiency in the production of congenital anomalies. Sontag and Garn (289) pointed out that dietary deficiencies experimentally induced during pregnancy have produced a wide range of developmental abnormalities in the offspring of laboratory animals. Giroud (115) reviewed the relationship between avita-minosis and malformation. Cohlan (60) showed that excess vitamin A, as well as a deficiency, may produce congenital malformations. Excess vitamin A fed during the seventh to tenth day of gestation in the rat produced encephaly, eye malformations, cleft palate, shortening of the mandible and maxilla, spina bifida with meningocele, or hydrocephalus in 52 percent of the 148 offspring in the experimental group as contrasted with no congenital anomalies in 1201 control young. He concluded that the developing embryo requires nutrients at critical developmental stages for the orderly progress of normal fetal differentiation and that teratologic effects may be produced by either a deficiency or an excess of specific nutrients at critical developmental stages. Lepkovsky and Borson (194) concluded that in humans nutritional supplements should be provided during the early prenatal period because organogenesis is practically complete 10 weeks after conception. Hartman (138) reported on experiments furnishing evidence against the notion that all prenatal abnormalities are due to defective maternal environment. His technic consisted in evaluating the growth of companion eggs in

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the same maternal (uterine) environment. He then attributed embryonic death to inherent lack of growth potential or specific defects in the germ plasm if along with the death the other cell developed into a normal live individual. Nelson (237) reported that the folic acid antagonist, X-methyl pteroylglutamic acid, when administered to pregnant rats on a folic aciddeficient diet, produced brain injuries including encephaly and many other congenital abnormalities. Several experimenters (97, 103) administered cortisone to pregnant mice and rabbits. Large doses caused resorption of litters and smaller doses produced a variety of congenital defects with cleft palate the most common visible gross defect. Genetic make-up of the animals influenced the results markedly as did the gestational stage of the mother. The effect of diverse chemicals on mammalian embryos was studied by Hamburgh (130). The teratologic effects of ionizing radiation have been verified by several workers. Brent (40) found that irradiating embryos in shielded mothers produced the same types of abnormalities produced by irradiating the mother. He found some abnormalities would occur only if the embryo was irradiated at certain stages of embryogenesis. Most malformations were found to have a wide range of susceptibility, particularly central nervous system malformations. Hicks (146), Hicks, O’Brien, and Newcomb (147), Russell and Russell (277), and Wilson (335) experimenting with animals found the teratogenic potentiality of ionizing radiation to be high when exposure occurred during the time of major organogenesis. They also found that in some stages of embryonic development a certain amount of damage repair occurred following small total radiation dosages. Levinson (195) studied the effects of differing amounts of X irradiation at each of five stages of prenatal development upon maze-learning performance in rats. He found such learning decreased with increasing X irradiation and that the learning behavior was most impaired if X irradiation was administered on the thirteenth day of gestation. Blattner and his co-workers (31) studied developmental defects in the chick embryo following infection with Newcastle disease virus. They concluded that three key factors were involved: the site of the inoculation, the developmental stage of the embryo, and the amount of virus inoculated. Buck (44) reported on the exposure to virus diseases in early pregnancy and the resultant congenital malformations. Sontag and Garn (289) pointed out that some, if not all, viruses are able to cross the placental barrier with resultant embryological defects if the mother becomes infected during the first trimester, the duration and severity of the infection determining the nature and extent of the ensuing abnormalities. They also pointed out that a great many substances, including sex hormones and antibiotics, may pass the barrier and that the current widespread use of hormones and antibiotics to enhance the chances of a given conception

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coming to full term may well result in a concomitant increase in the number of defective offspring. It will be noted that to date, experimental work in this area has produced abnormalities largely, if not entirely, on the debit side. It is certainly conceivable that circumstances exist which would produce deviations from “normal” on the positive side, for surely the present environment is not usually the ultimately optimal.

Vital Statistics The United Nations published two volumes of accumulated data and interpretations in the field of mortality of young children (318, 319). These volumes included a mass of information from over 60 countries on all continents on birth rates; still-born; neonatal and infant death rates; and correlations of mortality rates with income, illiteracy, parity, nutritional levels, and other factors. In some countries 20 percent of all live-born children failed to survive the first five years. In 35 countries, where infant mortality rates were comparable, 10 had rates of 100 to 200 per 1000 live births, and in only five countries was the rate less than 50. Strom (295) reported that Sweden leads the world in low infant mortality having a total infant mortality rate of about 20 per 1000. Bundesen (45) reported the results of a 14-year study based on 10,000 neonatal deaths. He made the interesting point that there was practically no reduction in the mortality of the first day of life over this period of time. Abnormal pulmonary ventilation caused 54.3 percent of the deaths, and birth injuries accounted for 18.2 percent of the total. The correlation with prematurity was found to be high for all causes of death. Cenci (53) studied infant mortality in the city of Castello for the years 1938 to 1951. He found that infant mortality rose in the war years but has since fallen altho there was no decrease in prematures or infants with congenital or neonatal disorders. Wegman (330) reported on birth rate, death rate, marriage license rate, and infant mortality rate for 1952 and 1953 in the United States. Douglas (81) published data on trends in the risks of childbearing and in the mortalities of infants during the last 30 years in Scotland. Landucci (186) found that the birth rate in the province of Siena gradually dropped during the years 1946 to 1951 while the mortality among children under one year of age dropped from 68 to 49 per 1000 live births. Deaths in infants under one year of age accounted for 92.8 percent of the deaths of all children under 12 years of age during the year 1951. Both birth and death rates in Italy as a whole were higher than in Siena. Kendall and Rose (175) suggested an organization and procedure for studying neonatal mortality. Silverman, Fertig, and Kraus (283) proposed a method of computing standardized death rates for premature nurseries, which makes

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due allowance for distribution differences in the structure of premature nursery populations. Essentially they proposed that the statistical technics of standardization which are used in the study of mortality rates of other population groups be applied to the problem of hospital nursery rates. Age-specific death rates for infants of differing birth weight, gestational age, sex, color, place of birth, and similar data obtained from the pooled experiences of many hospital nurseries were suggested as a basis for a set of standard rates. Maternal and perinatal mortality in New York City decreased from 1944 to 1951 altho there was no change in the incidence or weight distribution of prematurity (326). Lewis (196) analyzed data from U. S. Life Tables of the Bureau of the Census on the mortality of white males and females in the different eras of 1890, 1900, 1910, 1920, 1930, 1940, and 1949. He reported continuous improvement in survival in younger and older age groups with a significant decrease in the older age groups in 1940 and in 1949, except for white males aged 50 to 65 years. White females manifested a significantly higher survival rate at all ages thruout the period from 1890 to 1949, the difference being a major one after the age of 50. Wegman (331), after studying vital statistics for the United States, concluded that at a given weight Negro babies are more mature than white, children born to parents of lower socioeconomic status more mature than children from higher levels, and plural births more mature than single births. He also concluded that Negro babies of a given weight have a distinctly greater chance of survival than white babies of the same weight and that, as infant mortality rates continue to decline, the relative importance of the newborn period and particularly the problem of prematurity and postmaturity become greater. Wilson (336) stressed the growing importance of neoplasms as a cause of childhood maturity pointing out that in all age groups from 3 to 10 years, tumors (including leukemia) are one of the first four principal causes of death, tumors of the central nervous system being most common.

Multiple Births Guttmacher (121) reviewed the literature on the incidence of multiple births in man and some of the multipara with special reference to the variation in the frequency of twin births in various ethnic and social groups. Relative frequency of twin births for 12 countries with available data placed Norway highest with 14.5 per 1000 total births and Japan lowest with 6.5 per 1000 total births. Nichols (241, 242) combed the literature for accounts of quintuplet and sextuplet births in the United States. He judged most of the reported cases as not well authenticated. Miettinen (229) reported that 633 sets of triplets and 20 sets of quadruplets were born in Finland from 1905 to 1952. Digby (78) described what he believed to be the first case of quintuplet pregnancy in the British Isles.

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Lilienfeld and Pasamanick (199) studied the relationship between twin births and race and socioeconomic status. They found an increased frequency of both mono- and dizygotic twin births in the nonwhite population as compared with the white population after making necessary adjustments for economic status, birth order, and maternal age. McArthur (209) reported a tendency for monovular twinning to increase with maternal age and parity in young mothers in Italy from 1949 to 1950. Maternal age played a larger role than parity. Binovular twinning increased to a maximum at ages 35 to 39 years, the frequency increasing after birth ranks one and two. Binovular twinning was more closely related to modal maternal age than modal birth rank. Karn (174) found that mean birth weight of twins increased with the mother’s age up to the fourth birth and that there was an almost linear relationship between birth weight and gestation time. Low birth weight, first birth, and low age of mother all increased mortality. Friedman (105) pointed out that the possibility of solving the problem of the etiology of Mongolism would be greatly aided by cases of Mongolism in one or both twins provided data were available with respect to their zygosity.

Multiparity Miller and Oxom both reported studies of mutiparity. Miller (230) studied 563 grand multiparas in New Mexico and found that the incidence of prematurity was 8.8 percent and that it increased with the age of the mother. Oxom (253) found 1056 grand multiparas (seven or more children) in an examination of 63,140 confinements from 1926 to 1952 in Montreal. Eighty percent of the mothers were over 35 years of age and 25 percent over 40. He compared the more common and serious complications in the grand multipara with those in women who had given birth to fewer than eight children.

Prematurity and Postmaturity Such aspects of prematurity as the general background of the problem, the respiratory exchange, immunology, haemotology, development of enzyme systems, endocrinology, and the prevention of premature births were discussed at a conference attended by 30 scientists from all over the country who met to exchange information, to discuss research methods, and to clarify common objectives in the pursuit of basic knowledge germane to prematurity and related areas (151). Another general and extensive source of information on prematurity is the second edition of Dunham’s Premature Infants (87). Clifford (59) summarized present obstetric and pediatric knowledge by which a further reduction in the neonatal mortality rate may be brought about. He agreed with Bloch and his co-workers (32) that the occurrence of premature birth is primarily a public health problem involving the nutritional

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condition and the socioeconomic status of the mother. Less frequent causative factors were obstetric conditions, ethnic origin, and maternal illness. Taff and Wilbar (305) reported that the neonatal death rate for immature infants of all races was about 30 times that of mature infants in his Chinese, Japanese, and Filipino populations. Racial immaturity rates ranged from 6 percent for Japanese to 12.1 percent for Filipinos, with a 7.3 percent over-all average. Verrotti (324) analyzed the causes and frequency of prematurity which occurred during the years 1936 thru 1950 in Siena, Italy. The incidence of prematurity varied from 3.5 percent in the prewar years to 7.3 percent in the early postwar period and then dropped to 5.2 percent in 1950. During the war the rate was 5 percent. There was no increase in death rate during the period studied. Thirty-six percent of the children were born before the eighth month of pregnancy and 55 percent were born of uniparae. Houghton and Ross (153), who studied birth weights and prematurity rates in Southern Rhodesia, suggested that the much higher rate which they found among Africans might be due to poor diet and social conditions. Llewellyn-Jones (201) reported that, if the international standard of prematurity were used in the tropics, abnormally favorable mortality rates would ensue and the percentage of premature babies would be abnormally high. The problem of the long-term prognosis for prematurely born children was studied by Alm (9) and by Douglas and Mogford (82). Alm found the mortality rate considerably higher among prematures especially those who were plural born. After this initial inequality, he found more brain-injury disorders in those surviving three years, altho there was no greater incidence of other disabling diseases. At 20 years the prematures were shorter and weighed less than the controls. In the matter of social adjustment he found no deficiency in the prematures at 20 as compared with normals. Douglas and Mogford (82) followed a national sample of premature children from birth to four years of age. Thruout this period the children were compared with a closely matched group of controls. There was a tendency for the smallest premature children to be most successful in eliminating their handicaps, and by the age of four years 36 percent of the premature children equaled or surpassed their controls in weight and 44 percent did so in height. Children who by four years of age had eliminated their initial height and weight handicaps were found to have mothers who were as tall and as heavy as the mothers of the controls. Children still lagging behind at the age of four years had mothers who were significantly smaller and lighter than the mothers of the controls. If the children whose mothers were smaller were really just smaller babies, rather than “prematures,” this would be further evidence of the inadequacy of the generally used criterion of prematurity which is birth weight of less than 2500 grams. Longo and Vianello (202) studied 18 reflexes commonly used in clinical tests in 50 immature and premature newborn infants on the first, fifth,

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fifteenth, thirtieth, forty-fifth, and sixtieth days of life. Reflexes of defense and medullary automatism were found to be more accentuated than in normal infants, but otherwise differences were slight. Postmaturity, which has an incidence of approximately 5 percent, ranks second only to prematurity as a cause of fetal and neonatal mortality. Like prematurity it is also arbitrarily defined; an infant delivered after a gestational period of 300 days being generally considered post-mature. In a sample of 2178 women included in a statistical study (57) where the incidence of neonatal death was approximately 15 percent, prematurity accounted for 36 percent of the perinatal deaths and postmaturity for 30 percent Postmaturity seems to be a hazard unique to primigravida; in one sample 73 percent of the primigravida whose gestation period lasted at least 300 days did not become pregnant again during the following 10 years (58). An analysis of 4401 consecutive primigravid labors at term, of which 482 had a prolonged first stage (30 hours plus), showed that stillbirths and neonatal death rates increased with prolongation of labor and that each passing hour beyond 30 increased the number of disasters (251). Higgins (148) recorded a well-authenticated case of pregnancy which lasted for one year and 24 days. It ended in delivery of an anencephalic monster. Ley (197) discussed criteria of postmaturity in addition to the 300-day gestation requirement. He reported a definite relation between prolonged gestation and dry peeling skin in the newborn, excessively long fingernails, small amounts of subcutaneous fat, and increased length in relation to body weight. Walker (325) reported that recent physiologic studies showed that the supply of oxygen in a clinically normal pregnancy fell gradually up to the fortieth week and thereafter declined rapidly leaving practically no reserve by the forty-third week, A review of 11,051 deliveries from 1948 to 1952 made it apparent that obstetric death was three times as high at 43 weeks as at 40, and that increased stillbirths accounted for the difference. In a study (266) of babies; weighing over nine pounds who were born during the years from 1949 to 1954, it was found that such large babies were two to three times commoner in white than in Negro patients. Eighty-seven percent of the mothers were multiparas and 20 percent grand multiparas. Nine percent of all mothers delivering infants weighing over 10 pounds were diabetic.

Neonatal and Early Postnatal Development In a 1953 publication of the Association for the Aid of Crippled Children (151) the sections devoted to birth injury dealt with the clinical impact and pathology of late pregnancy hemorrhage, pathologic changes in the infant in anoxia, placental transfer of oxygen, fetal tolerance of anoxia, and transverse narrowing of the pelvis as a cause of dystocia.

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Hewitt and Stewart (145) standardized the first-year weight records of 298 boys and 282 girls in the Oxford Child Health Survey for sex, parity, and parental size. Such standardization reduced the estimated weight differences between infant groups according to maternal efficiency, home amenities, social class, and sickness experience altho bottle-fed infants were still found to outweigh breast-fed infants at the age of one year. Anderson (10) in another British study found some evidence that birth weight and incidence of prematurity were related to social class. He also found that birth weight increased with parity and age of mother and that subsequent development was affected by birth weight as indicated by height and weight measurements at school entry. This problem was also studied by Herdan (144) who found that birth weight accounted for not more than 25 percent of the variations in weight found at three years of age. He found that the relationships between birth weight and subsequent weight decreased systematically with the age of the child. Schlesinger and Allaway (278) studied the combined effect of birth weight and length of gestation on neonatal mortality in a large number of single live births with over 20 weeks of gestation. They found longer gestation increased the chance of survival within each birth weight group of 2500 grams or less and that high birth weight had a similar effect within each gestation group of 36 weeks or less. Cawley and his co-workers (51) studied the relationship between weight and length at birth and at 3, 6, 9, 12, and 24 months, and the duration of gestation, birth rank, and maternal age in 334 boys and 307 girls. Gestation correlated positively with both weight and length at birth but decreased after birth. Similarly, positive correlations at birth between birth rank and length and weight were found to disappear rapidly after birth. Maternal age had virtually no influence on height and weight at birth, but the correlations became slightly positive at two years. They (52) also reported the relationship between parental stature and birth weight. The influence of prenatal environment on the correlation between birth weight and parental height was studied by McKeown and Record (213). Humphreys (156) analyzed the maternal and fetal weight factors in normal pregnancy in 1000 mothers who delivered 1002 babies.

Mongolism (Congenital Acromicria) Mongolism is the most common of the congenital growth deficiencies, occurring three to four times in every 1000 births, yet it is poorly understood (28). Ingalls (161) examined 27 references in a critical review of the problem. Lande-Champain (185) studied detailed case histories of 150 Mongoloids and concluded that advanced maternal age did not play a dominating role, “functional” age being more important. Actually she found that Mongoloids who were the first-born children of young mothers outnumbered the “menopausal”

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babies. Young mothers who had a late menarche or menstrual irregularities before marriage seemed especially to run the risk of Mongolism in a first child. In such instances it was concluded that the Mongoloid did not mark the end but the beginning or regaining of fertility (in some older women). The Mongoloid develops at the border line between sterility and fertility as the result of fertilization of a subnormal ovum. Penrose (262) reviewed 35 references pertaining to the relationship of this growth abnormality to the mother’s age. Oster (250) analyzed the case records of 1000 Mongols traced from 1925 to 1949. Friedman (105) studied Mongolism in 42 twins of unlike sex and 71 twins of like sex. He also reviewed both germ cell theories and environmental theories pertaining to the disease. Benda and Mann (28) used radioactive iodine in a biochemical and biophysical investigation of Mongolism. They also compared 54 Mongoloids with 54 non-Mongoloid but defective children living in the same institution, in addition to comparing the Mongoloid group with 1039 normal subjects matched as to age and sex. They found Mongolism was not characterized by unusual serum lipid patterns. Simon and his associates (284) found no significant differences in the serum protein-bound iodine levels of Mongoloid children as compared with controls of the same age. They did find that mentally retarded children, including Mongols as a group, had significantly higher serum cholesterol levels than did normal children. The level of large molecule lipoproteins of the Sf 12–20 class provided the most marked differences among Mongoloids, normals, and control children. The Mongoloids had the highest level, the cases of undifferentiated mental deficiency an intermediate level, and the normals the lowest level.

Erythroblastosis Fetalis A major problem in erythroblastosis caused by Rh incompatibility is stillbirth since about 20 percent of the fetuses in these cases die in utero (8). Workers on this problem found that high maternal titer and an unfavorable previous history influenced the outcome adversely and concluded that in these cases the only known method of reducing the frequency of stillbirth late in pregnancy, was delivery before term. Neither cortisone nor other drugs had any value in preventing stillbirths due to Rh incompatibility. Day and Haines (73) studied the effects upon intelligence quotients of replacement transfusion in recovered cases of erythroblastosis fetalis. A later depression in mean IQ was found even when children with detectable nervous system defect (palsy and the like) were excluded from the population. Depression in IQ was found to be significantly related to the degree of jaundice but not to the degree of anemia. The children who were rated severely ill by both the criteria of severe anemia and severe jaundice averaged 23.1 points less in IQ than did their siblings.

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Neonatal Anoxia Of 85 children who were treated at a children’s hospital for asphyxia neonatorium, 18 died during the newborn period. Of the remainder, 19 showed signs of a permanent cerebral injury such as convulsions, mental retardation, and spasticity. A conspicuously bad prognosis was found for children who had convulsions, changed tonus, bulging fontanel, or reduced sucking capacity during the newborn period (141). Apgar and others (11) recorded objective data relating to neonatal anoxia in an unselected series of 404 infants and then followed their subsequent intellectual development. Using the microgasometric method of Raughton and Scholander to determine oxygen content, and the Gesell Developmental Schedule and the Revised Stanford-Binet Scale, they found no significant correlation between the levels of blood oxygen content measured in the first three hours after birth and intelligence in early childhood in the 275 cases who returned for psychological tests. Becker and Donnell (24) asphyxiated guinea pigs in utero after one animal in each litter had been delivered as a control. Some of the asphyxiated animals were released in time for spontaneous recovery while others were asphyxiated to the point where administration of a gas mixture was required for revival. Eight to 10 weeks later all animals were tested in problem and learning situations of two levels of difficulty. Learning and retention performance were inferior for the experimental as compared with the control animals. Ingalls and his co-workers (163) found that maternal environment and genetic constitution interacted during prenatal development. Using mouse embryos of different strains, they found strain specific differences in susceptibility to hypoxia-induced anomalies of growth. Leedham (191) reported on glutamic acid in the treatment of mental deficiency. His study is a good presentation of the current status of the problem. Using Form L of the Stanford-Binet, Sequin’s Form Board and Critchton’s Vocabulary Scale on 12 matched pairs of children aged 4½ to 17½ years, ranging from 2 years 11 months to 7 years 7 months in mental age, he evaluated the effect of 10 grams of glutamic acid given daily for six months to one individual of each pair. The control received a similar quantity of saccharine lactate. No significant difference between the two groups was found. Earlier studies were reviewed and criticized.

Retrolental Fibroplasia (Retinopathy of Prematurity) Retrolental fibroplasia, which was first described by Terry in 1942, is basically an exuberant retinal vascular overgrowth which is invariably bilateral and results in characteristic retrolental membranes, retinal detachments, and other gross ocular changes leading to impaired vision or total blindness. It is now the

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commonest late complication of premature birth (143). It ranks first among the causes of blindness in children in the United States and in the case of premature infants is the foremost problem after death itself (189). Ten to 20 percent of babies weighing less than 3½ pounds at birth become blind within the first year after birth as a result of retrolental fibroplasia (89). Retrolental fibroplasia is a true developmental defect tho extra-uterine in origin. It is more hazardous the greater the degree of immaturity. Sontag and Garn (289) pointed out that the fact that retrolental fibroplasia is brought about by an environmental insult intended to support life (oxygen therapy) should lead to caution and careful evaluation of the use of hormonal therapy, vitamin therapy, antibiotics, and bacteriostatic agents, particularly in connection with the least developed newborn. There is certainly a serious possibility that such agents may artificially induce disturbances of growth in incompletely developed organisms. Several authors reviewed the literature dealing with retrolental fibroplasia (118, 142, 162, 189, 259, 260). Quite recently a symposium (252) on retrolental fibroplasia was held which dealt with the clinical course of the disease, the pathology involved, pediatric considerations, the etiology of retrolental fibroplasia, experimental studies in the field, and management of the disease. It was concluded that the incidence of retrolental fibroplasia is positively associated with the use of oxygen, that limiting the amount of oxygen used to that required for clinical emergency in prematures is without effect on the survival rate, and that much of the harmful effect appears to be associated with exposure to oxygen during the first 10 days of life. Retrolental fibroplasia is most likely to develop in infants beginning life with a birth weight of less than 1500 grams and almost never develops in those with a birth weight exceeding 2000 grams (289). Several workers produced or evaluated evidence pertaining to the rate of oxygen administration in the etiology of retrolental fibroplasia (1, 14, 15, 16, 17, 80, 89, 93, 113, 118, 122, 123, 124, 125, 155, 162, 177, 179, 187, 189, 219, 252, 259, 260, 342). There seems to be little question but that oxygen plays a major role tho the exact mechanism is not clear. Vascularization of the retina occurs during the fourth to eighth month of fetal life, and it has been suggested that the condition of the capillaries at birth, the rate of growth, and dietary components like electrolytes or tocopheral content may play auxiliary roles (118). Hepner (143) held that any factor which overstimulated vascular growth or which caused intra-ocular bleeding might produce or aggravate the acute vascular lesions of retrolental fibroplasia. Gyllensten and Hellstrom (124) contended that oxygen could not be the only cause of the disease and that it may act by way of an intermediate mechanism which can be provoked by other agents in exceptional cases. Variability in genetic components may also alter susceptibility (163). Eastman (89) pointed out that the normal fetus in utero exists in an environment in which the oxygen pressure is so low that a healthy adult could survive for only a few moments. He concluded that high and vacillating pressures

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produced by oxygen therapy might readily have injurious effects in an organism which normally would not encounter such tensions until much further developed. The anoxia which adversely affects immature neural tissue may be produced by inadequate oxygenation of the blood or by exposure to high oxygen concentration followed by rapid withdrawal, an alternation which may result in irreparable damage before physiological acclimatization can take place. Jim and Krause (169) used electroencephalography in the study of retrolental fibroplasia, and Fortier (102) evaluated the therapeutic possibilities of carbon dioxide in the prevention of the disease.

Anthropometric Measurements General discussions of growth were published by Ojemann (246), Washburn (327), and Weech (329). Concepts of growth and what they mean for the classroom teacher were dealt with by Blommers and others (33), Jackson and Kelly (164), Olson (247), Olson and Lewellen (248), and Themen (310). Krogman (181) published an article on physical development and growth in relation to student success which may be of especial interest to educators. Tuddenham and Snyder (314) recently published a study of the physical growth of California boys and girls from birth to 18 years. For use in classroom planning and equipment design, Martin (223), and Martin and Thieme (224) published 45 measurements on 3318 school-age children in Michigan. The 45 tables were arranged by age and school grade. Clements (55) analyzed growth data to determine the age of children when growth in stature closes. Eppright and Sidwell (94) published means and standard deviations of five body measurements for 1200 Iowa school children aged 6 to 18 years. Meredith and Meredith (228) published data on 16 traits of size and form of presentday white boys and girls attending public elementary schools in WestCentral Oregon. Age changes in body form, sex differences in body form, and delineation of the body form of individuals were discussed. Abramson and Ernest (2) found that school boys in Stockholm had increased 15 centimeters in height and about 12 kilograms in weight during the past 70 years altho the increase of height of the adult population was scarcely half as great. A significant correlation between height and social class was reported. Gesell (114) reported a continuity of functional and physical development during the prenatal and postnatal growth periods up to the tenth year of life. He stressed the evolutionary development of eyes and hand-eye coordinations, basing his conclusions on longitudinal records of premature infants. Acheson and Hewitt (4) compared the physical development in the English and in the American preschool child using data from the Oxford and the Brush Foundation Surveys. Low (205) published measurements, at various intervals, of 66 boys and 60 girls who were born between 1923 and 1927. The fact that no data were published on the social and economic background of

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this population throws into relief the difficulty of maintaining constancy of methods in longitudinal studies over considerable periods of time or of providing duplication over an adequate period if advances in technics require it. Acheson and others (5) studied height, weight, and skeletal maturity in the first five years of life. Hammond (133) measured weight gain during the first year of life of a sample of 451 babies. Weights were measured at four-week intervals, and gain was found to be largely independent of birth weight. Boys gained about one pound more than girls and full-term babies less than prematures. Taller than average babies and those of leptosome type also gained more. Weight gain was independent of social class, was reduced by illness including colds and rashes, and decreased with increasing birth rank. Paiva (254) found that the pattern of growth of breast-fed infants did not differ significantly from that of bottle-fed infants from birth to six to seven months of age. Meredith (227) reviewed the literature on the comparative size and growth of North American Negro and white infants with respect to eight anthropometric measurements during the first postnatal year. Staton (292) reviewed 95 publications dealing with the physical growth and health of the adolescent under such headings as growth assessment and anthropometry, morphological and puberal development, nutritional status, physique and motor performance, physique and personality, physiological efficiency, and health. Gallagher and Gallagher (106) published tables relating height and weight values and increments to chronological and skeletal age in adolescents. They also discussed the psychological potentialities of wide divergences in size and growth from the point of view of the adolescent. Johnston (170) and Jones (171) also dealt with physical development during adolescence. Nicolson and Hanley (243) analyzed data on 180 boys and girls who were measured from the first to the eighteenth year of life. They were concerned with the derivation and interrelationships of indexes which could be used to assess progress along the hypothetical maturational continuum. They used factorial analysis of the intercorrelations among the various measures of maturation in their attack upon the question of generality in adolescent physical growth. They found a high degree of relationship among very different indexes of maturation.

Somatotyping Sheldon’s most recent volume in the “Human Constitution” series (280) is largely a tabular and pictorial presentation of somatotype variations. Part I deals with the nature of somatotypes and general theoretical considerations. Part II contains front, side, and rear photographic views of 1175 men photographed in identical postures, age-height-weight tables, and accompanying

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text arranged according to the Sheldonian somatotypic scale. This section was based on studies of 46,000 American males between the ages of 18 and 65, Equipment and procedures in somatotyping are also described. Germain, Browne, and Bellows (112) described several physical profiling systems. Lindegård (200) published a new method of describing individual body build. He used four objectively determined variables, length, sturdiness, muscle, and fat factor, to express both outer body configuration and structure. He used both somatometric and X-ray cephalometric procedures in measuring his factors. This report analyzed 114 references. Hammond (132) compared physique and development of boys and girls in independent and council schools in England. Weights, heights, and other body measurements for boys and girls from 5 to 18 years of age in independent schools (Group A) were compared with pupils up to 14 or 15 years of age in council schools in the best (Group B) and worst areas (Group C) of several industrial towns. Group A was about two years’ growth ahead of Group C in height and most length measurements. Group A was also about two years ahead in “shape,” but for girth measurements the differences were much less. Differences in rate of development rather than specifically social differences were suggested to account for the different mature physiques of the social groups. Hammond (131) in another study used Burt’s multiple factor analysis technic to distinguish three body types in a population of 2967 British and American school children between the ages of 5 and 18. Remeasurements after three years indicated constancy of body type to be very high. A study (95) of the relationship between levels of nutrient intake of Iowa school children and physical and biochemical measurements showed children on diets which conformed fully to the recommended allowances of the National Research Council to be slightly taller, heavier, and larger in leg girth than the children on diets at the other extreme which averaged below the allowances. No significant differences in hemoglobin or serum alkaline phosphatese concentrations were reported. The mean serum concentration of ascorbic acid and carotenoids reflected the intakes of these two substances by the two groups of children. The differences in nutritional status of children noted in this study were small, but the significance of these differences to the long-time health and well-being of children who are on diets which are suboptimal in nutrient content should be assessed thru longitudinal studies. Bayley (23) studied parent-child similarities in height and weight for children in the Berkeley Growth Study whose records were complete thru 21 years. She concluded that there must be a core of parent-child similarities to account for the increasing similarities which occur up thru the teens even tho such similarities may not have been present during the first year or two of life. Lorr and Fields (203) published a factorial analysis of the 15 “purest” body types found in a group of 90 psychotic males. They reported the existence of three distinguishable groups of morphological trait patterns that closely resembled the patterns descriptive of Sheldon’s components. They also

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concluded that the 76 somatotypes identified by Sheldon could more simply and economically be defined in terms of measurements on only two type factors. Three new methods for calculating the surface area of the body were described by Schmitz (279). Trotter and Gleser (313) described a differential procedure for estimation of stature from the long bones of American whites and Negroes. Tanner (308) studied the reliability of anthropometric estimates of somatotype both in the same observer at different times and between different observers. Observers agreed within half a rating on a 7-point scale for 90 percent of the cases; mesomorphy proved the hardest component to rate, and ectomorphy the easiest. Changes in body build and form with age were pictorially and graphically presented by Boyd (38) both in terms of changes in proportion due to different rates of growth of the parts of the body and in comparison with the proportion and rates of growth of others. Dupertius and Michael (88) made a comparative study of the physical growth of 26 ectomorphs and 28 mesomorphs who were somatotyped at the age of 21. The ectomorphs averaged 2 3⁄16 inches taller and 267⁄10 pounds lighter in weight than the mesomorphs. The mesomorphs outweighed the ectomorphs at each age from 2 to 17 years while the ectomorphs were taller from 4 to 17 years of age. The height of the puberal spurt was reached one year earlier by the mesomorphs while the ectomorphs grew in height over a longer period of time. The mesomorphs grew at a more rapid rate. It was concluded that somatotypes as indicated by measures of height and weight remain fairly constant thruout childhood at least for ectomorphs and mesomorphs. Parnell (256) reviewed some of the difficulties associated with somatotyping and then described a short physical anthropometric method of estimating Sheldonian somatotype in young men in which the taking of the needed measurements required but five minutes of time. Pugh (267) discussed the charting of growth by means of the Wetzel Grid. Garn (108) studied individual and group deviations from “channel-wise” grid progression in girls and concluded that constancy of channel position was not a usual phenomenon. In his longitudinal series, the proportion still in the “starting” channel was only 50 percent after one year and 19 percent after two years. The short-term series revealed deviations of one or more channels in over 50 percent of the cases in a one-year period and deviations of two or more channels in 9 percent of the cases. In both series, Garn reported a downchannel trend during the earlier years and up-channel trend in later years.

Environment and Physique Kaplan (172) reviewed 25 articles dealing with the relationship between environment and human physique. She concluded that climate, diet, and altitude all had a significant effect upon the growth patterns of the populations

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studied. The effect was most marked when one or two vital features of the environment were radically changed. Newman and Munro (240) measured 15,000 men at the time of their induction into the army and then analyzed the relationship of climate to body size. They concluded that there was a definite association between elements of body size and temperature of habitat. Body size was larger in colder climates, and this relationship was more highly correlated with January than with either annual or July temperatures. Heights and weights of 392 boys and 409 girls in a South African nursery school measured at quarterly intervals between the ages of 2¾ and 6½ years for periods ranging from 1936 to 1951 were found to be definitely related to socioeconomic status (264). At all ages children from the lower income group were significantly shorter and lighter, the differences being 3 to 5 pounds and 2 to 2½ inches for the boys, and 2 to 4½ pounds and 1¼ to 3½ inches for the girls. Roberts (274) found a highly significant inverse relationship between body weight in indigenous populations and mean annual temperature, both before and after correction for the influence of group affinity and the influences of stature. Roberts concluded that the relationship between mean weight and environmental temperature made the use of universal “norms” of weight based upon European standards inappropriate in nutrition and growth studies of other populations in other areas of the world. A close fit between lean body weight and metabolism was found by Behnke (25). When lean body weight was used as a reference, the usual sex differences in basal metabolic rate tended to disappear. Because lean body weight is thought not to change during adult life, Behnke suggested that it could serve as one property of the individual to which many other variables could be related. Garn, Clark, and Portray (109) studied the relationship between body composition and basal metabolic rate in 49 boys and 49 girls ranging from 6 to 18 years of age. He found that correlations between radiographic measurements of muscle size and basal metabolic rate equaled or exceeded those with measures of height, weight, or surface area. On the basis of his results he suggested the use of tissue measurements in establishing metabolic reference standards. Wedgwood and his colleagues (328), after pointing out that the commonly used relationship between basal metabolic rate and surface area is not a primary one, analyzed basal metabolic rate and estimates of the fluid compartments of the body in 17 healthy young men. They concluded that basal metabolic rate could be predicted from the volume of body fluid compartments as well as, or better than, from surface area of the body. Peckos (261) studied 28 endomorphs, 21 mesomorphs, and 37 ectomorphs to determine the relationship between caloric intake and physique in children. She found that the relation of the observed energy intake to body

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build was opposite to that expected and concluded that weight reductions in an endomorph may require a dangerously low energy intake. Lasker (190) pointed out the importance of ascertaining the extent to which the diagnostic criteria for each new typology are subject to change under the influence of environmental conditions. He concluded that the description given by Howell’s Factor I, is that of the nutritional state and that it accounts for the major fraction of the variance in extreme somatotypes. Škerlj, Brožek, and Hunt (285) studied subcutaneous fat and age changes in body build and body form in 84 women, 18 to 67 years of age. They concluded that the deposition of inner fat may be an important aspect of the complex phenomenon of aging and that the index of total body fat to subcutaneous fat may measure the aging process. They were unable to determine how much of the increase occurred because of accumulations of fat in depots and how much was the result of fatty infiltration of organs. Iliff and Lee (159) found that generally pulse rates, respiratory rates, and body temperatures decreased in children between two months and 18 years of age. Goldstein (116) reviewed the genetic and environmental evidence bearing on the differences in mortality and health status between whites and nonwhites and found notable progress in the instances of both American whites and nonwhites. He found, among other things, a marked reduction in the rate of mortality among nonwhites in the urban South in recent years. The relationship between physique and physical performance has been examined by several workers. Bookwalter (36) compared the physical fitness scores of 1977 Indiana elementary-school boys with their physique and developmental level as determined by the Wetzel Grid. Boys of thin or medium physique and those who were very large performed equally well physically. Maximum size and shape did not produce maximum fitness, but a relationship between physique and developmental level did seem to exist. Loveless (204) studied the relationships between scores on the Navy Standard Physical Fitness Test and age, height, and weight in 5669 randomly selected cases among enlisted personnel and officers. Height seemed to have less effect than age or weight. Age and test scores were largely unrelated below the age of 30 with a slight relationship above 30 indicated by consistently lower scores. Scores in the more strenuous exercises were adversely affected by weight over 190 pounds. Pere, Kunnas, and Telkkä (263) studied the correlation between performance and physique in 172 top-ranking track and field athletes. They found that physique had little effect upon performance, high achievement in a given field of athletics being reached by athletes of very different physiques. Throwers were tallest and seemed to benefit from height. Positive correlations were found between relative upper limb length and performance in throwers and in long distance runners and between relative chest circumference and performance in throwers. Negative correlations were found between

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relative shoulder breadth and performance in throwers and between relative chest circumference and performance in sprinters. Lamp (184) correlated physical size and maturity with volleyball skills of junior high-school students. Tanner (307) examined the effect of weight training on physique. He concluded that the arm muscles in man seem to have a considerably greater growth potential than do the leg muscles. This is an interesting problem, for we have practically no quantitative data on the degree to which human muscles may be increased in size by exercise. We do not know whether the growth of a child’s muscles or other tissues or organs is affected temporarily or permanently by muscular exercise. Rarick (270) published a review of the literature dealing with the problem of maturity indicators and the development of strength and skill.

Physical Disability Barker and her colleagues (21) compared three sources of information as to the frequency of physical disability in children: laymen, teachers, and physicians. Teachers proved to be the best informants, reporting 76.5 percent of the total number of subsequently discovered disabled children. Laymen reported 48.2 percent and physicians 17.7 percent. No survey involving medical examinations was made. A recent volume (22) on the somatopsychological aspects of the adjustment to physical handicaps and illness contained extensive bibliographies and reviewed a large number of researches in this field. It is a major source book in this area and covers such topics as differences in physical size, strength, and attractiveness; crippling; the tubercular; impaired vision; social psychology of acute illness; and employment of the disabled. Wenar (333) studied the effects of a motor handicap on the integrative ability by administering Buhler’s World Test to a group of handicapped and nonhandicapped children. He found a significant decrease in integrative ability in children with a motor handicap and also a tendency for a decrease in integrative ability with increased severity of handicap. No evidence that motor handicap is associated with a particular kind of deviate thinking was found. Bruckner (41) published a book for parents and others interested in children with a handicap. The author is the mother of a child born a congenital bilateral arm amputee.

Skeletal Maturation Several writers dealt with the times of appearance of ossification centers. Dedick and Caffey (75) published several charts, based on roentgen findings, which showed the incidence of ossification centers in the skull and chest in 1030 newborn infants. Ellis and Joseph (91) published an account of the

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time of appearance of the centers of ossification of the tibular epiphyses. Harding (135) used roentgenograms of a group of children taken from birth to 14 years to establish the fact that the appearance and fusion of a second accessory center of ossification of the calcaneus appeared in most instances beginning at one and one-half years before menarche and was fused about one year after menarche. Noback (244) published a critical summary of the current status of data on the times of appearance of ossification centers and the fusion of bones. He urged that more accurately documented data be made available and that population samples be fully described. He particularly called attention to the fact that times of first observation of a center are usually recorded as times of first appearance whereas the actual first appearance time may have been months before. Acheson (3) and Harding (134) published methods of assessing skeletal maturity from radiographs. Harding studied approximately 50,000 X-ray films of 323 children from birth to 14 years of age. She published the percentages of boys and girls having a certain osseous center at various ages and the range of appearance of such centers. On the basis of her experience she also described a simple method of estimating osseous development which is of particular value in longitudinal research. Her subjects tended to maintain fairly constant rates of development over long periods of time. Mainland (217) and Mainland and Mainland (218) evaluated the skeletal age method of estimating children’s development, from the point of view of both systematic error and variable errors. To check on systematic error, expert assessments were compared with each other, and it was concluded that even expert assessment had not reached a desired degree of stability. The variable error in a single observer was studied by means of 1124 readings of 326 films for 233 subjects between the ages of 16 months and 17 years. No significant difference was associated with the Todd or Greulich-Pyle Atlas, the age of the child, sex, differences between skeletal age and chronological age, differences between children, differences between roentgenograms of the same child, or the speed with which the assessment was made. Greulich (119) reviewed the relationship between skeletal age (based on carpal X rays) and bodily maturity in normal growth, in precocious puberty, and in endocrine dysfunction. He stressed the complications produced by the presence of both early maturing and late maturing strains in the same population. This genetic diversity and nutritional differences tend to make standards which fit a particular group inapplicable to other groups. Cotellesa and DeToni (64) evaluated the weight, height, and skeletal development records of 500 normal and pathologically abnormal children and adolescents in terms of accepted norms and concluded that skeletal age should afford a useful index of general body maturity. In Japan, children who were skeletally above average were found to have a greater number of permanent teeth as compared with children who were

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skeletally below average. At each age level, girls had a greater number of erupted permanent teeth than did the boys (303). Sutow (302) compared the skeletal maturation in 1200 healthy Hiroshima boys and 1150 healthy Hiroshima girls aged 6 to 19 years with the skeletal maturation in American children. He found that the skeletal development of these Hiroshima children, none of whom had been exposed to the atomic bomb, was consistently slower than that of American children of the same chronological ages. In another study in which the skeletal ages of West African Negro boys from 9 to 20 years of age were compared with those of American boys, an average retardation of 16 months was found (332). Findings of apparent retardation, such as were revealed by these studies, raise the interesting theoretical question as to whether or not we are dealing with a retarded group in the non-American sample or an accelerated American population. Whichever it may be, the further question of the desirability of such acceleration or retardation, in terms of optimum subsequent development, becomes of vital importance.

Growth of Body Segments and Tissues A number of workers studied the growth of various body segments and tissues: bones, the head, endocrines, teeth, the eye, the human diencephalon, skin, the blood, subcutaneous fat, and hematopoietic tissues. Stewart (293) studied the relationship between metamorphosis of the joints of the sternum and age changes in other bones. Moss (232) discussed the differential growth analysis of bone morphology as a useful technic for the study of bone growth. Gardner (107) studied prenatal development and growth of bones in man. Maresh (221) followed the linear growth of the long bones of the extremities from infancy thru adolescence. Park (255) dealt with the effects of health and disease upon bone growth, stressing the need to think of bone in the early period as a very much alive and sensitive tissue. He also pointed out that in times of stress, bone tissue can cease its growth activity and so relieve the general body growth of that added burden. MacDonald (210) related the head measurements of 1272 infants to estimated periods of gestation and concluded that growth continues in each of the diameters after the thirty-fifth week and that sex does not affect the rate of growth at that stage. In another study (211) he reported that for corresponding lengths of gestation and corresponding birth weights the head of the male fetus is larger and harder. As a sequel to this work, Meredith (226) published a review of more than 50 investigations of the growth in head width during the first 12 years of life in normal North American infants and children. He reported that thruout the period girls have slightly lower means and smaller standard deviations than boys.

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Lanman (138) summarized the developmental course, comparative anatomy, and possible physiologic functions of the fetal zone of the adrenal gland. Swingle and Kleinberg (304) reviewed studies of the effects of the growth hormone. Smith (287) studied the action of relaxin on growth of the mammary gland in the rat. Clements and Zuckerman (56) studied the order of eruption of the permanent teeth in 166 gorillas and 188 chimpanzees and compared the order of eruption with that of 2792 English children. Jeffreys (165) reported on the dental status of children in Delaware. Leicester (192) pointed out that teeth are extremely responsive to systemic and metabolic changes during their early developmental period before eruption and only very slightly responsive to such bodily changes thereafter. He also discussed caries etiology in some detail. Toverud and others (312) published a very extensive review of all phases of dental caries. McLean (214) and Ridley (273) published material on the growth and development of the lens of the eye. Fletcher (98) studied the pattern of development of the eye in a series of 320 small, premature infants over the period from April 1950 to January 1953 paying especial attention to the developing fundus oculi. She concluded that there were critical periods of development in the eye when it was particularly prone to retrolental fibroplasia. Kuhlenbeck (183) published a summary of the development, structure, function, and pathology of the human diencephalon. His monograph presented a detailed anatomical study of the epithalamus, dorsal thalamus, thalamus ventralis, and hypothalamus. This work contained a 421-item bibliography. Banfield (20) used the electron microscope to study the width and length of collagen fibrils during the development of human skin and in the skin of adult animals. He used human embryos, human fetuses, a three-year-old child, and adults 65, 68, and 85 years of age. Hale (126) published a quantitative and qualitative description of the morphogenesis of volar skin in 122 human fetuses ranging in size from 40-millimeter crown-rump length to 350-millimeter length. Duggins (86) reported age changes in head hair from birth to maturity using seven boys and nine girls in a longitudinal study. He found that refractive indexes of hair were of some value in indicating the approximate age and the sex of the individuals. Kiil (178), after studying frontal hair direction in American Chinese, Indian, and Negro populations, concluded that frontal hair direction in man may be a result of competition between two growth inducing centers of the skin. Two books dealing extensively with the distribution of blood groups in man were published recently (233, 268). Mourant’s volume (233) included a 97-page bibliography of 1716 items. It was intended to cover practically all existing works on distributions of blood groups. These volumes supplement each other in an excellent fashion.

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The relationship between age changes and subcutaneous fat was studied by Eichorn and McKee (90), Reynolds (271), and Škerlj, Brožek, and Hunt (285). Osgood (249) studied the development and growth of the hematopoietic system using a method of analysis which took into consideration the relativity of biologic time.

General or Specific Variables Affecting Growth DeWijn (76) published a general review of factors governing the development of children. He discussed the relationship of environmental influences, climate, secular changes, socioeconomic status, racial factors, illness, psychic influences, and educational neglect to child growth. The effect upon growth patterns of chronic nutritive failure was the subject of a series of researches in which indexes, such as height, weight, and skeletal maturation, in children with chronic nutritive failure were compared with those in children without nutritive deficiency, One group of workers (84) found that the degree to which chronic nutritive failure affected the growth of a child was determined by an interplay of genetic and environmental influences, for, altho striking differences between mean height and weight and speed of growth values were found between the groups with and the groups without nutritive failure, within each age group individual children showed overlapping. It was also found that attainment of maximum physical development depended upon a child’s ability to remove any accumulated deficit before the epiphyses and diaphyses of the major long bones fused. In another study (290) it was found that prolonged nutritive failure produced a retarding effect on skeletal development which was largely reversible provided the amount of the nutrient supplement was increased beyond that needed to maintain equality in skeletal maturation between the children in the treated group and the normal controls. Skeletal maturation which was accelerated thru the feeding of essential nutrients did not equally affect all bone centers in the hand and wrist. Consequently it was concluded that all centers of a skeletal area must be evaluated to appraise fully and accurately the growth-promoting effects of nutritional therapy in children with chronic nutritive failure (85). Campbell and McLaughlan (48) published a review of the relationship between vitamin B12 and the growth of children. Crump and Tully (66) administered partial vitamin supplements daily to a group of 50 children who had a clinical diagnosis of malnutrition and anorexia associated with chronic illness. Comparing clinical impressions of improvement with analysis by Wetzel Grid charts, they concluded that the inability of clinical impression to differentiate between actual and apparent growth failure emphasized the need for objective standards in appraising response to nutritional therapy.

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In a similar study of growth failure in school children, Wetzel and his colleagues (334) found a significant growth response to nutritional therapy in children previously manifesting retarded growth. Improvement in growth response was not correlated with physical performance as measured by grip, leg, or back strength. Howe and Schiller (154) graphed data on height and weight for school children in Stuttgart, Germany, from 1915 to 1948 and related these data to changes in diet and environmental factors. The data covered changes in average weight and stature over a long period which included two world wars. Height and weight were diminished during both world wars and increased after each. No data on the adult height of these individuals were given. The effect of climate vectors in growth and development was also studied by Dodson (79). French and his co-workers (104) studied the effect of dietary fat and carbohydrate on growth and longevity in the rat. The life span of both male and female rats ingesting high fat diets was decreased markedly, tho more so in the case of the males. Their data also indicated that a high fat diet decreased the life span without noticeably altering the cause of death. Increased caloric intake per se was not associated with decrease in life span. Horn (152) found that rats on a protein-free diet were prevented from reaching sexual maturity. Ershoff (96) reported that proper nutrition is essential for functional integrity of the reproductive system. Bakwin (19) briefly reviewed the psychological aspects of various dietary deficiencies. In a survey of 285 cases drawn from a total population of 218,693 babies in Birmingham, MacMahon and McKeown (216) found harelip, with or without cleft palate, to be nearly four times as frequent in the offspring of old mothers (38 years of age and older) as in those of young mothers. Li (198) summarized the chemical and biological properties of the growth and adrenocorticotropic hormones of the anterior pituitary. Soffer and Gabrilove (288) reviewed 433 references on the important role of the endocrines in growth. In another comprehensive review of chemical growth in infancy and childhood, Forbes (101) pointed out the gaps in our presentday knowledge of chemical growth in children. Gaunt (110) recently discussed the endocrine factors which affect growth in a somewhat specific way. He dealt with the pituitary growth hormone, the adrenal cortex, the gonads, and the thyroid. In mammals the major growth stimulant is the pituitary growth hormone. The thyroid hormone is essential for the full action of the growth hormone and also for normal growth and development especially in the young. If it is absent, most food is deposited as fat rather than used for growth processes. The gonads and the adrenal cortex also produce definite but limited stimulants of general body growth. MacKay (212) reviewed 196 references dealing with the relationships between the endocrine and the nervous systems. Caldwell (47) found that intellectual functioning, speed and flexibility of reactions, and attitudes and

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interests showed improvement in aged women following sex hormone administration. Thirty women whose mean age was 75 years were divided into two groups for the experiment, the control group receiving a placebo and the other, female sex hormones. Heller (140) reported that cortisone reduced the resistance of mammals to bacteria, fungi, viruses, and bacterial toxins. Jervis and his colleagues (168) reported the results of operations for revascularization of the brain in 25 mental defectives aged 3 to 20 years. Postoperative observations were made for two years or more, and no improvement in intelligence quotients, electroencephalographic findings, or clinical symptomatology was found. They also reviewed the data obtained by other investigators in a total of 331 cases and discussed theoretical aspects of the problem. Tyler and Armstrong (315) reviewed the evidence dealing with the metabolic aspects of some neurological and muscular disorders. They concluded that many studies of metabolic changes in schizophrenia suggest that a metabolic derangement underlies the disease process. They also pointed out that there is good evidence that patients with several types of endocrine abnormalities manifest an unusually high incidence of psychoses altho the position that all psychotic patients show an abnormality of endocrine function is much less well established.

Methodology and Technics Asdell (13) reported that several methods for controlling ovulation time in mammals were available. These procedures, combined with technics for preserving tissues indefinitely and transplantation developments, should open up a whole new era in the study of growth. Indeed, in his foreword to a recent volume entitled Mammalian Germ Cells Folley (100) stated that the technical advances foreshadowed by the researches reported in this volume might well be fraught with as much significance for man, and danger of misapplication, as the discoveries on how to release the energy of the atomic nucleus. The advantages of the longitudinal method of studying growth, and the problems associated with the approach, are well known. Bell (26, 27), in partial solution of some of these difficulties, suggested a method of combining the cross-sectional and longitudinal technics in such a manner that longrange developmental changes may be estimated in a relatively short period of time. He suggested that groups be selected so that final measurements on a younger group could be made at the same age as the initial measurements of the next-older group. By this procedure, for example, a longitudinal study which normally would take eight years might be accomplished over a twoyear period if four slightly overlapping age groups were used. Both absolute measurements and directions of development could be ascertained and used

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to help answer the question of whether or not age changes only were involved. He pointed out that such a short-cut method would be particularly helpful in studying transient populations or relatively uncooperative groups and wherever extensive study leads to undesirable contamination of the population universe. It could also be used to point up special problems and to obtain initial results to further experimental design in longitudinal studies. Suarez (296, 297), Suarez and Peva (298, 299, 300), and Suarez and Tirjeira (301) critically evaluated methods used to show growth and development beginning with Quetelet in 1871 and including the grids of DeToni, Gobesi and Tatafiori, and Wetzel. The authors also presented a new method of graphic representation of growth which permitted the visualization of developmental age, velocity, and direction of growth. LeLong and his colleagues (193) also published a new method of graphically recording growth which made it possible to compare the child with himself at successive ages and to attack some of the unsolved problems of auxology. Bryan and Greenberg (43) investigated methods suitable for crosssectional determination of immaturity points and sexual maturation. They compared three methods: logits, probits, and Karber’s method, and critically evaluated each. In a later paper Bryan (42) published methods for analyzing and interpreting physical measurements of groups of children. Tyler (316) critically evaluated various concepts of organismic growth with regard to their statistical bases, their psychological meaning, the validity of interpretation of basic data, and their important educational implications. He stressed the need for further empirical studies in this area. In another publication, Tyler (317) suggested the use of P-Technic in the study of the interrelatedness of rates of growth of children. Organismic concepts, such as “unity of growth” and “interrelatedness of the growth process,” have been given great educational and psychological significance, yet, referring, as they do, to intra-individual growth, they cannot be verified by data about interindividual growth. Nicolson and Hanley (243) published a factorial analysis of the intercorrelations among a group of indexes of physiological maturity obtained in an urban sample of 180 boys and girls who were measured annually from their first to eighth years and thereafter semiannually until they were 18 years of age. In this study of the generality of growth a high degree of relationship among very different indexes of maturation was found. Kerlinger (176) recommended the use of analysis of variance with child development data, particularly those data covering ages from 6 to 10 years. He reported that analysis of variance could be used to determine the significance of the difference between growth ages and between organismic ages, and within certain limitations, could give the degree of homogeneity and heterogeneity. Electroencephalography was used to study the developing brain of the intact prenatal human fetus (30, 37), the developing heart in the intact prenatal human fetus (29, 72), the normal aged adult (245, 281), brain phenomena in

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senile psychoses (235), and in retrolental fibroplasia (169). Blum (34) checked on the reliability of electroencephalographic judgments by having 10 sets of electroencephalographic records evaluated by five experienced neurologists. He found the reliability to be low and emphasized the need for further research in this area. Geoghegan (111) devised a system by which a wide range of body measurements, surface area, and total and partial body volumes could be obtained from photographs of subjects in certain postures. The body specific gravity value could also be calculated if body weight were known. In a paper which was concerned with regularities in growth curves including rhythms and allometry, Sholl (282) made the point that accurate knowledge of growth of individuals or the growth process itself cannot be accomplished by deriving a kind of average curve from measurements of different individuals at different ages. No amount of mathematical theory or statistical practice can ever supplant the endless experimentation which lies at the base of progress in biological science. Peculiarities of growth data may be due to a peculiarity of the individual, a characteristic of growth of human beings, or an error of observation. In an important methodological note, Davies (71) pointed out that mortality statistics based on either cross-sectional or longitudinal data are limited in answering the question as to how fast man is aging at different periods of his life. Indeed, he concluded that the average rate of inherent aging will probably never be known because ideal environmental conditions which would be optimal for all, and hence not shorten life, do not exist now, nor are they likely to appear in the forseeable future. This is just another way of saying research must be continuous, that we shall not run out of problems, and that progress consists of a series of approximations or approaches to an ever receding and more enticing goal.

Bibliography 1. Aalde, O., and Innerslund, O. “Retrolental Fibroplasia and Treatment of Oxygen.” Acta Paediatrica 43: 553–56; November 1954. 2. Abramson, Ernest, and Ernest, Eva. “Height and Weight of Schoolboys at a Stockholm Secondary School, 1950, and a Comparison with Some Earlier Investigations.” Acta Paediatrica 43: 235–46; May 1954. 3. Acheson, R. M. “A Method of Assessing Skeletal Maturity from Radiographs.” Journal of Anatomy 88: 498–508; October 1954. 4. Acheson, R. M., and Hewitt, D. “Physical Development in the English and the American Pre-School Child: A Comparison Between Findings in the Oxford and the Brush Foundation Surveys.” Human Biology 26: 343–55; December 1954. 5. Acheson, R. M., and Others. “Height, Weight, and Skeletal Maturity in the First Five Years of Life.” Lancet 268: 691–92; April 2, 1955. 6. Adams, C. E. “Some Aspects of Ovulation, Recovery and Transplantation of Ova in the Immature Rabbit.” Mammalian Germ Cells. (Edited by G. E. W. Wolstenholme.) Boston: Little, Brown and Co., 1953. p. 198–216.

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7. Adams, Theodore W. “Intrauterine Roentgenography as an Aid in Determining Fetal Age.” Obstetrics and Gynecology 5: 43–48; January 1955. 8. Allen, Fred H., JR.; Diamond, Louis, K.; and Jones, A. Richardson. “Erythroblastosis Fetalis: IX. The Problems of Stillbirth.” New England Medical Journal 251: 453–59; September 16, 1954. 9. Alm, Ingvar. “The Long-Term Prognosis for Prematurely Born Children.” Acta Paediatrica Supplementum 42: 1–116; May 1953. 10. Anderson, A. “Some Observations on Birth Weights.” Medical Officer 89: 15–17; January 10, 1953. 11. Apgar, Virginia, and others. “Neonatal Anoxia: I. A Study of the Relation of Oxygenation at Birth to Intellectual Development.” Pediatrics 15: 653–62; June 1955. 12. Armitage, S. G. “The Effects of Barbiturates on the Behavior of Rat Offspring as Measured in Learning and Reasoning Situations.” Journal of Comparative and Physiological Psychology 45: 146–52; April 1952. 13. Asdell, S. A. “The Effect of Controlled Ovulation upon the Fertility of the Mammalian Egg.” Mammalian Germ Cells. (Edited by G. E. W. Wolstenholme.) Boston: Little, Brown and Co., 1953. p. 170–79. 14. Ashton, N. “Pathological Basis of Retrolental Fibroplasia.” British Journal of Ophthalmology 38: 385–96; July 1954. 15. Ashton, N. “Retrolental Fibroplasia.” American Journal of Ophthalmology 39: 153–59; April 1955. 16. Ashton, N.; Ward, B. A.; and Serpell, G. “Effect of Oxygen on Developing Retinal Vessels with Particular Reference to the Problem of Retrolental Fibroplasia.” British Journal of Ophthalmology 38: 397–432; July 1954. 17. Ashton, N.; Ward, B. A.; and Serpell, G. “Role of Oxygen in the Genesis of Retrolental Fibroplasia: A Preliminary Report.” British Journal of Ophthalmology 37: 51–320; September 1953. 18. Baker, Harry J. Introduction to Exceptional Children. Revised edition. New York: Macmillan Co., 1953. 500 p. 19. Bakwin, H. “Psychologic Aspects of Dietary Deficiency States.” Journal of Pediatrics 45: 110–14; July 1954. 20. Banfield, William G. “Width and Length of Collagen Fibrils During the Development of Human Skin, in Granulation Tissue and in the Skin of Adult Animals.” Journal of Gerontology 10: 13–17; January 1955. 21. Barker, Louise S., and others. “The Frequency of Physical Disability in Children: A Comparison of Three Sources of Information.” Child Development 23: 215–26; September 1952. 22. Barker, Roger G., and others. Adjustment to Physical Handicap and Illness: A Survey of the Social Psychology of Physique and Disability. New York: Social Science Research Council, 1953. 440 p. 23. Bayley, Nancy. “Some Increasing Parent-Child Similarities During the Growth of Children.” Journal of Educational Psychology 45: 1–21; January 1954. 24. Becker, R. Frederick, and Donnell, William. “Learning Behavior in Guinea Pigs Subjected to Asphyxia at Birth.” Journal of Comparative and Physiological Psychology 45: 153–62; April 1952. 25. Behnke, Albert R. “The Relation of Lean Body Weight to Metabolism and Some Consequent Systematizations.” Annals of the New York Academy of Science 56: 1095–1142; November 17, 1953. 26. Bell, Richard Q. “Convergence: An Accelerated Longitudinal Approach.” Child Development 24: 145–52; June 1953. 27. Bell, Richard Q. “An Experimental Test of the Accelerated Longitudinal Approach.” Child Development 25: 281–86; December 1954.

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28. Benda, Clemens, E., and Mann, George V. “The Serum Cholesterol and Lipoprotein Levels in Mongolism.” Journal of Pediatrics 46: 49–53; January 1955. 29. Bernstine, Richard L., and Borkowski, Winslow J. “Prenatal Fetal Electrocardiography.” American Journal of Obstetrics and Gynecology 70: 631–38; September 1955. 30. Bernstine, Richard L.; Borkowski, Winslow J.; and Price, A. H. “Prenatal Fetal Electroencephalography.” American Journal of Obstetrics and Gynecology 70: 623–30; September 1955. 31. Blattner, Russell J., and others. “Developmental Defects in the Chick Embryo Following Injection with Newcastle Disease Virus.” American Journal of Diseases of Children 88: 654; November 1954. 32. Bloch, Harry, and others. “Reduction of Mortality in the Premature Nursery: II. Incidence and Cause of Prematurity; Ethnic, Sociometric and Obstetric Factors.” Journal of Pediatrics 41: 300–304; September 1952. 33. Blommers, Paul, and others. “Organismic Age Concept.” Journal of Educational Psychology 46: 142–50; March 1955. 34. Blum, Richard H. “A Note on the Reliability of Electroencephalographic Judgments.” Neurology 4: 143–46; February 1954. 35. Boell, Edgar J., editor. Dynamics of Growth Processes. Princeton, N. J.: Princeton University Press, 1954. 304 p. 36. Bookwalter, Karl W. “The Relationship of Body Size and Shape to Physical Performance.” Research Quarterly of the American Association for Health, Physical Education, and Recreation 23: 271–79; October 1952. 37. Borkowski, Winslow J., and Bernstine, Richard L. “Electroencephalography of the Fetus.” Neurology 5: 362–65; May 1955. 38. Boyd, Edith. “Pictorial and Graphic Analysis of the Body Build of One Boy.” American Journal of Diseases of Children 89: 332–40; March 1955. 39. Breckenridge, Marion E., and Vincent, E. Lee. Child Development. Third edition. Philadelphia: W. B. Saunders Co., 1955. 497 p. 40. Brent, Robert. “X-Ray-Induced Embryonic Malformations in the Rat: An Application to the Human Malformation Problem.” American Journal of Diseases of Children 88: 654–57; November 1954. 41. Bruckner, Leona S. Triumph of Love. New York: Simon and Schuster, 1954. 213 p. 42. Bryan, A. Hughes. “Methods for Analyzing and Interpreting Physical Measurements of Groups of Children.” American Journal of Public Health 44: 766–74; June 1954. 43. Bryan, A. Hughes, and Greenberg, B. G. “Methodology in the Study of Physical Measurements of School Children: II. Sexual Maturation—Determination of Immaturity Points.” Human Biology 24: 117–44; May 1952. 44. Buck, C. “Exposure to Virus Diseases in Early Pregnancy and Congenital Malformations.” Journal of the Canadian Medical Association 72: 744–46; May 15, 1955. 45. Bundesen Herman N. “Natal Day Deaths: The Long Neglected Field of Infant Mortality.” Journal of the American Medical Association 153: 466–73; October 3, 1953. 46. Bunge, R. G., and Sherman, J. K. “Frozen Human Semen.” Fertility and Sterility 5: 193–94; March-April 1954. 47. Caldwell, Bettye M. “An Evaluation of Psychological Effects of Sex Hormone Administration in Aged Women: II. Results of Therapy after Eighteen Months.” Journal of Gerontology 9: 168–74; April 1954. 48. Campbell, J. A., and MCLaughlan, J. M. “Vitamin B12 and the Growth of Children: A Review.” Journal of the Canadian Medical Association 72: 259–63; February 15, 1955. 49. Carmichael, Leonard, editor. Manual of Child Psychology. Second edition. New York: John Wiley and Sons, 1954. 1295 p. 50. Casida, L. E. “Some Factors Affecting Fertilization and Embryonic Death.” Mammalian Germ Cells. (Edited by G. E. W. Wolstenholme.) Boston: Little, Brown and Co., 1953. p. 262–74.

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229. Miettinen, Maija. “On Triplets and Quadruplets in Finland.” Acta Paediatrica 43: 493–96; September 1954. 230. Miller, E. “Grand Multiparas.” Obstetrics and Gynecology 4: 418–25; October 1954. 231. Moricard, R. “Meiosis and Fertilization Studies of Mammalian Ova in Vivo and in Vitro.” Gynaecologia 38: 310–36; August 1954. 232. Moss, Melvin L. “Differential Growth Analysis of Bone Morphology.” American Journal of Physical Anthropology 12: 71–75; March 1954. 233. Mourant, Arthur E. The Distribution of Human Blood Groups. Springfield, Ill.: Charles C. Thomas, 1954. 438 p. 234. Muller, H. J. “Damage to Posterity Caused by Irradiation of the Gonads.” American Journal of Obstetrics and Gynecology 67: 467–83; March 1954. 235. Mundy-Castle, A. C., and others. “The Electroencephalogram in the Senile Psychoses.” Electroencephalography and Clinical Neurophysiology 6: 245–52; May 1954. 236. Needham, Joseph. “Developmental Physiology.” Annual Review of Physiology. (Edited by Victor E. Hall.) Stanford, Calif.: Annual Reviews, 1955. p. 37–60. 237. Nelson, Marjorie M. Mammalian Fetal Development and Antimetabolites. Paper presented to 120th A. A. A. S. Meeting, Medical Science Section, Boston, Mass., December 26–27, 1953. 238. Nelson, Marjorie M.; Lyons, William R.; and Evans, Herbert M. “Comparison of Ovarian and Pituitary Hormones for Maintenance of Pregnancy in PyridoxineDeficient Rats.” Endocrinology 52: 585–89; May 1953. 239. Nelson, Warren O. “Reproduction.” Annual Review of Physiology. (Edited by Victor E. Hall.) Stanford, Calif.: Annual Reviews, 1955. p. 443–58. 240. Newman, Russell W., and Munro, Ella H. “The Relation of Climate and Body Size in U.S. Males.” American Journal of Physical Anthropology 13: 1–17; March 1955. 241. Nichols, John B. “Plural Births in the United States.” Western Journal of Surgery, Obstetrics and Gynecology 61: 229–36; May 1953. 242. Nichols, John B. “Quintuplet and Sextuplet Births in the United States.” Acta Geneticae Medicae et Gemellologiae 3: 143–52; May 1954. 243. Nicolson, A. B., and Hanley, C. “Indices of Physiological Maturity: Derivation and Interrelationships.” Child Development 24: 3–38; March 1953. 244. Noback, Charles R. “The Appearance of Ossification Centers and the Fusion of Bones.” American Journal of Physical Anthropology 12: 63–69; March 1954. 245. Obrist, Walter D. “The Electroencephalogram of Normal Aged Adults.” Electroencephalography and Clinical Neurophysiology 6: 235–44; May 1954. 246. Ojemann, Ralph H. “Child Growth and Development.” Children in Focus: Their Health and Activity. 1954 Yearbook. Washington, D. C.: American Association for Health, Physical Education, and Recreation, a department of the National Education Association, 1954. p. 47–55. 247. Olson, Willard C. “Recent Research Findings in Human Growth and Development as They Apply to Teacher Education.” Sixth Yearbook, 1953. Oneonta, N. Y.: American Association of Colleges for Teacher Education, a department of the National Education Association (Secy.-Treas.: Edward C. Pomeroy, 11 Elm Street), 1953. p. 46–63. 248. Olson, Willard C., and Lewellen, J. B. How Children Grow and Develop. Chicago: Science Research Associates, 1953. 48 p. 249. Osgood, Edwin E. “Development and Growth of Hematopoietic Tissues: With a Clinically Practical Method of Growth Analysis.” Pediatrics 15: 733–51; June 1955. 250. Oster, Jacob. Mongolism. Copenhagen: Danish Science Press, 1955. 206 p. 251. Ostry, E. I. “The Effect of Delay in the First Stage of Labour on the Forceps Rate and on the Stillbirth and Neonatal Mortality Rates: An Analysis of 4,401 Consecutive Primigravid Labours at Term with 482 Cases Having a Prolonged First Stage.” Journal of Obstetrics and Gynaecology of the British Empire 62: 115–16; February 1955.

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252. Owens, William C., and others. “Symposium: Retrolental Fibroplasia (Retinopathy of Prematurity).” American Journal of Ophthalmology 40: 159–89; August 1955. 253. Oxom, Harry. “Hazards of Grand Multiparity.” Obstetrics and Gynecology 5: 150–56; February 1955. 254. Paiva, Silvio L. “Pattern of Growth of Selected Groups of Breast-Fed Infants in Iowa City.” Pediatrics 11: 38–47; January 1953. 255. Park, Edwards A. “Bone Growth in Health and Disease.” Archives of Disease in Childhood 29: 269–81; August 1954. 256. Parnell, R. W. “Somatotyping by Physical Anthropometry.” American Journal of Physical Anthropology 12: 209–39; June 1954. 257. Patt, H. M. “Radiation Effects on Mammalian System.” Annual Review of Physiology. (Edited by Victor E. Hall.) Stanford, Calif.: Annual Reviews, 1954. p. 51–80. 258. Patten, Bradley M. Human Embryology. Second edition. London: J. and A. Churchill, 1953. 798 p. 259. Patz, Arnall. “Oxygen Studies in Retrolental Fibroplasia: IV. Clinical and Experimental Observations.” American Journal of Ophthalmology 38: 291–308; September 1954. 260. Patz, Arnall, and others. “Oxygen Studies in Retrolental Fibroplasia: II. The Production of the Microscopic Changes of Retrolental Fibroplasia in Experimental Animals.” American Journal of Ophthalmology 36: 1511–22; November 1953. 261. Peckos, Penelope S. “Caloric Intake in Relation to Physique in Children.” Science 117: 631–33; June 1953. 262. Penrose, L. S. “Mongolian Idiocy (Mongolism) and Maternal Age.” Annals of the New York Academy of Science 57: 494–502; January 1954. 263. Pere, Soini; Kunnas, Mlkko; and Telkkä, Anti. “Correlation Between Performance and Physique in Finnish Athletes.” American Journal of Physical Anthropology 12: 201–208; June 1954. 264. Phillips, H. T. “Some Social and Ethnic Variations in the Physique of South African Nursery School Children.” Archives of Disease in Childhood 28: 226–31; June 1953. 265. Plummer, George. “Anomalies Occurring in Children Exposed in Utero to the Atomic Bomb in Hiroshima.” Pediatrics 10: 687–93; December 1952. 266. Posner, A. C; Friedman, S.; and Posner, L. B. “The Large Fetus.” Obstetrics and Gynecology 5: 268–78; March 1955. 267. Pugh, M. C. “Charting Growth with the Wetzel Grid.” Research Quarterly of the American Association for Health, Physical Education, and Recreation 25: 47–48; March 1954. 268. Race, R. R., and Sanger, Ruth. Blood Groups in Man. Springfield, Ill.: Charles C. Thomas, 1954. 400 p. 269. Rand, Winifred, and others. Growth and Development of the Young Child. Fifth edition. Philadelphia: W. B. Saunders Co., 1953. 523 p. 270. Rarick, G. L. “Maturity Indicators and the development of Strength and Skill.” Education 75: 69–73; October 1954. 271. Reynolds, Earle L. The Distribution of Subcutaneous Fat in Childhood and Adolescence. Evanston, Ill.: Child Development Publications, 1952. 189 p. 272. Reynolds, S. R. M.; Paul, W. M.; and Huggett, A. “Physiological Study of Monkey Fetus in Utero: A Procedure for Blood Pressure Recording, Blood Sampling and Injection of the Fetus under Normal Conditions.” Bulletin of the Johns Hopkins Hospital 95: 256–68; November 1954. 273. Ridley, Harold. “Further Observations on Intraocular Acrylic Lens in Cataract Surgery.” Transactions of the American Academy of Ophthalmology and Otolaryngology 57: 98–106; January-February 1953. 274. Roberts, D. F. “Body Weight, Race and Climate.” American Journal of Physical Anthropology 11: 533–58; December 1953.

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275. Rubin, I. C. “Third Generation Follow-up in Women Receiving Pelvic Irradiation.” Journal of the American Medical Association 150: 207–209; September 20, 1952. 276. Rugh, Roberts. “Genetic Hazards in Ovarian Radiation.” Journal of Obstetrics and Gynaecology of the British Empire 62: 461–63; June 1955. 277. Russell, L. B., and Russell, W. L. “An Analysis of the Changing Radiation Response of the Developing Mouse Embryo.” Journal of Cellular and Comparative Physiology 43: Supplement 1, 103–49; May 1954. 278. Schlesinger, Edward R., and Allaway, Norman C. “The Combined Effect of Birth Weight and Length of Gestation on Neonatal Mortality among Single Premature Births.” Pediatrics 15: 698–704; June 1935. 279. Schmitz, Karl L. “The Calculation of the Body Surface.” Zeitschrift Für Biologie 106: 325–29; March 1954. 280. Sheldon, William A. Atlas of Men: A Guide for Somatotyping the Adult Male at all Ages. New York: Harper and Brothers, 1954. 357 p. 281. Sheridan, F. P., and others. “Electroencephalography as a Diagnostic and Prognostic Aid in Studying the Senescent Individual: A Preliminary Report.” Journal of Gerontology 10: 53–59; January 1955. 282. Sholl, D. A. “Regularities in Growth Curves, Including Rhythms and Allometry.” Dynamics of Growth Processes. (Edited by Edgar J. Boell.) Princeton, N. J.: Princeton University Press, 1954. p. 224–41. 283. Silverman, William A.; Fertig, John W.; and Kraus, Arthur. “A Proposed Method of Computing Standardized Death Rates for Premature Nurseries.” Pediatrics 15: 467–78; April 1955. 284. Simon, A., and others. “Metabolic Studies in Mongolism: Serum Protein-Bound Iodine, Cholesterol and Lipoprotein.” American Journal of Psychiatry 111: 139–45; August 1954. 285. Škerlj, Bozo; Bžozek, Josef; and Hunt, Edward E., JR. “Subcutaneous Fat and Age Changes in Body Build and Body Form in Women.” American Journal of Physical Anthropology 11: 577–600; December 1953. 286. Smith, Audrey U., and Parkes, A. S. “Storage and Homografting of Endocrine Tissues.” Preservation and Transplantation of Normal Tissues. (Edited by G. E. W. Wolstenholme and Margaret P. Cameron.) Boston: Little, Brown and Co., 1954. p. 76–85. 287. Smith, Thomas C. “The Action of Relaxin on Mammary Gland Growth in the Rat.” Endocrinology 54: 59–70; January 1954. 288. Soffer, Louis J., and Gabrilove, J. Lester. “Endocrinology.” Annual Review of Medicine. (Edited by Windsor C. Cutting.) Stanford, Calif.: Annual Reviews, 1954. p. 115–66. 289. Sontag, Lester W., and Garn, Stanley M. “Growth.” Annual Review of of physiology. (Edited by victor E. Hall.) Stanford, Calif.: Annual Reviews, 1954. p. 37.50. 290. Spies, Tom D., and others. “Skeletal Maturational Progress in Children with Chronic Nutritive Failure.” American Journal of Diseases of Children 85: 1–12; January 1953. 291. Spratt, Nelson T., Jr. “Developmental Physiology.” Annual Review of Physiology. (Edited by Victor E. Hall.) Stanford, Calif.: Annual Reviews, 1953. p. 21–38. 292. Staton, Wesley M. “The Adolescent: His Physical Growth and Health.” Review of Educational Research 24: 19–29; February 1954. 293. Stewart, T. D. “Metamorphosis of the Joints of the Sternum in Relation to Age Changes in Other Bones.” American Journal of Physical Anthropology 12: 519–35; December 1954. 294. Stodard, Roy. “Prenatal Environment and Congenital Malformations.” Bulletin of the Tulane Medical Faculty 12: 123–27; May 1953. 295. Strom, Justus. “The Decrease of Infant Mortality in Sweden and Its Causes.” Nordisk Medicin 50: 1285–94; September 17, 1953.

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296. Suarez, Manuel. “Growth: Critical Study of Graphic Methods of Expression of Growth.” Revista Española de Pediatria 8: 595–605; September-October 1952. 297. Suarez, Manuel. “Growth: Critical Study of Somatometry.” Revista Española de Pediatria 8: 571–94; September-October 1952. 298. Suarez, Manuel, and Peva, J. “Growth: Correlation Between Creatininuria and Radiographic Muscular Area as Index of Muscle Development.” Revista Española de Pediatria 8: 663–73; September-October 1952. 299. Suarez, Manuel, and Peva, J. “Growth: Creatininuria as Index of Muscular Development.” Revista Española de Pediatria 8: 633–52; September-October 1952. 300. Suarez, Manuel, and Peva, J. “Growth: Radiographic Index of Muscle Development.” Revista Española de Pediatria 8: 653–62; September-October 1952. 301. Suarez, Manuel, and Tirjeira, J. “New Method of Graphic Presentation of Growth.” Revista Española de Pediatria 8: 439–50; July-August 1952. 302. Sutow, Wataru W. “Skeletal Maturation in Healthy Japanese Children, 6 to 19 Years of Age: Comparison with Skeletal Maturation in American Children.” Hiroshima Journal of Medical Science 2: 181–93; 1953. 303. Sutow, Wataru, W., Terasaki, Taro; and Ohwada, Kenji. “Comparison of Skeletal Maturation with Dental Status in Japanese Children.” Pediatrics 14: 327–33; October 1954. 304. Swingle, W. W., and Kleinberg, William. “The Pituitary and Adrenals.” Annual Review of Physiology. (Edited by Victor E. Hall.) Stanford, Calif.: Annual Reviews, 1955. p. 367–92. 305. Taff, M. A., Jr., and Wilbar, C. L., Jr. “Immaturity of Single Live Births According to Weight, with Particular Reference to Race.” American Journal of Diseases of Children 85: 279–84; March 1953. 306. Talbot, Nathus B., and others. Functional Endocrinology from Birth Through Adolescence. Cambridge, Mass.: Harvard University Press, 1952. 638 p. 307. Tanner, J. M. “The Effect of Weight-Training on Physique.” American Journal of Physical Anthropology 10: 427–60; December 1952. 308. Tanner, J. M. “Reliability of Anthropometric Somatotyping.” American Journal of Physical Anthropology 12: 257–65; June 1954. 309. Terner, C. “Aerobic Metabolism and Semen Quality.” Mammalian Germ Cells. (Edited by G. E. W. Wolstenholme.) Boston: Little, Brown and Co., 1953. p. 46–58. 310. Theman, V. “Emerging Concepts of Child Growth and Development: What They Suggest for Classroom Practice.” The American Elementary School. Thirteenth Yearbook, John Dewey Society. New York: Harper and Brothers, 1953. p. 57–86. 311. Thompson, George G. Child Psychology. New York: Houghton Mifflin Co., 1952. 667 p. 312. Toverud, G., and others. A Survey of the Literature of Dental Caries. Washington, D. C.: National Research Council, 1952. 567 p. 313. Trotter, Mildred, and Gleser, Goldine C. “Estimation of Stature from Long Bones of American Whites and Negroes.” American Journal of Physical Anthropology 10: 463–514; December 1952. 314. Tuddenham, Read D., and Snyder, Margaret M. Physical Growth of California Boys and Girls from Birth to Eighteen Years. Publications in Child Development, Vol. 1, No. 2. Berkeley: University of California Press, 1954. p. 183–364. 315. Tyler, Frank H., and Armstrong, Marion D. “Diseases of the Nervous System: Metabolic Aspects of Some Neurological and Muscular Disorders.” Annual Review of Medicine. (Edited by Windsor C. Cutting.) Stanford, Calif.: Annual Reviews, 1954. p. 207–22. 316. Tyler, Fred T. “Concepts of Organismic Growth: A Critique.” Journal of Educational Psychology 44: 321–42; October 1953. 317. Tyler, Fred T. “Organismic Growth: P-Technique in the Analysis of Longitudinal Growth Data.” Child Development 25: 83–90; June 1955.

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318. United Nations, Department of Social Affairs, Population Division. Foetal, Infant and Early Childhood Mortality. Volume I: The Statistics. New York: Columbia University Press, International Documents Service, 1955. 137 p. 319. United Nations, Department of Social Affairs, Population Division. Foetal, Infant and Early Childhood Mortality. Volume II: Biological, Social and Economic Factors. New York: Columbia University Press, International Documents Service, 1955. 44 p. 320. U.S. Department of Health, Education, and Welfare, Social Security Administration, Children’s Bureau, Research Relating to Children: January 1, 1952–October 31, 1952. Bulletin II. Washington, D.C.: Superintendent of Documents, Government Printing Office, 1953. 459 p. 321. U.S. Department of Health, Education, and Welfare, Social Security Administration, Children’s Bureau. Research Relating to Children: January 1, 1952-March 31, 1954. Bulletin II, Supplement No. 1. Washington, D.C.: Superintendent of Documents, Government Printing Office, 1954. 76 p. 322. U.S. Department of Health, Education, and Welfare, Social Security Administration, Children’s Bureau. Research Relating to Children: November 1, 1952-May 31, 1954. Bulletin II, Supplement No. 2, Washington, D.C.: Superintendent of Documents, Government Printing Office, 1954. 195 p. 323. U.S. Department of Health, Education, and Welfare, Social Security Administration, Children’s Bureau. Research Relating to Children: April 1, 1954-January 31, 1955. Bulletin II, Supplement No. 3. Washington, D.C.: Superintendent of Documents, Government Printing Office, 1955. 235 p. 324. Verrotti, M. “An Analysis of the Causes and Frequency of Prematurity, Especially During the War Years.” Lattante 24: 449–57; July 1953. 325. Walker, James. “Foetal Anoxia.” Journal of Obstetrics and Gynaecology of the British Empire 61: 162–80; April 1954. 326. Wallace, Helen M., and others. “Trends in Maternal and Perinatal Mortality in New York City.” Journal of the American Medical Association 155: 716–19; June 1954. 327. Washburn, Alfred H. “Human Growth, Development, and Adaptation.” American Journal of Diseases of Children 90: 2–5; July 1955. 328. Wedgwood, Ralph J., and others. “Relationship of Body Composition to Basal Metabolic Rate in Normal Man.” Journal of Applied Physiology 6: 317–34; December 1953. 329. Weech, A. A. “Signposts on the Highway of Growth.” American Journal of Diseases of Children 88: 452–57; October 1954. 330. Wegman, Myron E. “Public Health, Nursing and Medical Social Work: Vital Statistics in the U.S.A.—1953.” Pediatrics 13: 588–89; June 1954. 331. Wegman, Myron E. “Weight at Birth and Survival of the Newborn.” Pediatrics 14: 396–400; October 1954. 332. Weiner, J. S., and Thambipillai, V. “Skeletal Maturation of West African Negroes.” American Journal of Physical Anthropology 10: 407–18; December 1952. 333. Wenar, Charles. “The Effect of a Motor Handicap on Personality: II. The Effects on Integrative Ability.” Child Development 25: 287–94; December 1954. 334. Wetzel, Norman C., and others. “Growth Failure in School Children: Further Studies of Vitamin B12 Dietary Supplements.” Journal of Clinical Nutrition 1: 17–31; September-October 1952. 335. Wilson, James G. “Differentiation and the Reaction of Rat Embryos to Radiation.” Journal of Cellular and Comparative Physiology 43: Supplement 1, 11–37; May 1954. 336. Wilson, James L. “Pediatrics.” Annual Review of Medicine. (Edited by Windsor C. Cutting.) Stanford, Calif.: Annual Reviews, 1954. p. 389–404. 337. Wolff, Etienne, and others. “The Culture of Embryonic Organs in Synthetic Media.” Journal of Embryology and Experimental Morphology 1: 55–84; March 1953.

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338. Wolstenholme, G. E. W., editor. Mammalian Germ Cells. Boston: Little, Brown and Co., 1953. 302 p. 339. Wolstenholme, G. E. W., and Cameron, Margaret P., editors. Preservation and Transplantation of Normal Tissues. Boston: Little, Brown and Co., 1954. 236 p. 340. Workany, Josef. Congenital Malformations Induced by Maternal Dietary Deficiency. Harvey Lectures Series. New York: J. B. Lippincott, 1952–53. 273 p. 341. Yamazaki, James N.; Wright, Stanley W.; and Wright, Phyllis M. “Outcome of Pregnancy in Women Exposed to the Atomic Bomb in Nagasaki.” American Journal of Diseases of Children 87: 448–63; April 1954. 342. Zacharias, L., and others. “The Incidence and Severity of Retrolental Fibroplasia in Relation to Possible Causative Factors.” American Journal of Ophthalmology 38: 317–36; September 1954. 343. Zubek, John P., and Solberg, Patricia A. Human Development. New York: McGraw-Hill Book Co., 1955. 476 p.

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21 Mental Development during the Preadolescent and Adolescent Periods Gordon Hendrickson

Summaries and General Treatments

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egel (57) prepared a well-organized monograph on the development of intellectual abilities in the adolescent period. He based his discussion on a wide variety of sources as well as on some new data. Other surveys of the research literature may be found in the revised Encyclopedia of Educational Research (42), and in articles by Jones and Bayley (33), and Thorndike (68). Textbooks on child or adolescent psychology by Averill (3), Breckenridge and Vincent (10), Cole (14), Cole and Morgan (15), Hurlock (30), and Olson (46) included sections on mental development. Olson drew many inferences for school practice from the research literature of the period. Anderson (2) proposed a classification for literature pertinent to child development. Gesell and Ilg (25) combined their earlier works on infant and child development, stressing characteristics of successive age levels up to age 10. Jenkins, Shacter, and Bauer (31) prepared a popular treatment of the characteristics of children at each age from five to 11, and Schnell (55) wrote a digest on psychological characteristics of youth at four age levels from 10 to 21. In a pamphlet addressed to adolescents, Bouthilet and Bryne (9) discussed the factors in general intelligence and provided a helpful orientation for prospective test-takers.

Source: Review of Educational Research, XX(5) (1950): 351–360.

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The Organization of Intellectual Powers Theoretical discussions as well as research on the organization of intellect have revolved around the problems of traits or factors. Burt (12) argued that the evidence points to a hierarchy of a general factor and a small number of broad group factors, subdivided into narrower group factors; i.e., to factors arranged by levels. To the primary group abilities recognized by Thurstone, Burt (11) would add a general factor. Anastasi (1) regarded traits as results of learning and, due to the greater cultural standardization of intellectual activities, considered traits as more consistent and easier to identify in the intellectual aspects of behavior than in the emotional aspects. New tests produced in this period were chiefly analytical in character and generally yielded several scores. Notable are the SRA Primary Mental Abilities tests (PMA) by the Thurstones (69, 70), appropriate for subjects from the junior high school thru the college level, and a set of seven differential aptitude tests by Bennett, Seashore, and Wesman (5).

Adolescent Intellectual Abilities A number of studies attempted to isolate specific adolescent intellectual traits or dealt with their relationships. Johnson (32) studied problem-solving abilities in arithmetic at the eighth-grade level. Of the PMA tests, the vocabulary test gave the highest correlation with arithmetic problem tests. The flow of words in writing was studied by Taylor (66). Taylor analyzed fluency for highschool seniors into two factors: word fluency, i.e., facility in producing single, isolated words; and ideational fluency, facility in expressing ideas by means of words and their meanings. Murray (44) employed a multiple correlation procedure to analyze the geometric ability of high-school boys. He found spatial ability, as measured by the Minnesota Paper Form Board, and reasoning, as measured by the PMA tests, contributed less to success in geometry than numerical or verbal ability, as measured by the Modified Alpha Examination. Fattu and Fox (24) found the ability of ninth-grade pupils to interpret data to be closely associated with factors which make up typical group measures of intelligence and achievement. A unique approach to traits is found in two French studies by Michaud (38, 39). He was concerned with the interpretation which pupils give to geometric figures, and asked children aged nine to 14 to interpret the thickness of squares drawn on a blackboard or on paper. He found the percentage of realistic responses to diminish, and the percentage of rational responses to increase with age. Realistic responses, wherein subjects aged 10 to 15 were asked what would happen if a triangle which they imagined drawn on the ground were superimposed upon another imagined triangle, were also more characteristic of younger children in the second study.

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Factor Analysis Studies The most frequent procedure in attacking the problem of intellectual organization continued to be that of factor analysis. This procedure has been used to study changes with age, the relative importance of various factors for prediction, and other issues. In a group of studies, Swineford (62, 63, 65) reported the results of test batteries administered to pupils in Grades V to X and repeated at various intervals. Six tests were given to pupils in Grades VII or VIII and repeated when the pupils were in Grade IX. After one or two years factor analysis revealed no material change in the factor composition of the tests. The general factor apparently increased both in its absolute and in its relative contribution to the total test variance. For a group of pupils who took nine tests in the sixth grade and again in the ninth grade, three bi-factors persisted as entities but grew at different rates; the general factor most, the verbal factor to some degree, the spatial factor not at all. The means for the general factor increased repeatedly and steadily with school grade level from Grade V to X. The means for the verbal factor increased gradually and irregularly. Retarded pupils were markedly inferior in the general factor, less so in the verbal factor, and equivalent to the normal group in the spatial factor. According to Swineford the general factor is the only one which predicted school marks with any consistency. Another report by Swineford (64) dealt with a number factor revealed by data from 19 tests given to ninth-grade pupils. This factor may be related to the pupil’s mental set in approaching a task, a set determined by his liking or dislike for numbers. Swineford also inferred from the data that girls are more affectively sensitive to numbers than boys. Curtis (16) also presented data emphasizing the importance of a general factor. His data on nine- and 12-year-olds failed to support Garrett’s hypothesis that “abstract or symbol intelligence changes in its organization as age increases from a fairly unified and general ability to a loosely organized group of abilities or factors.” On the other hand, Segal (57) accepted Garrett’s conclusion and presented new data to show that differentiation among traits is more pronounced for bright ninth-grade pupils than for dull ones. Diamond (21) used a factor analysis procedure which he believed showed that the Wechsler-Bellevue subtests may serve as indicators of linguistic, clerical, and spatial aptitudes.

Development in Specific Traits A number of investigators compared subjects at various ages by measures which were designed to reveal growth in particular traits. Webber and Hunnicutt (74) studied improvement in the ability to perceive change of color in painting with subjects from Grades I thru IX. Birch (6) found the Goodenough

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drawing test valuable in studying the processes of concept formation in a group of borderline or mentally defective children aged 10 to 16 years. Three investigators were interested in moral traits. Turner (71) developed a scale of altruism and found no improvement from age nine to age 16. Beller (4) studied the attitudes toward honesty of boys aged nine, 12, and 15 years. On the basis of verbal problems, Dowd (22) studied moral reasoning in Catholic girls from Grades VIII to XII. Hilden (27) reported a study of 100 children from birth, 30 of whom had taken several mental tests by the age of 16. The mean IQ of the subjects was 119 with a range of differences between repeated tests from seven to 64 points. On the average there was a slow and reliable rise in score not accounted for by practice effects. Hilden suggested that the highest IQ score prior to puberty might be more representative of mid-adolescent status than scores on the early test. Another retest study by Kvaraceus and Lanigan (36) reporting data on the Iowa Every-Pupil Tests of Basic Skills administered at half-year intervals in the junior high school, indicated that individual performance at any one testing period should be interpreted with discretion; in some cases scores drop for a test period. A European study presented results from tests of subjects of various age levels. Vernon (72) found general intelligence increasing more rapidly and to a later age among boys who continue in school to the age of 17 and beyond, and among men in “intellectual” occupations. In general, Vernon concluded that abilities depend largely on the extent to which they are used.

Gains in Intelligence during College Years Retests of students on the American Council on Educational Psychological Examination were reported from several colleges. In general, the investigators concluded that gains over and above practice effects do occur. Thorndike (67) found such gains occurring to the age of 20 and probably beyond. Projections of the growth curve for his data indicated either age 21 years, six months or 25 years, nine months as a point of zero gain, depending upon the mathematical treatment of the data. Shuey (59) found gains for college students.

Prediction of Academic Success Studies on prediction of academic success in high school and college ranged from those which employed simple correlations of test scores and grades to multiple correlation and factor analysis studies. Shaw (58) used multiple correlation and Beta coefficients in treating data from the PMA tests and 13 measures of achievement for 591 high-school students. He found verbal-meaning to be highly related to every achievement measured, with reasoning in second

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place but not closely so. Little power to predict achievement was found for number, word fluency, space, and memory scores. A study evaluating several tests for prediction of high-school achievement was reported by Bolton (7). At the college level Remmers, Elliott, and Gage (48) found certain tests developed at Purdue (Placement Test in English, Mathematics Training Test, Physical Science Test) more predictive of grade point averages of freshmen than scores on the American Council on Education Psychological Examination. Lanigan (37) found that the ACE differentiated better between highachieving and low-achieving college students than the Otis test or the Minnesota Speed of Reading Tests. At the University of Wisconsin, Milligan, Lins, and Little (40) also found the ACE especially useful for identifying students at the upper and lower ends of the distribution of intelligence. They reported the ACE helpful in predicting achievement for nonhigh-school graduates admitted to the university. Borg (8) reported low positive correlations between the ACE and success in a college of arts and crafts. Investigators using the ACE repeatedly on the same group were warned by Muntyan (43) that the norms for a first testing cannot be justifiably used in interpreting the results of a retest.

Miscellaneous Relationships of Intellectual Abilities Kendall (34) reported that there was no significant relationship of scores on a memory-for-design test with retardation in reading for a group of children aged six to 16. Hobson (29) gave PMA tests in Grades VIII and IX. Significant sex differences were found. Boys were superior in spatial orientation, and girls excelled in word fluency, inductive reasoning, and visual memory. Wheeler and Wheeler (75) inferred from correlations between ACE and reading test scores of university freshmen that ACE performance is highly influenced by reading skill. A Dutch version of the National Intelligence Test was used by de Groot (20) to study the effects of war upon the intelligence of youth. His 13- and 14-year-old subjects averaged four IQ points lower than similar subjects tested in prewar years.

Intellect in Relation to Social Factors Davis and Havighurst (19) prepared a general report on cultural factors claimed to produce differential test results in various socio-economic groups. These writers continued to present evidence on this problem as well as general discussions of their theoretical position (18). Schulman and Havighurst (56) found a correlation of .46 between vocabulary size and socioeconomic status for children in Grades IX and X in a midwestern community.

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Durea (23) presented some evidence indicating that the mental retardation of delinquent boys aged 11 to 18 may be a reflection of the sub-par socioeconomic conditions from which the delinquents come.

Intellectual Growth of Feeble-minded Children By the publication of claims that feeble-minded children had been made normal thru education, Schmidt (52, 53) precipitated one of the most violent psychological controversies of recent years. Popular articles on Schmidt’s work by Stern (61) and Clark (13) challenged long-settled beliefs concerning the improvement of the mentally deficient. Schmidt reported an eightyear study of 322 school children, aged 12 to 14, ranging in IQ from 27 to 69, including experimental and control groups. For three years the experimental subjects were taught in a school environment planned to decrease nervous tensions, to remove emotional blocks, to further social interaction, and to develop self-confidence and a sense of personal worth. Regular school subjects plus hand work were taught at a slower rate than normal to a control group. A five-year follow-up permitted study of out-of-school or later school adjustment of these individuals. The results for the experimental group included (a) gains in social adjustment and maturity and in Bernreuter scores, (b) the completion of a four-year high-school course by 27 percent of the group, (c) a good employment record for children out of school, and (d) an increase in IQ from an initial mean of 52.1 to 71.6 after three years of training and to 89.3 after the five-year follow-up. Kirk (35) reviewed Schmidt’s study in the light of an investigation of pertinent data in board of education records in Chicago, where Schmidt was a teacher. Kirk raised questions concerning: (a) the correspondence of the initial IQ distributions for the subjects with the statistics for Chicago special classes as a whole; (b) the appropriateness of the Bernreuter test for pupils of the mental status of the subjects; (c) certain statistical anomalies in the presentation of the data; and (d) the professional status of Schmidt at the time of the study. Schmidt (54) replied in general terms stressing scientific method, similar results reported by other investigators, and professional ethics. A survey by Nolan (45) revealed considerable doubt of the validity of the results on the part of several well-known psychologists. Other evidence on intellectual changes in mental defectives is conflicting. Rudolf (49) reported that on the Wechsler-Bellevue verbal scale and on the Vineland Social Maturity Scale 395 defectives showed more rises than declines on retests six months or more after initial tests. The inference was drawn that defectives should be given continued education after the age of 16. Guertin (26) reported on the mental growth curve of 25 institutionalized defectives whose IQ scores showed marked increase over a period of time in comparison with the IQ performance of 25 controls who failed to show improvement.

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A report by Hill (28) on retests of 107 special-class children in Des Moines showed occasional significant changes in IQ, possibly due to the social environment, but there were no consistent gains such as those reported by Schmidt. Sloan and Harman (60) studied 1446 institutionalized mental defectives, for whom the median chronological age at initial testing was 14.4, at final testing, 17.6; the corresponding median IQ’s were 51.9 and 47.4. Cutts and Lane (17) reported that 57 defectives who had been hospitalized for seven years received lower scores on the Wechsler-Bellevue verbal scale than 57 defectives hospitalized for one year. Two studies dealt with educational programs for adolescent special-class pupils. Mones (41) discussed 10 years of experience in Newark, New Jersey, where a specially adapted program at the junior-high-school level proved profitable to special-class children.

Chemical Regulators of Intellectual Growth Glutamic acid has been claimed by several investigators to stimulate intellectual growth on the part of mental defectives. Waelsch (73) reviewed 23 references on this subject. Zimmerman, Burgemeister, and Putnam (77) reported on a series of clinical cases, ranging from infancy to adolescence, and concluded that glutamic acid accelerates mental functioning in human beings, chiefly in the first six months of treatment. A ceiling of improvement is apparently reached after one year of therapy. Zimmerman (76) suggested a definite dosage and claimed that the treatment had value for children in the 70 to 80 IQ range. Quinn and Durling (47) reported small gains (three to five IQ points) for institutionalized defectives treated with glutamic acid for six months. Rudolf (50, 51) investigated the value of thiamine treatment. Out of 90 defectives who had not improved for over a year, all of whom were treated with thiamine, 17 showed some increase in IQ, and 20 showed an increase in social age.

Unsettled Issues Few issues in this field can be regarded as closed, but a list of a few unsolved problems in which there is current interest may be helpful. Several of the following research areas were suggested by Segel’s review (57): (a) The existence or significance of a general factor in intellect; (b) Increase of differentiation among traits with age; (c) Relative variation of traits within the individual and within groups; (d) Stability of the mental growth of individuals; (e) Independence of time cycles for growth of various traits; (f) Existence of definite interest areas in early adolescence and their relationship to intelligence; and (g) Relationship of intellectual traits and level to socio-economic factors.

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Bibliography 1. Anastasi, Anne. “The Nature of Psychological ‘Traits.’ ” Psychological Review 55: 127–38; May 1948. 2. Anderson, John E. Classification and Index for the Child Development, Human Development, and Psychology Areas., Minneapolis, Minnesota: Institute of Child Welfare, 1947. 98 p. 3. Averill, Lawrence A. The Psychology of the Elementary School Child. New York: Longmans, Green and Company. 459 p. 4. Beller, Emanuel K. “TWO Attitude Components in Younger Boys.” Journal of Social Psychology 29: 137–51; May 1949. 5. Bennett, George K., Seashore, Harold G., and Wesman, Alexander G. Differential Aptitude Tests. New York: Psychological Corporation, 1947. 6. Birch, Jack W. “The Goodenough Drawing Test and Older Mentally Retarded Children.” American Journal of Mental Deficiency 54: 218–24; October 1949. 7. Bolton, Floyd B. “Value of Several Intelligence Tests for Predicting Scholastic Achievement.” Journal of Educational Research 41: 133–38; October 1947. 8. Borg, Walter R. “A Study of the Relationship Between General Intelligence and Success in an Art College.” Journal of Educational Psychology 40: 434–40; November 1949. 9. Bouthilet, Lorraine, and Bryne, Katherine M. You and Your Mental Abilities. Chicago: Science Research Associates, 1948. 48 p. 10. Breckenridge, Marian E., and Vincent, E. Lee. Child Development; Physical and Psychological Growth Through the School Years. Philadelphia: W. B. Saunders Company, 1949. 622 p. 11. Burt, Cyril. “Critical Notice of Thurstone’s ‘Multiple Factor Analysis.’ ” British Journal of Educational Psychology 17: 163–69; November 1947. 12. Burt, Cyril. “The Structure of the Mind; A Review of the Results of Factor Analysis.” British Journal of Educational Psychology 19: 176–99; June 1949. 13. Clark, Thomas, B. “They Are Feeble-minded No Longer.” Reader’s Digest 51: 111–15: September 1947. 14. Cole, Luella. Psychology of Adolescence. New York: Rinehart and Company, 1948. 650 p. 15. Cole, Luella, and Morgan, John J. B. Psychology of Childhood and Adolescence. New York: Rinehart and Company, 1947. 416 p. 16. Curtis, Hazen A. “A Study of the Relative Effects of Age and of Test Difficulty upon Factor Patterns.” Genetic Psychology Monographs 40: 99–148; August 1949. 17. Cutts, Richard A., and Lane, Margery O’Kelley. “The Effect of Hospitalization on Wechsler-Bellevue Subtest Scores by Mental Defectives.” American Journal of Mental Deficiency 51: 391–93; January 1947. 18. Davis, W. Allison. Social-Class Influences upon Learning. Iglis Lecture, 1948. Cambridge, Massachusetts: Harvard University Press, 1948. 100 p. 19. Davis, W. Allison, and Havighurst, Robert J. “The Measurement of Mental Systems. (Can Intelligence Be Measured?)” Science Monthly 66: 301–16; April 1948. 20. de Groot, A. D. “The Effects of War upon the Intelligence of Youth.” Journal of Abnormal and Social Psychology 43: 311–17; July 1948. 21. Diamond, Solomon. “The Wechsler-Bellevue Intelligence Scales and Certain Vocational Aptitude Tests.” Journal of Psychology 24: 279–82; October 1947. 22. Dowd, M. Amedeus. “Changes in Moral Reasoning Through the High School Years.” Studies in Psychology and Psychiatry from the Catholic University of America 7, No. 2; 1948. 120 p. 23. Durea, Merrin A., and Taylor, G. J, “The Mentality of Delinquent Boys Appraised by the Wechsler-Bellevue Intelligence Tests.” American Journal of Mental Deficiency 52: 342–44; April 1948.

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24. Fattu, Nicholas A., and Fox, William R. “Scores on the Interpretation of Data Test: Their Relation to Measures of Achievement, Personality, and Interest.” Indiana University School of Education Bulletin 25: 1–54; May 1949. 25. Gesell, Arnold, and Ilg, Frances L. Child Development: An Introduction to the Study of Human Growth. New York: Harper and Brothers, 1949. 475 p. 26. Guertin, Wilson H. “Mental Growth in Pseudo-Feeblemindedness.” Journal of Clinical Psychology 5: 414–18; October 1949. 27. Hilden, Arnold H. “A Longitudinal Study of Intellectual Development.” Journal of Psychology 28: 187–214; July 1949. 28. Hill, Arthur. “Does Special Education Result in Improved Intelligence for the Slow Learner?” Journal of Exceptional Children 14: 207–13; April 1948. 29. Hobson, James R. “Sex Differences in Primary Mental Abilities.” Journal of Educational Research 41: 126–32; October 1947. 30. Hurlock, Elizabeth B. Child Growth and Development. New York: McGraw-Hill Book Company, 1949. 374 p. 31. Jenkins, Gladys G., Shacter, Helen, and Bauer, William W. These Are Your Children: How They Develop and How To Guide Them. Chicago: Scott, Foresman and Company, 1949. 192 p. 32. Johnson, John T. “On the Nature of Problem-solving in Arithmetic.” Journal of Educational Research 43: 110–15; October 1949. 33. Jones, Harold E., and Bayley, Nancy. “Growth, Development, and Decline.” Annual Review of Psychology 1: 1–8; 1950. 34. Kendall, Barbara S. “A Note on the Relation of Retardation in Reading to a Performance on a Memory-for-Designs Test.” Journal of Educational Psychology 39: 370–73; October 1948. 35. Kirk, Samuel A. “An Evaluation of the Study by Bernardine G. Schmidt Entitled: ‘Changes in Personal, Social, and Intellectual Behavior of Children Originally Classified as Feebleminded. ’ ” Psychological Bulletin 45: 321–33; July 1948. 36. Kvaraceus, William C., and Lanigan, Mary A. “Pupil Performance on the Iowa EveryPupil Tests of Basic Skills Administered at Half-Year Intervals in the Junior High School.” Educational and Psychological Measurement 8: 93–100; Spring 1948. 37. Lanigan, Mary A. “The Effectiveness of the Otis, the A.C.E. and the Minnesota Speed of Reading Tests for Predicting Success in College.” Journal of Educational Research 41: 289–96; December 1947. 38. Michaud, S. “L’ Enfant et les Figures Geometriques.” (The Child and Geometric Figures.) Journal de Psychologie Normale et Patholagique 40: 154–68; April–June 1947. 39. Michaud, E. “L’ Interpretation de Figures Geometriques par I’Enfant.” (The Child’s Interpretation of Geometric Patterns.) Journal de Psychologie Normale et Pathologique 42: 295–308; July-September 1949. 40. Milligan, Edward E., Lins, L. Joseph, and Little, Kenneth. “The Success of Non-High School Graduates in Degree Programs at the University of Wisconsin.” School and Society 67: 27–29; January 10, 1948. 41. Mones, Leon. “The Binet Pupils Get a Chance.” School and Society 67: 281–83; April 10, 1948. 42. Monroe, Walter S., editor. Encyclopedia of Educational Research. New York: The Macmillan Company, 1950. 1520 p. 43. Muntyan, Milosh. “A Study of the Re-test Factor in the Illinois Statewide High School Testing Program.” Journal of Educational Research 41: 183–92; November 1947. 44. Murray, John E. “An Analysis of Geometric Ability.” Journal of Educational Psychology 40: 118–24; February 1949. 45. Nolan, William J. “A Critique of the Evaluations of the Study of Bernardine G. Schmidt Entitled: ‘Changes in Personal, Social, and Intellectual Behavior of Children Originally Classified as Feebleminded.’ ” Journal of Exceptional Children 15: 225–34; May 1949.

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46. Olson, Willard C. Child Development. Boston: D. C. Heath and Company, 1949. 417 p. 47. Quinn, Karl V., and Durling, Dorothy. “Twelve Months’ Study of Glutamic Acid Therapy in Different Clinical Types in an Institution for the Mentally Deficient.” American Journal of Mental Deficiency 54: 321–32; January 1950. 48. Remmers, Herman H., Elliott, Donald N., and Gage, Nathaniel L. “Curricular Differences in Predicting Scholastic Achievement: Applications to Counseling.” Journal of Educational Psychology 40: 385–94; November 1949. 49. Rudolf, G. de M. “Retesting of the Intelligence Quotient and the Social Age.” Journal of Mental Science 95: 696–702; 1949. 50. Rudolf, G. de M. “The Treatment of Mental Defectives with Thiamine.” Journal of Mental Science 95: 910–19; 1949. 51. Rudolf, G. de M. “The Treatment of Mental Defectives with Anemia for One Year.” Journal of Mental Science 96: 265–71; January 1950. 52. Schmidt, Bernardine G. “Changes in Behavior of Originally Feeble-Minded Children.” Journal of Exceptional Children 14: 67–72, 94; December 1947. 53. Schmidt, Bernardine G. “Changes in Personal, Social, and Intellectual Behavior of Children Originally Classified as Feeble-Minded.” Psychological Monographs 60, No. 5; 1946. 144 p. 54. Schmidt, Bernardine G. “A Reply.” Psychological Bulletin 45; 334–43; July 1948. 55. Schnell, Dorothy Maclary. Characteristics of Adolescence. Minneapolis, Minnesota: Burgess Publishing Company, 1947. 68 p. 56. Schulman, Mary Jean, and Havighurst, Robert J. “Relations Between Ability and Social Status in a Mid-Western Community. IV: Size of Vocabulary.” Journal of Educational Psychology 38: 437–42; November 1947. 57. Segel, David. Intellectual Abilities in the Adolescent Period. U. S. Office of Education Bulletin, 1948, No. 6. Washington, D. C: Superintendent of Documents, U. S. Government Printing Office, 1948. 41 p. 58. Shaw, Duane C. “A Study of the Relationships Between Thurstone Primary Mental Abilities and High School Achievement.” Journal of Educational Psychology 40: 239– 49; April 1949. 59. Shuey, Audrey M. “Improvement in Scores on the American Council Psychological Examination from Freshman to Senior Year.” Journal of Educational Psychology 39: 417–26; November 1948. 60. Sloan, William, and Harman, Harry H. “Constancy of I.Q. in Mental Defectives.” Pedagogical Seminary and Journal of Genetic Psychology 71: 177–85; December 1947. 61. Stern, Edith M. “Feeble-Minded Children Can Be Cured.” Woman’s Home Companion 74: 34–35; September 1947. 62. Swineford, Frances. “General, Verbal, and Spatial Bi-factors After Three Years.” Journal of Educational Psychology 40: 353–60; October 1949. 63. Swineford, Frances. “Growth in the General and Verbal Bi-factors from Grade VII to Grade IX.” Journal of Educational Psychology 38: 257–72; May 1947. 64. Swineford, Frances. “A Number Factor.” Journal of Educational Psychology 40: 157–67; March 1949. 65. Swineford, Frances. “A Study in Factor Analysis: The Nature of the General, Verbal, and Spatial Bi-Factors.” Supplementary Educational Monographs 67: 1–70; 1948. 66. Taylor, Calvin W. “A Factorial Study of Fluency in Writing.” Psychometrika 12: 239–62; December 1947. 67. Thorndike, Robert L. “Growth of Intelligence During Adolescence.” Pedagogical Seminary and Journal of Genetic Psychology 72: 11–15; September 1948. 68. Thorndike, Robert L. “Individual Differences.” Annual Review of Psychology 1: 87–104; 1950.

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69. Thurstone, Louis L., and Thurstone, Thelma G. SRA Primary Mental Abilities. Chicago: Science Research Associates, 1947. 70. Thurstone, Thelma G., and Thurstone, Louis L. SRA Verbal Form. Chicago: Science Research Associates, 1947. 71. Turner, William D. “Altruism and Its Measurement in Children.” Journal of Abnormal and Social Psychology 43: 502–16; October 1948. 72. Vernon, Philip E. “Changes in Abilities from 14 to 20 Years.” Advancement of Science 5: 138; July 1948. 73. Waelsch, Heinrich. “A Biochemical Consideration of Mental Deficiency. The Role of Glutamic Acid.” American Journal of Mental Deficiency 52: 305–13; April 1948. 74. Webber, Vera J., and Hunnicutt, Clarence W. “Children’s Ability to Perceive Change of Color in Painting.” Elementary School Journal 48: 494–97; May 1948. 75. Wheeler, Lester R., and Wheeler, Viola D. “The Relationship Between Reading Ability and Intelligence Among University Freshmen.” Journal of Educational Psychology 40: 230–38; April 1949. 76. Zimmerman, Frederick T. “The Glutamic Acid Treatment of Mental Retardation.” Quarterly Review of Psychiatry and Neurology 4: 263–69; October 1949. 77. Zimmerman, Frederick T., Burgemeister, Bessie B., and Putnam, Tracy J. “The Ceiling Effect of Glutamic Acid upon Intelligence in Children and in Adolescents.” American Journal of Psychiatry 104: 593–99; April 1948.

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Section II: Curriculum, Instruction and Learning

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22 Making Sense of Curriculum Evaluation: Continuities and Discontinuities in an Educational Idea David Hamilton

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t first encounter the reviewer’s task for this chapter appears impossible. Curriculum evaluation is a field which lacks a strong sense of boundary. The growing corpus of published and unpublished material tends to foster feelings of unease and incompetence rather than insight and optimism. The more secluded corners of the academic garden seem to offer a greater sense of security. Further reflection, however, indicates the shallowness of such withdrawal. Disengagement is neither a solution to the problems of the researcher nor an adequate representation of the process of intellectual inquiry. Curriculum evaluation – like any other educational activity – is guided by the accumulated experience (or inexperience) of its participants and focused by their individual or group aspirations. The purpose of this chapter is to examine the conventional, but often tacit, wisdom of curriculum evaluation. In short, it is an attempt to demystify the invisible college. Two broad strategies are open to a reviewer. The first is to adopt the style of a 19th-century anthropologist and set out, as it were, to unearth the totality of cultural artifacts embedded in a bygone age. Unfortunately, however, product-centered reviews of this type are often trapped by their own rhetoric. By claiming to provide an exhaustive account they also become impossible to complete. Posthumous or partial publication is their most conspicuous outcome. Source: Review of Research in Education, 5 (1977): 318–347.

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The second strategy is to focus upon the processes of research. Reviews of this type make no particular claim to catalog the myriad manifestations of an endeavor but rather seek to characterize the generative elements that help to create them. This chapter follows the second strategy. Its aim is to make sense of the present through an appraisal of the past. A historical perspective is believed to be a valid and useful heuristic for establishing the processes that activate curriculum evaluation. No claim is made that this account provides an allembracing history of evaluation. Indeed, to do so would be to switch to the encyclopedic stance of the erstwhile anthropologist. There is also a more profound sense in which this account cannot be complete. As a recurrent feature of educational life, curriculum evaluation necessarily prefigures a past that has yet to come. Thus, insofar as the interpretations of this paper are sensitive to the future as well as to the past, they must remain, in Cronbach’s cautionary phrase, “more provocative rather than authoritative” (1963, p. 672). Guided by these initial assumptions, this chapter is divided into four sections. The first part, “Some Perspectives for the Study of Curriculum Evaluation,” offers a set of conceptual prisms for differentiating the relatively unchanging features of curriculum evaluation. Primarily, its purpose is to delimit the concerns of this review. The second part, “The Origins of Curriculum Evaluation,” outlines the beliefs and practices which came to dominate evaluation research after World War II, arguing that many of these concerns had remained unaltered since the 19th century. The third part, “Curriculum Evaluation and the Image of Consensus,” considers the incorporation of these earlier ideas into the education reform movements of the 1960s and early 1970s. It focuses on the relationship between evaluation as course improvement and evaluation as social auditing. The fourth part, “Curriculum Evaluation and the Image of Pluralism,” documents and comments upon some of the contrary perspectives that have arisen along with the consensus assumptions of the 1960s. Accordingly, it suggests that recent developments reveal the existence of a major disjunction in both the theory and practice of curriculum evaluation. Overall, the aims of this chapter are to distill the ideas and events of the past 150 years and to provide a parsimonious yet comprehensive review of contemporary practice in curriculum evaluation.

Some Perspectives for the Study of Curriculum Evaluation The substance of this review is held together by a number of different unifying ideas. These relate to evaluation as a form of practical morality, evaluation and social change, evaluation and curriculum development, the internal and external dynamics of evaluation, the politics of evaluation, and evaluation and pluralism.

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The foremost assumption of this chapter is that curriculum evaluation falls within the sphere of practical morality. As such, it responds not only to the ethical question “What should we do?” but also to the empirical question “What can we do?” Traditionally, however, these two questions have been held at arm’s length by the educational research community. Value statements have been regarded as something quite different from factual statements. As Scriven (1974, p. 4) has noted, many of the debates surrounding curriculum evaluation have been created through the interpenetration of these hitherto separately considered concerns. The second assumption is that societal concern for evaluation is heightened, if not created, by the facts of social change. Evaluation is meaningless without the possibility or requirement of alternative courses of action. Almost by definition, social change engenders such options. Evaluative actions are as old as social life. They occur whenever there is a social setting and someone in a position to change it. Curriculum evaluation takes place at all levels in the education system. A kindergarten child’s decision to do math rather than painting is, in principle, just as much an evaluation as a superintendent’s decision to spend more money on science and less on the arts. In each of these examples, a choice is made by weighing the options against a set of criteria. These illustrations, however, indicate only part of the story. Throughout the history of schooling continuous attempts have been made to translate these informal decision processes into explicit rules and formal procedures. Governments have made evaluation compulsory; federal agencies have formulated guidelines; universities have trained evaluators; textbooks have supplied methodologies; journals have established accepted practices. And so on. Thus social change creates not only new options but also new traditions and institutions. The third assumption is that curriculum evaluation can be seen as functionally related to curriculum development. For instance, if curriculum developers attend to the production of instructional packages, evaluators seem to respond in analogous fashion (e.g., the “Product Evaluation Profile” in Scriven, 1974). If, however, development becomes the preparation of delivery systems, then procedures developed for the evaluation of packages may become devalued and inoperative. The fourth assumption is that curriculum evaluation has both an internal and external dynamic. It can be discussed, for instance, within the restricted concerns of program operation as well as within the broader boundaries of social policy. These overlapping realms of thought and action do not always operate in concert. There is always the possibility of disagreement among the various parties to the evaluation, such as researchers, sponsors, and audiences. A fifth assumption is that evaluation is directly linked to the distribution of resources in the education system. As such it is essentially a political process. The history of curriculum evaluation can be seen as part of the struggle by different interest groups – educationalists, teachers, administrators,

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industrialists – to gain control and exercise power over the forces that shape the practices of schooling. In these terms, a review of curriculum evaluation has also to be concerned with the distribution of power in the education system. The final assumption of this review relates to the idea of evaluation as a social process. As indicated above, evaluations conducted by individuals with respect to their own practice are usually based on a single set of criteria. If, however, more than one person is involved, the process takes on a completely different complexion. The participants may not agree upon the selection of criteria. Consensus can no longer be assumed. To this extent, value differences are crucial to the organization and enactment of educational change. They figure prominently in this review.

The Origins of Curriculum Evaluation An appropriate starting point is the work of John Stuart Mill (1806–1873). In the first half of the 19th century Mill laid down some of the most important ground rules of Western thought. Together with the ideas of colleagues and contemporaries like Bentham, Carlyle, Whewell, Herschel, Comte, and de Tocqueville, Mill’s notions have exerted a major, though often unrecognised, influence on 20th-century social philosophy. As a journal editor and member of parliament, and as a philosopher and economist, Mill was a pivotal figure in the linking of scientific practice to social administration. His writings – notably A System of Logic (1843), Principles of Political Economy (1848) and Utilitarianism (1861) – were both a defense and an elaboration of the liberal ideologies that took shape during the periods of revolutionary social change in North America and Western Europe. They were an “attempt . . . to embody and systematize” the “best ideas of the epoch” (A System of Logic, preface). Although more than a century has passed since Mill reached the height of his career, the issues he addressed are still a source of contention among theorists, administrators, and politicians. Mill’s impact on curriculum evaluation can be traced to three related concerns. First, he provided a coherent rationale for the conduct of the social sciences. Second, he developed a naturalistic (i.e., empirically rooted) theory of ethics. And third, he laid the philosophic foundations for what would now be termed the welfare state.

Science, Values, and State Intervention A System of Logic was published in eight editions during Mill’s life time. It was the first comprehensive formulation of the newly fashionable empirical method, and the “best attacked” book of the time (Nagel, 1950, p. xvii). In the final

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section (“On the Logic of the Moral Sciences”) Mill sought to rescue the “proper study of mankind” from what he regarded as the inadequacies of philosophy and theology. He emphasized that the moral (i.e., social) sciences should follow the same methods and strive for the same goals as the natural sciences. By this reasoning, Mill articulated what would now be called the scientific approach (Kerlinger, 1964) to the study of social phenomena. Mill’s utilitarian theory of ethics was directly related to his views on scientific and logical method. In its most general form utilitarianism embodies two assumptions: first, that principles of conduct can be adduced from the canons of experimental inquiry, and second, that social behavior can be unequivocably judged against an overarching (and “self-evident”) moral principle. These assumptions enabled Mill to measure morality against a one-dimensional ordinal scale. The “Greatest Happiness” can be established unequivocally by reference to what “competent judges” consider to be “desirable” (Utilitarianism, part 2). By adopting this form of moral yardstick Mill was able to overcome what would now be called the criterion problem in evaluation: There must be some standard by which to determine the goodness or badness, absolute or comparative, of ends or objects of desires. And whatever that standard is, there can be but one; for if there were several ultimate principles of conduct, the same conduct might be approved by one of these principles and condemned by another; and there will be needed some more general principle as umpire between them (A System of Logic, final chapter).

Mill’s views on the state also drew support from his moral precepts. He believed that in certain areas of social life (e.g., elementary education, the alleviation of poverty), the free-trade assumptions of laissez-faire government were contrary to the overall “Greatest Happiness” of society. As a result, Mill held that the welfare state should act as a counterbalancing force by supporting the charitable efforts of “private and voluntary agency” (Principles of Political Economy, Book 5, Chapter 11; there were seven editions during Mill’s lifetime). In 1859, the publication of Charles Darwin’s The Origin of Species gave Mill’s methodological and political ideas a fresh lease on life. The Darwinian precept that differences between members of the same species provide the mainspring of biological evolution gave a new impetus to the empirical study of human characteristics. Soon after Mill’s death in 1873, Francis Galton began a series of anthropometric and psychometric surveys which helped to establish not only a psychology of individual differences but also a new inferential calculus (correlational analysis) for the codification of empirical associations (see Hamilton, 1974). The United States first learned of Galton’s ideas – and those of his associate Karl Pearson – through the efforts of J. McKeen Cattell, who coined the term mental test in 1890, and

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Edward L. Thorndike, who used their ideas in the construction of achievement tests (see Joncich, 1968, pp. 290–293). Although Mill and Galton shared a common belief in the scientific methods of the 19th century, their political theories were mutually at variance. Mill stressed the shaping influence of environmental forces; Galton emphasized the primary importance of heredity. Nevertheless, the methodological unity of the two schools of thought meant that their ideas could be tested using the same equipment and procedures. A rash of social investigations in late 19th-century Britain were the outcome of this common concern. The crucial question was whether social assistance increased or decreased the self-help capacities of the urban poor. One of the most prominent investigators of the time was Charles Booth, a wealthy shipping magnate. Booth conducted a series of inquiries which were reported in the 17 volumes of Life and Labour of the People of London (1889–1903). Although he began his research by siding with Galton, Booth later came out in favor of state intervention in the affairs of the “helpless and incompetent” (Webb, 1926, pp. 260–261). Booth and his assistants used questionnaires, official census data and “personal (i.e., participant) observation” to document and portray the extent of poverty in London. Furthermore, part of their work focused on the preparation of a “social diagnosis” (or evaluation) of various “experiments” in poor-law relief. The influence of Booth’s work, like that of Galton, also spread to the United States, giving strong support to the settlement movements in Chicago and New York (see Cremin, 1961, chap. 3).

Pragmatism and Social Change in the United States By the end of the 19th century the social and economic forces which had made Britain the world’s leading industrial power began to stir more vigorously in the United States. In the wake of the Civil War, Darwin’s ideas – transposed to the realm of social evolution by Herbert Spencer – were the focus of long and vigorous debate. The dominant viewpoint was that “survival of the fittest” (Spencer’s term) should be retained, through laissez-faire government, as the most efficient mechanism for social improvement. Other commentators – notably the pragmatists – took a position that was close to Mill’s (William James’s Pragmatism was dedicated to J. S. Mill). They felt that Spencer’s evolutionist views were a one-sided interpretation of Darwinism and, moreover, a thinly disguised biological apology for the excesses of laissezfaire government. Above all, they rejected the assumption that the social environment was outside the realm of human control. If Spencer offered a philosophy of inevitability, the pragmatists replied with a vision of possibility (see Hofstadter, 1955, p. 103). In an era of rapid social change, characterized by such movements as accelerating urbanization, massive immigration, economic boom and bust, and labor unrest, they put forward proposals

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which could serve to coordinate the disparate elements of an ungainly social system. Education rather than competition was advocated as the most effective instrument of social improvement (Feinberg, 1973). The major architect of practical pragmatism was John Dewey. Like Mill, Dewey addressed a wide range of concerns in the realms of social science, ethics, and government policy. Briefly, he believed that logic could be redefined as the theory of inquiry, that moral knowledge was a species of empirical knowledge, and that social life could be enhanced through the use of a political technology (see White, 1972, p. 277 ff ). During his stay at the University of Chicago (1896–1904), Dewey not only founded the Laboratory School (1896) but also developed his education theories within a new philosophical and psychological framework. For instance, in his 1899 presidential address to the American Psychological Association (“Psychology and Social Practice,” 1900) Dewey argued for a “fuller” understanding of the relationship between the “new education” and the elements of “psycho-physical mechanism.” Through its new-found knowledge, psychology provided education with a “statement of mechanism” through which “ethical ends” could be “realized” (p. 121): . . . the more thorough-going and complete the mechanical and causal statement, the more controlled, the more economical are the discovery and realization of human aims. It is not in spite of, nor in neglect of, but because of the mechanical statement that human activity has been freed, and made effective in thousands of new practical directions, upon a scale and with a certainty hitherto undreamed of. (p. 118)

Dewey’s rhetoric resonated not only with the aspirations of an emerging industrial society but also with the debates taking place inside education, such as the NEA’s “Committee of Ten” on colleges and secondary schools, initiated in 1892, and the “Committee of Fifteen” on elementary education, initiated in 1893. The established curriculum of the 19th-century secondary school was based on faculty psychology and the related concept of transfer of training. By the last decade of the century faculty psychology was forced onto the defensive (see Krug, 1969, chaps. 1 & 2). Criticism came from two sides. Psychologists such as Thorndike and Woodworth, at the conference presided over by John Dewey, claimed that there was limited experimental evidence for transfer of training from the old disciplines such as Latin to the new ones. The other attack came from those inside and outside education who wanted the curriculum to respond more adequately to the social efficiency movements gaining ground in the spheres of industrial and administrative life.

Evaluation and the Cult of Efficiency In due course, the education system responded to these concerns with a welter of innovations. Examinations began to replace school accreditation as

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a means of selecting students for college (the College Entrance Examination Board, for example, was founded in 1900). Individualization became a key concept of school theory and practice (Joncich, 1968, p. 311); age-grade statistics were collected to measure the productive quality of school systems (Tyack, 1974, p. 199 ff.); mental tests were used to categorize school children (Karier, 1973, p. 115); and industrial and vocational schooling came into greater prominence (Lazerson, 1971, chaps. 5–7). The net result of these developments was the centralization of education, locally and nationally, and the growth of an administrative and managerial elite – many of whom were recruited from expanding graduate institutions like Teachers College, Columbia, the University of Chicago, and Stanford University (Joncich, pp. 216–231; Tyack, pp. 182–198). The rallying cry and self-justification of these “administrative progressives” (Tyack’s term) was that streamlined efficiency would be achieved in all spheres of education through a more rigorous application of the scientific method. As the new century grew a little older, school superintendents began to see themselves more as business executives than as scholars or statesmen (Callahan, 1962, pp. 7–8). The practical consequences of this trend took various forms. For instance, the National Education Association appointed four committees between 1904 and 1911 to study the classification and progress of children; E. L. Thorndike published his first achievement scale (on handwriting) in 1908; and New York City established a Bureau of Research in 1912 to conduct a continuous built-in survey of the school system, using “the new measurement techniques” (Seguel, 1966, p. 75). In turn, the administration of education shifted from a rural model of lay community control toward one that stressed professional training and bureaucratic expertise (see Tyack, 1974, passim). The control of the school curriculum underwent a similar change. In the late 19th century curriculum and pedagogical decisions rested with two agencies: local school boards and college accreditation committees, initiated by the University of Michigan in 1871. By 1910, the impact of the reform movement in schooling meant that the school curriculum fell increasingly under the influence of the business ethic. It spoke for the captains of industry, and their lieutenants, the superintendents, and not for the belowdecks personnel such as parents, teachers, and students. A further wave of support for the scientific movement followed the publication of F. W. Taylor’s The Principles of Scientific Management (1911). Taylor’s ideas were brought to the attention of educational administrators through The Supervision of City Schools, The 12th Yearbook of the National Society for the Study of Education (1913). The yearbook’s editor was Franklin Bobbitt, who in later years began to focus more specifically on the organization of school subjects (e.g., The Curriculum, 1918; and How to Make a Curriculum, 1924). Bobbitt’s interests were shared by W. W. Charters (e.g., Curriculum Construction, 1923), who, unlike Bobbitt, came to curriculum design from an interest in teaching rather than administration.

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Bobbitt and Charters developed a conveyor-belt system of curriculum making. They believed, following Taylor, that educational efficiency could be increased through a detailed analysis of the skills a child must acquire to become a socially mature adult. Further, they held that educational goals could be established by reference to “common aims” (Bobbitt, 1913, quoted in Seguel, 1966, p. 99) rather than to the concerns of any particular interest group such as principals or teachers. By this appeal, Bobbitt and Charters were able to unite teachers and administrators in a common technological task – the facilitation of effective schooling. In one respect, therefore, they were successful in taking politics out of the curriculum. In another respect, they also enhanced the value-neutral image of research. Evaluation of a scholastic activity could be regarded as a technical achievement equivalent to the evaluation of a mathematical expression. Within the rhetoric of educational efficiency, the main purpose of curriculum construction was to facilitate the production (or reproduction) of an ideal adult. For their curriculum blueprints, Bobbitt and Charters looked to the superintendents and teachers; for their quality control they looked to the growing measurement community.

Evaluation and Curriculum Design Both Bobbitt and Charters had connections with the University of Chicago. Charters had been a graduate student of John Dewey, and Bobbitt was to serve the university as a professor of educational administration from 1912 to 1941 (Jackson, 1975). In 1919 Charters became director of a research bureau for retail training at the Carnegie Institute of Technology in Pittsburgh. The strong vocational concerns of the bureau (and the availability of federal grants for trade and industrial training under the Smith-Hughes Act, 1917) allowed Charters to extend his earlier pedagogic enquiries toward the analysis of adult occupations and the construction of suitably related curricula and teaching methods. After a second period at Chicago in the 1920s, Charters moved to the directorship of the Bureau of Educational Research at Ohio State University. While at Ohio State, Charters shared his job analysis interests with Ralph Tyler, a former doctoral student at the University of Chicago. Tyler’s crucial contribution to the work of the bureau derived from his graduate training. Unlike many earlier researchers, he combined expertise in both testing theory (the responsibility of psychologists) and curriculum construction (the responsibility of administrators and teachers). The title of Tyler’s 1927 doctoral thesis – “Statistical Methods for Evaluating Teacher-Training Curricula” – symbolizes this unification. Tyler’s complementary interests were successfully combined when he became research director of the Committee on Evaluation and Recording

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of the Eight-Year Study (1932–40). This was a curriculum experiment commissioned at the height of the Great Depression by the Progressive Education Association (honorary president, John Dewey) and supported by private funds from the Carnegie Foundation and the Rockefeller-initiated General Education Board. Over 300 colleges agreed with the PEA to relax their formal entrance requirements, and 29 experimental schools reciprocated by redesigning their curricula along “progressive” lines. Tyler’s evaluation rationale was that an “appraisal of an educational institution is fundamentally only the process by which we find out how far the objectives of the institution are being realized” (Tyler, in Smith & Tyler, 1942, p. 5). The first half of the study followed this “objectives” model. The evaluation staff was “primarily concerned with developing means by which the achievement of students in the schools could be appraised” (p. 5). The second half used a different rationale. The relative merits of progressive and traditional courses were adduced by means of a comparative design. The college careers of the participating students were compared with those of a matched sample of 1,475 nonexperimental students. To practising educationalists the apparently favorable results of the Eight-Year Study indicated the efficacy of progressive methods. To the research community, they signaled the emergence of a sophisticated paradigm for the design and evaluation of school and college curricula (Tyler, 1949). Tyler’s specific contributions to the Eight-Year Study reflected both his training and his experience. As a colleague of Charters, he placed high priority on the analysis of curriculum goals and activities (see Charters, 1926). As a recently trained psychologist, he argued that objectives should be prespecified in behavioral terms (see Anderson, 1975, p. 143). And as a witness to the industrial collapse of the depression he held to a much broader conception of education – one that included affective as well as cognitive and vocational components (see Cremin, 1961, chap. 7; Smith & Tyler, 1942). After World War II the public success of the Eight-Year Study stimulated other cooperative investigations of a similar kind, such as the American Council on Education Project on Evaluation in General Education (McKim, 1957; Taylor & Cowley, 1972). Tyler’s concern for schoolwide behavioral objectives received a fresh boost in 1956 with the publication of Handbook 1 of the Taxonomy of Educational Objectives (Bloom, 1956). This short but seminal work was the result of a seven-year collaborative project set up in 1949 by two of Tyler’s co-workers: J. Thomas Hastings, of the University of Illinois, and Benjamin S. Bloom, of the University of Chicago. In a sense the encyclopedic efforts of Bloom and his colleagues marked both a beginning and an end. Their work had an air of completeness and finality about it. Yet, in combination with the postwar growth of factor analysis (Thurstone & Thurstone, 1941), psychometric theory (Lindquist, 1951), and experimental design (Stanley, 1966), it also offered an awesome prospect for the future.

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This section has sketched some of the recurrent themes and precipitating events that accompanied educational change and evaluation in the period prior to the mid-1950s. At the risk of underestimating the influence of counter-currents, it has argued that the dominant ideas of the day were translated into educational terms by John Dewey, fostered by private and state investment in education, operationalized (not always with Dewey’s approval) by Thorndike, Bobbitt, and Charters, and reproduced by the generations of administrators and professors who passed through the portals of Teachers College, Chicago, Stanford, and elsewhere. The ideas of the founding fathers were clearly articulated and efficiently disseminated. Relatively little, however, is known about the translation of their prescriptions into the realm of classroom practice. Given the “lack” of “empirical studies on the conduct of evaluation during this period” (Lortie, 1970, p. 155), the stipulation remains clearer than the deed. It is also probably true, though perhaps a little unjust, that the founding fathers are better remembered by their technologies than by their aspirations. In these terms, the new wave of educational evaluators created in the late 1950s and 1960s had much to contend with. The groundwork had been done; a plateau had been reached (see Taylor & Cowley, 1972, p. 1). In some senses, then, the history of evaluation had drawn to a close. From another standpoint, however, it had hardly begun.

Curriculum Evaluation and the Image of Consensus The ethos of the Eight-Year Study carried through into the postwar years. But, according to Hagen and Thorndike (1960), there was a shift from a “research oriented attempt to develop new and better evaluation procedures to an action research oriented attempt to involve school personel in evaluating their own educational programs” (1960, p. 482, emphasis in original; see Smith & Tyler, 1942, p. 30). In time, however, this new “school of thought” (Cronbach, 1963, p. 674) came under sharp attack. It was claimed for instance that general academic standards were being eroded and, more specifically, that the average college entrant’s performance had begun to decline. From a vocational perspective, it was also claimed that colleges were failing to fill the demand for scientific personnel in industry (Cremin, 1961, chap. 9). The response to this “crisis in popular schooling” (Cremin’s phase) was slow but sure. In 1951, for example, the University of Illinois Committee on School Mathematics received funds from the Carnegie Corporation to enable faculty members to give guidance to high school teachers. Similar stirrings occurred within other specialized fields (Goodlad, 1966). In the process, curriculum development shifted away from the techniques of course construction toward a concern for the substance of course content. The new curriculum mandarins were drawn from the ranks of subject specialists, not management technologists or educational psychologists.

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This emphasis on discipline-centered curriculum reform eventually received official and financial recognition from Congress (Hurd, 1969, p. 14). In 1958 the sputnik-prompted National Defense Education Act released federal funds to the National Science Foundation for the improvement of science, mathematics, and (in part) social science curricula.

Evaluation and Course Improvement The circumscribed nature of curriculum development’s funding and the materials-based character of its commitments meant that it gradually became synonymous with program development. Task forces of specialists were convened to produce packages of ideas and procedures which, if required, could be transmitted intact to the farthest corners of the school system. At first the overall merit of the revised curricula was taken to be self-evident. Evaluation remained an informal iterative process directed toward course improvement and conducted by members of the subject team in association with teachers in trial schools. Command of the endeavor remained in the hands of subject specialists. The superior intellectual prestige of the pure sciences enabled the developers to be a self-policing, self-evaluating community. Curriculum projects tended to reject or ignore the conventional wisdom of the evaluation traditions that had flowered in the 1930s (Atkin, 1963). In the early 1960s evaluation issues began to be raised more sharply as the earlier curriculum projects penetrated through the school system. A typical complaint was that the new schemes had no visible impact (Goodlad, 1968; Provus, 1971, Chap. 1). In this respect, doubt was cast upon the pedagogical rather than the intellectual viability of the programs. Merit could no longer be taken for granted; it had to be made manifest. Insignificant or equivocal results made curriculum developers more willing to enlist the support of ideas from within the realms of behavioral research (see Weir’s account of the Biological Science Curriculum Study project, 1976). At that time the educational research community was dominated by psychologists trained in the experimental or individual-differences traditions. Not surprisingly, therefore, these traditions began providing the basic blueprints used in curriculum evaluation. Concern about the visibility of programs also influenced the organization of curriculum research: Evaluation gradually became a specialist activity.

Evaluation and Social Auditing In 1965 the emergent evaluation community received a boost when continued financial support under Title 1 and Title 3 of the Elementary and Secondary Educational Act was made contingent upon submission of evaluation reports

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by local program operators. With this administrative device, consolidated and extended in subsequent legislation, curriculum evaluation took a new turn. Its major concern ceased to be course improvement and became instead educational auditing. The range of objectives to be scrutinized was much more limited than the “comprehensive” range of objectives envisaged by Tyler in Basic Principles of Curriculum and Instruction (1949, p. 5 ff.). At the same time, de facto control of the curriculum was taken out of the hands of subject specialists and located closer to the heart of the federal administration, itself undergoing a period of reappraisal. In the early 1960s, Secretary of Defense Robert McNamara, newly recruited from the Ford Motor Company, introduced a form of evaluation termed Planning, Programming, Budgeting System into the decision-making processes of his own department. The essential feature of this innovation was that it shifted the basis of decision making from input to output budgeting – that is, from indices such as class size to measurements such as pupil performance. In 1965 this type of cost-effective appraisal was extended to all federal departments and agencies (Williams & Evans, 1969). Until that time most federal and local educational agencies had evaluated the results of their endeavors using the same internally organized procedures as curriculum developers used. Both groups saw their primary task as the production of a visible program; every dollar spent on evaluation was a dollar lost to program development (McDill, McDill, & Sprehe, 1972, p. 148). As suggested above, the legislation of the mid-1960s foreshadowed a rapid growth of administrative involvement in evaluation. This interest was particularly evident with respect to poverty programs funded under the Economic Opportunity Act of 1964 and with respect to the growth of statewide accountability schemes and the nationwide Program for the Assessment of Educational Progress. From its inception, the Office of Economic Opportunity (OEO) contained a section for Research Plans, Programs and Evaluation (Glennan, 1972, p. 188). The evaluation efforts of this section during its early years were directed toward servicing the requests of program participants and consumers. No attempts were made to question the existence of any given program. In 1966 (presumably in response to the introduction of PPBS) the Research Plans, Programs, and Evaluation (RPP&E) office began a series of program evaluations. These studies were precipitated by an internal request for evidence which could assist with decisions over the alteration, curtailment, or discontinuation of programs. Whatever its espoused intention, this request had the effect of luring the evaluators’ allegiances from the concerns of the program teams to those of the program sponsors. In effect, the evaluation agency was charged with the task of eliciting visible results which could be displayed in the company prospectus and itemized in the annual balance sheet. In such a climate, it is not surprising that evaluators became more concerned with visible products than inferred processes.

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These social auditing concerns were formalized in an OEO “instruction” of March 1968 which established a major component of evaluation as “determining the extent to which programs are successful in achieving basic objectives” (Glennan, 1972, p. 189). Specific responsibility for this evaluation strategy was invested in RPP&E, which was established as a separate division of OEO in 1967. The separation between outcome evaluation and program development was also formalized in a decision that RPP&E should automatically receive a small proportion (0.16%) of any program budget (Glennan, 1972, p. 190). This division of labor soon provoked its own contradiction. When asked to design an evaluation of Head Start, the RPP&E evaluators, who needed data for decision making, came into conflict with program staff, who believed the evaluators’ proposals were too narrow. After “much internal debate” the director of OEO “ordered” the study. A contract was made with the Westinghouse Learning Corporation and Ohio University in June 1968. Eight months later (i.e., prior to the completion of this study), former President Richard Nixon’s economic opportunity message to Congress revealed that the long-term effect of Head Start appeared to be “extremely weak” (Williams & Evans, 1969, p. 124). This statement provoked a storm of controversy. In its wake, the population at large became more aware that social scientists were divided among themselves as to the implications of the Westinghouse-Ohio results. Along with controversies about the relationship between schooling and educational achievement (Coleman, Campbell, Hobson, McPartland, Mood, Weinfeld, & York, 1966; Jensen, 1969), the Head Start investigation did little to validate the activities of professional evaluators. Nevertheless, as Nixon’s statement suggests, the research community’s power to legitimate political decision making remained as strong as ever. Thus, despite its evident technical shortcomings, the Ohio-Westinghouse study increased rather than decreased the attention focused on curriculum evaluation. As the Vietnam War drew to an end and no new revenue was made available for federal spending (see Glazer, 1973), evaluation began to serve more sharply in an auditing function. At a time of economic stagnation it became an agent of program contraction (or rationalization) rather than a patron of program promotion.

Monitoring the Curriculum Statewide accountability schemes were a further instance of centralized monitoring of the curriculum. Educational accountability, as it developed in the 1970s (see Sciara & Jantz, 1972), rested upon the logic that educational processes – rather like productive mechanisms – can be broken down into their constituent parts and specified in terms of operational criteria. Uniform

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and heightened efficiency was taken to be the end which justifies the means. As in the days of Bobbitt and Charters, educational technology of this kind had strong links with business management and centralized control. Just as the earlier generation appealed to the division of labor, so their descendants utilized the language of cybernetics. Assistant Commissioner of Education Leon M. Lessinger’s references in 1970 to logistics, systems analysis and human factors engineering are examples (see Popkewitz & Wehlage, 1973, p. 49). The National Assessment of Education Progress (NAEP) took shape in the mid-1960s. In 1965 an exploratory committee (under the chairmanship of Ralph Tyler, and supported by the Carnegie Corporation and the Ford Foundation) set out to prepare educational objectives and assessment procedures which would embrace the entire school curriculum (Flanagan, 1969, p. 223). The goal of the NAEP was to develop indices of educational output, like economic indices such as the gross national product, which might serve as a basis for social planning. The first results of NAEP were announced in 1970. They had been generated by a national sample of about 100,000 children and adults who had responded to test items drawn from 460 exercises in science, citizenship, and writing. A significant feature of the NAEP is that its indices do not refer to particular students, classes, schools, or school systems, but rather to the overall (aggregate) attainment of a large number of people. To this extent, the information directly serves the generalized interests of administrative bureaucracies, not the specific concerns of students, teachers, or schools. As with the utilitarian theory of J. S. Mill, the needs of the system are held to be congruent with the needs of the individual (Britton, 1969, p. 53).

Educational Research and Curriculum Evaluation Confronted with the problems and opportunities offered by these wider developments, the educational research community reacted with not unsurprising speed. In 1964 (i.e., before evaluation was mandated by the Elementary and Secondary Education Act) L. J. Cronbach, then the president of the American Educational Research Association, appointed an ad hoc committee to study the contribution the association could make to the growing interest in evaluation. A year later, President Benjamin S. Bloom commissioned a committee to develop evaluation guidelines and model procedures. The following year’s committee (extended by President Julian S. Stanley) rejected Bloom’s concerns in favor of a more eclectic stance. One of the outcomes of these deliberations was the AERA Monograph Series on Curriculum Evaluation, seven volumes of which were published between 1967 and 1974 (see Stake, 1967b, pp. 8–12). The Monograph Series bears witness to the diversity of opinions expressed within the research community. In the early days there were two competing

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schools of evaluation thought. On the one hand there were those (like Cronbach) who argued in favor of a modified Tylerian rationale (see Tyler, 1949), whereby a study is made of the “post-course performance of a well described group with respect to many important objectives and side effects” (Cronbach, 1963, p. 676). On the other hand, there were those (like Stanley) who advocated comparative studies which used control and treatment groups (see Campbell & Stanley, 1963). Although the experimental model had the most persuasive scientific appeal, few studies of the new curricula achieved the required levels of randomization and control. According to Welch and Walberg (1974), only 4 out of 46 government-sponsored course development projects had used “true” experiments in their evaluation strategies by 1969 (p. 113). Nevertheless, the comparative assumptions of the experimental paradigm still served to underpin the “two most frequently used models in large-scale program evaluation” (Light & Smith, 1970, p. 9). The first of these models – post hoc quasiexperimentation, as in the Head Start evaluation – establishes experimental and control groups after the treatment has been applied. The second model – post hoc sample surveying, as in the Coleman report (1966) – relies on a large data base which, because of its size and variability, can be subsequently analyzed to account for the various designated treatments.

Comparative versus Tylerian Rationales Objections to the comparative model (Cronbach, 1963; Guba, 1969) rested on the argument that it was both technically and philosophically inappropriate to the nature of curriculum evaluation. Cronbach and Guba, for instance, maintained, like J. S. Mill, that group comparisons may give equivocal results if more than one variable is studied (i.e., the control group may appear superior on one variable, the experimental group on another). Other critics (e.g., Walker & Schaffarzick, 1974) argued against comparative evaluation designs on the grounds that the new curricula set their own goals and standards. In principle, Tylerian evaluation models avoid these problems: The innovative curriculum is measured against agreed internal standards, not against the results achieved by another (possibly nonequivalent) program. Scriven identified some of the epistemological weaknesses of the Tylerian and comparative rationales in the first AERA Monograph (Scriven, 1967). The Tylerian approach does not solve the comparison problem since, as in the Eight-Year Study, curriculum objectives are always established by reference to (or in reaction to) the objectives and achievements of other programs and sets of standards. In effect, Scriven demonstrated that the question “Does it meet the standards laid down by program staff?” is, in principle, no different from the question “Is it better than Brand X?” Scriven

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also reiterated the argument that two-group comparative designs give ambiguous results in that there is no intrinsic mechanism for separating the impact of the actual treatment from that of the associated Hawthorne and John Henry effects (p. 68). By analyzing the complementary weaknesses of the preeminent rationales, Scriven was able to outline some possible solutions. First, he maintained that the opinion of subject specialists should count more heavily in the validation of Tylerian objectives and criteria. Second, he suggested that simple designs based on experimental versus control groups should be replaced by designs with more than one experimental group. Finally, Scriven offered a solution to J. S. Mill’s multiple-criterion problem by arguing that individual criteria could be differentially weighted and then combined to form a single criterion measure. Scriven’s theoretical appraisal of the criterion problem was both elegant and appropriate. In 1970, however, Glass claimed that the practical implementation of Scriven’s solution required “evaluation techniques still not discovered” (p. 23). Glass examined possible procedures (such as minimax techniques) but was unable to devise a further technique for choosing among them. He concluded that “human judgment” (p. 29) was the only valid arbiter. Although the science of human conduct had come a long way since the days of John Stuart Mill, it continued to run a ground on the shifting sands of human values. Confronted by such a tangle of epistemological, empirical, and statistical problems (Lord, 1967; Campbell & Erlebacher, 1970), certain educationists began to look for evaluation models beyond the conventional boundaries of postwar educational research. Among the more successful forays have been those into the realms of management theory, literary criticism, jurisprudence, and consumer science.

Evaluation and Management Theory Management models for evaluation (Provus, 1969; Rippey, 1973; Stufflebeam, Foley, Gephart, Guba, Hammond, Merriman & Provus, 1971) are program, organization, or system centered. They take as their basic aim the improvement of rational decision making. Evaluations are designed to reduce “institutional conflict” (Rippey, p. 14), to “facilitate quality control and improvement” (Stufflebeam et al., p. 217), or to “determine whether to improve, maintain or terminate a program” (Provus, p. 245). Although their methodologies may vary, the data and performance criteria of management models relate to the “total system” (Stufflebeam et al., 1971, p. 238) rather than to individual pupils or teachers. As such, they reflect the aspirations of personnel with programwide responsibility, not the immediate concerns of classroom practitioners.

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Management-oriented evaluation models hark back to Bobbitt’s writings on the administration of school systems. Bureaucratic (i.e., management) efficiency tends to be blended with educational efficiency. In Provus’ revealing formulations, the evaluator is like a “management engineer” (p. 245), and the evaluation functions as a “watchdog of program management” (p. 260).

Evaluation and Literary Criticism The influence of literary criticism as a role model for evaluation (Eisner, 1972; Kelly, 1971) also grew out of dissatisfaction with existing paradigms. Evaluations of this kind – a “supplement to the use of scientific procedures” (Eisner, 1975) – draw upon an artistic tradition of “connoisseurship and criticism.” They incorporate ways of seeing rather than ways of measuring. The evaluator (or “critic”) aims to sensitize the individual practitioner (or reader) by “rendering” an account of the program, using the “vehicles” of “suggestion, simile, and metaphor” (Eisner, 1972, p. 586). Nevertheless, despite these important methodological differences, Eisner’s “new” approach was, in essence, just as much an abstruse technology (with specialist training, journals, books, studentships) as the procedures it sought to supplement (see Eisner, 1975). This is not altogether surprising, since Eisner’s concern for “judgment . . . grounded in reasons” harks back to Dewey, whom he quotes approvingly, just as Cooley and Lohnes’ (1976) call, equally Dewey-inspired, for evaluation research that is “multi-variate, large-sample and longitudinal” (p. 5). Eisner also solves the criterion problem in a way that is similar to the manner of Cooley and Lohnes. Judgments are established externally – by reference to prior “human needs” (Cooley & Lohnes, p. 13) or values derived from “tradition and habit” and “the nature of artistic virtue” (Eisner, 1975).

Evaluation and Jurisprudence Legal or adversary models for evaluation (Kourilsky, 1973a; Levine, 1973b; Wolf, 1974) use the notion that courts of law have well-established principles of procedure which can be used to regulate and administer the processes of decision making. They can be seen as an attempt by the evaluation community to institutionalize the kind of debates that typically occur following the publication of an evaluation report. The most significant theoretical feature of these models is that they legitimate the existence of discrepant accounts presented by advocates and adversaries. Different models, however, embody different concepts of decision making. Kourilsky (1973b) saw the “goal” of adversary evaluation as the generation of “properly informed” decisions, whereas Levine (1973a) regarded the adversary model simply as a means of conducting debates about educational programs. Kourilsky focused on the “technology” of decision making, such as “selecting appropriate information”

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(1973a, p. 4), or “empanelling jurors” (see Wolf, 1974, preface). Levine emphasized the “politics of decision making” (1973b, p. 8; Levine, 1974). As shown below, such a distinction is crucial to this review.

Evaluation and Consumer Science Consumer science provides a model for evaluation in cases where the curriculum is studied in terms of its value to the user, rather than in terms of the intentions (or goals) of the producer. Consumption is the ultimate criterion, not production. Evaluations of this kind examine payoffs rather than precepts. By comparison with the Tylerian rationale, the “actual effects” of the program are given priority over its objectives or “alleged effects” (Scriven, 1972b, p. 2, emphasis in original). In these instances the judgmental criteria are not prespecified by the curriculum developer. They are applied post hoc by the evaluator who uses external “standards of merit” derived from “the needs of the nation” (Scriven, 1972b, p. 2). Scriven (1972b) coined the term Goal Free Evaluation to describe this type of study. However, its ancestry stretches back through the evaluation of broad-aim programs of social action (see Weiss & Rein, 1969) to the social diagnoses conducted by Booth and others in the 19th century (see Caro, 1971).

Evaluation and the Problem of Consensus Despite superficial differences, the evaluation models discussed in this section share a number of attributes. Each one draws upon a consensual image of social life. They assume that, in principle, the goals of a curriculum and the criteria for its success can be agreed upon. Their credibility rests on the stability of this assumption. In practice, consensus is usually arrived at by allowing surrogate interest groups, such as the evaluation community, to speak for the “welfare of society as a whole” (Scriven, 1967, p. 81). Whether consent is in fact assumed or established will vary from case to case. In most instances, however, there seems to be a tendency for course developers and evaluators to play a particularly strong role. The Tylerian tradition, for example, relies heavily upon the “curriculum maker” for its objectives (Tyler, 1949, passim; Stake, 1970, p. 187). Likewise, Scriven’s comparative evaluation model uses criteria validated by “highly qualified experts” and “professionally competent evaluators” (1967, pp. 58, 53). The national assessment program follows a similar pattern. It utilizes objectives identified by committees of “subject matter specialists” sprinkled with “thoughtful lay-persons” (Merwin & Womer, 1969, p. 315). Management systems approaches also subscribe to a similar view of consensus. Responsibility for defining performance criteria is

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delegated to “skilled operating personnel” (Rippey, 1973, p. 13) or to the “program manager” (Provus, 1969, p. 251). Even the literary criticism, legal, and consumer models take an equivalent stance. Eisner, for instance, looks to “connoisseurship” for his ultimate criteria; decision-oriented legal models are suffused with the consensus image of unanimous verdicts (see Wolf, 1974, p. 62 ff.); and goal-free evaluation allows the evaluator to infer the “goals of the consumer or the funding agency” (Scriven, 1972b, p. 2). Given the assumption of goal consensus, the implementation of an evaluation rationale hinges upon the comparison of various means to achieve such ends. From John Stuart Mill and John Dewey, to Ralph Tyler and Michael Scriven, the possibility of realizing a theory of evaluation rested upon this assumption. For them, the dualistic separation of fact and value is incorrect and unacceptable. From Mill’s “Greatest Happiness” principle to Scriven’s “system of principles aimed at maximizing long-run social utility” (1967, p. 81), the assumptions and logic have remained essentially the same. Throughout, this vision of consensus has been well formulated, overtly rational, and immensely powerful. All the evaluation models discussed in this section stress the importance of agreement about objectives and/or criteria. As a consequence, they tend to play down the possibility that criteria might be mutually exclusive. This does not mean, of course, that they ignore areas of antagonism – merely that they regard them as potentially or pragmatically resolvable. Although Scriven, for instance, has acknowledged that different individuals may have an “opposite preference” (1972b, p. 2), his main thrust has been that evaluators should focus preferentially upon areas of agreement (1972a, p. 84). Stufflebeam et al. (1971) made a similar point. They proposed that the decision maker should go into the “value web of the larger world only as far as necessary to find a common value among his constructs” (p. 116). Both these strategies presume that values which are shared are more significant than discrepant values. There is no logical reason why this should be the case. Such a presumption may offer an expedient solution to the criterion problem, but it has difficulty in resolving the prior value question, Who decides that consensus has been achieved? As this suggests, consensus models tend to be justified by appeals to representative democracy, yet, as in the days of J. S. Mill, it is still not clear in each case whether everyone has achieved the right to vote or to sit on the jury panel.

Curriculum Evaluation and the Image of Pluralism Just as Herbert Spencer raised objections to Mill’s social theories, and various contemporaries of Bobbitt and Charters expressed concern with the curriculum-building model (e.g., Rugg, 1931), so certain commentators

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articulated doubts about the consensus assumptions of recent evaluation theories. Ideas about pluralism and politics were brought to the forefront. A connection between consensus and politics was clearly identified by the early critics. For instance, in a review of “Research Styles in Science Education,” Atkin (1967–68) noted that one of the “major shortcomings” of the systems model of curriculum development was its reluctance to “recognize the competition among diverse value systems and power groups” (p. 341). Around the same time Stake’s “The Countenance of Evaluation” (1967a) paper included the pluralist argument that “part of the responsibility of evaluation is to make known which standards are held by whom” (p. 535). In retrospect, developments of this kind can be seen as a turning point in the recent history of evaluation. Atkin and Stake (both at the University of Illinois) were accepted leaders in, respectively, the fields of curriculum development and curriculum evaluation. It was as if the winners of the tournament had suddenly begun to question the rules that had made them victorious. The rediscovery of values was also fueled by Scriven’s contemporaneous argument that “evaluation proper” must also include the “evaluation of goals.” The idea that goal evaluation should be an “equal partner with the measuring of performance against goals” implied a radical shift of concern (Scriven, 1967, p. 52). Within Scriven’s rationale, evaluation was not merely “the process of determining to what extent the educational objectives are being realized” (Tyler, 1949, p. 105); it also included the post hoc scrutiny of the prespecified objectives.

Value Analysis and Value Pluralism The possibilities of value analysis and value pluralism opened up new perspectives and new problems for curriculum evaluation. The major premise that evaluation is the ascription of worth with reference to a given set of standards was joined by a new assumption: that a uniformity of standards may not be attainable in social situations. The introduction of this second assumption nullified the conventional wisdom of earlier theory. The notion of evaluator as “watchdog” became difficult to advocate, since its flavor was too reminiscent of autocratic, hired-hand research. Likewise, evaluators could no longer claim to provide categorical answers acceptable to all parties or to furnish prepackaged instruments suitable for every occasion. A longestablished technology became unwieldy, if not unsafe. In the search for more “democratic” models (see MacDonald, 1976) certain evaluators sought to redefine the evaluation problem, renegotiate their role, and reformulate their strategies for information gathering and data analysis. The new perspectives tended to acknowledge that evaluation is as much a sociopolitical as a methodological process (e.g., “The Process

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and Ideology of Valuing in Educational Settings,” Apple, 1974; School Evaluation: The Politics and the Process, House, 1973; “Racism and Educational Evaluation,” Jenkins, Kemmis, MacDonald, & Verma, 1977; and “Politics, Ethics and Ideology,” Sjoberg, 1975). At the present time, evaluation models with a pluralistic concern are still relatively limited in their impact. They occupy either an interstitial or a subordinate status in the education system. In the former case they tend to be employed (and funded) where Tylerian and experimental models are empirically, financially, or politically less attractive – as in studies of alternative schools (Black & Geiser, 1971), programs in aesthetic education (Stake, 1975), extracurricular activities (Stake & Gjerde, 1974) and multicultural projects such as the Teacher Corps (Fox, 1976). The second context for the utilization of pluralist models is as a complement to Tylerian or experimental designs, as in the evaluation of Home Start (Love, Nauta, Coelen, Hewett, & Rupp, 1976) and the evaluation of a school-based computer-aided instruction curriculum (Smith & Pohland, 1974). In these latter cases, however, the pluralist assumptions were overshadowed by the consensus concerns of the dominant models. Despite a declared eclecticism, the conflicting priorities of consensus and pluralist models are rarely (if at all) resolved in a manner that honors the aspiration of all parties (see, e.g., Wehlage, 1976). In practical terms, pluralist evaluation models (Parlett & Hamilton, 1972; Patton, 1975; Stake, 1967a) can be characterized in the following manner. Compared with the classical models, they tend to be more extensive (not necessarily centered on numerical data), more naturalistic (based on program activity rather than program intent), and more adaptable (not constrained by experimental or preordinate designs). In turn they are likely to be sensitive to the different values of program participants, to endorse empirical methods which incorporate ethnographic fieldwork, to develop feedback materials which are couched in the natural language of the recipients, and to shift the locus of formal judgment from the evaluator to the participants.

Problems of Pluralism At first, such models were rarely self-conscious or explicit about their pluralism. They were more likely to emerge in isolation as a response to the methodological weaknesses of the traditional models. Through time, however, they began to develop an epistemological and logical identity of their own: a theory about evaluation rather than a theory of evaluation. This, in turn, raised a series of specific problems for pluralist practitioners. One such difficulty stemmed from the separation of program development from program evaluation. What role, for instance, can a pluralist evaluation play with regard to a Tylerian rationale which has as one of its “basic

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assumptions” that “an educational program is appraised by finding out how far the objectives of the program are actually being realized” (Tyler, in Smith & Tyler, 1942, p. 12)? The evaluation team would be predisposed to scrutinize the objectives of the program – something that the development team would consider to be illegitimate. How can such a tension be resolved? The possibility of practitioner disagreement over curriculum objectives also means that pluralist evaluations are likely to work from program practices to program goals. In effect, a figure-ground reversal takes place. As in the case of goal-free evaluation, the learning milieu is regarded as containing the substance of educational innovation, not, as is sometimes implied, its pale or distorted shadow. The program shapes the evaluation methodology, not vice versa. As noted above, this can create serious difficulties, since it is no longer possible to prescribe specific methodological procedures without a knowledge of the context in which they are to be used. The preparation of training programs for pluralist evaluators is vitiated by this problem. In what sense is it possible to talk about a methodology in the absence of a complementary theory of the situation? A third issue resulting from the adoption of pluralist evaluation models relates to the establishment of evaluative criteria. Given the unacceptability of standards unilaterally offered by “experts,” the problem facing the evaluator is not so much which criteria and how as it is whose criteria and why. A fourth issue hinges on the interpretation of pluralist. In the extreme, pluralist evaluation models could be taken to mean that all viewpoints are equally valid, and all interest groups are equally powerful. This position creates profound problems, since the very essence of evaluation is making statements about the relative merits of different perspectives. To espouse this cause is to be committed to a relativist, value-free evaluation (see NowellSmith, 1971).

A Pluralist Theory of Evaluation? Doubts about relativism lie at the heart of debates about pluralist evaluation: In what sense is a pluralist evaluation possible? Will objective data be honored? In what sense can a pluralist evaluation be fair to all parties? Whose logic will be followed? These questions were posed very sharply in “Justice and Evaluation” (House, 1976). Although critical of the utilitarian basis of consensus models, House noted the absence of an explicit pluralist theory of evaluation. His paper offered such a theory, using notions derived from A Theory of Justice (Rawls, 1971). If J. S. Mill claimed that “utility” should be the “first principle” of ethics, Rawls countered with the concept of “justice as fairness.” The value of a curriculum is not measured against its effectiveness, as with aggregate changes in test score, but against its fairness. Furthermore, individual persons

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are taken to be the basic focus of analysis, rather than social institutions such as “schools and colleges,” as in the Eight-Year Study (see Smith & Tyler, 1942, p. 5) or “geographic regions,” as in the National Assessment Educational Progress (see Education Commission of the States, 1974, p. 2). This last point allows for the fact that different individuals can pursue a “plurality of ends” (House, 1976, p. 97). Justice as fairness aspires to be the pluralist counterpart of utilitarianism. In these terms House confronted the problem of relativism by arguing that the viewpoint of the “least advantaged” (p. 84) should be given priority over the values of other groups. House had already tested these ideas in his evaluation of the Michigan Accountability System (House, Rivers, & Stufflebeam, 1974). This evaluation was commissioned by certain recipients of the scheme (the NEA), rather than by its originators (Michigan Department of Education). House acknowledged that his analysis “ignored” some of the “philosophic difficulties” (p. 98) surrounding the notion of justice as fairness (e.g., it presupposes the existence of a consensus in favor of pluralism). Nevertheless, his paper had the important distinction of forcing the relativism problem out into the open. Other models of pluralist evaluation have tried to resolve the relativist dilemma by stressing the information-gathering rather than the judgmentmaking aspects of evaluation. Still others have idealized the evaluator as a free-floating, independent intellectual. Both these positions (reviewed in House, 1976) are weak and unsatisfactory. At root they embody a kind of concealed consensus. The former comes very close to the hired-hand role of management-oriented evaluation, and the latter, by its appeal to expertise, is highly reminiscent of elitist variants of the consensus model. One final and perhaps more coherent response to the problems of pluralist evaluation has been to hand over responsibility for the control of an evaluation to those who have to live with its consequences. Evaluation is conducted by the participants rather than for the participants. Models of this kind, such as Black & Geiser’s (1971) notion of “peer research,” and Scheyer & Stake’s (1976) image of a “self-evaluation portfolio,” undermine the de facto hierarchical and bureaucratic processes of consensus evaluation. They respond to an alternative conception of accountability – one that locates community rather than centralized control at its core. They imply that lifelong experience of social relationships in inner-city schools may generate a more sophisticated account of educational practice than proficiency at multivariate analysis. And they also imply that the tacit knowledge of practitioners may be more significant to program operation than the generalized statements of theoreticians. In a sense, the history of curriculum evaluation has come full circle. Just as the curriculum was taken out of lay control in the 19th century, so, at a new level, demands are heard for its restoral. Curriculum evaluation has played a part in both these movements.

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Summary and Conclusions In his introduction to a 1970 review symposium on educational evaluation, Denny (1970) noted the absence of investigations into the “historical growth of evaluation methodology.” This chapter, written by a participant rather than a bystander, has tried to respond to that shortcoming. Through an examination of the historical forms embraced by curriculum evaluation, it has related some of their more fundamental features. At the same time, it has suggested that recent events indicate a crucial differentiation of these institutional forms. On the one hand, there are evaluation models which rely for their coherence on notions of consensus; on the other, there are others (here termed pluralist) which treat consensus as a problematic assumption. As befits their origins in 19th-century liberal and pragmatic thought (Gouldner, 1971, passim; Karier, 1974, p. 280), consensus models are strong on technology, reformism, and social engineering. They regard evaluation as a technical accomplishment: the demonstration of empirical/logical connections between what is and what (we all agree) ought to be. If an evaluation is to be successful, there has to be “consensus on the key issues of the hierarchy of purposes of education and the rules of evidence” (Cooley & Lohnes, 1976, p. 5). The internal logic of these models guarantees the strength of their truth statements. Given agreement on educational ends, the unambiguous selection of appropriate means (i.e., the curriculum) is simply a technical problem. A crucial feature of pluralist models, however, is that they are skeptical of these “preconditions” (Cooley & Lohnes, p. 5). To this extent, the differences between consensus and pluralist models are epistemological rather than methodological. One explanation for the emergence of pluralist theories is that, as in the 1930s, they reflect a general crisis in the realm of social values. They are an expression of doubt and reflection rather than certainty and action. As such, they tend to be strong on conflicting interpretations, value differences, and incomplete closure. Evaluation is offered as an unfinished blueprint rather than a perfected technology. It generates issues, not solutions. It is about information rather than confirmation. By their openness, however, pluralist models also leave a number of questions unanswered. Is pluralism consonant with an overarching theory of values? Or is it incompatible with such a universalistic notion? Is the rise of pluralism a temporary phase? Will it be replaced by a new consensus? Or has crisis become a necessary feature of any social system that has gone “beyond the stable state” (Schon, 1971)? To address questions such as these is to reach out far beyond the problems of research technique and goal identification. Yet, if acts are best comprehended by establishing their context, then tasks of this magnitude are essential to an adequate understanding of curriculum evaluation.

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Tyler, R. W. Basic principles of curriculum and instruction. Chicago: University of Chicago Press, 1949. Tyler, R. W. (Ed.). Educational evaluation: New roles, new means. 68th yearbook of the NSSE, part 2. Chicago: University of Chicago Press, 1969. Walberg, H. J. (Ed.). Evaluating educational performance: A sourcebook of methods, instruments, and examples. Berkeley: McCutchan, 1974. Walker, D. F., & Schaffarzick, J. Comparing curricula. Review of Educational Research, 1974, 44, 83–111. Webb, B. My apprenticeship. Harmondsworth, England: Penguin Books, 1971. (Originally published, 1926.) Wehlage, G. The ethics and politics of evaluation: Patrons, clients and casualties. Paper presented at the meeting of the American Educational Research Association, San Francisco, April, 1976. Weir, E. An experimental approach to curriculum evaluation: The BSCS population genetics field trial. In R. E. Stake (Ed.), Case studies in the evaluation of educational programmes. Paris: Organization for Economic Cooperation and Development, 1976. Weiss, R. S., & Rein, M. The evaluation of broad-aim programs: A cautionary case and a moral. Annals, 1969, 385, 133–142. Welch, W. W., & Walberg, H. J. A course evaluation. In H. J. Walberg (Ed.), Evaluating educational performance. Berkeley, Cal.: McCutchan, 1974. White, M. G. Science and sentiment in America: Philosophical thought from Jonathan Edwards to John Dewey. New York: Oxford University Press, 1972. Williams, W., & Evans, J. W. The politics of evaluation: The case of Head Start. Annals, 1969, 385, 118–132. Wolf, R. L. The application of select legal concepts to educational evaluation. Unpublished doctoral dissertation, University of Illinois at Champaign–Urbana, 1974.

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23 Psychology of Learning Environments: Behavioral, Structural, or Perceptual? Herbert J. Walberg

B

efore overviewing the purpose and parts of this chapter, it may be helpful to mention the general features of the three models of learning process that are contrasted in the discussion (Figure 1). In the behavioral model the teacher presents stimuli to the student, observes or psychometrically assesses the responses, and selectively reinforces them by reward and punishment. In the structural model, the preprogrammed development of internal mechanisms mainly determines the course of learning; the teacher stimulates the maturation of these mechanisms, draws them out, or provides the environment in which they can be acted upon or be concretized. The perceptual model allows for behavioral and structural mechanisms but holds that the student’s conscious perception of internal and external stimuli and his choices are the proximate, mediating determinants of learning. Because behaviorism has increasingly dominated psychological thought about education since John B. Watson’s (1913) famous paper, it should be critically examined. The natural sciences now raise questions about its assumptions, and certain theoretical presuppositions of behaviorism in education that have been neglected should be made explicit. Accordingly, the first section of this review critically questions behaviorism from a structural perspective. The second section describes the intriguing but rudimentary character of structuralism itself, as either a scientific theory of psychology or a workable basis of educational practice. The third section proposes perception as a useful, transactional concept between structure and behavior in research on Source: Review of Research in Education, 4 (1976): 142–178.

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1. Behavioral Model

Behavior

Instruction

External Stimuli 2. Structural Model

Learning

Instruction

Internal Structures 3. Perceptual Model External and Internal Stimuli Perception

Learning

Emergent Structures

Figure 1: Three models of the learning process

classroom learning environments, and the fourth proposes a framework for future empirical work. The fifth section discusses several analytic problems of perceptions of learning environments and examples of training, intervention, and evaluation applications. Thus the first three sections of this review concentrate on theoretical issues of psychology and their consequences for educational research, and the last two sections take up questions concerning research methods in classroom perceptions. An attempt has been made to avoid topics and issues that have been recently reviewed elsewhere. See Moos (1973) and Insel and Moos (1974) for an analytic treatment of psychological research on a variety of human settings; W. J. Campbell (1970) and Marjoribanks (1974) for valuable but neglected collections of substantive work on learning environments carried out in Australia, Canada, England, and the United States; Khan and Weiss (1973), Randawa and Fu (1973), Shulman and Tamir (1973), and Walberg (1971, 1974a) for substantive and methodological reviews; and Walberg (1974b) for a source book of learning environment instruments and evaluations by several research groups.

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Structural Criticism of Behaviorism One way of sorting psychological theories of education is to ask whether learning is more the enactment of structural potentialities or the external shaping of behavior. This classification leaves out some theories, and the dichotomy oversimplifies for some purposes. Nevertheless, it enables us to see the historical continuity of two fairly distinct psychological traditions that trace back to the origins of Western thought and inquiry. They remain philosophically and scientifically unreconciled, and they conflict in their implications for educational practice. Alfred North Whitehead observed that Western philosophy (which until this century included psychology as a special topic) may be considered footnotes to Plato. Indeed, historically oriented reviews of social and developmental psychology (Allport, 1968; Riegel, 1972; Walberg, 1973) and psychological theories of instruction (Walberg, 1975) show that Plato’s Republic posed many of the enduring questions of educational psychology. Plato’s dualism sharply distinguished mind and matter, and he held that ideas alone endure. Perhaps influenced by Oriental beliefs in reincarnation, he held that ideas are present in the child’s mind at birth. Since “knowledge is but remembrance,” the Socratic teacher acts as a “midwife of ideas.” The original meaning of educate – to draw forth rather than to stamp in – follows from Plato’s theory of ideas. Education, for him, is the soul’s re-cognition, more precisely, stagewise apprehension and integration of abstract ideas, of which the empirical flux is but a series of images. However odd these notions appear at first glance, they underlie a vital tradition of structuralism in Continental Europe that challenges Anglo-American behaviorism today. Structuralists, represented by Sig-mund Freud, Carl Jung, and Jean Piaget, posit innate or a priori structures (ideas, qualities, or sophisticated capacities) and often stress their stagewise development or integration in the individual or in society. Educational cultivators of the structural tradition include Rousseau, Pestalozzi, Froebel, Montessori and open educators who call for child-centered instruction in contrast to that which is centered in the external authority of the subject matter, teacher, or society (Riegel, 1972; Walberg, 1975). The tenets of behaviorism can be traced to Aristotle, who was skeptical of Plato’s concept of ideas. Perhaps because of his early interest in biology, he favored explanation in terms of empirically discriminable qualities, classification rather than integration of subject matter for inquiry, descriptive taxonomies, and an atomistic concept of mind as the aggregation and association of mental elements by induction from external reality. John Locke’s portrayal of the child’s mind as a “blank tablet” descends from Aristotle. Other English and American inheritors of the Aristotelian tradition include Bacon, Hume, Newton, and other English empiricists; Hobbes, Darwin, Spencer, Summer, Pearson, and Galton, who described ideal types that survive in competitive environments; and Galton, Thorndike, Watson, Gesell, Hull, Terman, and Skinner, who sought to identify, norm, or amass items of knowledge or discrete behaviors.

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To be sure, structuralism and behaviorism have stimulated one another, and the important theorists are not all geographically, philosophically, or linguistically separated by the English Channel. Starting with Aristotle, who tried to harmonize his ideas with Plato’s, many synthesizers had first-hand contact with the two traditions: William James, John Dewey, and Henry Murray were directly influenced by Continental thought; Ludwig Wittgenstein, Kurt Lewin, Egon Brunswik, Erik Erikson, Bruno Bettelheim, and Paul Lazarsfeld were Continental migrants to England and the United States. But there has been no grand, enduring synthesis. Structuralism is substantive and methodologically alien from the psychologies of the English-speaking countries, and we often seem to misunderstand or distort it. Some examples illustrate the point. Although Freud is one of the great minds of this century, and perhaps of all time, he was rightly pessimistic about American psychologists’ understanding of psychoanalysis, and there are few psychology departments today where it may be studied in depth. The simple tests Alfred Binet devised or adapted to pick out those few Parisian children who might not benefit from regular schooling led to the American educational psychologists’ apparent preoccupation with testing and ranking of verbal achievement, among both hereditarians and environmentalists. Contrary to Piaget’s (1971) position, behaviorists attempt to norm and accelerate the development of his schemata. Proponents of open or informal education whose premises are structural, as evidenced in content analysis of their writings (Walberg, 1975), resist the behavioral confusion of their ideas with open space, criterion-referenced tests, and permissiveness. Notwithstanding these misunderstandings, structuralists can constructively stimulate behaviorists. Each position, in my opinion, is more like a complex of aesthetic preferences than a scientific model that can be rigorously tested. The structuralist insistence on theoretical coherence, scope, and invariance challenges and balances behaviorist inclinations toward classification and taxonomy, unrelieved empiricism, and the vision of prediction and control as ultimate scientific criteria. As Shulman (1974) points out, E. G. Boring, historical spokesman for American psychology’s hard-nosed experimental wing, cites Freud as the most important psychologist of the first half of the 20th century. McGuire (1973) admires Freud and Piaget for their direct confrontation of empirical reality, in a sense going behaviorists one better on their own ground, rather than studying indirect or secondary evidence of behavior such as the closing of relays, EKG recordings, and filled-in blanks. Moreover, psychoanalysis and the méthode clinique require ongoing experimental interventions. Since our sympathies may be more strongly linked to behaviorism, we ought to examine critically our position from the other point of view. Accordingly, a structural reconsideration of the behavioral concepts of atomism, flux, evolution, control, and reductionism is in order. The discussion is prefaced by noting that not all behaviorists would subscribe to the extreme behavioral views described below (perhaps none would), and neither do

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structuralists constitute a pure type. The differences in views are sharply drawn to illustrate the theoretical issues.

Atomism Both Aristotle and Locke drew on Democritus’ atomism in formulating models of learning. Locke, who began the atomistic tradition of English empiricism, followed Bacon’s and Newton’s utmost caution in theorizing. Newton, it may be recalled, took pride in refusing to speculate about a causal structure to explain gravitational attraction. As the biologist Pantin (1968) points out, even Darwin’s “theory” of evolution must be considered an inductive generalization based on an immense number of empirical instances; the explanation in terms of genetic structure came much later. Physics and chemistry began to be made exact and mature at the time of Locke and Newton by progressively finer analysis of discrete particles and briefer phenomena (Pantin, 1968). Influenced by the unquestionable breakthroughs of the natural sciences, psychology and the other nascent human sciences sought revelations first in psychobehavioral (mind-matter) atomism. Later, Watson (1913) and others tried to purge the concept of mind (or psyche) from psychology because of its subjectivity; they aimed for purely behavioral atomism. E. L. Thorndike, who explained the transfer of learning from one situation to another by the overlap of identical elements, broke the subject matter into elements and represented them on exercise and test items (Walberg, 1975). Similarly, many contemporary educational psychologists advocate lists of behavioral objectives and criterion-referenced items to formulate curricula and study teaching by classifying the teacher’s behavior every few seconds. Clark Hull’s (1943) theory of learning is the paragon of behavioral atomism: repeated responses of the organism to external stimuli (cues), reinforcement by external stimuli (rewards), and chains of association that produce an internal copy of external cues. Piaget (1971) holds that if the purpose of education is to elicit the repetition of transmitted cues, the machine can indeed replace the teacher, but he argues that if the purpose is personal comprehension, then “to know by heart is not to know” (p. 139). The molecular biologist Stent (1975) argues that behaviorism’s insistence on treating only raw elements of sensory data and direct inductive inferences has restricted the human sciences to taxonomic or descriptive disciplines with little explanatory power. Recent neurological studies show that information about the world reaches consciousness not as elements of raw data but as transformed patterns, and the transformations proceed according to a pattern that preexists in the brain. Koestler (1964) and Langer (1973) marshal natural science findings supporting the existence of a priori mental structures. There is little reason to doubt that adaptive capacities and complex patterns of behavior are passed on

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genetically through DNA and RNA (B. Clark, 1975; Stent, 1975; Kolata, 1975; Piaget, 1970). For example, Konrad Lorenz’s Nobel Prize work in comparative ethology shows that innate mechanisms such as imprinting in ducks are preprogrammed for release at critical times. Many complex patterns of singing, courtship, navigation, and other activities in insects, birds, fish, and mammals are also innate; and the environment ordinarily plays a comparatively minor permissive or activating role rather than a determining one (Kolata, 1975). Since Immanuel Kant’s transcending concepts – time, space, and causality – are adaptive for a wide variety of human environments, there is reason to believe that they may have been selected for evolutionary fitness (Stent, 1975). Moreover, the psycholinguist E. H. Lenneberg (1969) believes that a structural capacity for language is a priori, in view of the fact that though human environments vary enormously, there is a nearly universal mastery of complex language rules among children. It does seem that humans innately possess certain universal conceptual capacities or frameworks for the interpretation of experience and for complex response patterns. These can be explained neither as randomly emitted, atomistic behaviors nor as products of discrete cues or reinforcements.

Flux Atomism often goes with flux in psychological theories because both derive from extreme empiricism. The pre-Socratics stated the issue, which is still vital in philosophy of science: Heraclitus believed all is flux; you cannot step into the same river twice. But Parmenides held that transitory appearances conceal mutual relations of deeper, unchanging realities. Acceptance of flux as the main stuff of psychological and educational science can be found in several recent reviews, as well as in many contemporary research papers. Scriven (1956), Glass (1972), and Cronbach (1975) believe that explanations of human behavior remain short-lived, in contrast with the enduring theories of physical scientists. Gergen (1973) holds that psychology does not originate but merely reflects the thoughts of any historical period of time. Cronbach (1975) again calls for a search for trait-by-treatment interactions and now holds that such interactions vary across decades of history. In accord with these notions, McGuire (1968) measures the maturity of a science by the complexity of its interactions. On the other hand, S. S. Stevens, an American inheritor of Continental structural psychophysics, taught that “the scientist’s contest with nature has prospered to the degree that simplicities and uniformities (invariances) have been detected amid the complexities that afflict observation and experiment” (quoted in Lockhead, 1975). The French sociologist Boudon (1971) contrasts structural analysis of enduring essentials with phenomenal description of transitory appearances. Structural equation models, of which path analysis is

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a special case, specify the invariant causal mechanisms that generate observable variables and their relations (Goldberger & Duncan, 1973). Albert Einstein dismissed hypotheses generated solely to fit a particular data set and stuck to his relativity theory, despite the early appearance of apparently disconfirming evidence, because he believed his equations were encompassing, parsimonious, and beautiful (Clark, 1971). Similarly, educational psychologists may do better by adhering to theoretical invariances more often than to disconfirming data, because measurement error, reciprocal causality, and inadequate control prevent the more definitive empirical tests of theory carried out in the natural sciences.

Evolution Clark (1975) notes the irony that following the centuries in which the Bible was considered the final word on biology, interpretations and misinterpretations of Darwin’s Origin of the Species became an evolutionary basis of psychology, religion, ethics, politics, and sociology. The “evolutionary” behaviorists can be divided into two groups. Although they disagree vehemently, they both make the same error, the narrowing of the quality of human nature or cultural progress to a single criterion. The original hereditarian group – Herbert Spencer, Francis Galton, and Karl Pearson – lived in a socially stratified English society in which evolution was a ready explanation for the ideal social types as well as the poor or deviant. Perhaps influenced by Calvinistic ideas of predestination, they sought to describe, measure, and norm hereditary potential for acquiring and connecting elements of external reality. The environmentalist side was originally and audaciously stated by Watson; residing in Chicago, where some poor migrants became rich while others did not, he claimed he could train any healthy infant to become a doctor or a thief, regardless of his heredity. (The statement following his famous contention is seldom quoted but is still true today: “I am going beyond my facts and I admit it, but so have advocates of the contrary and they have been doing it for thousands of years” (Watson, 1925, 104). The testing movement gave behaviorists on both sides of the naturenurture question an axis for the controversy. Because standardized ability and achievement tests are built on the item-total score correlation and are evaluated most often for internal consistency, they tend to measure a single characteristic, what English psychologists call verbal-educational ability. Lohnes and Marshall’s (1965) factor and canonical analyses show that grades and test scores in student records are mainly accounted for by this one source of variance. Since some teachers measure their students’ progress by this single criterion, the tests reinforce a typically academic verbal parochialism, what Piaget (1970) calls “a proliferation of pseudo-ideas loosely hooked on to a string of words lacking all real meaning” (p. 157). Perhaps this judgment

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is harsh; but it is true that, although verbal-educational ability and grades afford moderately accurate predictions of subsequent grades and educational attainment in years, for persons with a given education, neither factor seems to predict occupational or other kinds of adult success (Walberg, 1974b, l974d; Taylor and Ellison, 1975). The exception is school-like tasks, and success in these is predicted very inaccurately, with validities in the .20s (Taylor & Ellison, 1975). Verbal-educational ability has been overemphasized at the expense of other traits such as problem solving, integrity, courage, leadership, and practical knowledge; and it does not follow that what is conveniently or conventionally measurable or observable is most important. A new and persuasive caution is set forth by evolutionary geneticists (for example, B. Clark, 1975). Contrary to behavioral premises, evolution is not an orderly progression of successively superior types that can be ranked on a single dimension but a complex set of shifting equilibriums between the genes and the environment. By a process of polymorphism, evolution does not converge on pure types suited only for one environment but instead generates genetically diverse populations, able to adapt to changing, heterogeneous environments. Samples of individuals even from a single ethnic group will differ “at hundreds of chromosomal loci, and possibly at thousands” (B. Clark, 1975, p. 60). These differences result in an enormous diversity among individuals, which is to be celebrated rather than simplified or suppressed in a free, cosmopolitan society. Thus, it would be a great mistake to allow the purpose of education to become the genetic, environmental, or even interactionist optimization of verbal-educational ability or those few psychometric traits and behavioral norms that can be conveniently assessed. Rather, one of the general purposes of education should be to enhance the best qualities of the child’s inheritance and early experience. Plato and Piaget say this is best accomplished by drawing the child out rather than stamping knowledge in; and we can look to the educational tradition of Rousseau, Pestalozzi, Froebel, Montessori, and Piaget for more specific clues on how it might be done. But even if this view is unconvincing, it does appear that, analogous to Cannon’s (1932) Wisdom of the Body, the child’s mind consists in part of homeostatic components which preserve identity and individuality against the vicissitudes of the environment (see Bloom, 1964, on the stability of a wide variety of traits after school age). The surpassing adaptability of these components, a product of evolution and early environment, may account for our difficulties in replicating enduring main or interaction effects of behavioral variables in the classroom (Walberg, 1975).

Control Chomsky (1959) and Bowers (1973), among others, point out that there is nothing wrong with the Skinnerian definition of a reward or reinforcer as

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“a stimulus that increases the probability of a response,” but they maintain that to say “behavior that is acquired and maintained is reinforced” not only does not follow but completes a vicious circle. Aside from tautology, the evidence for the generality of reinforcement is limited. Some behaviorists wish to make it the guiding principle for the perplexed individual or society. As evolution has shaped species throughout the ages, so it is claimed, contingencies of reinforcement shape the individual during his lifetime, increasing the frequency of adaptive behaviors and extinguishing maladaptive ones. One way to demonstrate reinforcement is by reductionism: modeling psychological research on experimental physics, seeking artificially pure species and stable environmental conditions, and selecting highly manipulable reinforcers. Connectionists can “deprive the white rat (Strain number 02×Y ) of a third of its body weight” (to avoid the mentalistic concept of “hunger”) and show that food pellets control the speed at which it runs (not “memorizes”) the maze. Rats, however, cannot be controlled by water deprivation in this way because, in the rat’s environment, the location of food varies, although water is usually found in the same place (Bermant, 1973); the “independent variable” is unsuitable for the rat’s life-style as it has evolved. If each species requires a different reinforcer, then comparative behaviorism faces a difficult task of descriptive taxonomy, because science requires either theoretical generalization or enumerated instances. Enumeration would be still more difficult if allowances were to be made for differences in drive and maturation levels and other factors within species. Moreover, since humans are more complex than rats and pigeons, and since social environments afford more cues, competing reinforcers, and opportunities for action that individuals perceive and weigh differently in reaching decisions, the task of behavioral application becomes indeed awesome. No wonder, then, that operantly shaped patient behaviors typically revert to base line after the end of therapy, that such behaviors as smoking, stuttering, and autism are remarkably resistant to control by reinforcement (Bowers, 1973), and that main and interaction effects of behavioral programs have been difficult to extend to natural settings. If, on the other hand, behaviorists posit some general theory of mechanism of reinforcement instead of the mere empirical regularities called for by Skinner (1950) in “Are Theories of Learning Necessary?” then perhaps there is less of a quarrel with structuralists. Freud, for example, offers “the pleasure principle” that is not only a part of a grand explanation of human nature but is accompanied by cautions that it applies mostly to infants and regressed adults in extreme situations. Freud, one imagines, would want to question Watson’s repudiation of all forms of subjective experience and ask, with Murray (1959), how the semantic somersaulting of “cognitive behavior,” “emotional behavior,” and even “dream behavior” clarifies psychological theory. On the educational wisdom of behaviorism, aside from its efficacy, George Orwell and Aldous Huxley have envisioned 1984 and Brave New World as

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examples of reinforcement that are as abhorrent as genetic control. Nor does it seem wise to control young children in this way simply because they are docile. As Whitehead warned: The result of training is that qualities essential at a latter stage of a career are apt to be stamped out at an earlier stage. This is only an instance of a more general fact, that necessary technical excellence can only be acquired by training which is apt to damage those energies of mind which should direct the technical skill. This is a key fact of education and the reason for most of its difficulties. (1929, p. 96)

Reductionism During the past half century, behavioral tenets gained much of the allegiance of psychologists in the United States and in other English-speaking countries, even to the extent of identifying psychology as that “behavioral” science closest to the natural sciences. (Ironically, “psychology” first meant the study of the soul or mind; strictly speaking, the science of behavior is kinesiology, a branch of physical education.) Stent (1975) exaggerates in saying: “Now, in retrospect, at a time when such tenets appear to be moribund, it seems surprising that these views ever did manage to gain such a hold on the human sciences” (p. 1054). On the contrary, it is understandable: psychologists had been tearing themselves away from philosophy, armchair speculation, and “subjective mentalism,” even from phenomenological experiences of everyday life. They emulated what they understood to be the objective, atomistic, esoteric spirit of the natural sciences. Whether or not the psyche should be purged from psychology is a question that goes beyond psychology. Two eminent scientists whose thought survives the test of time, Darwin and William James, warn against behavioral reductionism. Skinner (1971) desires to “follow the path taken by physics and biology by turning directly to the relation between behavior and the environment and neglecting supposedly mediating states of minds” (p. 15). Before him, Watson (1925) took James to task for departing from the “Darwinian” model of “thoroughly objective and behavioristic descriptions of emotional reactions” (p. 29). In fact, as his recently published notebooks (Gruber & Barrett, 1974) show, Darwin made much use of subjective experience, recorded and interpreted his own dreams, conducted one of the earliest questionnaire studies, and aided Galton in questionnaire investigations of introspective experiences. Darwin criticized views of learning as blind trial and error or as specialized instinct and did not hesitate to attribute intelligence, even to worms: To sum up, as chance does not determine the manner in which objects are drawn into the burrows, and as the existence of specialized instincts for each particular case cannot be admitted, the first and most natural supposition is that worms try all methods until they at last succeed; but many

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appearances are opposed to such a supposition. One alternative alone is left, namely, that worms, although standing low in the scale of organization, possess some degree of intelligence. (1881, pp. 92–93)

Moreover, as Gruber and Barrett (1974) point out, Darwin went into great detail in The Origin of the Species (1859) about cuckoos laying their eggs in other birds’ nests, ants making slaves of other ants, and the like to make the point that many complex adaptive patterns among infrahumans are intelligent. James insisted that mind is an active force. The knower, he held: . . . is not simply a mirror floating with no foothold anywhere, and passively reflecting an order that he comes upon and finds simply existing. The knower is an actor, and coefficient of the truth on one side, whilst on the other he registers the truth which he helps to create. Mental interests, hypotheses, postulates, so far as they are bases for human action – action which to a great extent transforms the world – help to make the truth which they declare. In other words, there belongs to mind, from its birth upward, a spontaneity, a vote. It is in the game, and not a mere looker-on. (1899, pp. 148–149)

Since the basic form of classroom instruction – the short factual recitation of text or what the teacher says – appears to have been unchanged since the turn of the century (Hoetker & Ahlbrand, 1969), James might protest today that the student needs something more than choosing the “one best answer”; he or she needs to compose his or her own answer, or better, his or her own question. For personal meaning and choice are the essences of education. As James says in Talks to Teachers (1889): “The solid meaning of life is always the same eternal thing – the marriage, namely, of some unhabitual ideal, however special, with some fidelity, courage, and endurance; with some man’s or woman’s pains” (pp. 88–89; emphasis added).

Structuralism: Problems and Possibilities Although structuralism may eventually provide the conceptual keys that will unlock the human mind, it is by no means evident that this will be accomplished in the next few generations. The arguments for structuralism, like those for behaviorism, are hypothetical, based on evidence from unusual circumstances or unverifiable analogies from research on infrahumans. Even though the biological sciences provide indications of the evolution and inheritance of innate mental structures in the human race, there is little consensus within the structural tradition as to exactly what they are: Kant’s a priori ideas, Freud’s principles or structures, Jung’s archetypes, Piaget’s schemata, or Levi Strauss’s universals. Nor can it be claimed that factor analysis, a kind of “objective” Anglo-American methodological search for psychological structure, has established the number and nature of mental components.

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Mature science implies consensus, and it is not apparent. Moreover, psychological structuralists, apart from introspectionists, depend on behaviorism in the sense that the latencies they seek can only be inferred from what is observable or in the sense that they are obligated to specify the strings that connect the outside of the machine to the ghost inside. Structuralists and behaviorists seem generally to be going their separate ways. Educators, of course, cannot wait for a grand synthesis; and many find behaviorism preferable to structuralism as the basis for practice. Behaviorism indicates how to split up the curriculum; gives the teacher (or mechanism of instruction) a pragmatic, active, dominant role; and delivers visible (though not necessarily generalizable) results. Behaviorists have also made themselves, their ideas, commodities, and services, available to the schools, first in the form of tests and exercises and today in the form of behavioral objectives and systems of instruction. In contrast, leading structural theorists seem preoccupied with ascertaining the nature of the internal structures and are less directly interested in education. There are no widespread educational programs based on structural premises comparable to the federally supported, behaviorally oriented systems that are designed for adoption by whole schools or districts. A teacher is most likely to pick up structural ideas from a Socratic professor, an inspiring colleague, a summer trip to Bristol, England, or Grand Forks, North Dakota, to see an open classroom, or a chance reading of Summerhill or Piaget. But this informality can easily be underestimated. Comparing the contemporary structural theory represented in, say, Piaget (1970) or a content analysis of the scattered writings on open or informal education (Walberg & Thomas, 1972) with observations of nominally open classes in England and the United States reveals rough correspondences: 1. Direct encounters with science materials and the nonacademic environment, as well as vicarious and verbal experience. 2. Group-developed standards of mutual aid, discipline, and justice. 3. Teacher-student and student-student collaboration in determining the goals and means of learning, within teacher constraints. 4. Diagnostic notes and samples of the student’s work to evaluate and redirect learning. 5. Allowances for maturation and individuality on the basis of teacher questioning, observation, and judgments, rather than on norm- or criterionreferenced tests. 6. An enriched environment and stress on the integration of knowledge. 7. The use of critical discussion and reflection to encourage reason and selfdetermination rather than belief in authority to replace egocentrism. These practices can be seen in classes where behavioral principles are evident, and the observer can only stand in admiration of the genius of school men

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and women who have generally been able, as Ravitch (1974) suggests, to find a workable compromise or reconciliation of the conflicting pressures on education. Such is likely to be the case before structural and behavioral theorists settle their differences.

Perception and Learning Between the external flux of behavior and the mysteries of deep structures is what Hebb (1974) declares psychology is all about: the mind. Acceptance of this and related phenomenological concepts may help us to rediscover that psychology is “truly William James, ’ ‘science of mental life,’ not merely John B. Watson’ ‘science of behavior’ ” (Shulman, 1974, p. 334), or structural metaphysics. This section selectively treats one aspect of mental life: perception. Perception is a broad, complex subject of psychological inquiry; Carterette and Friedman (1973) enlisted more than 200 authors to write 159 chapters treating perception in ten prospective volumes. Obviously this work cannot be summarized here. Rather, this section discusses how perception of environments can “account” for considerable variation in classroom learning and some of the research complexities that investigators of educational perceptions face.

Accounting for Learning Variation How much of the variation in rate or amount of learning among students is attributable to their aptitudes, to educational treatments, and to the interaction of these two sets of variables? In an influential paper, “ The Two Disciplines of Scientific Psychology,” Cronbach (1957) identified aptitudes and treatments as the two important areas of psychology that had been studied separately (by correlators of psychometric measures and by experimenters who manipulate stimuli, respectively) and which would have to be brought together for a comprehensive analysis of the organism’s change in relation to the environment. Not only should psychologists investigate the main (or separate) effects of aptitudes and treatments (b1 and b2 ) on, say, learning in the regression equation: 1 = b1a + b2t + b3 (at) + constant + error, they should also study the possibly differential effects of treatments on students with different levels of aptitudes (interactions at). Such an analysis makes sense for a number of reasons, even if a parsimonious two-term additive model is preferred to one containing the product. Aptitude often serves as a potent covariate, decreases the error of estimate, and increases the power of the analysis to detect treatment effects. The weight (b3) for the product term provides an indication of the equality of the dependence of learning on aptitude in the different treatment groups, a possibility that should

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be routinely checked in covariance. Most important, educators recognize individual differences in students and would like to have hints about which treatments are most suitable for students with different levels of aptitudes. Cronbach’s notion of aptitude–treatment interaction is perhaps the most influential research paradigm in educational psychology in several decades. It not only sensitized many to the possibility of interaction; it also led to hundreds of behavioral studies. In brief, the research showed that the effects of behavioral treatments (aside from content opportunities) and their interactions with aptitudes were difficult to replicate (Berliner & Cahen, 1973; Cronbach & Snow, 1976) if not small and inconsistent (Walberg, 1971). On the other hand, the estimates of b2 in the equation 1 = b1a + b2 p + constant + error reveal large, consistent “effects” of student perceptions of the social environment of learning that “account” in 11 analyses for a median of 30% (range = 13 to 46; all significant) of the variance in cognitive, affective, and behavioral postcourse measures, beyond that accounted for by parallel precourse measures. By contrast, IQ accounts for only a median of 7% (range = 0 to 9) of the residualized variance (Anderson & Walberg, 1974). Efforts at generalizing these results suggest consistency across different school subjects (Anderson & Walberg, 1974) and different languages and cultures (United States, Canada, Australia, Brazil, and India). Although in classroom research the usual treatment (aside from content opportunity) and its interaction with aptitude add little to the explanation of learning variation beyond that accounted for by aptitude, student perceptions of the social environment of learning add considerably. It can be hoped that measures of aptitude, perception, and exposure to content measured on the criterion test will account for most of the reliable variance in standard measures of learning outcomes.

What Perception Encompasses What does perception include that treatment omits? Brunswik (1956) recognized that, for Lewin, the “exterior field” (including treatments) is completely inside the person and argued that we selectively perceive our own and others’ behaviors, perceptions, intentions, emotions, ideas, traits, abilities, and memories. As James (1899) pointed out, restricting ourselves to atomistic, objective behavior omits the main subject of psychology, the conscious, perceiving mind. Even cosmopolitan Murray (1959) felt the need for “objective observer judgments,” in view of the possibility that “the internal is morbid delusion” or “unrelated to realistic or congruent estimation of the external situation” (p. 27). Such reasoning might better be reversed: what is objectively counted or measured should be weighed and justified by what is subjectively perceived, insofar as individual learning is concerned.

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Specifically, what are some things subjective phenomenology encompasses that the “objective observer” who counts cues or behaviors might miss? They include the following: 1. The Chicago social psychological school’s (Cooley, Mead, Dewey) observations that we modify our potential reactions in view of the way others might react to them and that we consciously alternate between the self and this other point of view (D. T. Campbell, 1963). 2. The well-known phenomenon from human and infrahuman psychology that organisms can ignore stable or inconsequential stimuli in the environment (Pantin, 1968). 3. That a mean man is kind when he doesn’t hurt you (Tagiuri, 1969), that we damn with faint praise, and that some teachers give great rewards with a smile and others observably gush to no avail. 4. That discriminations can sometimes be made not only on the basis of single, consistent, atomistic attributes (Aristotelian man as featherless biped) but that in natural settings we most often distinguish, rightly or wrongly, on the basis of imperfectly consistent groups or patterns of cues such as the recognition of family resemblances in faces (Wittgenstein, 1953). 5. That different cues in the same environment may lead people to the same (or different) perceptions, conclusions, or actions. 6. That perception, though imperfectly related to veridical environments, and imperfect attribution of causality, intention, and consistency adaptively simplify and stabilize complex social environments for the individual (Tagiuri, 1969). 7. That perception itself is readily adaptable; for example, the respective advantages of weightlifters and watchmakers in judging different ranges of weights are soon lost with practice (Tresselt, 1948). Behavioral psychology, with its origins in simple, controllable laboratory experimentation, seems to carry into education the simplistic notion of one-way causation, of teacher as first or only cause, the dispenser of cues and rewards (Figure 2, Model 1a) who sometimes delegates control over the student to the text or workbook. Much research on instruction relates counts of teacher behaviors every few seconds to residualized learning, on the assumption of equal one-way, univariate causal effects on students (Figure 2, Model 5). Such models, of course, do not consider programmed systems (see Talmage, 1975) which determine teacher role or the influence of student on teacher or of students upon one another, as common sense and the other models would suggest. Psychology, as McGuire (1973) argues, must begin to take account of such bidirectionality as well as reverberation and feedback among humans in natural settings. (See Fiedler’s, 1975, pioneering paper on classroom interaction.) But the task has to be as complicated and difficult as the foregoing examples suggest only if we insist on studying behavior alone. Murray (1959) is worth quoting on the point:

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1. Teacher centered

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In due course, I assumed that correctness of prediction is the best index of the worth of different methods. I did a few impromptu experiments and found empirically that the most dependable single operation I could perform in attempting to foretell what a behaviorist would do next or in the near future was to ask him. But the commonsensical avowal I wish to make here is this: that first as a doctor and second as a psychologist I have never ceased to elicit direct expressions and reports of interior experiences – somatic, emotional, and intellectual. . . . (p. 10)

Similarly, Donald T. Campbell, in an interview with Tavris (1975), when asked about the legitimacy of Unobtrusive Measures (Webb, Campbell, Schwartz, & Sechrest, 1966), said: When I started out, I was, like many social scientists, obsessed with gadgets. I believed I was being more scientific if the people I studied didn’t know what I was up to, and if they didn’t realize what they were telling me.

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After 20 years research, I feel that this is an unworthy stance for a scientist. It exploits others and implies social distance between researchers and the people they study. In addition, all the evidence shows that when you ask people to cooperate with you and tell you their attitudes, you get greater validity than with all this mumbo-jumbo. (p. 47)

Thus one way to find out about, say, the suitability of the learning environment is to ask students, as well as teachers and trained observers. The student, however, stands at a superior vantage point; what he takes in makes the difference in learning. By the age of ten he has encountered a variety of educational environments; he is with his teacher for many hours during the year; he is a sophisticated judge with plenty of information to weigh. His perceptions, as partaker of classroom social transaction, are of great value, and it is easy enough (and incrementally valid) to ask him for them. As Fiedler’s (1975) study of classroom transactions shows, students’ perceptions of their own influences on the class, but not observer estimates of the same, predict academic gains. This is not to say that “objective” tests, counts of teacher behavior, and the like must be put aside, but only that by themselves they may be an inefficient approach to understanding. Behavioral efforts to link classroom treatment with learning are numerous, expensive, and difficult. Investigating the links of perception with treatment and learning may yield more revealing clues to the puzzle of optimizing classroom learning.

A Framework for Research on Classroom Perceptions Another appeal for incorporating perceptual measures into classroom-effects research is scientific and practical parsimony. Even if psychometrists could agree that there are ten aptitude factors, and if instructional theorists established ten aspects of treatment, the number of main effects and first-order interactions of aptitude and treatment alone, 120, would be unparsimonious, to say nothing of other interactions such as aptitudes with one another, treatments with one another, and aptitudes and treatments with student moods and developmental levels and teacher characteristics. Moreover, the possibility of higher order interactions and sequences of treatments would raise the number of theoretical effects by many magnitudes.

Mediation There is one instrument that can help us to deal with such complexity – the human mind. Students seem quite able to perceive and weigh stimuli and to render predictively valid judgments of the cohesiveness, democracy, goal direction, friction, and other psychological characteristics of the social environments of their classes. These molar judgments may mediate the multiplicity of

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molecular events of instruction and other classroom activities and properties. Notwithstanding the extremely general appeal of atomic description, the physicist chooses larger units of analysis for problems of astronomy and mechanics; he and the engineer, of course, feel free to draw on fine-grain analysis when it seems helpful in accounting for macrophenomena. And moot physiological questions about the linkage of the five or ten processes that regulate blood clotting do not deter the physician from cleansing a wound or applying a tourniquet. Similarly, perception can usefully and simply index the complex match of internal and external elements, structures, and sequences that optimizes learning. How can the validity of this mediation be tested? Unlike a path diagram, which requires an indisputable list of all causal variables plus an unequivocal indication of their causal direction, the mediation diagram (Figure 3) simply asserts that, in ordinary classroom instruction, no variable explains any sizable variation in immediate learning outcomes beyond that accounted for by aptitude, content opportunity, and student perception of the learning environment, provided the variables are validly and reliably measured. Thus other variables (including products and quadratics to test for interaction and curvature) added to a regression containing these variables will not replicably increase R2. Similarly, extended outcomes are mainly accounted for by immediate outcomes, perceptions, and past (and possibly present) student background. In samples of ordinary middle-school, high school, and college lecturediscussion classes that meet three to five times a week for a term or a year, it may be found that a relevant postcourse learning measure will regress significantly on at least one aptitude measure; and the residual from this equation will regress significantly on at least one opportunity measure (measured by the degree of exposure to criterion content). The second residual will significantly regress on a perception measure. This hypothesized sequence of stagewise entry means that content opportunity will account for variance in the learning criterion, independent of that large amount, perhaps 40 to 60%, which overlaps aptitude and pretest. Similarly, perception will account for learning variance independent of that accounted for by the other two terms. The weighting of the terms in the regression will depend on the covariations of the variables with one another and with the outcome. However, covariations are functions of their variations; a variable must vary reliably, or it cannot covary. For example, if the learning outcome is unreliably measured or is an easy test on which all students get very high scores, then the outcome measure detects very little variation that can covary with aptitude, opportunity, or perception. Concluding that individual differences have been removed or that the latent variables do not covary is based on the fallacy that what is poorly measured does not exist. Whether or not more than one term from each set enters the regression will in part depend on the covariations of the variables within sets. For example, within the aptitude set, IQ alone will generally account for much of the variation

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Posttest understanding Posttest attitude Structural stage

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Figure 3: A mediation diagram for student learning. (This figure is not a path diagram and thus does not identify all causal variables and paths.)

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in the usual outcomes; only if the variation in pretest knowledge, understanding, or attitude covaries sufficiently with outcome variation independent of that accounted for by IQ will one of these variables be significant. Such psychometric measures are reliable, but they have little incremental validity beyond one another in prediction. For example, only recently has it been possible to conclude that small classes and discussions are educationally more beneficial, because much of the older research employed measures of superficial verbaleducational achievement rather than deeper understandings (McKeachie & Kulik, 1975). Weighting the variables within sets equally or by principal or canonical components has empirical appeal. However, theory-guided stagewise, stepwise regression would seem the method of choice in theoretically based research. An extended illustration is given below; here, a word about the reasoning is in order. Just as advocates of perception should allow perceptual variables the worst chance in the regression, that is, allow them to enter in the third stage, after aptitude and opportunity, so should variables within sets be allowed chances to enter in a guided stepwise sequence. To take the aptitude set as example: since IQ predicts learning so generally, it should be allowed to enter first, and the burden of proof should be placed on the incremental explanatory power of questionable variables such as pretest attitude and structural measures. However, these educationally valued but questionably measured variables can serve as tentative outcome indicators; greater effort should go into making them differentially valid.

Educational Cautions In concluding this section two critical points are worth noting. First, even if the mediation hypothesis survives repeated empirical probes by independent investigators, we could not infer that changing perceptions would change rates of learning. Similarly, we cannot unequivocally conclude that cigarette smoking causes lung cancer, even from prospective surveys that link them, and control for rival explanations such as social class and area pollution levels. Statistically controlled correlation does not prove causation, and the mechanism of carcinogens at the cell level still has not been elucidated. Nonetheless, a prudent person who smokes cigarettes reconsiders the possible consequences of smoking on the basis of imperfect correlational evidence before the definitive experiment on humans in natural settings is carried out. Should we do as much in educational psychology? (Some practical experiments and evaluations are described in the final section of this chapter.) Second, the mediation hypothesis may be wrong in holding perception of the learning environment as a means to behavioral or structural ends. The limitations of measurement of such ends and the fallacy of equating what is most measurable or most often measured with what is most important have

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already been mentioned. Beyond these points, it can be argued that certain perceptions (of cohesiveness and democratic practices, for example) are worthy ends in their own right, or that certain perceptions optimize learnings that presently cannot be measured. Either case suggests that educators may find evaluations of perception useful, and additional deliberation and research on these points are certainly in order.

Developments in Research on Classroom Perceptions The works cited in the beginning of this review discuss a number of substantive and methodological issues of research on learning environments. There is little need to repeat points in these reviews. This final section seeks out what is neglected elsewhere, specifically points of emerging consensus or controversy and developments, especially in works that are somewhat inaccessible, that suggest interesting possibilities for future research.

Dimensions of Perception What aspects of perception should be investigated? Shulman (1974) and Walberg (1974c) infer from a number of sources that we normally only hold a few things in conscious perception. Moreover, several possibly similar dimensions of classroom perceptions can be discerned in several research programs that differ greatly in theoretical starting points, operationalization of surface variables, and methods of synthesis. Bales, from unpublished factor analyses of observed behaviors of members of small self-analytic classes at Harvard (personal communication, 1968), posited three factors: Affection, Achievement, and Status. His quasi-spatial coordinates serve as metaphors for individual acts: a member expresses Achievement by forward moves: Status by upward moves; and Affection by moves to the right. These broad classes of behavior presumably summarize much of the individual behavior in groups that could be minutely analyzed. Upon learning of these dimensions in 1968, Ahlgren and Walberg, in unpublished work, tried an orthogonal rotation of factors of student perceptions of the social environments of their classes to an a priori Bales structure. Notwithstanding the fact that Bales factored individual acts and Ahlgren and Walberg factored the means of class perceptions, the rotation produced a close match, in the sense of explaining nearly as much variance as the original components. Independently, Moos (1973) and Insel and Moos (1974) found that the variables represented on instruments measuring perceptions of a wide variety of human environments could be reasonably classified in three categories – Interpersonal Relations, Personal Development, and System Maintenance and Change, which seem close to the factors of Bales.

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Although canonical analysis is more concerned with the number and nature of orthogonal dimensions of association between two sets of variables (rather than within one set, as in factor analysis), it lent support to the first two Bales and Moos dimensions in one study. Walberg (see Anderson & Walberg, 1974) found that student perceptions of satisfaction and cohesiveness were associated with strong interest in physics at the end of the course; a second variate, independent of the first, linked perceived difficulty and pace of the class to cognitive achievement. Because of psychology’s ancient distinction between emotion and cognition, tracing back to Plato, the story might have happily ended with some consensus about the two factors, with not only historical but face, factorial, and predictive validity. Unfortunately, later explorations, reviewed by Anderson and Walberg (1974), questioned such a neat distinction. In new samples on other subjects, perceptions of high cohesiveness and other affective qualities, but not cognitive perceptions, predicted cognitive achievement. Other research (Anderson & Walberg, 1974) suggested that affective and cognitive perceptions are fused in one canonical variate; classes high on the variate contain students who have major accomplishments and interest in the subject. The authors of the perceptual instruments that have been factored tried to make the scales mutually uncorrelated; so it is somewhat contradictory to search for structure in them. Moreover, the scales are reliable enough to treat individually in regression, and because two variables load similarly on a factor does not mean they will correlate similarly with a criterion or predictor. Yet having factors that are comparable across subjects, samples, and instruments would enable replication and consolidation of findings. It is clear that the search for perceptual structure will continue. Future work should avoid the continual exploratory factoring that in ability and personality measurement has led to nearly as many sets of factors as the number of major investigators. The results of purely empirical factoring, such as the popular components and varimax rotation, are highly dependent on the accidents of variance and covariance described in a previous section. Moreover, removing a few outlying observations can greatly change the factor solution. For these reasons (and contrary to its purposes), exploratory factoring seems to have multiplied rather than coordinated or synthesized psychological theory. To avoid such flux in perceptual research, one should start with a priori schemes, either prior empirical solutions or theoretical frameworks. The analyst might either seek confirmation of one or pit one against another, using confirmatory factor or canonical analysis (Mulaik, 1975). The threefactor models of Bales and Moos are good starting points.

Analytic Models The school class is obviously a social group composed of individuals, and it can be analyzed at the group or individual levels. The analysis of perceptions

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of the social environment of the class or school presents some interesting problems that should be considered in future research on social perceptions and other group properties. In educational research around 1965 to 1970 there were four positions on the units question: 1. Sociologists, nearly hypnotized by Robinson’s (1950) seminal paper, cautioned that analysis (correlation, regression, or ANOVA, for example) of means (and other group properties such as percentages in various social stata in census tracts) cannot be generalized to individuals; indeed, they correctly pointed out that regression weights for group means and individuals may differ in sign as well as magnitude. 2. In contrast, psychologists began to realize that the analysis of individuals in groups, which characterizes a large fraction of quantitative research in psychology and education, violates the assumption of discrete, independently responding units for statistical inference. Hence group means seemed to be the unit of choice. 3. Many seemed to be uninformed of the problem or ignored it and arbitrarily chose mean or individual analysis. 4. The Harvard Project Physics (HPP) group got conflicting advice from eminent scholars and so carried out analyses both ways and tried to compare them (see Anderson & Walberg, 1974, for a summary of early analyses). In general, as others have found, means show stronger relations than do individual scores; that is, the ratios of regression weights to their standard errors are larger. Anderson (1970) carried out an integrated analysis. Assuming that perception of the social environment is a class property and IQ is an individual property, he punched the class-mean perception on each student’s card and regressed postcourse individual learning on it and the student’s IQ. Anderson’s insight that different levels can be analyzed simultaneously led to a test of the possibility that perception of the class merely indexes the idiosyncratic match of each individual to the environment. In a set of Montreal data (Anderson & Walberg, 1974) the mean perceptions of a random group of students in the class were nearly as closely related to the mean post-course learning (residualized for IQ) of another random group in the class as they were to the same measure for the same group. Thus, class-mean perceptions are realistic or congruent in Murray’s (1959) sense. Walberg (unpublished tables, 1968) regressed individual learning scores across classes on perceptions, with their class-mean property removed by computing within-class Z scores. The data for these analyses were obtained on the first HPP instrument to measure perceptions of the social environment, and the reliabilities of the scales were low to moderate; hence the research went unpublished. In correspondence about the analysis, Cronbach

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(personal communication, 1969) suggested the concomitant analysis of the class-mean perception and the raw difference of the individual’s perception from his or her class mean. Z scores measure the individuals distance from the class mean in units standardized within class. Raw difference scores measure the distance in the original metric of the variable; they are easier to compute ( x − x ) than Z scores, and have no disadvantage unless standardized ranking is theoretically critical. But what does decomposition of x into x and ( x − x ) tell us? Figure 4a shows the expected situation in which three parallel statements can be made: 1. The more cohesive the class is perceived, the greater the class learning (both measured by means). 2. The extent to which the individual differs from the means in his perception is reflected in his learning deviation, as measured by difference or Z scores. If he perceives the class as more cohesive than his classmates do, he will learn correspondingly more. 3. The higher the individual’s score in perception of cohesiveness across classes, the greater his learning.

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Figure 4B illustrates a reversed situation: 1. The greater the class cohesiveness, the greater the class learning, as before. 2. To the extent that the individual exceeds the class mean in his perception of cohesiveness, however, the less is his learning in relation to the mean of his classmates. 3. The individual’s perception of cohesiveness across classes is unrelated to his learning. Such an odd reversal might be attributable not to the general benefits of cohesiveness to the class as a whole but to the harm of channeling psychic energy into socializing rather than learning by members who see more cohesion in the class than others do. The two effects cancel each other out and produce a zero correlation or flat regression on individuals across classes. Learning might also regress oddly on IQ or anxiety, and it is worth testing the possibility with mean-difference analysis. Another important notion that has implications for perceptual research and other analyses of individuals in groups is Lohnes’s (1972) regression of reading posttest means on pretest means and standard deviations (and other shape indicators). For example, a smaller variation on the pretest might be associated with greater mean gains, because instruction can be targeted on the narrow spread of beginning achievement. To investigate shape and difference effects, Walberg and Singh (1974) regressed individual posttest achievement in social studies and science on means, standard deviations, and differences of aptitude, perception, and control variables, plus their products and squares (to test for interaction and curvature), using large samples of classes in Rajasthan, India. Such regressions, without the information loss of leveling in analysis of variance, test such complex possibilities that brighter classes or students brighter than the rest of their class achieve better or worse under conditions of varying IQ heterogeneity. Such complex analyses, however, generate many variables; unguided stepwise regression should be avoided, and select equations should be cross-validated on independent sets of data. Thus, the order of possible stagewise and stepwise entry should be determined by such rules as placing well-established variables, for example, IQ, before hypothesized variables such as perception; means before standard deviations and difference scores; main effects before interactions; and linear variables before quadratics. The cross-validated Rajasthan results showed that students who learn more are in bright classes, are brighter than their classmates, are with teachers perceived as effective by their classmates, see their teacher as more effective than do their classmates, and are seen as studious but not misbehaving by their classmates. Whereas prior work on learning environments obtained perceptions of the social environment, to which teacher and student contribute, the Rajasthan study showed it is possible to partition significant variation associated with student perception of the teacher and each student. Although separating the objects of perception may be useful, for example, to gain

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formative insights on how the teacher or student is perceived, the separate effects are probably mediated by the student perception of the generalized social environment of learning. The Rajasthan analysis tested the possible entry of 51 different variables but showed that only 6 were significant in the theoretically guided ordering. Sex, socioeconomic status, class size, and interactions were mediated by the main effects of the variables in the select equation, and no curvature among the variables was significant. Moreover, although unrecognized by Walberg and Singh at the time, the results could have been further simplified. The raw regression weights for mean and difference terms were approximately equal (parallel as in Figure 4A rather than reversed as in 4B), and the terms did not interact with one another or any other variable. Therefore, the individual score for IQ and for student perception of the teacher, that is, the simple individual score x (ignoring class), would account for learning as well as both class mean x and individual difference x − x . The analytic points made above are based on two value judgments that coincide with educational practicality. We should strive for simplicity: we should prefer main effects over interactions, linearity over curvature, parallel rather than reversed effects at group and difference levels of analysis, and, in general, few over many variables. But such parsimony and practicality should be accepted only if analyses show that assumptions of complexity are unwarranted.

Analytic Outcomes The discussion has dealt with the regression of educational outcomes on aptitude, perception, and other variables and complexities and possible simplifications among these regressors. The cases of complexity in the criteria or regressands also call for discussion. In addition to using moment-based shape indicators of the pretest, Lohnes (1972) used such indicators of the posttest distribution as regressands. He drew no educational implications about shape indicators – location, spread, and skew – as outcomes, but his method is suggestive for research on educational perceptions specifically and educational effects research in general. It is ironic that the sociological controversy of recent years on educational opportunity has left researchers unsensitized to the possibilities of shape indicators other than a school or class mean or individual score as outcome. As Lohnes points out, shape indicators go back at least as far as Ronald Fisher’s early work. Moreover, instructional psychologists who have recently aimed for equality of results should be considering spread as outcome. And another indicator that deserves consideration is skew; by definition, at least in relative terms, excellence implies one or a few individuals far above the rest (a cosmopolitan educator, however, entertains many aspects of excellence). In addition to such shape indicators, subgroup similarity and contrast effects may warrant analysis when learning or perception is the regressand.

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Walberg, Sorenson, and Fischbach (1972) developed a multivariate, multiple regression procedure to investigate such effects. If we are interested in the relation of an input, such as school size or innovation, associated with differential perceptions of subgroups within a school, such as boys and girls or students of high and low socioeconomic status, we can calculate the means of the subgroup perceptions for each school. Then we can calculate the sums of the subgroup means which indicate, as a dependent variable, the general effect of the independent variable, and the differences between subgroup means which indicate the advantage or disadvantage to one group compared to the other at various levels of a regressor. Sums and differences could also be calculated on learning measures of subgroups within classes and regressed on perceptions and other variables. For example, we can ask how much cohesiveness benefits learning in general and also how much it benefits boys more than girls, or vice versa. Such sum-difference analyses, unlike mean-difference analyses, do not violate the independence assumption and do not have ambiguous numbers of degrees of freedom, as long as multivariate tests are carried out. Three final analytic points should be mentioned. The first is the problem of unreliability corrections. If two variables differ in reliability, it can be misleading to compare their relations to a third variable without adjusting for the differences in reliability, because the measure of relation (r,b,t,F) is diminished by unreliability. Moderately complex analyses seem to call for extraordinarily complex adjustments (Cronbach, Gleser, Nanda, & Rajaratnam, 1972), and the extension of these adjustments to the cases above is far from obvious. How to correct for such varying degrees of unreliability of means, deviations, differences, and products in the presence of colinearity is a difficult and important problem that remains unresolved. Second, although the mean is the standard estimate of location, the Princeton group (Andrews, Bickel, Hampel, Huber, Rosers, & Tukey, 1972) nominates it as “clear candidate” for the “worst estimator” (p. 239) among 68 studied for robustness; even mild skew and slight outliers vitiate it (s, r, and b are even more susceptible). Because of this problem, differences would be better measured from the median, and Lohnes’s location and spread would be better measured by the median and the semi-interquartile range (see Tukey, 1972). These replacements are less correlated with one another than are means and standard deviations in the presence of skew or outliers, which makes them more estimable regressors. Third, research on perceptions of learning environments usually deals with many correlated independent and dependent variables, and much of the published work (see reviews cited in the first section of this chapter) makes use of multivariate techniques not only for their summary power, which is often useful, but to guard against a few chance findings among many investigated, on either side of the equations. But some research regrettably is exploiting such chance relations, and even atheoretical, unguided, uncross-validated stepwise regressions could be cited.

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Evaluation Notwithstanding the great amount of theoretical and analytic research that is necessary, perception instruments have proven to be useful in applied evaluative studies. Eash and Talmage and their associates make student perception of learning environments a focus of their evaluative research. In a rare true educational experiment, Eash, Sparkis, and Rasher (1975) evaluated a public school district’s “alternative” junior high school featuring self-direction, learning contracts, and community involvement. Although achievement tests and self-concept measures showed borderline significance levels, the experimental students rated their classroom environments as being sharply more cohesive, democratic, and goal directed and as having less apathy, disorganization, and favoritism than did students who remained in control schools. The persistence of the differences over the several years of follow-up helped to discount Hawthorne effects. The standard approach to evaluating National Science Foundationsponsored in-service training programs is to administer before and after achievement tests to the teachers. Eash and Talmage (1975) engaged the teachers directly in the evaluation of an “investigative mathematics” program by having each participant administer preceptual measures to her or his class and a control class, discuss the comparison of the class with the control and those of the other participants, and study the changes in perception during the year. It was found that the investigative classes gained more in cohesiveness and less in friction and competitiveness. The teachers found the perceptual data valuable in comparing their initial status to that of others and in charting their comparative progress. Eash and Talmage also evaluated the changes in the perceptions of their classes by black and white students over a period of several years in a suburban school district which carried out an integration plan. The general trend of the significant changes was increasing satisfaction and decreasing friction; and the perceptual data revealed several troubled classes and schools in which the alerted staff was able to remedy conditions. Well-designed correlational evaluations can be as valuable as experiments, but they must be judged on different criteria. Man – A Course of Study, the controversial social studies program, is undergoing an imaginative, methodologically sophisticated correlational evaluation by H.R. Cort (personal communication, 1975). Although the national sample of Man and control classes, the time series design, test sampling, and convergent-divergent measurement are strong points of the evaluation, the perceptual emphasis is of most interest here. Since the objectives of Man are too open ended and implicit to operationalize uniformly, both teachers and students are asked repeatedly during the year to describe in some detail what they think they are accomplishing, how they are doing it, and, in the case of teachers, why they are doing it. In effect, goals are

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inferred, within limits, from what is being learned and then compared to what is avowed. Diversity of comment is solicited, and a full range of apparent outcomes is sought. These are being related to scales measuring the student perceptions of the social environment (Anderson & Walberg, 1974) and perceptions of the emphasis given to levels of the Bloom taxonomy of cognitive objectives (Steele, House, & Kerins, 1971). Cort is also experimenting with mean, difference, and variation analyses.

Measurement Since a number of methodological questions have been considered above, this subsection is brief and treats only developments in measurement. Doctoral dissertations by Gardner (1974) at Monash University (Victoria, Australia), Holsinger (1972) at Stanford, and Zussman (1976) at McGill indicate some new directions for measuring perceptions of learning environments. All three works are large-scale empirical studies based on a priori theoretical frameworks that hold bright promise as beginning research programs. Gardner developed a perceptual instrument based on Murray’s (1959) needs-press theory and brought this personality-social-psychological perspective into curriculum research, with interesting results. Zussman’s dissertation, which is still in progress, shows that reducing the 15 scales from the Learning Environment Inventory (LEI; Anderson & Walberg, 1974) from seven to three items each, and hence the time from about 25 to 11 minutes, results in little reliability loss. Holsinger translated only 7 of the 105 LEI items to Portuguese and found the drastically shortened scale had greater predictive validity than a socioeconomic status index with student cognitive performance and modernity as criteria for a sample of Brazilian primary school children. Ahlgren (personal communication, 1974), at the University of Minnesota, increases rather than reduces the number of scales and items; adapting items from the LEI and the semantic differential, he uses optically scannable sheets that yield several hundred perceptual responses concerning the school plant, program, staff, and subjects in the curriculum. It is easy for younger children to fill in smiling, neutral, or frowning faces to indicate their evaluations, and the result is scannable for conversion to punched cards or tape. Ahlgren designs graphic transparencies to convey the information to school staff committees for evaluation and planning. Barclay’s instrument (personal communication, 1975) is sociometrically based, and his and other such work merit review in a separate treatment. However, a feature of his methodology at the University of Kentucky that has implications for research on perception of educational environments is the use of numerical ratings to trigger computer readouts of diagnosticprescriptive, natural language statements on classes and individual students, as well as computer-drawn profiles for use by the school staff.

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Such technical developments allow not only quicker, more efficient, and more comprehensive mutual enrichment of theory and data but also various practical applications. Harnessing computer memory, analyses, and graphic displays to these developments and to time series and student sampling should, with due restraint for privacy, offer many possibilities for research, training, and evaluation. Groups at two universities base their research design and measurement on social psychological theory. Johnson and Johnson of Minnesota and DeVries, Edwards, and Slavin of Johns Hopkins focus on classroom competition and cooperation and measure perception of the social environment of learning as well as academic performance and other variables as dependent variables. Johnson and Johnson’s (1974, 1975) masterful integrative review of psychological research (including their own and the early Hopkins work) and classroom application is a model of applied social science. Since the Johnsons’s review is readily accessible, only one point will be drawn from it as an introduction to the later work of the Hopkins group. The Johnsons conclude that when class members compete as individuals for grades, they often become hostile and hinder one another; the social environment becomes less wholesome and constructive. And, as Slavin (1975) shows, grades given to groups as wholes do not uniformly bring out the best in each member when individual accountability is unclear. The Hopkins group invented a mixed cooperative and competitive classroom reward structure called Teams-Games-Tournament (TGT). In TGT, students placed on five-person teams (a cooperative reward structure) compete with members of other teams at three-person “tournament tables” (a competitive reward structure). Points won by students at the tables by competing on course-content-relevant games contribute to a team score. The student at the tournament table, like the baseball player at bat, strives toward a group goal and is individually and publicly accountable for his contribution. Unlike a “group grade” contingency, a student cannot easily afford to coast and let his teammates carry him along. DeVries and Edwards’s (1974) true experiments show that TGT can increase academic achievement, cross-sex and cross-race help and friendship, and favorable perceptions of satisfaction and mutual concern. The Hopkins group finds perceptual measures prove not only quite sensitive to treatments but also more convenient to use than behavioral counts, which are expensive to obtain, and custom-made or published achievement tests, which require special standardizing to compare achievement in units of lessons of different lengths, at various grade levels, and in several subjects in the curriculum. Finally, some brief comments on several on-going investigations may give some indication of the future of perceptual research on learning environments. Robert Ellison of the Institute for Behavioral Research in Salt Lake City, William Genova of TDR Associates in Newton, Massachusetts, and

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Walter Hathaway and Stephen Murray of the Northwest Regional Laboratory in Portland are carrying out state-wide assessments of educational environments. Ellison developed a perceptual instrument based upon eclectic psychological theory and educational needs in Utah and is using a systematic sampling frame to assess classroom environments throughout the state for educational planning. Genova, working with the Massachusetts Department of Education and parent advisory groups, is reviewing existing instruments to formulate a school environment measure that will be used by school staff and parent groups to assess their schools and plan data-based improvement programs. Hathaway and Murray, under contract with the National Institute of Education, are constructing perceptual instruments to measure the intended and unintended effects of the state-mandated competency-based programs in the Oregon high schools. Murray, unlike many psychometrists who assume a purely methodological, atheoretical stance, is also carrying out several sophisticated evaluations of programs to increase the interpersonal sensitivity of teachers; his theoretical models, conceived in collaboration with program developers, relate prior characteristics of teachers, group-formulated goals, social perceptions in the training groups, group effectiveness ratings, changes in classroom behavior, and student perceptions of classroom climate. Empirical results identify breakdowns in the hypothesized causal framework that suggest improvements in materials and procedures. Among the several hundred investigators using the Learning Environment Inventory, several in the United States, Australia, India, and Canada require at least brief mention. Chad Ellet and David Payne of the University of Georgia set forth and refined more than 1,000 behavioral competencies of school principals which are being validated against student perceptions of the classroom environment and achievement. More than 80 percent of the conceptually-hypothesized directions of relations of competencies to perceptions were born out with a sample of 60 schools. Continuing analyses of such relations, with statistical controls for school socioeconomic status and other factors, are laying the basis for systematic assessment and training programs for principals. Colin Powers of the University of Queensland and Richard Tisher of Monash University in Australia are collaborating on replication and extension of multivariate analyses of aptitudes, perceptions, and achievement originally done in the United States and Canada. Rampal Singh of the Jialal Institute of Education in Ajmer, Rajasthan, India is replicating earlier predictive validity studies with a Hindi version of the Inventory. Vidya Bhushan of the University of Montreal translated the Inventory to French and had an independent translation made back to English as a check; he is investigating the cross-cultural validity of prediction and of the Bales-Moos factors discussed in an earlier section. Such national and international hybrid vigor of theory, substance and method bodes well for the future of perceptual research on learning environments.

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Conclusion Hints on the possible utility and direction of perceptual research on classroom teaching and learning may be drawn by analogy from Counsilman’s (1968) analysis of competitive swimming, a sport that has shown steady progress during this century. Physics provides the inverse-square law of resistance and Newton’s law of action-reaction to suggest ways of improving stroke mechanics; and physiological principles set forth regimens for improving aerobic and anaerobic conditioning. But what seems critical among swimmers of international rank is the ability to regulate lap times to the fraction of a second by the “feel of the water.” To be sure, teaching is far more complex than swimming, but perception may be no less critical. What may be required to evaluate teaching techniques based on structural and behavioral theories are perceptual measures of the “feel of the class,” say, its cohesiveness, level of participation, and pace of achievement. Such proximal, immediate measures may yield sensitive indications of effects and conditions that will cumulate in distal medium- and longterm educational consequences. The mystique of the master teacher may be partially attributable to perceptiveness, the ongoing assessment of posture, tone of voice, and subtle patterns and changes in the class. The multivariate association of such behavioral complexes with educational perceptions may reveal the general factors that characterize the social environment, that optimize various learnings, and that can lead to a theory of teaching based on perceptual mediation.

References Allport, G. W. The historical background of modern social psychology. In G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (Vol. 1). Reading, Mass.: AddisonWesley, 1968. Anderson, G. J. Effects of classroom social climate on individual learning. American Educational Research Journal, 1970, 7, 135–152. Anderson, G. J., & Walberg, H. J. Learning environments. In H. J. Walberg (Ed.), Evaluating educational performance. Berkeley: McCutchan, 1974. Andrews, D. F., Bickel, P. J., Hampel, F. R., Huber, P. J., Rosers, W. H., & Tukey, J. W. Robust estimates of location. Princeton, N.J.: Princeton University Press,1972. Berliner, D. C., & Cahen, L. S. Trait-treatment interaction and learning. In F. N.Kerlinger (Ed.), Review of research in education, 1. Itasca, Ill.: F. E. Peacock,1973. Bermant, G. (Ed.). Perspectives on animal behavior. Glenview, Ill.: Scott, Foresman, 1973. Bloom, B. S. Stability and change in human characteristics. New York: Wiley, 1964. Boudon, R. The uses of structuralism. London: Heineman, 1971. Bowers, K. S. Situationism in psychology: An analysis and critique. Psychological Review, 1973, 80, 307–336. Brunswik, E. Perception and the representative design of psychological experiments. Berkeley: University of California Press, 1956. Campbell, D. T. Social attitudes and other acquired behavioral dispositions. In S. Koch (Ed.), Psychology: A study of a science (Vol. 6). New York: McGraw-Hill, 1963.

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Johnson, D. W., & Johnson, R. T. Learning together and alone: Cooperation, competition, and individualization. Englewood Cliffs, N.J.: Prentice-Hall, 1975. Kahn, S. B., & Weiss, J. The teaching of affective responses. In R. M. W. Travers (Ed.), Second handbook of research on teaching. Chicago: Rand McNally, 1973. Koestler, A. The act of creation. New York: Macmillan, 1964. Kolata, G. B. Behavioral development: Effects of environments. Science, 1975, 189, 207–209. Langer, S. K. Mind: An essay on human feeling (Vol. 2). Baltimore: Johns Hopkins Press, 1973. Lenneberg, E. H. On explaining language. Science, 1969, 164, 635–643. Lockhead, G. Psychophysics. Science, 1975, 189, 451. Lohnes, P. R. Statistical descriptors of school classes. American Educational Research Journal, 1972, 9, 547–556. Lohnes, P. R., & Marshall, T. O. Redundancy in student records. American Educational Research Journal, 1965, 2, 19–23. Marjoribanks, K. (Ed.). Environments for learning. London: National Foundation for Educational Research, 1974. McGuire, W. J. Personality and susceptibility to social influence. In E. F. Borgatta & W. W. Lambert (Eds.), Handbook of personality theory and research. Chicago: Rand McNally, 1968. McGuire, W. J. The yin and yang of progress of social psychology: Seven koan. Journal of Personality and Social Psychology, 1973, 26, 446–456. McKeachie, W. J., & Kulik, J. A. Effective college teaching. In F. N. Kerlinger (Ed.), Review of research in education, 3. Itasca, Ill.: F. E. Peacock, 1975. Moos, R. H. Conceptualizations of human environments. American Psychologist, 1973, 28, 652–664. Mulaik, S. A. Confirmatory factor analyses. In D. A. Amick & H. J. Walberg (Eds.), Introductory multivariate analysis. Berkeley: McCutchan, 1975. Murray, H. A. Preparations for the scaffold of a comprehensive system. In S. Koch (Ed.), Psychology: A study of science, Vol. 3. New York: McGraw-Hill, 1959. Pantin, C. F. A. The relations between the sciences. London: Cambridge University Press, 1968. Piaget, J. Science of education and the psychology of the child. New York: Viking, 1970. Piaget, J. Structuralism. New York: Basic Books, 1971. Power, C., & Tisher, R. Variations in the environment of self-paced science classes: Their nature determinants, and effects. Paper presented at the Annual Conference of the Australian Association for Research in Education, Adelaide, Australia, November, 1975. Randhawa, B. S., and Fu, L. L. W. Assessment and effect of some classroom environment variables. Review of Educational Research, 1973, 43, 303–321. Ravitch, D. The great school wars. New York: Basic Books, 1974. Riegel, K. F. Influence of economic and political ideologies on the development of developmental psychology. Psychological Bulletin, 1972, 78, 129–141. Robinson, W. Ecological correlations and the behavior of individuals. American Sociological Review, 1950, 15, 351–357. Scriven, M. A possible distinction between traditional scientific disciplines and the study of human behavior. In H. Feigl & M. Scriven (Eds.), Minnesota studies in the philosophy of science (Vol. 1). Minneapolis: University of Minnesota Press, 1956. Shulman, L. S. The psychology of school subjects: A premature obituary. Journal of Research in Science Teaching, 1974, 11, 319–339. Shulman, L. S., & Tamir, P. Research on teaching in the natural sciences. In R. M. W. Travers (Ed.), Second handbook of research on teaching. Chicago: Rand McNally, 1973. Skinner, B. F. Are theories of learning necessary? Psychological Review, 1950, 57, 193–216. Skinner, B. F. Beyond freedom and dignity. New York: Knopf, 1971.

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Slavin, R. E. Classroom reward structure: Effects on academic performance and social growth. Unpublished doctoral dissertation, Johns Hopkins University, 1975. Steele, J. M., House, E. R., & Kerins, T. An instrument for assessing instructional climate through low-inference student judgments. American Educational Research Journal, 1971, 8, 447–466. Stent, G. S. Limits to the scientific understanding of man. Science, 1975, 187, 1052–1057. Tagiuri, R. Person perception. In G. Lindzey & E. Aronson (Eds.), The handbook of social psychology (Vol. 3). Reading, Mass.: Addison-Wesley, 1969. Talmage, H. Systems of individualized education. Chicago: National Society for the Study of Education, 1975. Tavris, C. The experimenting society. Psychology Today, 1975, 9, 44–50. Taylor, C. W., & Ellison, R. L. Moving toward working models in creativity. In I. A. Taylor and J. W. Getzels (Eds.), Perspectives in creativity. Chicago: Aldine, 1975. Tresselt, M. E. The effect of the experiences of contrasted groups upon the formation of a new scale of judgment. Journal of Social Psychology, 1948, 27, 209–216. Tukey, J. W. Exploratory data analysis. Reading, Mass.: Addison-Wesley, 1972. Walberg, H. J. Optimizing and individualizing instruction. Interchange, 1971, 2, 15–27. Walberg, H. J. An overview of social psychology. In J. Culbertson (Ed.), Social science content for preparing educational leaders. Columbus, Ohio: Charles E. Merrill, 1973. Walberg, H. J. Educational process evaluation. In M. W. Apple et al. (Eds.), Educational evaluation: Analysis and responsibility. Berkeley: McCutchan, 1974. (a) Walberg, H. J. (Ed.). Evaluating educational performance: A sourcebook of methods, instruments, and examples. Berkeley: McCutchan, 1974. (b) Walberg, H. J. Learning models and learning environments. Educational Psychologist, 1974, 11, 102–109. (c) Walberg, H. J. Psychological theories of educational individualization. In H. Talmage (Ed.), Systems of individualized education. Berkeley: McCutchan, 1975. Walberg, H. J., & Singh, R. Teacher quality perceptions and achievement in Rajasthan. Alberta Journal of Educational Research, 1974, 20, 226–232. Walberg, H. J., Sorenson, J., & Fischbach, T. Ecological correlates of ambience in the learning environment. American Educational Research Journal, 1972, 9, 139–148. Walberg, H. J., & Thomas, S. C. Open education: An operational definition and validation in Great Britain and United States. American Educational Research Journal, 1972, 9, 197–208. Watson, J. B. Psychology as a behaviorist views it. Psychological Review, 1913, 20, 158–177. Watson, J. B. Behaviorism. Chicago: University of Chicago Press, 1925. Webb, E. J., Campbell, D.T., Schwartz, R. D., & Sechrest, L. Unobtrusive measures: Nonreactive research in the social sciences. Chicago: Rand McNally, 1966. Whitehead, A. N. The aims of education and other essays. New York: Macmillan, 1929. Wittgenstein, L. Philosophical investigations. New York: Macmillan, 1953. Zussman, D. The convergent-divergent abilities of students and their teachers. Unpublished doctoral dissertation, McGill University, 1976.

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24 Thought and Two Languages: The Impact of Bilingualism on Cognitive Development Rafael M. Diaz

B

y the end of 1979, approximately 3.6 million children in the United States were judged to be in need of special linguistic assistance to cope with the regular school curriculum (Pifer, 1980); at the time, however, roughly 315,000 children were participating in some kind of bilingual education program. Despite the fact that federal spending on bilingual education is comparatively low, and that existing programs reach only a fraction of eligible children, bilingual education is presently under considerable attack. Indeed, “few other educational experiments in recent years have managed to arouse such passionate debate – so much so, in fact, that the future of this promising educational tool is uncertain” (Pifer, 1980, p. 4). The attack against bilingual education can be explained mostly in terms of political, cultural, and socioeconomic variables (see Fishman, 1977). A discussion of such variables is well beyond the scope of this paper. Nevertheless, for our purposes it should be noted that psychological and educational research on the effectiveness of bilingual education often has provided the attackers with sophisticated weapons. For example, an influential study of bilingual education projects sponsored by the Office of Education in 1976 (American Institute for Research, 1977) showed that many existing programs were not providing academic gains for students and, in some cases, were allowing

Source: Review of Research in Education, 10 (1983): 23–54.

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students to fall behind. Although the study has been criticized severely for basic methodological flaws, it has contributed significantly to a negative mood against bilingual education efforts in the nation (Blanco, 1977). Tucker and D’Anglejan (1971) outlined four commonly held beliefs regarding the effects of bilingual education: (1) Children who are instructed bilingually from an early age will suffer cognitive or intellectual retardation in comparison with their monolingually instructed counterparts. (2) They will not achieve the same level of content mastery as their monolingually instructed counterparts. (3) They will not achieve acceptable native language or target language skills. (4) The majority will become anomic individuals without affiliation to either ethnolinguistic group. (as cited in Cummins & Gulutsan, 1974, p. 259). Some of these beliefs are just that – beliefs. Others are based on studies that were poorly designed and that failed to control for relevant confounding variables such as children’s actual knowledge of their two languages or bilingual-monolingual group differences in socioeconomic status. At present, almost everyone in the field agrees that research on the effects of bilingual education in this country is relatively scarce and, at best, inconclusive (Paulston, 1977). Nevertheless, studies of doubtful validity and ill-founded conclusions are much too often used by legislators and politicians to decide the future of social and educational programs for children. In the near future, the Bilingual Education Act will come up for reauthorization, and once again research findings will be used (and misused) to attack or support decisions regarding the future of bilingual education. There is an urgent need to carefully review the validity of present research findings, in light of their theoretical assumptions and research methodologies, to enlighten policy decisions with scientific facts. This paper attempts, in part, to respond to this need by reviewing the literature on the effects of bilingualism on children’s cognitive development. The review focuses on the psychological literature relating bilingualism and secondlanguage learning to children’s cognitive performance rather than on formal educational evaluations of existing bilingual education programs. Special attention is given to research showing the cognitive advantages of becoming bilingual, bringing to surface the underlying theoretical models relating children’s bilingualism to positive cognitive gains. After all, the rationale for bilingual education rests heavily on the belief that true bilingualism, rather than “semilingualism” or the gradual loss of the first language, is advantageous to children’s learning and cognitive development. It is my hope that this review will not only stimulate further and more rigorous research in the area, but also serve as a guide and inspiration to educational policymakers.

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Bilingualism and Intelligence: Early Studies Systematic studies on the relationship between bilingualism and intelligence began in the early 1920s, parallel to the flourishing of psychometric tests of intelligence. Because the measurement of intellectual potential was, and still is, heavily dependent on verbal abilities, psychologists and educators were concerned about the validity of such tests for bilingual children. The main concern was that bilingual children would suffer from some kind of language handicap, and this, in turn, would be an obstacle to a fair assessment of their intellectual abilities and potential. The overwhelming majority of studies prior to 1962 found, indeed, strong evidence for the so-called “language handicap” in bilingual children (see reviews by Arsenian, 1937; Darcy, 1953, 1963; Macnamara, 1966). When compared to monolinguals, bilingual children appeared inferior on a wide range of linguistic abilities. Among other things, bilinguals were shown to have a poorer vocabulary (Barke & Perry-Williams, 1938; Grabo, 1931; Saer, 1923), deficient articulation (Carrow, 1957), lower standards in written composition, and more grammatical errors (Harris, 1948; Saer, 1923). Interestingly enough, evidence of a language handicap in bilingual children did not lead to a questioning of the validity of psychometric tests of intelligence for this population. Rather, the consistent findings about bilinguals’ deficient linguistic performance quickly led to statements about the negative effects of bilingualism on children’s intelligence. For a long time, children’s bilingualism was considered as some kind of social plague (Epstein, 1905), “a hardship devoid of apparent advantage” (Yoshioka, 1929, p. 476). The language handicap of bilinguals was interpreted as a linguistic confusion that deeply affected children’s intellectual development and academic performance up to the college years (Saer, 1923). Beliefs about the negative effects of early bilingualism were further confirmed when several studies showed that bilinguals also performed significantly lower than monolinguals on tests of nonverbal abilities, such as tests of dextrality (Saer, 1931) and mathematical competence (Carrow, 1957; Manuel, 1935). Most early studies in this area, however, suffer from a wide range of methodological problems; so much so that at present most investigators in the field regard the findings of early studies as totally unreliable (see Cummins, 1976). Many early studies, for example, failed to control for group differences in socioeconomic status between bilingual and monolingual samples. As early as 1930, McCarthy pointed out that bilingualism in the United States was seriously confounded with low socioeconomic status. She found that more than half the occurrences of bilingualism in school children could be classified as belonging to families from the unskilled labor occupational group. Along the same lines, Fukuda (1925) alerted researchers to the fact that high-scoring, English-speaking subjects were mostly in the occupational and executive classes; he reported a correlation of .53 between the Whittier

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(socioeconomic) Scale and the Binet IQ for this population. Nevertheless, prior to the early 1960s, most studies investigating the effects of bilingualism on children’s intelligence did not account for group differences in socioeconomic status. A second major methodological flaw of early studies is that investigators consistently ignored children’s actual degree of bilingualism. An extreme example is a study by Brunner (1929) where degree of bilingualism was determined by the foreignness of parents. Brunner divided his bilingual sample into three categories: (1) both parents born in this country, (2) one parent born here and the other abroad, and (3) both parents born abroad. The classification was simply assumed to represent children’s varied degree of bilingualism. In other studies, the sample’s bilingualism was assessed through family names or even place of residence (see Darcy, 1953, for a review). As present investigators have stated repeatedly, it is impossible to ascertain if the bilingual subjects of many studies were indeed bilingual or just monolingual of a minority language. A few studies, however, were conducted with controls for socioeconomic variables and attempted more refined measures of subjects’ bilingualism. Fritz and Romkin (1934), for example, tested 201 junior high school students in Kansas on the Otis Self-Administering Test of Mental Ability, the New Stanford Achievement Test, and the Sims Socio-Economic Score Card. The sample consisted of two different groups: an “only-English-speaking” group and a “usually-foreign-speaking” group. As expected, the results showed that the monolingual English-speaking group was at a definite advantage in all achievement and IQ variables, as well as in socioeconomic status. To make the two language groups more comparable, Fritz and Romkin matched 12 children from each group on relevant variables such as sex, age, mental ability, and socioeconomic status. Once again, the results showed that “foreign-speaking” children performed at a lower level than monolinguals on all sections of the achievement test. Although the matched samples were small, and the matching procedure never guarantees that groups are equivalent on all relevant variables, this study shows that the language handicap of bilinguals was evident even when socioeconomic variables were controlled somewhat. The methodological problem remained, however, with the fact that the selection of foreign-speaking subjects does not guarantee that the bilingual sample masters both languages at age-appropriate levels to be considered truly bilingual. Other studies attempted such strict controls that comparisons between bilingual and monolingual samples on cognitive variables became meaningless. Hill (1935) compared Italian children who heard and spoke only Italian at home with Italian children who heard and spoke only English at home. The sample’s degree of bilingualism was ascertained by questionnaires and tests of comprehension of spoken Italian and Italian word meaning. The two groups of children were equated on age, sex, educational environment,

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mental age, and intelligence quotient. As could be reasonably expected, the results showed no reliable differences between the two groups of children in verbal and nonverbal scores. Arsenian (1937) argued that Hill’s (1935) results are basically meaningless, because matching the groups on an IQ measure that is based on both verbal and nonverbal performance guarantees a lack of difference result in verbal and nonverbal abilities. This study, however, is an excellent example of the dilemma faced by both early and present investigators in the field. To date, it is not clear how to control for group differences between bilingual and monolingual intellectual abilities and at the same time study meaningful group differences in both cognitive and linguistic abilities. One possible solution is to use subjects as their own controls and study cause–effect relationships between degree of bilingualism and cognitive variables using a longitudinal design. Unfortunately, there are very few longitudinal studies that shed light on these cause-effect relationships.

Lessons from Four Decades of Research The severe methodological problems of early studies resulted in few clear facts about the effects of bilingualism on children’s intelligence and intellectual development. On the other hand, early studies yielded a great deal of wisdom about the complexity of the issues. The first few decades of serious systematic studies in the field have alerted researchers to simplistic theories and methodologies regarding the phenomenon of bilingualism and recognize the variables that mediate its effects on children’s cognitive development. As early as 1937, Arsenian argued against a unidimensional construct of bilingualism and argued that variations between different bilingual experiences could make a big difference in the types of effects observed in children’s cognitive performance. Specifically, Arsenian proposed that for scientific research purposes, bilingual samples should be defined along the following dimensions: Degree of bilingualism. Bilinguals vary in degree of proficiency in their two languages. Some bilingual children are just beginners in learning the second language, while others have achieved age-appropriate levels of proficiency in both languages. Furthermore, the bilingualism of a given person may vary with time; for example, in some bilingual situations increased competence and mastery of a second language gradually replaces the use and abilities of the first language. The effects of such variations within bilinguals should be the object of scientific investigation rather than simply ignored. Degree of difference between the two languages. Two languages from different language families vary along more dimensions than two languages within the same language family. Spanish, for example, is closer to other IndoEuropean languages such as Italian, French, and Rumanian than it is to

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English or Japanese. It is clear that more cognitive effort is required from a Spanish child to learn the morphology, grammar, and phonetics of English than for the same child to learn Italian. Furthermore, the degree of difference between two languages might represent deeper cultural differences that the child must assimilate and accommodate to achieve proper mastery of the language. In Arsenian’s. (1937) words: The degree of difference between the two languages of a bilinguist is important from the point of view not only of the learning mechanism, but also of the thinking process; because the difference between two languages usually denotes a difference in the culture and civilization of the two peoples using them, and hence denotes also a difference in the connotation of words which will influence the direction and the content of thought in the two languages. (p. 20)

It should not be surprising, therefore, that the degree of difference between two languages might mediate the effects of a bilingual experience on children’s cognitive development. The effects of this variable must be considered carefully when attempting to generalize from one bilingual experience to another. Age when learning a second language. Although it is not clear what age is best (or worst) to learn a second language, most likely the experience of becoming bilingual will have different cognitive effects, depending on the learner’s age. For example, the experience of infants exposed to two languages simultaneously (Leopold, 1949a, 1949b) seems to be qualitatively different from the experience of a monolingual 6- or 7-year-old who is faced with the task of learning a second language to understand the school curriculum. The question regarding the best age to learn a second language is, indeed, an unresolved issue in current research. By the same token, it is not clear if the age of the second-language learner is an important variable mediating the possible positive or negative effects of bilingualism. Those who argue in favor of a critical period hypothesis in language acquisition, and the relative ease of acquiring a language during this period, tend to postulate different cognitive effects of second-language learning depending on whether the learner is within or beyond this critical period (see Lenneberg, 1967; Penfield & Roberts, 1959). Others argue that the introduction of a second language at an early age, when the child has not yet achieved a certain degree of competence in his first language, might be detrimental to the child’s cognitive development, while positive cognitive gains should be expected from bilingualism if the second language is introduced after the child has achieved a certain threshold level of competence in his first language (Cummins, 1976). It is important to note that certain dependent variables in studies of bilingualism and cognition might be particularly sensitive to age effects. For example, several studies have shown that a bilingual’s vocabulary in both the first and second language is smaller than the vocabulary of monolinguals (Grabo, 1931; Saer, 1923; Sanchez, 1934). However, on the basis of the data from several other studies, Arsenian (1937) showed that this

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apparent deficit is closely related to a given age group of bilinguals, and therefore is a temporary effect of second-language learning at a young age. The same effects simply are not found in older bilinguals (Murdoch, Maddow, & Berg, 1928). Method of learning the second language. Arsenian (1937) insisted that researchers should be attentive to whether the bilingual child had learned the two languages simultaneously or whether the second language had followed the first. Relevant to this dimension is the distinction between acquiring and learning a second language. Briefly stated, second-language acquisition refers to the process of acquiring a second language in a natural environment, outside of formal instruction; second-language learning refers to the process of formal language education where one aspect of the grammar is introduced at a time, and systematic feedback with error correction is provided (McLaughlin, 1978). There are few empirical findings regarding the cognitive effects of acquiring versus learning a second language. Probably, in most situations, bilinguals both acquire and learn different aspects of the second language. However, there is some scattered evidence that certain features of language acquisition might ease the process of formal second-language learning. In one of the earliest studies in the area, Saer (1923) tested approximately 1,400 children from ages 7 to 12 in five rural and two urban districts in Wales. Saer obtained the following results on the Stanford-Binet scale:

Monolingual Bilingual

Urban

Rural

99 100

96 86

According to Saer’s data, differences in the performance of bilingual and monolingual children seem to exist only in the rural sections. Saer explained his findings in the following way: For the rural Welsh-speaking children, Welsh is the language of home, play, and Church and, therefore, a language with strong affective connotations. When these children are exposed to a second language at school, a conflict is raised between the child’s “selfregarding sentiment or positive self-feeling” and his “negative self-feeling or his instinct for submission” (p. 37). On the other hand, for the Welsh-speaking child in the urban areas this conflict is played down by the fact that they come in contact and play with English-speaking children at an early age, before a formal learning contact with the second language at school. Although there is no evidence to support Saer’s psychodynamic assertions, his data do indeed suggest that opportunities to acquire a second language might mediate the effects of second-language learning on cognitive development. More recent studies show that children who begin bilingual education programs with a fair amount of knowledge of the second language perform significantly better on several cognitive measures than children with little or no previous experience in the second language (Diaz & Hakuta, Note 1).

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Attitudes toward the second language. Bilingual experiences vary significantly in terms of the social, political, and religious sentiments connected with the first and second languages. As Saer’s (1923) conclusions suggested, having to learn a second language might threaten a person’s self-esteem when the second language is identified in any way with a colonizing or assimilating force. In such situations, a negative attitude toward the second language might play a crucial role in determining children’s linguistic and academic performance. Arsenian believed, therefore, that when defining a given bilingual situation, researchers must include a detailed description of the national, religious, and political significance of the second language for the bilingual sample involved (see also Fishman, 1977). Although Arsenian (1937) at an early stage outlined the five dimensions mentioned above, the majority of studies in the field prior to 1962 lacked adequate assessments of the sample’s actual degree of bilingualism or proficiency in both languages. Also, as a rule, bilinguals were treated as a homogeneous group with no adequate consideration of the variability in second-language learning or acquisition histories. Furthermore, results from studies of specific bilingual situations were grossly generalized as effects of the universal aspects of bilingualism. Toward the end of the 1950s, research on the effects of bilingualism showed consistent findings. Monolinguals performed significantly higher than bilinguals on measures of verbal intelligence. Some studies showed that monolinguals were also at an advantage on measures of nonverbal ability, but group differences on this variable were not consistent across studies. On one hand, the findings suggested that at certain stages of second-language learning, bilinguals suffered from a “language handicap.” On the other hand, it was not clear if this linguistic disadvantage in bilinguals was a true intellectual deficit of a permanent nature, or just a temporary manifestation of the struggle to cope with two different language systems at a relatively young age. Further research to clarify these issues seemed extremely important on two counts. First, the question was obviously and directly relevant to educational policy in several countries. Second, the negative findings contradicted linguists’ case studies and theoretical statements regarding the effects of early bilingualism. The best-known linguistic study of a child’s simultaneous acquisition of two languages is Leopold’s monumental investigation of his daughter Hildegard (Leopold, 1939, 1947, 1949a, 1949b). Hildegard lived most of the time in an English-speaking environment, but her father spoke to her in German and her mother in English. As was the case in similar earlier studies (see e.g., Pavlovitch, 1920; Ronjat, 1913), Leopold’s study found little interference between Hildegard’s two languages, and no evidence at all of any serious linguistic retardation in either language. Hildegard shifted languages with relative ease and developed strategies to use new words appropriately in the context of their respective languages. Leopold (1949b) noted in his last

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volume that by age 3 both his daughters had an awareness of dealing with two separate languages, and from then on both languages seemed to develop adequately as two independent systems. Furthermore, Leopold regarded his daughters’ bilingualism as a genuine asset to their mental development. He felt that bilingual children must learn very early to separate the sound of the word from its referent, and this, in turn, forced the child to focus on essentials, on “content instead of form” (p. 188). Leopold’s conclusion implies that bilingualism accelerates the development of abstract thinking by freeing the child’s thought from the concreteness and “tyranny” of words. Similar claims can be found in the work of Evans (1953) and Vygotsky (1962). Nevertheless, because the majority of studies before 1962 showed that bilinguals performed lower than monolinguals on linguistic, cognitive, and academic variables, the first four decades of psychological research on the effects of bilingualism were loaded with the notion that bilingualism was detrimental to children’s intelligence and cognitive development. In the early 1960s, however, new experimental procedures and more controlled sample selection procedures led to very different conclusions. Peal and Lambert’s study in 1962 marked the turning point.

Bilingualism and Pseudobilingualism: Peal and Lambert (1962) Aware of the potential advantages of bilingualism for children’s cognitive development, Peal and Lambert (1962) attributed the negative findings of early studies to the failure of researchers to differentiate “pseudo-bilinguals” from truly bilingual children. “The pseudo-bilingual knows one language much better than the other, and does not use his second language in communication. The true bilingual masters both at an early age and has facility with both as means of communication” (p. 6). Guided by O’Doherty’s (1958) writings, Peal and Lambert believed that while pseudobilingualism might be a serious problem that could result in intellectual retardation, genuine bilingualism may be a real asset to children’s intellectual development. Because early studies had been lax in their definition of bilingualism and in the assessment of their sample’s degree of bilingualism, negative findings could be attributed to a situation of pseudobilingualism. To test their hypotheses, Peal and Lambert (1962) administered several measures of degree of bilingualism to 364 10-year-old children in Canada. Three tests were used to determine whether children were “balanced” bilinguals, that is, equally skilled in French and English, or whether they were monolingual. Children’s self-ratings of their ability in the second language were taken into account also. The final sample was composed of 164 subjects: 75 monolinguals and 89 (genuine or balanced) bilinguals. Children in the sample were administered a modified version of the Lavoie-Laurendau

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(1960) Group Test of General Intelligence, the Raven’s Coloured Progressive Matrices, and a French version of selected subtests of the Thurstone and Thurstone (1954) Primary Mental Abilities Test. In addition, several measures of attitudes toward English Canadians, French Canadians, and the self were administered to the subjects. Contrary to the findings of earlier studies, the results of the Peal and Lambert study showed that bilinguals performed significantly better than monolinguals in most of the cognitive tests and subtests, even when group differences in sex, age, and socioeconomic status were appropriately controlled. Bilingual children performed significantly higher than monolinguals on tests of both verbal and nonverbal abilities; the bilinguals’ superiority in nonverbal tests was more clearly evident in those subtests that required mental manipulation and reorganization of visual stimuli, rather than mere perceptual abilities. A factor analysis of test scores indicated that bilinguals were superior to monolinguals in concept formation and in tasks that required a certain mental or symbolic flexibility (the notion of cognitive flexibility will be discussed in detail in a later section). Overall, bilinguals were found to have a more diversified pattern of abilities than their monolingual peers. Peal and Lambert’s (1962) findings must be considered, however, with a certain degree of caution. First, as Macnamara (1964, 1966) pointed out, the process of subject selection might have introduced a bias in favor of the bilingual sample. Peal and Lambert’s bilingual sample included only children who scored above a certain determined level in the English Peabody Picture Vocabulary Test, a test commonly used to measure intelligence in monolinguals. It is possible that in a situation like Canada, the intelligence of French-Canadian children might be reflected in a measure of English (the second language) vocabulary. Second, on the average, the bilingual sample belonged to a higher grade than the monolingual sample; perhaps the superiority observed in bilinguals was the result of their having longer exposure to formal education. And third, the frequency distribution of the Raven’s test scores was very different for both groups of children; it was negatively skewed for bilinguals, while the opposite was true for monolinguals. In short, the cognitive advantages observed in Peal and Lambert’s balanced bilingual sample could have been inflated by several artifacts in their subject selection procedures. As Peal and Lambert admitted, A partial explanation of this [the results] may lie in our method of choosing the bilingual sample. Those suffering from a language handicap may unintentionally have been eliminated. We attempted to select bilinguals who were balanced, that is, equally fluent in both languages. However, when the balance measures did not give a clear indication of whether or not a given child was bilingual, more weight was attached to his score on the English vocabulary test. Thus some bilinguals who might be balanced, but whose vocabulary in English and French might be small, would be omitted from our sample. The less intelligent bilinguals, those who have not acquired as large an English vocabulary, would not be considered bilingual enough for our study. (p. 15)

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Nevertheless, Peal and Lambert’s (1962) empirical distinction between bilinguals and pseudobilinguals made a significant (and much needed) methodological contribution to the field. Their distinction has forced recent investigators to select their bilingual samples with greater care and to measure the sample’s actual knowledge of the two languages. Peal and Lambert’s study also alerted researchers to the possible positive and negative effects of bilingualism depending on the bilingual situation involved. Recently, more attention has been given to descriptions of different types of bilingual experiences that might have different effects on children’s cognitive development (see Cummins, 1976). One such situation results in “semilingualism.” Semilinguals are children whose second language gradually replaces the native tongue. Therefore, at a given point, these children are neither fluent speakers of the first language nor have mastered the second language with age-appropriate ability. Along these lines, Macnamara (1966) noted that in certain Irish-English bilingual situations in Ireland, competence in the second language was attained at the expense of competence in the first language. Macnamara named this process the “balance effect,” which must be carefully distinguished from those situations where children move toward balanced bilingualism, that is, age-appropriate abilities in both languages. Recent studies in Scandinavia (e.g., Hansegard, 1968; Skuttnabb-Kangas, Note 2) have shown that semilingualism has negative emotional, cognitive, linguistic, and scholastic consequences (see Paulston, 1975, for a review of Scandinavian research on semilingualism). When trying to understand the situation of minority bilingual children in the United States, one must look carefully for signs of semilingualism or the balance effect. The main reason is that semilingualism is usually associated with the bilingualism of the poor economic classes. Sociolinguists have often made a sharp distinction between the bilingualism of upper- and lower-class children in terms of “elitist” versus “folk” bilingualism (Fishman, 1967; Paulston, 1975). As a rule, elitist bilingualism is a matter of choice for the educated classes and has not presented any educational problems. On the other hand, folk bilingualism is “the result of ethnic groups in contact and competition within a single state” (Cummins, 1976, p. 19). Folk bilingualism also is associated with several sociocultural factors, such as negative attitudes and actual discrimination against the use of a minority language, which probably prevent the adequate development of genuine or balanced bilingualism.

Cognitive Advantages of Balanced Bilinguals Although the Peal and Lambert (1962) study had some serious methodological difficulties, it must be pointed out that their findings regarding the positive effects of balanced bilingualism have been replicated in more recent studies that have carefully assessed the sample’s actual knowledge of the

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two languages. Indeed, when compared to monolinguals, balanced bilingual children show a wide range of advantages in different cognitive tasks. These studies will be carefully reviewed here.

Cognitive Flexibility Several studies have concluded that bilinguals are more cognitively “flexible” than monolinguals; the construct “cognitive flexibility,” however, has never been adequately defined. The notion of flexibility has been loosely used and abused to account for bilinguals’ superior performance on a wide range of cognitive tasks. For example, the term was used by Peal and Lambert (1962) to describe bilinguals’ performance on tests of general reasoning; by BenZeev (1976, 1977a) to describe bilinguals’ improved attention to structure and detail; by Balkan (1970) to describe performance on perceptual and “set changing” tasks; and by Landry (1974) to describe divergent thinking skills measured by tests of creativity. (See Cummins, 1976, for a discussion of the conceptual confusion underlying the term cognitive flexibility.) Nevertheless, this poorly defined construct is now widely used, and many students and researchers in the field argue that bilinguals are, indeed, more cognitively flexible than monolinguals. It is important, therefore, to trace the history of the term’s usage, as well as to clarify the nature of the tasks where bilingual children seem to perform more “flexibly” than monolinguals. In the literature on bilingualism and cognitive development, the term cognitive flexibility was used first by Peal and Lambert (1962) to describe bilinguals’ performance on measures of general intelligence. Specifically, the term was used to explain a puzzling finding, namely, that bilinguals performed significantly better than monolinguals on several nonverbal tests of intelligence. On the basis of earlier linguistic studies, the superior performance of balanced bilinguals on verbal tests could be explained rather easily by the linguistic advantages of knowing two different languages, such as the early separation between sound and meaning. However, a similar explanation was not available for the effects of bilingualism on nonverbal abilities. Bilinguals’ need to switch languages and a resulting mental flexibility proved to be a logical and attractive explanation. Because bilinguals outranked monolinguals on both verbal and nonverbal tests, an alternative explanation would have been to simply admit the (nonintuitive) conclusion that bilinguals in the study were more intelligent than the monolinguals. Such an explanation, however, would have cast further doubts on Peal and Lambert’s sample selection procedures. After submitting their data to a factor analysis, Peal and Lambert (1962) noted that the nonverbal advantages of balanced bilinguals appeared more clearly on tests requiring some manipulation and reorganization of symbols, rather than on tasks requiring perceptual or spatial abilities. Previous analyses

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of nonverbal tests of ability (Ahmed, 1954; Anastasi, 1961) suggested that spatial visualization and mental manipulation of visual symbols are independent abilities. Moreover, Ahmed (1954) described this second ability “as if it consisted of mental flexibility which is involved in the process of mentally reorganizing the elements of a problem situation” (as cited in Peal & Lambert, 1962, p. 14; italics added by Peal & Lambert). Peal and Lambert went a step further and cleverly explained the newly discovered flexibility of bilinguals in terms of their habitual language switching: The second hypothesis is that bilinguals may have developed more flexibility in thinking. Compound bilinguals typically acquire experience in switching from one language to another, possibly trying to solve a problem while thinking in one language, and then, when blocked, switching to the other. This habit, if it were developed, could help them in their performance on tests requiring symbolic reorganization since they demand a readiness to drop one hypothesis or concept and try another. (p. 14)

Implied in Peal and Lambert’s explanation is the assumption that bilingual children would perform verbally the mental manipulation of visual symbols required by nonverbal tests like the Raven’s Progressive Matrices. More specifically, their hypothesis involves three basic (and untested) assumptions: (1) that bilingual children are thinking verbally while performing these nonverbal tasks, (2) that bilinguals switch from one language to the other while performing these tasks, and (3) that bilinguals’ habit of switching languages while performing these tasks stimulates the ability to more readily discard doubtful hypotheses and formulate new ones to find a correct solution to the problem involved. In support of their explanatory hypothesis, Peal and Lambert cite the case of a Gaelic-speaking boy of 11 (originally cited in Morrison, 1958), who had just taken the Raven’s Progressive Matrices test. According to Morrison, when the boy was asked whether he had done his thinking in Gaelic or in English, the boy replied, “Please Sir, I tried it in the English first, then I tried in the Gaelic to see would it be easier; but it wasn’t so I went back to the English” (p. 280). Recent research on the Raven’s Progressive Matrices suggests that the matrices can be solved by performing either verbal or nonverbal operations on the elements involved (see Hunt, 1974). However, research on children’s performance on the Raven’s Matrices (Kirby & Das, 1978) suggests that, most likely, children rely on visual-spatial strategies when solving the matrices. Kirby and Das found that even the items that are more prone to verbal processing, such as terms requiring some kind of analogical reasoning, are highly correlated with tests of pure spatial abilities in fourth-grade monolinguals.

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Although Peal and Lambert’s (1962) assumptions are fascinating and suggestive hypotheses in themselves, it is clear that they cannot be taken at face value. This writer is currently investigating bilinguals’ use of verbal and spatial strategies when solving problems like those encountered in the Raven test. It is possible that, because of their unique linguistic experience, bilingual children prefer to process information and to solve nonverbal tasks verbally; in fact, some preliminary data analyses suggest that this might be the case. Hopefully, this kind of research will shed some light on bilinguals’ superior performance in nonverbal tests. Nevertheless, it is too early to tell whether bilingual and monolingual children do indeed differ in their informationprocessing strategies. Peal and Lambert’s conclusions regarding bilinguals’ flexibility, therefore, must be taken with great caution. One of the most frequently cited studies of bilinguals’ cognitive flexibility is a study conducted by Balkan in Switzerland. Balkan (1970) administered several tests of nonverbal abilities that purportedly measured cognitive flexibility. The bilingual group, as expected, performed significantly higher than the control monolingual group in two of these measures. One task, Figures Cachees, similar to the familiar Embedded Figures Test, involved the ability to reorganize a perceptual situation. The other task, Histoires, involved sensitivity to the different meanings of a word. Interestingly, the positive effects of bilingualism on these measures were much stronger for children who had become bilingual before the age of 4. The differences between monolinguals and children who had become bilingual at a later age were in favor of the latter but did not reach statistical significance. Balkan’s study implies, as earlier linguistic studies had suggested, that bilingualism might have the most beneficial cognitive effects for those children who learn their two languages simultaneously. However, to consider bilinguals’ superior performance on these very different cognitive tasks a sign of their cognitive flexibility might be stretching things too far. On one hand, because balanced bilinguals have two different words for most referents, it is not surprising that they show a greater sensitivity than monolinguals to the possible different meanings of a single word, as shown in the Histoires task. On the other hand, Balkan’s study offers no clue as to how or why bilingualism should contribute to a greater ability to reorganize and reconstruct perceptual arrays, as shown in the Figures Cachees task. As Peal and Lambert’s (1962) conclusions suggest, the clue might be in bilinguals’ tendency to use verbal mediation when performing these visual-spatial tasks. Ben-Zeev’s (1977b) study with Hebrew-English bilingual children provides further evidence of bilinguals’ so-called cognitive flexibility. When compared to monolinguals, the bilingual children in this study showed a marked superiority in symbol substitution and verbal transformation tasks. The symbol substitution task involved children’s ability to substitute words in a sentence according to the experimenter’s instructions. In a typical instance, children were asked to substitute the word “I” with the word “spaghetti.”

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Children were given correct scores when they were able to say sentences like “Spaghetti am cold,” rather than “Spaghetti is cold,” or a similar sentence that, although grammatically correct, violated the rules of the game. The verbal transformation task involved the detection of changes in a spoken stimulus that is repeated continuously by a tape loop. Warren and Warren (1966) reported that when a spoken stimulus is presented in such a way, subjects older than 6 years report hearing frequent changes in what the taped voice says. The authors attributed this illusion to the development of a reorganization mechanism that aids the perception of ongoing speech. The bilingual children in Ben-Zeev’s study also outperformed the monolingual group on certain aspects of a matrix transposition task; bilinguals were better at isolating and specifying the underlying dimensions of the matrix. No group differences were found, however, on the rearrangement of figures in the matrix. The two comparison groups also performed similarly on the Raven’s Progressive Matrices. It should be noted that the bilinguals in Ben-Zeev’s study showed cognitive advantages only in measures that were directly related to linguistic ability and on the verbal aspects of the matrix transformation task. Ben-Zeev (1977b) noted that throughout the study bilingual children seemed to approach the cognitive tasks in a truly analytic way. They also seemed more attentive to both the structure and details of the tasks administered, as well as more sensitive to feedback from the tasks and the experimenter. Ben-Zeev explained these improved abilities in terms of bilinguals’ confrontation with their two languages. She argued that to avoid linguistic interference, bilinguals must develop a keen awareness of the structural similarities and differences between their two languages as well as a special sensitivity to linguistic feedback from the environment. Supposedly, this more developed analytic strategy toward linguistic structures is transferred to other structures and patterns associated with different cognitive tasks. Ben-Zeev summarized her results as follows: Two strategies characterized the thinking patterns of the bilinguals in relation to verbal material: readiness to impute structure and readiness to reorganize. The patterns they seek are primarily linguistic, but this process also operates with visual patterns, as in their aptness at isolating the dimensions of a matrix. With visual material the spatial reorganizational skill did not appear. however. (p. 1017)

In conclusion, the nature or meaning of cognitive flexibility is far from being understood; the studies just reviewed, however, suggest that the flexibility noted in bilinguals could stem from language-related abilities such as a precocious use of verbal mediation in solving nonverbal tasks or an early awareness of the conventionality and structural properties of language. The next section will review in greater detail the linguistic and metalinguistic abilities that have been related empirically to the bilingual experience.

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Linguistic and Metalinguistic Abilities As mentioned earlier, linguists’ case studies (Leopold, 1961; Ronjat, 1913) concluded that early bilingualism was advantageous to children’s cognitive and linguistic development. In particular, Leopold suggested that bilingualism promoted an early separation of the word sound from the word meaning, “a noticeable looseness of the link between the phonetic word and its meaning” (1961, p. 358). Furthermore, Leopold postulated a fascinating connection between the semantic and cognitive development of bilingual children; namely, the separation of sound and meaning leads to an early awareness of the conventionality of words and the arbitrariness of language. This awareness could promote, in turn, more abstract levels of thinking. Vygotsky (1935/1975) saw the cognitive advantages of bilingualism along the same lines; in his own words, bilingualism frees the mind “from the prison of concrete language and phenomena” (as cited in Cummins, 1976, p. 34). Leopold’s observations were tested empirically by Ianco-Worrall (1972) in a remarkably well-designed and controlled study of English-Afrikaans bilingual children in South Africa. The bilingual sample consisted of nursery school children who had been raised in a one-person, one language environment, similar to the situation of Leopold’s daughter Hildegard. The sample’s degree of bilingualism was determined by several measures, including detailed interviews with parents and teachers as well as a direct test of the children’s vocabulary in both languages. Two comparable monolingual samples, one English and one Afrikaans, were included in the study. In a first experiment, children were administered a semantic-phonetic preferences test. The test consisted of eight sets of three words. A typical set was the words cap, can, and hat. Children were asked questions such as: Which word is more like cap, can or hat? Choosing the word can or hat was an indication of the child’s phonetic or semantic preference in analyzing word similarities. The capacity to compare words on the basis of a semantic dimension is regarded as more advanced developmentally than comparing words along a phonetic dimension. The results of Ianco-Worrall’s (1972) experiment showed not only that semantic preferences increased with age, but also that bilinguals outranked monolinguals in choosing words along a semantic rather than a phonetic dimension. As Ianco-Worrall reported, “of the young 4–6 year old bilinguals, 54% consistently chose to interpret similarity between words in terms of the semantic dimension. Of the unilingual groups of the same age, not one Afrikaans speaker and only one English speaker showed similar choice behavior” (p. 1398). Ianco-Worrall concluded that bilingual children who are raised in a one-person, one-language environment reach a stage of semantic development 2 to 3 years earlier than monolingual children. In a second experiment, using Vygotsky’s (1962) interviewing techniques, Ianco-Worrall (1972) asked her subjects to explain the names of different

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things (e.g., why is a dog called dog?). She also asked children whether or not names of things could be arbitrarily interchanged. For the first question, children’s responses were assigned to different categories, such as perceptible attributes, functional attributes, social convention, and so forth. The results of this experiment, however, showed no reliable differences between bilingual and monolingual children in the types of explanations offered. For the second question, the differences favored the bilingual children; bilinguals replied that names of objects could in principle be changed, while the opposite was true for monolingual children. As part of the same experiment, Ianco-Worrall played a “game” with her young subjects where the names of objects were actually changed. She then asked questions about the qualities and properties of the newly named objects. For example, “Let us call a dog, cow. Does this cow have horns? Does this cow give milk?” (pp. 1394–1395). The results indicated that there was no difference between bilinguals and monolinguals in their capacity to separate in play the qualities of objects from their names. In the study just described, bilinguals exceeded monolinguals in their capacity to analyze the similarity of words along semantic rather than acoustic dimensions. Also, bilingual children seemed more aware than monolinguals of the conventional nature of words and language. This awareness or flexibility with respect to the use of language was also evident in bilinguals’ responses to Ben-Zeev’s (1977b) symbol substitution task, mentioned above. In another study (Feldman & Shen, 1971), bilingual 5-year-olds were better than their monolingual peers at relabeling objects and expressing relations between objects in simple sentences. Further evidence of the positive effects of bilingualism on verbal and linguistic abilities can be found in the work of Casserly and Edwards (Note 3) and in the reports of the St. Lambert experimental bilingual project in Canada (Lambert & Tucker, 1972; Lambert, Tucker, & D’Anglejan, 1973). Casserly and Edwards reported that firstthrough third-grade children in bilingual programs showed definite advantages on several psycholinguistic measures when compared to children attending regular school programs. By the same token, bilingual children in the St. Lambert project outperformed monolinguals when tested on verbal tests of intelligence. Several investigators have explored the effects of bilingualism on the development of metalinguistic awareness. Metalinguistic awareness refers to the ability to analyze objectively linguistic output, that is, “to look at language rather than through it to the intended meaning” (Cummins, 1978, p. 127). Indeed, as children develop, they become more capable of looking at language as an objective set of rules, an objective tool for communication. Because bilingualism induces an early separation of word and referent, it is possible that bilingual children also develop an early capacity to focus on and analyze the structural properties of language. Vygotsky (1935/1975, 1962) suggested that because bilinguals could express the same thought in

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different languages, a bilingual child would tend to “see his language as one particular system among many, to view its phenomena under more general categories, and this leads to an awareness of his linguistic operations” (1962, p. 110). Similarly, Ben-Zeev (1977b) hypothesized that bilinguals develop an analytic strategy toward language to fight interference between their two languages. Lambert and Tucker (1972) noted that children in the St. Lambert bilingual experiment engaged in some sort of “contrastive linguistics” by comparing similarities and differences between their two languages. Cummins (1978) investigated the metalinguistic development of thirdand sixth-grade Irish-English bilinguals. Children in the sample came from homes where both Irish and English were spoken; all children received formal school instruction in Irish. An appropriate monolingual comparison group was selected that was equivalent to the bilingual group on measures of IQ and socioeconomic status. A first task investigated children’s awareness of the arbitrariness of language. Similar to the measure used by Ianco-Worrall (1972), children were asked whether names of objects could be interchanged; children were then asked to explain or justify their responses. The results indicated that at both third- and sixth-grade levels bilinguals showed a greater awareness of the arbitrary nature of linguistic reference. In a second task, children were presented with several contradictory and tautological sentences about some poker chips that were either in view of the child or hidden. The sentences varied in two additional dimensions: true versus false and empirical versus nonempirical. Nonempirical statements refer to sentences that “are true or false by virtue of their linguistic form rather than deriving their truth value from any extra-linguistic state of affairs” (p. 129). The task was chosen as a measure of metalinguistic awareness because previous research had shown that to correctly evaluate contradictions and tautologies, it is necessary to examine language objectively. Although the results for this measure were not clear-cut in favor of the bilinguals, sixth-grade bilingual children showed a marked superiority in correctly evaluating hidden nonempirical sentences. The monolinguals “analyzed linguistic input less closely, being more content to give the obvious ‘can’t tell’ response to the hidden nonempirical items” (p. 133). In a second experiment with balanced Ukranian-English bilinguals, Cummins (1978) investigated children’s metalinguistic awareness using a wide variety of measures including analysis of ambiguous sentences and a class inclusion task. Contrary to previous findings, the bilinguals in this study did not show advantages on the Semantic-Phonetic Preference Test or on the arbitrariness of language task. However, “the results of the Class Inclusion and Ambiguities tasks are consistent with previous findings in that they suggest that bilingualism promotes an analytic orientation to linguistic input” (p. 135). Diaz and Hakuta (Note 1) investigated two different types of metalinguistic awareness; namely, bilingual children’s awareness of grammatical errors

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in their first language and their ability to perceive their two languages as two independent and different language systems. In this study, a group of Spanish-English balanced-bilingual children were compared to a group of Spanish-speaking children who were just beginning to learn English as a second language at school; therefore, the comparison group could be considered relatively monolingual children who were at beginning stages of secondlanguage learning. The two groups of children were equivalent in their Spanish ability, lived in the same neighborhoods, and attended the same kindergarten and first-grade bilingual classes. The metalinguistic awareness tasks consisted of eight ungrammatical Spanish sentences and eight Spanish sentences with one English word in each (e.g., La teacher está en la clase or El dog es grande); several correct Spanish sentences were intermixed within each set of wrong sentences. For the first set of sentences, children were asked to give a correct or grammatical version of the sentences presented. The results showed no differences between the two groups of children in their ability to detect grammatical errors in their native language. However, balanced bilinguals showed a greater ability to make grammatical corrections and to detect confusions between their two languages. Contrary to popular belief that early bilingualism causes confusion and interference between the two languages, the balanced-bilingual children in this study showed an awareness of the independence and proper separate usage of their two languages.

Concept Formation By far, the most detailed descriptions of concept formation in childhood are those by Jean Piaget. His theory of cognitive development emphasizes the importance of four different factors in the development of intelligence: maturation, experience, social interaction, and equilibration (Flavell, 1963). Although Piaget’s theory implies the existence of stages with a universal invariant sequence in development, his interactionist formulations allow for the role of experience and social interaction in the acceleration or retardation of different cognitive abilities. Using a Piagetian theoretical framework, and capitalizing on the fact that bilinguals are exposed to a unique and complex “two worlds of experience,” Liedtke and Nelson (1968) investigated differences between bilinguals and monolinguals on a concept formation task. Based on tasks similar to those used by Piaget, Inhelder, & Szeminska (1960), Liedtke and Nelson (1968) constructed a test on concepts of linear measurement. The test measured six different aspects of linear measurement: (a) reconstructing relations of distance, (b) conservation of length, (c) conservation of length with change of position, (d) conservation of length with distortion of shape, (e) measurement of length, and (f ) subdividing a straight line. The test was administered to English-French bilingual and

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English monolingual first-grade children in Canada. The bilingual sample consisted of children who were exposed to the two languages at home; that is, simultaneous learners of the languages. The monolingual subjects came from monolingual homes and had no functional knowledge of a second language. Subjects’ IQs, socioeconomic status, as well as a measure of their kindergarten attendance, were carefully controlled. Subtests a to d yielded a measure of children’s ability to conserve length, while subtests e and f yielded a measure of children’s ability to measure length. On both measures, bilinguals performed significantly better than their monolingual counterparts. After such strict experimental controls, the results were clearly in favor of the bilingual children; so much so, in fact, that the authors were carried away in their enthusiasm for bilingual education: If bilingualism increases intellectual potential and is beneficial to concept formation [as the study shows], then a second language should be introduced during the early years when experience and environmental factors are most effective in contributing to the development of intelligence. (p. 231)

In a modest attempt to reconcile Piaget’s and Vygotsky’s conceptions of thought and language, Bain (1974) examined the effects of bilingualism on “discovery learning” tasks (see Gagne & Brown, 1961, for a detailed description of such tasks). The paradigm of Bain’s study was to discover the rules that lead to solution of linear numerical problems such as, A. 1, 3, 7, 15, 31, _____ B. 1 2, 1, 1 1 2 , 2, 2 1 2 , 3, ⎯⎯⎯ C. 1, 2, 4, 8, 16, 32, ——— Children were presented with two sets of items on 2 different days. On the second day of testing, children were told to “use the rules that you learnt last day to help you solve the problems” (p. 123). The task was chosen because it involved the ability to discover a rule and then use the rule to deduce a certain outcome. Also, a second round of testing with similar items demanded transferring the newly derived rules to novel situations. In Piagetian terms, the task involved concept formation abilities such as classification and generalization of rules. Bain’s sample consisted of French-English balanced bilinguals and a control group of monolingual English speakers. Besides controlling for group differences in variables such as IQ, socioeconomic status, and school grades, Bain controlled for his sample’s developmental level of operations. Over a 1-week period, he administered conservation tasks to both bilingual and monolingual children and selected only subjects whose explanations for conserving mass, weight, and volume placed them at the concrete-operational level of thought. Bain’s research question could then be reformulated as follows: Do differences in linguistic experience (bilingual vs. monolingual) affect the cognitive performance

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of children who are at similar levels of cognitive development? According to Bain, if concrete operational bilingual children perform better than comparable monolinguals on tasks requiring formal operations, then one could conclude that linguistic experiences do indeed affect the development of cognitive structures, and therefore Vygotsky’s position would be supported. Before the test was administered, children were asked to proceed as fast as they could, but to complete one item before going to the next. Two measures of response latency were taken: discovery time, the time it took subjects to complete the first set of items; and transfer time, the time it took to complete the second set of items at a later date. Bilinguals completed the first set of items approximately 8 minutes earlier than their monolingual peers (discovery time = 31.25 minutes for bilinguals vs. 39.48 minutes for monolinguals). The difference, however, failed to reach statistical significance ( p = .17). There were no substantial group differences on the transfer time measure. Unfortunately, the results of this experiment are difficult to interpret for two reasons. First, the sample was rather small, including only 20 children, 10 subjects in each comparison group. Second, Bain does not report whether children responded to the items correctly. Without this information, a faster discovery time could also mean that bilinguals were more impulsive, that is, faster than their monolingual peers at the expense of accurate performance. Nevertheless, assuming that Bain’s (1974) findings are valid, and taken together with Liedtke and Nelson’s (1968) results, it seems that balanced bilinguals do enjoy some advantages over monolinguals in concept-formation abilities. In summary, bilinguals demonstrate a greater grasp of linear measurement concepts and a greater facility to discover additive rules in a string of numbers than their monolingual counterparts. More important, the findings from the experiments reviewed in this section give modest support to Vygotsky’s contention that language influences the development of new cognitive structures.

Divergent Thinking Skills and Creativity With few exceptions, the majority of studies that have investigated the relationship between bilingualism and creative abilities have used the Torrance Tests of Creative Abilities (Torrance 1966a, 1966b) as their dependent variable. Although different definitions of creativity are available (see, e.g., Rothenberg & Hausman, 1976), it is no surprise that researchers interested in the effects of bilingualism chose Torrance’s formulations as their conceptual framework. For Torrance, creativity is closely identified with divergent productions and transformations with the ability to take different perspectives and different approaches to a given problem. Moreover, Torrance strongly believes that creativity can be trained and that it is, therefore, vulnerable to

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the influence of cultural factors. In fact, so close were his ideas of creativity to the abilities affected by bilingualism, that Torrance himself conducted a large-scale study comparing the creative functioning of bilingual and monolingual children in Singapore (Torrance, Wu, Gowan, & Aliotti, 1970) Influenced by Guilford’s “Structure of the Intellect” model and his concern regarding the measurement of thinking abilities involved in creativity (Guilford, 1967), Torrance developed tests that measured fluency, flexibility, originality, and elaboration, involving both verbal and visual stimuli. Although a detailed description of these abilities is beyond our purposes here, a brief outline of Torrance’s tests is called for to better understand and interpret the results of the studies to be reviewed. Figural Form A of the test consists of three 10-minute tasks: Picture Construction, Picture Completion, and Repeated Figures (Parallel Lines). The “ideational” form of the test involves verbal stimuli and ideas rather than figures. Figural flexibility, for example, would be a measure of the different patterns that a child can create using the same set of lines. Fluency (figural or ideational) refers to the number of associations to a given stimulus expressed in a given amount of time. Usually, six measures can be derived from children’s performance on these tests: verbal fluency, flexibility, and originality, as well as figural fluency, flexibility, and originality. A measure of elaboration can also be derived from these tests. However, the criteria for scoring elaboration are not too clear, and investigators shy away from such measure. Postulating both possible positive and negative effects of bilingualism on creative functioning, Torrance et al. (1970) tested 1,063 third- to fifth-grade bilingual and monolingual children in Singapore. The bilingual sample included Chinese-English and Malayan-English speaking children. Torrance and his coworkers hypothesized, on one hand, that bilingualism could have negative effects on fluency and flexibility skills. They believed that bilingualism fostered a competition of associations: that is, older associations could compete with the assimilation of new associations, a kind of “negative transfer” between the two languages. In their words, When a child reared during his early years in a particular culture learns to speak the language common with that culture, and then enters a school where instruction is in a different language and the practices and ways of thinking of a different culture predominate, one has a good example of this negative transfer. (p. 72)

On the other hand, Torrance et al. expected a positive correlation between bilingualism and originality. They argued that the competition between the two languages, between old and new association, should facilitate originality, especially if originality was assessed independently of fluency. As expected, the results of the study showed that monolinguals surpassed bilinguals on both measures of fluency and flexibility. In addition, as the authors hypothesized, bilinguals scored higher than monolinguals on both originality and

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elaboration. However, the group differences in originality, though obviously in favor of the bilinguals, failed to reach statistical significance. The results of the study just described must be evaluated with a great deal of caution. First, there were no measures of relevant variables such as IQ, socioeconomic status, or children’s actual knowledge of the two languages to insure that the two groups differed only in the bilingual versus monolingual dimension. Second, the authors do not specify what criteria they used to include children in the bilingual sample. It should be noted that the bilingual children in this study attended Malaysian-, Chinese-, or English-speaking schools. The children were not attending bilingual education programs where both languages are maintained and equally developed. It is most likely that the sample consisted of semilingual rather than bilingual children; that is, children whose native language was being gradually replaced by exposure and formal instruction in a second language. In fact, the situation of linguistic interference and negative transfer that Torrance and his coworkers described is a more accurate description of semilingualism than of genuine bilingualism. And third, one must be a bit skeptical about the construct “creative functioning” when there is so little relationship between subtests that purportedly measure creativity, especially when trends in subtest performance are so distinctly reversed within the same group of children. In a somewhat better controlled study, Landry (1974) examined the creative abilities of children who were learning a foreign language in elementary school. Landry compared children who attended both Foreign Language in the Elementary School (FLES) and regular school programs. To study the effectiveness of the FLES program in promoting creative abilities, Landry eliminated from the sample those children who had a bilingual home background; he tested both first and third graders, monolinguals and secondlanguage learners. As expected, there were no differences between the FLES and non-FLES first graders; Landry explained this finding in terms of first graders’ limited exposure to the second language. By the third grade, however, children learning a second language showed significant advantages on all measures of the Torrance test. Stretching the notion of cognitive flexibility a bit too far, Landry concluded that the flexibility produced by learning a second language was conducive to both divergent thinking and originality.

Cognitive Style Several investigators have been interested in the influence of bilingualism on children’s cognitive style (cf., Duncan & DeAvila, 1979; Ramirez, Castaneda, & Herold, 1974; Ramirez & Price-Williams, 1974). Cognitive style usually refers to “individual variations in modes of perceiving, remembering, and thinking, or as distinctive ways of apprehending, sorting, remembering, transforming and utilizing information” (Kogan, 1971, as cited in Duncan & DeAvila, 1979, p. 21). Involved in the conceptualization of cognitive style is the notion that

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there is diversity in cognitive performance; diversity, however, is regarded as value-neutral, with no implications of better or worse, bright or dull. Witkin and Goodenough (1977), for example, stress that each pole of the field dependence/independence cognitive styles has adaptive characteristics. It is not surprising, therefore, that minority researchers have made efforts to understand the effects of bilingualism on cognitive style and have advocated value-neutral formulations of cognitive performance. Among the many possible dimensions of cognitive style, field dependence/independence has been the most widely studied. Although measures of field dependence/independence are usually simple and straightforward, such as subjects’ performance on the familiar Embedded Figures Test, there are almost as many definitions of this construct as there are investigators in the field. Field independence, for example, usually refers to a measure of a subject’s ability to overcome the effects of a visually distracting background. Nevertheless, field independence has also been conceptualized as a personality characteristic of assertiveness, as a cognitive restructuring competency, and as an intellectual and perceptual segregation of the “me” and “not me” (Witkin & Goodenough, 1977; see also Cazden & Leggett, 1981; Duncan & DeAvila, 1979, for reviews of the pertinent literature). With this warning in mind, let us review the major formulations and empirical findings on the effects of bilingualism on field-dependent and independent cognitive styles. Ramirez (1973) argued that achievement and success in U.S. mainstream education are associated with characteristics of the field-independent person. He further claims that the academic failure of Mexican-American children can be attributed mainly to the predominantly field-dependent cognitive style of these children. Some studies (Buriel, 1975; Sanders, Scholz, & Kagan, 1976) have shown, indeed, that Mexican-American children tend to be more field dependent than their Anglo-American counterparts according to their performance on the Portable Rod and Frame Test. To emphasize the positive cognitive and social aspects of this style, Ramirez and Castaneda (1974) substituted the term “field dependence” with “field sensitivity.” In the social sphere, for example, field dependence is associated with more sensitivity to social feedback and a more developed repertoire of interpersonal behaviors. Following the same line of thought, Ramirez and his coworkers suggested that cognitive style varies with the degree of assimilation to the mainstream culture. Furthermore, they suggest that speaking two languages and belonging to two cultures fosters some kind of “bicognitivity”; that is, “in the same way that the bilingual child switches language codes in response to the demand characteristics of the socio-linguistic situation, so the bicognitive child switches cognitive styles as demanded” (Duncan & DeAvila, 1979, p. 25). Although these are fascinating theoretical formulations relating bilingualism to cognitive styles, the empirical evidence is rather weak and not convincing. First, the findings are not consistent across studies; in contrast to studies using the Portable Rod and Frame Test, some studies using the Children’s

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Embedded Figures Test (CEFT) did not find significant differences between bilinguals’ and monolinguals’ cognitive styles. In fact, when reviewing such studies, Kagan & Buriel (1977) argued that at this time it is meaningless to describe Mexican-American children as more field dependent than their Anglo-American peers. Second, most of these studies have not measured children’s language proficiency in either English or Spanish, so it is difficult to sort out the influence of linguistic variables from the effects of other cultural and socioeconomic variables on cognitive style differences found so far. To the best of my knowledge, only one study has looked at the relation between bilingualism and field dependence/independence, carefully controlling for the sample’s actual degree of bilingualism. Using the Language Assessment Scale, Duncan and DeAvila (1979) assessed the relative linguistic proficiency in English and Spanish in four groups of children of Hispanic background in grades one and three. The sample included urban and rural Mexican Americans, Puerto Ricans, and Cuban Americans. Through performance on the Language Assessment Scale, and according to their relative proficiency in English and Spanish, children were classified into five groups ranging from late language learners (poor in both languages) to proficient bilinguals. Of course, the sample included monolinguals of both languages. Field dependence/independence was assessed through two different measures: the CEFT and the Draw a Person Test (DAP). The results of the study showed that proficient bilingual children outperformed the monolingual children on both the CEFT and the DAP test. Proficient (i.e., balanced) bilingual children showed more advanced skills at perceptual disembedding and produced the most articulate or “field-independent” drawings. The investigators also found a positive linear relationship between degree of relative language proficiency in English and Spanish and field independence. It should be noted that in this study children who had not yet achieved an adequate balance between their two languages, that is, the partial and limited bilinguals, performed similarly to the monolingual group; there was no evidence of negative cognitive effects as a result of exposure to a second language. The authors concluded that their results support Cummins’ (1976) threshold hypothesis, namely, that a certain level of proficiency in both languages must be obtained before bilingualism can show its positive effects on cognitive variables.

Summary and Conclusion During the last two decades, many studies have presented evidence showing a positive influence of bilingualism on children’s cognitive and linguistic abilities. When compared to monolinguals, balanced bilingual children show definite advantages on measures of metalinguistic abilities, concept formation, field independence, and divergent thinking skills. Although the cognitive

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advantages of bilingual children have been explained in several ways, the empirical literature gives most support to the “objectification” hypothesis (see Cummins, 1977) that bilingualism accelerates cognitive development by fostering an early awareness of the objective and structural properties of language. Indeed, as several studies have shown, bilingual children demonstrate a keen awareness of the arbitrariness of language, as well as an early capacity to focus on linguistic structure and detail. Nevertheless, presently there are not enough data or adequate cause-effect analyses to accept the objectification hypothesis without further doubt. Generally, present-day investigators have been able to master most of the methodological difficulties encountered by early studies in the field. However, to adequately conclude this review, two major gaps in current research should be brought to surface. First, current research fails to look at the effects of bilingualism on nonbalanced bilinguals, that is, on children who have disparate abilities in the two languages. Many children who attend bilingual education programs in this country come to school with little or no knowledge of English. These children are nonbalanced bilinguals for a good number of years, and little is known about the immediate cognitive effects of gradually learning a second language during the early years of schooling. Moreover, it is not clear what percentage of these children will actually attain a reasonable degree of balance between their two languages to insure a positive effect of their bilingual experience. It is important to note that when one looks at balanced bilinguals only, one necessarily excludes children who are limited in their second-language proficiency for historical or environmental reasons; that is, children who recently have been exposed to a second language and are therefore at initial stages of second-language learning. At beginning stages of second-language learning, children must learn not only a new and different vocabulary, but also different syntactic rules and linguistic constraints. It is likely that during initial stages of second-language learning, children must exercise a great deal of cognitive effort to produce grammatically correct utterances. Once basic rules and linguistic constraints are mastered, second-language development proceeds through the less painful process of learning new vocabulary and idioms. Although there are no empirical data to support the above statements, one could logically hypothesize that initial stages of secondlanguage learning will produce the most dramatic, and perhaps negative, cognitive effects on young second-language learners. These effects must be temporary for those children who will develop toward balanced bilingualism; the issue, however, is still an open empirical question. A second gap in current research is that, to the best of my knowledge, there are no information-processing studies of young bilingual children. Because studies in this area have relied mostly on data from psychometric tests, it is not clear what cognitive processes or processing strategies, if any,

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truly differentiate bilingual from monolingual children. Most psychometric tests of ability really measure a whole array of different cognitive abilities and tap a wide variety of different processes. It is almost impossible, with our present knowledge, to develop a process model of how bilingualism affects children’s cognitive abilities or accelerates cognitive development. The development (and empirical support) of a detailed model relating bilingualism and cognitive development is still a few years ahead.

Reference Notes 1. Diaz, R. M., & Hakuta, K. Bilingualism and cognitive development: A comparison of balanced and non-balanced bilinguals. Paper presented at the meeting of the Society for Research in Child Development, Boston, April 1981. 2. Skuttnabb-Kangas, T. Bilingualism, semilingualism and school achievement. Paper presented at the Fourth International Conference of Applied Linguistics, Stuttgart, August 1975. 3. Casserly, S. M., & Edwards, H. P. Detrimental effects of grade one bilingual programs: An exploratory study. Paper presented at the annual conference of the Canadian Psychological Association, Victoria, June 1973.

References Ahmed, M. A. S. Mental manipulation. Egyptian Yearbook of Psychology, 1954, 1, 23–88. American Institutes for Research (AIR). Evaluation of the impact of ESEA Title VII Spanish/ English Bilingual Education Program. Palo Alto, Calif.: Author, 1977. Anastasi, A. Psychological testing (2nd ed.). New York: Macmillan, 1961. Arsenian, S. Bilingualism and mental development. New York: Columbia University Press, 1937. Bain, B. Bilingualism and cognition: Toward a general theory. In S. T. Carey (Ed.), Bilingualism, biculturalism, and education: Proceedings from the Conference at College Universitaire Saint Jean. Edmonton: The University of Alberta, 1974. Balkan, L. Les effets du bilinguisme francais-anglais sur les aptitudes intellectuelles. Bruxelles: Aimav, 1970. Barke, E. M., & Perry-Williams, D. E. A further study of the comparative intelligence of children in certain bilingual and monoglot schools in South Wales. British Journal of Educational Psychology, 1938, 8, 63–77. Ben-Zeev, S. Mechanisms by which childhood bilingualism affects understanding of language and cognitive structures. In P . A. Hornby (Ed.), Bilingualism: Psychological, social, and educational implications. New York: Academic Press, 1977. (a) Ben-Zeev, S. The influence of bilingualism on cognitive strategy and cognitive development. Child Development, 1977, 48, 1, 009–1, 018. (b) Ben-Zeev, S. The effects of bilingualism in children from Spanish-English low economic neighborhoods on cognitive development and cognitive strategy. Working Papers on Bilingualism, 1976, 9, 83–122. Blanco, G. The education perspective. In Bilingual education: Current perspectives (Vol. 4). Arlington, Va.: Center for Applied Linguistics, 1977. Brunner, E. D. Immigrant farmers and their children. New York: Doubleday, Doran, & Co., 1929. Buriel, R. Cognitive styles among three generations of Mexican-American children. Journal of Cross-Cultural Psychology, 1975, 6, 417–429.

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Carrow, S. M. A. Linguistic functioning of bilingual and monolingual children. Journal of Speech and Hearing Disorders, 1957, 22, 371–380. Cazden, C. B., & Leggett, E. L. Culturally responsive education: A discussion of Lau Remedies II. In H. T. Trueba, G. P. Gunthrie, & K. H. Au (Eds.), Culture and the bilingual classroom: Studies in classroom ethnography. Raleigh, Mass.: Newbury House, 1981. Cummins, J. The influence of bilingualism on cognitive growth: A synthesis of research findings and explanatory hypotheses. Working Papers on Bilingualism, 1976, 9, 1–43. Cummins, J. Metalinguistic development of children in bilingual education programs: Data from Irish and Canadian Ukranian-English programs. In M. Paradis (Ed.), The Fourth Locus Forum 1977. Columbia, S.C.: Hornbeam Press, 1978. Cummins, J. Cognitive factors associated with the attainment of intermediate levels of bilingual skill. Modern Language Journal, 1977, 61, 3–12. Cummins, J., & Gulutsan, M. Bilingual education and cognition. Alberta Journal of Educational Research, 1974, 20, 259–269. Darcy, N. T. Bilingualism and the measurement of intelligence: Review of a decade of research. Journal of Genetic Psychology, 1963, 103, 259–282. Darcy, N. T. A review of the literature on the effects of bilingualism upon the measurement of intelligence. Journal of Genetic Psychology, 1953, 82, 21–57. Duncan, S. E., & DeAvila, E. A. Bilingualism and cognition: Some recent findings. NABE Journal, 1979, 4, 15–50. Epstein, I. La pensee et la poligloise. Lausanne: Libraire Payot, 1905. Evans, S. J. Address of the Conference of Headmasters of Grammar Schools, Wales, 1906. In Central Advisory Council for Education (Wales), The Place of Welsh and English in the Schools of Wales. London: Her Majesty’s Stationary Office, 1953. Feldman, C, & Shen, M. Some language-related cognitive advantages of bilingual fiveyear-olds. Journal of Genetic Psychology, 1971, 118, 235–244. Fishman, J. A. Review of Bilingualism and primary education by J. Nacnamara. Irish Journal of Education, 1967, 1, 79–83. Fishman, J. A. The social science perspective. In Bilingual Education: Current Perspectives (Vol. 1). Arlington, Va.: Center for Applied Linguistics, 1977. Flavell, J. H. The developmental psychology of Jean Piaget. Princeton, N.J.: Van Nostrand, 1963. Fritz, R. A., & Romkin, N. R. The English handicap of junior high school pupils from foreign speaking homes, and remedial suggestions. Journal of Educational Research, 1934, 27, 412–421. Fukuda, T. A survey of the intelligence and environment of school children. American Journal of Psychology, 1925, 36, 124–139. Gagne, M., & Brown, L. Some factors in the programming of conceptual learning. Journal of Experimental Psychology, 1961, 62, 313–321. Grabo, R. P. A study of comparative vocabularies of junior high school pupils from English and Italian speaking homes. Bulletin No. 13. Washington, D.C.: U. S. Office of Education, 1931. Hansegard, N. E. Tvasprakighet eller Halvsprakighet (Bilingualism or semilingualism). Stockholm: Aldurs/Bonniers, 1968. Harris, C. W. An exploration of language skill patterns. Journal of Educational Psychology, 1948, 32, 351–364. Hill, H. S. The effects of bilingualism on the measured intelligence of elementary school children of Italian parentage. Unpublished doctoral dissertation, Rutgers University, 1935. Hunt, E. Quote the raven? Never more. In L. W. Gregg (Ed.), Knowledge and cognition. Hillsdale, N.J.: Lawrence Erlbaum Associates, 1974. Ianco-Worrall, A. D. Bilingualism and cognitive development. Child Development,1972, 43, 1,390–1,400. Kagan, S., & Buriel, R. Field dependence-independence and Mexican-American culture and education. In J. L. Martinez, Jr. (Ed.), Chicano psychology. New York: Academic Press, 1977.

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Kirby, J. R., & Das, J. P. Skills underlying the Coloured Progressive Matrices. The Alberta Journal of Educational Research, 1978, 24, 94–99. Lambert, W. E., Tucker, G. R., & D’Anglejan, A. Cognitive and attitudinal consequences of bilingual schooling: The St. Lambert project through grade five. Journal of Educational Psychology, 1973, 65, 141–159. Lambert, W. E., & Tucker, G. R. Bilingual education of children: The St. Lambert experiment. Rowley, Mass.: Newbury House, 1972. Landry, R. G. A comparison of second language learners and monolinguals on divergent thinking tasks at the elementary school level. Modern Language Journal, 1974, 58, 10–15. Lavoie, G., & Laurendau, M. Tests collectivs d’ intelligence generale. Montreal: Institut de Recherches Psychologiques, 1960. Lenneberg, E. H. Biological foundations of language. New York: Wiley, 1967. Leopold, W. F. Speech development of a bilingual child: A linguist’s record (4 vols.). Evanston, Ill.: Northwestern University Press, 1939, 1947, 1949a, 1949b. Leopold. W. F. Patterning in children’s language learning. In S. Saporta (Ed.), Psycholinguistics. New York: Holt, Rinehart, & Winston, 1961. Liedtke, W. W., & Nelson, L. D. Concept formation and bilingualism. Alberta Journal of Educational Research, 1968, 14, 225–232. Macnamara, J. The concession on Irish: Psychological aspects. Studies, 1964, 164–173. Macnamara, J. Bilingualism and primary education. Edinburgh: Edinburgh University Press, 1966. Manuel, H. T. A comparison of Spanish-speaking and English-speaking children in reading and arithmetic. Journal of Applied Psychology, 1935, 19, 189–201. McCarthy, D. A. The language development of the pre-school child: Minneapolis: University of Minnesota Press, 1930. McLaughlin, B. Second language acquisition in childhood. Hillsdale, N.J.: Lawrence Earlbaum Associates, 1978. Morrison, J. R. Bilingualism: Some psychological aspects. Advanced Science, 1958, 56, 287–290. Murdoch, K. A., Maddow, D., & Berg, N. L. A study of the relation between intelligence and the acquisition of English. The 27th yearbook of the National Society for the Study of Education (Part 1). Bloomington, Ill.: Public School Publishing, 1928. O’Doherty, E. F. Bilingualism: Educational aspects. Advanced Science, 1958, 56, 282–286. Paulston, C. B. Ethnic relations and bilingual education: Accounting for contradictory data. Working Papers on Bilingualism, 1975, 6, 1–44. Paulston, C. B. Viewpoint: Research. In Bilingual education: Current perspectives (Vol. 2). Arlington, Va.: Center for Applied Linguistics, 1977. Pavlovitch, M. Lelanguage enfantin: Acquisition du serbe et du francais par un enfant serbe. Paris: Champion, 1920. Peal, E., & Lambert, W. The relation of bilingualism to intelligence. Psychological Monographs, 1962, 76(546), 1–23. Penfield, W., & Roberts, L. Speech and brain mechanisms. Princeton, N.J.: Princeton University Press, 1959. Piaget, J., Inhelder, B., & Szeminska, A. The child’s conception of geometry. New York: Basic Books, 1960. Pifer, A. Bilingual education and the Hispanic challenge. The President’s (1979) Annual Report. New York: Carnegie Corporation of New York, 1980. Ramirez, M. Cognitive styles and cultural democracy in education. Social Science Quarterly, 1973, 53, 895–904. Ramirez, M., Castaneda, A., & Herold, P. L. The relationship of acculturation to cognitive style among Mexican Americans. Journal of Cross-Cultural Psychology, 1974, 5, 425–433. Ramirez, M., & Castaneda, A. Cultural democracy, bicognitive development, and education. New York: Academic Press, 1974.

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Ramirez, M., & Price-Williams, D. Cognitive styles in children: Two Mexican communities. International Journal of Psychology, 1974, 8, 93–100. Ronjat, J. Le development du language observe chez un enfant bilingue. Paris: Champion, 1913. Rothenberg, A., & Hausman, C. R. (Eds.). The creativity question. Durham, N.C.: Duke University Press, 1976. Saer, D. J. The effects of bilingualism on intelligence. British Journal of Psychology, 1923, 14, 25–38. Saer, H. Experimental inquiry into the education of bilingual peoples. In Education in a changing commonwealth. London: New Educational Fellowship, 1931. Sanchez, G. I. The implications of a basal vocabulary to the measurement of the abilities in bilingual children. Journal of Social Psychology, 1934, 5, 395–402. Sanders, M., Scholz, J. P., & Kagan, S. Three social motives and field independencedependence in Anglo American and Mexican American children. Journal of CrossCultural Psychology, 1976, 7, 451–462. Thurstone, L. L., & Thurstone, T. G. Primary mental abilities: Ages 7 to 11. Chicago: Science Research Associates, 1954. Torrance, E. P . Torrance tests of creative thinking: Directions manual and scoring guide. Figural Test Booklet A. Princeton, N.J.: Personnel Press, 1966. (a) Torrance, E. P . Torrance tests of creative thinking: Norms-technical manual (Research ed.). Princeton, NJ.: Personnel Press, 1966. (b) Torrance, E. P., Wu, J. J., Gowan, J. C, & Aliotti, N. C. Creative functioning of monolingual and bilingual children in Singapore. Journal of Educational Psychology, 1970, 61, 72–75. Tucker, G. R., & D’Anglejan, A. Some thoughts concerning bilingual education programs. Modern Language Journal, 1971, 55, 491– 493. Vygotsky, L. S. [Multilingualism in children] (M. Gulutsan & I. Arki, trans.). Edmonton: The University of Alberta, 1975. (The original essay appears in L. U. Zankov, Zh. I. Skif, & D. B. EI’konin [Eds.], Umstvennoe razvitie detei v protsse obucheviia, spornik statei [Mental development of children in the process of education, a collection of essays]. Moscow: State Pedagogical Publishing House, 1935.). Vygotsky, L. S. Thought and language. Boston: MIT Press, 1962. Warren, R. M., & Warren, R. P. A comparison of speech perception in childhood, maturity, and old age by means of the verbal transformation effect. Journal of Verbal Learning and Verbal Behavior, 1966, 5, 142–146. Witkin, H. A., & Goodenough, D. R. Field dependence revisited (Research Bulletin 77–16). Princeton, N.J.: Educational Testing Service, 1977. Yoshioka, J. G. A study of bilingualism. Journal of Genetic Psychology, 1929, 36, 473–479.

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25 Components of a Psychology of Instruction: Toward a Science of Design Robert Glaser

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t is a well-known historical fact that two major areas of scientific psychology, psychometrics and general experimental psychology, came out of different traditions and have developed in different ways. Psychometrics has become a major technological application of psychology, with primary effort being devoted to practical techniques and less effort to theoretical concerns. In contrast, the experimental psychology of learning and cognition has been almost exclusively a theoretical endeavor, with little effort devoted to application and the design of practical techniques for assisting in the conduct of human affairs. Although practical work has been carried out in educational psychology, industrial psychology, and human engineering, no integrated body of special technique of application has emerged. In recent years, however, there has been increasing interest in and social pressure for the development of professional techniques for the application of what knowledge there is of learning, cognitive processes, and human development. It appears that some linking of theory and practice needs to take place. It is of interest to note in this regard that John Dewey, in his presidential address before the American Psychological Association in 1899, expressed concern about developing a linking science between psychological theory and practical work. Dewey said the following:

Source: Review of Educational Research, 46(1) (1976): 1–24.

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“Do we not lay a special linking science everywhere else between the theory and practical work? We have engineering between physics and the practical workingmen in the mills; we have a scientific medicine between the natural science and the physician.”1 The sentences suggest . . . that the real essence of the problem is found in . . . [a] connection between the two extreme terms – between the theorist and the practical worker – through the medium of the linking science. The decisive matter is the extent to which the ideas of the theorist actually project themselves, through the kind offices of the middleman, into the consciousness of the practitioner. It is the participation by the practical man in the theory, through the agency of the linking science, that determines at once the effectiveness of the work done, and the moral freedom and personal development of the one engaged in it. (1900, pp. 110–111) It is the [teacher’s] inability to regard, upon occasion, both himself and the child as just objects working upon each other in specific ways that compels him to resort to purely arbitrary measures, to fall back upon mere routine traditions of school teaching, or to fly to the latest fad of pedagogical theorists – the latest panacea peddled out in school journals or teachers’ institutes – just as the old physician relied upon his magic formula. (pp. 112–113)

In this paper, my concern is similar to Dewey’s, and I would like to speculate on the nature of a “linking science” – a psychology of instruction – between the scientific knowledge of learning (including human cognition and development) and educational applications. As a further historical note, I refer to Edward L. Thorndike’s book, published in 1922, entitled The Psychology of Arithmetic. In the preface, Thorndike wrote as follows: Within recent years there have been three lines of advance in psychology which are of notable significance for teaching. The first is the new point of view concerning the general process of learning. We now understand that learning is essentially the formation of connections or bonds between situations and responses, that the satisfyingness of the result is the chief force that forms them, and that habit rules in the realm of thought as truly and as fully as in the realm of action. The second is the great increase in knowledge of the amount, rate, and conditions of improvement in those organized groups of hierarchies of habits which we call abilities, such as ability to add or ability to read. Practice and improvement are no longer vague generalities, but concern changes which are definable and measurable by standard tests and scales. The third is the better understanding of the so-called “higher processes” of analysis, abstraction, the formation of general notions, and reasoning. The older view of a mental chemistry whereby sensations were compounded into percepts, percepts were duplicated by images, percepts and images were amalgamated into abstractions and concepts, and these were manipulated by reasoning, has given way to the understanding of the laws of response to elements or aspects of situations . . . . This book presents the applications of this newer dynamic psychology to the teaching of arithmetic. (pp. v–vi)

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In this book, Thorndike applied his theory and findings about learning directly to the teaching process. The theory of stimulus-response bonds that made up complex chains of behavior was applied to the analysis of arithmetic tasks; the task of adding integers, for example, was carefully analyzed in terms of S-R bonds that could be taught and observed by the teacher. Thorndike also applied the results of his experimental work on transfer of training and reward in suggesting practical teaching techniques. He rejected the old notion of training general faculties and accepted the fact that training needed to be carried out in more specific contexts. He injected his notions of reinforcement by indicating that students should work on problems where, as a result of carrying out a successful response, a student could see the utility of his behavior. There is an important difference between Dewey and Thorndike, in terms of the publications I have cited, with respect to what it takes to translate science into practice. Dewey pressed for some kind of intermediate linking science. He conceived of a special structure that intervened between scientific theory and practical application. Thorndike, on the other hand, was concerned with the more direct application of what he knew about learning and psychological method to teaching practice. In addition to his general theory of learning, he brought to educational topics a scientific approach which involved careful analysis of the nature of the task, the design of teaching techniques as a function of his experimental findings, and measurement of what the task analysis indicated were the components of the performance being learned. Thorndike’s approach set a very special pattern: the combination in one person of the theoretical scientist and the applied scientist interested in designing instructional procedures. And since that time, for major advances in the psychology of instruction, we have come to look for individuals interested in both fields, particularly someone trained in the science of psychology who is motivated to look at problems in education. Such a tactic, however, has its shortcomings. It is a highly individualistic, noncumulative kind of venture which does not necessarily lead to the development of a linking science in which knowledge can be accumulated into a body of techniques and procedures for practical application by a professional. In contrast, my concern in this paper is with the possibilities for the development of a linking structure which, because of its own cumulative strength as a body of theory and practice, would be less dependent upon the sporadic interests and insights of individuals. In the sense described above, B. F. Skinner continued in the pattern of E. L. Thorndike, and most of those who became interested in programmed learning and teaching machines continued to work in this mode. As the field became popular, however, it took on a superficial momentum that separated it from the implicit theory that generated it; no substantial structure was built up into which new data, parameters of application, and boundary conditions could be placed.

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In the late 1950’s and early 1960’s, as part of a general Zeitgeist, the notion of a linking science was being nurtured. Bruner (1964) contrasted the nature of a theory of instruction with a theory of learning. He pointed out that a theory of learning is descriptive, whereas a theory of instruction is prescriptive in the sense that it sets forth rules specifying the most effective way to achieve knowledge or mastery of skills. A theory of learning describes, after the fact, the conditions under which some competence is acquired. A theory of instruction is a normative theory in that it sets up criteria of performance and then specifies the conditions required for meeting them. Skinner, too, had made this point in the course of his interest in the technology of teaching, since the nature of his approach to the study of behavior makes the development of procedures for prescribing conditions for learning almost indistinguishable from a theoretical description of learning. Most approaches to psychological knowledge emphasize both the theoretical and empirical description of learning, they have not been concerned with the problems of prescriptive science. There is, however, at the present time a growing feeling that a strong test of the adequacy of descriptive theory in the behavioral and social sciences can be made through attempts at application based upon the development of prescriptive theory for the design of social policy and social institutions, including education.

The Activity of Design The general characteristics of a prescriptive science of design have been discussed recently by Herbert Simon in his book, The Sciences of the Artificial (1969). Simon’s ideas on this matter are worth noting here. He points out that it traditionally has been the task of the sciences and other disciplines in the university to describe how things are and how they work, and it has been the task of professional schools to teach how to design and make things. The intellectual activity of design is involved not only in producing material artifacts as in engineering, but also in prescribing remedies for a sick patient, devising a sales plan for a company, constructing a new social welfare policy for a state, and designing a program of instruction for a school system. Simon writes: Design, so construed is the core of all professional training; it is the principal mark that distinguishes the professions from the sciences. Schools of engineering, as well as schools of architecture, business, education, law, and medicine, are all centrally concerned with the process of design. (pp. 55–56)

In view of the key role of design in professional activity, it is ironic, Simon argues, that prescriptive design sciences are less prominent in professional school curricula than they might be.

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Engineering schools have become schools of physics and mathematics; medical schools have become schools of biological sciences; business schools have become schools of finite mathematics. The use of adjectives like “applied” conceals, but does not change, the fact. (p. 56)

Curriculum topics are selected from disciplines that are thought to be most relevant to professional practice; but design, as distinguished from descriptive analysis, is not necessarily taught. To some extent, this phenomenon is a function of the professional schools being absorbed into the general culture of the university and hankering after respectability in terms of the prevailing norms of academic respectability. Descriptive theory and analysis is intellectually tough and prestigiously teachable. Design and application has generally appeared to be more intellectually soft, intuitive, and “cookbooky.” (I have before used the expression “by-the-numbers”; Simon’s word “cookbooky” is much better.) This certainly seems to be the existing state of affairs with respect to the application of psychology to the design of instruction. In an effort to explore the possibilities for design theory in psychology and education, a lead can be taken from certain intellectually rigorous practices that have been developed in other fields. The essence of design is to devise courses of action aimed at changing existing situations into preferred ones, and techniques called “optimization methods” have been developed in statistical decision theory, management science, and engineering design that are concerned with deciding upon optimal courses of action. In very general terms, the technique is this: Given a set of alternative goals or possibilities for action, certain fixed parameters and constraints of the situation, and a function that describes the relationship between these factors, find a set of values that provides the best means of attaining possible outcomes. A stock application of this paradigm, described by Simon, is to the so-called “diet problem.” Given the goal of losing a certain number of pounds; given parameters and constraints such as food prices and nutritional content; and given the relationship between the cost of a diet, calories per day, and minimum needs for nutritional requirements; find the kinds and quantities of food necessary to maximize utility – for example, no more than 2,000 calories per day with proper nutritional requirements. Once such a problem can be formalized in terms of a quantitative functional relationship, then standard mathematical techniques can be applied to maximize the outcome subject to the given constraints. On the basis of this solution, a course of action can be decided upon. In an exploratory way, the formal apparatus of optimization methods has been introduced into instructional design. Richard Atkinson and his students (Atkinson & Paulson, 1972; Groen & Atkinson, 1966) have described procedures for optimizing paired-associate list-learning of the kind found in initial reading tasks or in learning the vocabulary of a second language. This work makes it clear, however, that at the present time, the determination of optimal alternatives is a relatively easy matter only in “trivial” cases. Atkinson is careful to point out that formal uses of optimization routines developing out

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of linear programming theory, dynamic programming, and control theory are of little help for the complex performances and instructional procedures of most interest in education. However, his work to date on simple cases might help clarify some of the steps involved in devising and testing optimal instructional strategies. A significant problem in using optimization methods is the requirement for a formal description of the functional relationships involved. If one can employ a formal model like statistical learning theory, then standard optimization methods can be applied. However, such formal descriptions are not readily forthcoming for the complex cognitive tasks and instructional procedures that are of central interest to educators. For progress now, on the basis of our current knowledge and ability to model and describe the learning process, new kinds of prescriptive methods are required. But still, descriptive theory of some kind is a necessary prerequisite for prescriptive theory if the design procedures we will use in the design of instruction are to be at all like the procedures used in other professions. Of significant interest is that instructional design – the development of instructional procedures and methods – can also become a strong way of testing descriptive theory.

Questions for Instructional Psychology For the development of an instructional psychology, there are two questions that need to be asked, the first methodological and the second substantive. The first is: What can be learned about techniques to be used in the application of psychological knowledge to the design of instruction from the strategies of design used in other fields? One answer to this question recognizes the fact that an effective design strategy incorporates procedures for identifying admissible alternatives and then proceeds to make decisions about the most satisfactory of these alternatives. In this regard, a main lesson to be learned from the work to date is that design is not merely assembling a problem solution from what is known, but is rather a search for the most appropriate assembling of the components involved. The components of a design problem need to be assembled into a number of alternative procedures; exploration of these tentative paths then needs to be pursued so that the most promising ones can be followed up and the less promising ones given a lower priority. The design process essentially involves the generation of alternatives and the testing of these alternatives against practical requirements, constraints, and values. This is not done in a single generate-and-test cycle, but through an iterative series involving the generation of alternatives, testing them (through actual smallscale studies or through simulation), describing revised alternatives, testing them, and so on. This will take us away from the intuitive, one-shot innovation mode of educational reform to a mode of operation in which reforms are seen as actual or simulated experiments, with each experiment providing information for successive improvement and refinement of possible alternatives.

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A second question to be considered is: Given methodologies for deciding among possible alternatives, what are the substantive components that are required as the data to which these methodologies can be applied? This question is a large one for psychological research, and discussion of it will comprise the remainder of this paper. Regardless of the descriptive theory with which one works, four components of a prescriptive theory for the design of instructional environments appear to be essential: (a) analysis of the competence, the state of knowledge and skill, to be achieved; (b) description of the initial state with which learning begins; (c) conditions that can be implemented to bring about change from the initial state of the learner to the state described as the competence; and (d) assessment procedures for determining the immediate and long-range outcomes of the conditions that are put into effect to implement change from the initial state of competence to further development. These components of a psychology of instruction comprise the information – the parameters, constraints, and functional relationships – that is required for employing procedures to optimize instruction or for deciding between instructional alternatives. I shall discuss each of these in turn, but before doing so, let me give you some feeling for the general nature of the kind of individual cognitive development with which I am concerned here and to which the above components refer.

The Development of Competence The process of instruction, as distinguished from education in general, is, to a large extent, concerned with the development of the behaviors and cognitive structures that differentiate between the novice and the competent performer in a particular subject matter and intellectual skill. In attaining this knowledge and skill, the learner proceeds through a novitiate stage and then on to a stage of relative expertise; he or she learns to be a good reader, a competent mathematician, a deep thinker, a quick learner, a creative person, an inquiring individual, and so on. Competence in these activities is assessed according to criteria of expertise established by the school and the community; more specifically, it is assessed by subject-matter requirements, peer-group expectations, and the general social and professional criteria for what constitutes low, average, and high levels of competence. The educational and social community adjusts its expectations to the competence level of the learner so that initially awkward and partially correct performances are acceptable, whereas later, they are not. The changes that take place as an individual progresses from ignorance to increasing competence are of the following kinds: (a) Variable, awkward, and crude performance changes to performance that is consistent, relatively fast, and precise. Unitary acts change into larger response integrations and overall strategies. (b) The contexts of performance change from simple stimulus patterns with a great deal of clarity to complex patterns in which information must

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be abstracted from a context of events that are not all relevant. (c) Performance becomes increasingly symbolic, covert, and automatic. The learner responds increasingly to internal representations of an event, to internalized standards, and to internalized strategies for thinking and problem solving. (d) The behavior of the competent individual becomes increasingly self-sustaining in terms of skillful employment of the rules when they are applicable and subtle bending of the rules in appropriate situations. Increasing reliance is placed on one’s own ability to generate the events by which one learns and the criteria by which one’s performance is judged and valued. It is the understanding and facilitation of this process of change from ignorance to competence, from novice to expert, that is a major focus of the emerging psychology of instruction. Consider now the components required to facilitate this process.

Components of a Psychology of Instruction The Analysis of Competent Performance Central to a concern with instructional processes is the problem of task analysis; analytic description is required of what it is that is to be learned. What has a competent performer in a subject-matter domain learned that distinguishes him from a novice? What distinguishes a skilled reader from an unskilled one? When a task analysis identifies the properties of a certain class of performance, then inferences can be formulated and tested concerning optimal instructional processes for acquiring these performance abilities. Analyzing the content of instruction means studying tasks considerably more complex than those typically studied in the laboratory. It also requires techniques for the detailed analysis of performance in terms of the demands placed on cognitive processes and on knowledge and skills assumed to be in the learner’s repertoire as acquired through instruction, development, or self-learning. The requirement for the analysis of competent performance is related to the specification of behavioral objectives so strongly advocated by many educational psychologists. This salutary advice given by behavioral psychologists is now being taken seriously by cognitive theorists concerned with the cognitive components of criterion performance. There seem to be two main aspects to such an analysis. One is the identification of the information structures that are required for performance, and the other is a description of the processes and cognitive strategies – heuristics and algorithms – that need to be applied to this information, and which themselves are part of the information data base. As an interesting case in point, consider the work that has been going on in the cognitive simulation of expert chess players. An article by Simon and Chase (1973) summarizes differences between novice and average players,

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and masters and grandmasters in chess. They indicate that the most likely explanation for the extraordinary skill of the chess master is that he is acquainted with tens of thousands of familiar patterns of pieces, and he associates many of these patterns with plausible moves by taking advantage of the informational features represented by the patterns. The basic heuristics that guide the search for good moves are based upon the perceptual ability to recognize an informational pattern on the board. “For example,” Simon and Chase point out, “every chess player of even moderate skill is familiar with the advice: ‘If there’s an open file, put a Rook on it’ ”. (p. 402). The pattern of an open file triggers this heuristic and initiates a move in a heuristic search for the best move. For a chess master, hundreds of immediately recognized patterns may be associated with an algorithmic solution – i.e., moves that lead to the guaranteed win of a piece or a checkmate – so that a series of moves may be played almost by rote. The key to understanding chess skill lies in understanding the large perceptual vocabulary of piece configurations, the associated algorithms, and the particular perceptual processes involved in this skill. From an instructional point of view, the target behavior of interest is that the chess master’s performance seems to involve a buildup in long-term memory of a vast repertoire of patterns and associated plausible moves. Early in practice, these move sequences are arrived at by slow, conscious heuristic search – “If I take that piece, then he takes this piece . . . ” – but with practice, the initial condition is seen as a pattern, quickly and unconsciously, and the plausible move comes almost automatically. Such a learning process takes time – years – to build the thousands of familiar chunks needed for masterlevel chess. (Simon & Chase, 1973, p. 403)

It is to be noted further that grandmasters may possess exceptional talents along certain dimensions, but their talents are chess-specific. There is no evidence that masters demonstrate more than above-average competence on basic intellectual factors. Thus, the acquisition of chess skill depends, in large part, on building up specific recognition memory for many familiar chess patterns. In a psychology of instruction, this kind of contrastive analysis of the informational content and skills of competent performers and novices might be prototypic of the kind of research that is especially relevant to an understanding of the objectives of instruction. Consider another example of work on simple arithmetic problems and the nature of competent performance in addition and subtraction. Studies carried out by Suppes and Groen (1967); Woods, Resnick, and Groen (1975); and Resnick (in press) suggest an interesting relationship between what children are taught to do and how they eventually perform efficiently. Young children are generally taught to solve a single-digit addition problem such as 6 + 8 by an algorithm in which they count out six blocks, then count

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eight blocks, and then count to combine the set. With practice, children perform this smoothly; when the blocks are taken away, they frequently shift to counting on their fingers, and then eventually shift to internal processing. When the nature of this internal processing is examined, it is found that most children carry out addition by using what has been called a “choice model.” They appear to set a mental counter to the magnitude of whichever number is larger and then increment by the smaller number. Some children retain the earlier model used in instruction – that is, they increment six times, then increment eight more times, and then read their mental counter. The most efficient children, however, appear to be able, without direct instruction, to convert a routine that has been taught into a different routine – a routine that shows they have discovered commutativity and have developed a performance that requires fewer steps. It is to be noted that the initial teaching procedure reflected the rational “union of sets” definition of addition, and thus is a mathematically correct procedure that represents the subject matter clearly and provides a routine that is easy to demonstrate and learn. For an efficient performer, however, the routine is awkward and slow. Thus, the routine derived by rational analysis of the subject-matter structure is transformed to a performance routine that reflects a more sophisticated definition of the subject matter. What are the implications of this analysis? On the face of it, it would seem that we ought to abandon the algorithm suggested by direct analysis of tasks in favor of analysis of skilled performance. We can argue that the rational analysis of tasks may not match skilled performance and that it therefore should not be used as a basis for instruction. It would seem best to carry out detailed empirical analyses of skilled performance on subject-matter tasks and teach the routines uncovered by such analyses. However, in discussing her work, Resnick (in press) points out that such a conclusion could be in error, since it rests on the assumption that efficient instruction is necessarily direct instruction in skilled performance strategies rather than instruction in routines that put learners in a good position to invent or derive efficient strategies for themselves. So, it is implied that the teaching routines in elementary arithmetic were not poor ones that inhibited the acquisition of efficient performance, but may have been good ones that fostered the invention of more efficient algorithms. As suggested by the above examples, the work on the analysis of competent performance that is going on at the present time is of two kinds: the characterization of the information structures and cognitive processes of the skilled performer, and behaviorally oriented work on rational task analysis. Such analyses of human competence and subject-matter tasks may allow us to do two things regarding the optimization of instruction: (a) Specifying the structures and processes by which competent individuals might be performing a task may put us in a position to try to teach these processes to individual learners. (b) Knowing that a task is performed efficiently in one way

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rather than in another might enable us to design instruction so that the performance learned allows individuals to directly or indirectly transfer to the more efficient method. It would be a serious omission to leave the topic of task analysis without referring to the influential work of Robert Gagné on learning hierarchies (1962, 1970). This theory continues to be widely accepted as a framework for investigating instructional processes and for designing educational procedures and curricula in various subject matters. Gagné has presented us with a system for rational task analysis based upon a cumulative learning model that states that there are different types of learning, with the simpler types being prerequisite states for learning the more complex types. For example, problem solving, a complex higher-order type of learning, requires rule learning, a lower-order task, as a prerequisite; and rule learning, since rules consist of relationships between concepts, requires concept learning as a prerequisite; and so forth. In general, the lower-order task is defined as being prerequisite to a higher-order task when competence in the simpler task facilitates positive transfer in learning the more complex task. In addition to a clear-cut transfer relationship, there are, however, several possible relationships that might exist between prerequisite tasks and superordinate tasks. The lower-order task might be one of a number of components of the more complex task, each of which can be acquired independently of the others, but all of which must be combined to produce the higher-order performance. Alternatively, the lower-order tasks may themselves be hierarchically related to one another, constituting a sequenced progression leading to increasingly complex performance. Lower-order tasks may also be competencies which facilitate the learning of the more complex task, but which drop out in the more “skillful” performance. Furthermore, the lower-order tasks might function as heuristics for discovering or inventing procedures for carrying out the more complex task. Research along these lines, i.e., investigating the acquisition of complex performance on the basis of existing competencies, is especially relevant for instructional psychology.

Description of Initial State Instruction begins with an initial state of the learner, and instruction proceeds on this base toward the development of competent performance. There are two approaches to this component of instructional design: “immediate” and “long-term.” The immediate approach is to take seriously the fact that effective instruction requires careful assessment of the strengths, weaknesses, styles, and background interests and talents of individual learners. What are the details of what a child knows and does not know at particular points in his or her learning? What are the details of the skills that he or she is developing? What needs to be improved? What strengths can be capitalized on? What

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do various developmental levels and various cultural backgrounds mean for what should be taught and how it should be taught? Educational practices need to be designed so that answers to these kinds of questions are possible for all individuals attending school. Teachers and students need to be in a position to obtain and utilize this kind of information; with it, teachers can prescribe the instruction required, and students can assess their own abilities and select appropriate instruction. The use of procedures for providing this kind of information for teaching requires the adoption of an attitude that looks upon the information obtained as information for improving instruction, and not simply as a test for evaluating and classifying students. For this purpose, it has been useful to provide teachers with hierarchies of increasing competence in various school subjects (Resnick, Wang, & Kaplan, 1973). These take the form of “structured maps” into which a teacher can place a child and thereby direct attention to prerequisite skills that might need to be learned or advanced skills that the child might explore. The hierarchical map serves as a guide upon which both the teacher and the child can impose additional judgments. The provision of procedures for identifying the current competence and talents of the learner in a way that provides a basis for instruction is generally not done in current educational methods at a level of detail necessary for the effective guidance of individual learners. The implementation of such procedures is not only a matter of research, but also largely a matter of administrative change and the design of appropriate materials. The more long-term approach derives from the fact that aptitude and intelligence tests are the prevalent methods for assessing initial states that are, to some extent, predictive of eventual educational success, but these measures do not provide sufficient information about instructional processes (Glaser, 1972). Having been devised primarily for purposes of selection, these measures do not provide a basis for deciding how instruction might be designed to make the attainment of successful performance more probable. The significant requirement in this regard for a psychology of instruction is to describe the initial state of the learner in terms of processes involved in achieving competent performance. This would then allow us to influence learning in two ways: (a) to design instructional alternatives that adapt to these processes, and (b) to attempt to improve an individual’s competence in these processes so that he is more likely to profit from the instructional procedures available. There is, at the present time, a spurt of interesting research devoted to analyzing the underlying cognitive processes that contribute to intelligence and aptitude-like performance. Three illustrative examples will be presented. In a recent series of studies by Hunt, Frost, and Lunneborg (1973), students were classified into high- and low-verbal ability groups and into highand low-quantitative ability groups on the basis of a battery of tests used for selection for college entrance at the University of Washington. The individuals

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in each of these groups were then given a series of tasks employed in laboratory experiments on the experimental analysis of information processing models of memory. In this way, the characteristics of high-verbal ability and high-quantitative ability students, as defined by aptitude tests, were examined in terms of cognitive processes, as defined by tasks used to investigate particular theories of cognition. The conclusions from the studies tentatively indicate that there is a relationship between verbal ability and the rapidity and efficiency of data manipulation in short-term memory, and between quantitative ability and resistance to distraction while consolidating information in short-term memory. It is thus suggested that verbal and mathematical aptitude is related to the nature of information processing in memory, and the interesting question for an instructional psychology is whether we can proceed further and identify situations where the speed and other properties of such processing will be predictive of school achievement. Such an endeavor could have more significant implications than present correlationally derived relations between aptitude tests and school success because clues would perhaps be available about how verbal and mathematical ability processes might be modified or employed for learning. In a very recent paper, Estes (1974) discusses the digit-span test that appears on the Stanford-Binet. At year ten, the subject’s task is to repeat a sequence of random digits after they have been read aloud by the examiner. The test correlates satisfactorily with the usual validation criteria, but the interesting instructional question is: If an individual scores low on this test, what instructional procedure should we expect to be useful in improving this performance, performance that we know is correlated with academic accomplishment? Estes describes recent research and theory dealing with shortterm memory for sequences of items that indicate that the digit-span task appears to involve a hierarchical structure of representations in memory. A quote gives the gist and flavor of this: On presentation of the digit sequence of 691472, the individual is conceived to subgroup the sequence into two chunks, assigning a code to each which he maintains in memory, and within each chunk relating the items of the sequence to the ordinal numbers 1, 2, and 3. On a request to recall the string, the individual brings into memory his coded representations of the two chunks; each of these in turn activates recall of the individual digits and their associated serial positions. While this process goes on, the individual must hold the partially reconstructed sequence in an output response buffer by an inhibitory process until the decoding is complete and then emit the digits in the proper order. (p. 743)

Estes points out that such an analysis of performance on the digit-span task may have implications for assessing individual differences. Young or mentally retarded children might fail the test because of insufficient familiarity with the sequence of ordinal numbers or because of inexperience in ordering materials

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with the number sequence. An individual may not perform well because he has not developed an appropriate strategy of grouping (although he might utilize grouping when prompted by the examiner), is unable to accomplish the coding process necessary to take advantage of chunking, or lacks the capacity for selective inhibition in buffer storage necessary to order his output properly. Estes writes: Clearly, it would be possible with the advantage of added theoretical insight to augment the standard digit span test in such a way as to localize the source of difficulty for an individual who fails under the standard procedure. This augmentation would quite likely do little to improve the predictive value of the test, but it might be of considerable help in indicating how deficient performance in this and related tasks might be remedied. (p. 744)

Holzman (1975) has studied letter series completion problems of the sort used by the Thurstones (1941) in their factor analytic studies of intelligence. Letter series consist of a sequence of alphabetic characters running in a consistent pattern. In any one test item, usually about a dozen of these patterned letters are presented to the examinee followed by four blank spaces. The individual must fill in the four blanks with letters that are consistent with the pattern exhibited by the previously presented letters of that series. For example, the individual might see the problem “defgefghfghi . . . ” and be asked to fill in the blanks. Work on analyzing this task has been carried out by Simon and Kotovsky (Simon & Kotovsky, 1963; Kotovsky & Simon, 1973), who have obtained protocols of adolescents and adults solving these sorts of problems; then, based on these observations, they wrote computer programs to simulate humans’ solution routines. Four basic component routines are necessary for the simulation of correct solution. The first routine is the detection of relations between letters: Are letters identical, sequential, or sequential in reverse order? The second routine or subskill is the discovery of periodicity in a series. This involves noticing that letter relations repeat themselves at regular, predictable intervals. A third routine, called pattern description, assembles knowledge of letter relations and knowledge of periodicity into a rule that generates the series. The final routine required is extrapolation. This involves remembering the pattern description and using this rule to generate the appropriate letters for the blanks. Using this information about the possible cognitive processes involved, Holzman taught elementary school children to be very proficient in the detection of relations and the discovery of periodicity. As a result of their training, children were able to show substantial pretest to posttest gains on a typical letter series completion test. Most strikingly, the children were significantly more able than control subjects to demonstrate perfect posttest solutions to the types of problems which they found difficult on the pretest. Both the control subjects, as a result of repeated testing, and experimental

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subjects were able to make gains on easy problems, but the children trained on component subskills seem to have acquired an information management strategy that allowed them frequently to reach perfect solution even on difficult problems. The skills taught to the children in this study were quite specific; however, the question is raised about the possibilities for the analysis of abilities that are more general than these and that might provide a basis for truly generative intellectual abilities. Studies like those I have just described raise the possibility that measures of intelligence and aptitude, analyzed in terms of cognitive processes, will, as Hunt and his colleagues (1973) write, “move many psychometric predictions from static statements about the probability of success to dynamic statements about what can be done to increase the likelihood of success” (p. 118). And furthermore, “Hopefully [this] new viewpoint . . . will lead to measuring instruments which are diagnostic, in the sense that they tell us how the institution should adjust to the person, instead of simply telling us which people already are adjusted to the institution” (p. 120).

Conditions That Foster the Acquisition of Competence This third component of instructional design – the conditions that can be implemented to foster the acquisition of competence – essentially involves the procedures that assist learning and the techniques and materials that are designed into the environment in which learning occurs. In this regard, we should recognize that the little we do know about learning is known in terms of descriptive science. Little investigation has taken place from the point of view of utilizing this information for designing the conditions of instruction. Exceptions to this are the work on behavior modification, the work of Gagné, and the limited work referred to earlier on optimization models for paired-associate forms of learning. However, for the most part, these enterprises have not considered complex cognitive performance in any intensive way. What is required is that research on instruction be cast into the mold of a design science that attempts to maximize the outcomes of learning for different individuals. A new form of experimentation would be called for where the tactic is not to develop models of learning and performance, but to test existing models by using them for maximizing learning under various conditions. For this purpose, we need a theory of the acquisition of competent performance. Such a theory would be concerned with how an individual acquires increasingly complex performances by assembling the present components of his repertoire, by manipulating the conditions and events around him, and by employing his knowledge of how he learns. With the development of such a theory in mind, some very brief preliminary observations can be made on knowledge structures in memory, on generalized abilities for learning to learn, and on the nature of reinforcement.

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Knowledge structures. Some recent work on the semantic structure of information in memory (e.g., Greeno, in press) has been concerned with the semantic networks and information processing mechanisms that are available at different levels of subject-matter competence. If, at various levels of learning or stages of competence, the kinds of knowledge we wish to create in the minds of students can be specified in this way, then some interesting implications are suggested for the relationship among subject-matter structure, curriculum content, and instructional design. One such relationship can be seen by distinguishing between the structure of a subject-matter domain as it is organized by scholars studying that domain and the structure that is devised for teaching it (Glaser, 1973). The structure of a subject-matter discipline, as employed for the purpose of advanced scholarship, consists of theories, concepts, and definitions that serve to make the domain manipulable for the work of subject-matter experts. However, the structures employed for this purpose are not necessarily the most useful for facilitating the learning of an individual at a less advanced level of development or subject-matter sophistication. Good theory for the scholar may not be good pedagogical theory; what leads to knowledge for the expert may neither lead to knowledge for the novice nor help him to develop competence. It follows that a significant consideration for instructional design is the organization of curriculum sequences that provide knowledge structures optimally organized for moving the novice toward expertise. Appropriately designed structures for learning can reduce the amount of information that must be held in mind to comprehend the subject matter; for example, a verbal label, a conceptual formulation, or a rule or principle may help to organize and summarize a large number of observations. The rule can be thought of as a structure or representation by which an individual is directed or directs himself to look at the relevant features of what might otherwise be an unorganized task situation. As a consequence, a student can generalize across the superficial details of the limited set of experiences encountered in instruction (Gilbert, 1962). Some ways of organizing information may permit better memory retrieval than other ways and, as a result, facilitate the learner’s capacity to learn new things on the basis of what he has already learned and to access information readily for thinking and problem solving. The organization of subject-matter content can do for the learner what advanced theory does for the expert. Such organizations, however, are not readily available; they are sometimes devised by ingenious teachers and built by them into instructional procedures. I would further suggest that the nature and the design of these organizations or pedagogical structures are a unique province of study for a psychology of instruction. Teaching generalized learning-to-learn abilities. In the acquisition of competence, a significant instructional consideration is the way in which individuals use their current competence and components of their repertoire for learning new higher-order performance or for solving problems that lead to

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learning this higher-order performance. Thus, an appropriate concern for instruction is the possibility for teaching general strategies that will help individuals learn on their own and be less dependent on the instructor’s elegance in presenting particular tasks. An interest in teaching such general “learning to learn” abilities has been widely expressed by educators and psychologists, but at the present time, there is little scientific basis for such instruction. One possible basis can come from the studies already described on the process analyses of aptitude-like skills. Still another potential basis for such instruction might be provided by the growing number of information processing analyses of problem-solving tasks. In a recent paper, Resnick and Glaser (in press) argue that the processes involved in certain kinds of problem solving are probably similar to the processes involved in learning in the absence of direct or complete instruction, and that instruction in these processes might constitute a means of increasing an individual’s generalized learning-to-learn abilities. A model of problem solving was developed in which three interacting phases were identified: (a) problem detection, in which the inapplicability of “usual routines” for solving a problem is noted and a problem or goal is formulated; (b) feature detection, in which the task environment (the external situation, which includes both physical and social features) is scanned for cues that might lead to appropriate actions; and (c) goal analysis, in which goals are successively reformulated, partly on the basis of external task cues, in order to yield soluble subgoals that contribute eventually to solution of the problem as presented. A study by Schadler and Pellegrino (Note 1) has shown that requiring subjects to verbalize their goals and strategies in each of these phases, before making overt moves toward solution, greatly enhances the likelihood of problem solution. Along these lines, it seems reasonable to anticipate that ways can be found to make individuals more conscious of the role of environmental cues in problem solving. Individuals might be taught strategies of feature scanning and analysis that will enhance the likelihood of their noticing cues that prompt effective actions while somehow “deactivating” those cues that prompt ineffective actions. Such self-regulation could be a major characteristic of successful self-learning and problem solving. The specific suggestions that can be offered at this time for instruction of such generalized learning abilities are limited, since relatively little has been done on developing task analyses that characterize these general processes in instructable terms; but work on problem solving is especially relevant to this important goal of instruction. Related to this is work on reinforcement effects to which I now turn. Reinforcement. Contingencies of reinforcement pervade the acquisition of competence. However, with the strong emergence of cognitive psychology, and with awareness of the fact that the bulk of our knowledge about reinforcement is derived from animal studies in simple task situations and from human experimental contexts in which conditions constrain subjects to employ limited behavioral processes, we are in some danger of ignoring the potential influence

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of reinforcement on complex performance. There is, on the one hand, a strong suggestion of discontinuity in the operation of reinforcement when moving from simple to higher-order behaviors. On the other hand, the view that seems best supported at the moment is that the mechanisms of reinforcement are similar at all levels of development, but variations in response organization result in different phenotypic manifestations (Estes, 1971). As individuals mature, human behavior is organized into higher-order routines and strategies, and it is these large cognitive organizations whose probabilities of occurrence are modified by reinforcing contingencies. It is the nature of the unit of response that may distinguish the mature human learner, whereas the operation of the principles of reinforcement may be similar for different species and different levels of development and competence. From the point of view of a theory of instructional psychology, we should be further aware that in the natural settings of classrooms reinforcement occurs extensively within a social context. This highlights certain dimensions of the nature of reinforcement that need to be considered in instructional situations (e.g., Bandura, 1971). One aspect is that people continually observe the behaviors of others as this behavior is rewarded, ignored, or punished; and this observation influences the subsequent operation and effect of reinforcers on the observers. This is the phenomenon of modeling and vicarious reinforcement. A second aspect is that individuals regulate their own actions by mechanisms of self-reinforcement. Self-generated anticipatory consequences allow possible future contingencies to influence present behavior, and self-evaluations of the consequences of one’s own actions influence behavior as these consequences are made apparent by classroom reinforcement contingencies.

Assessment of the Effects of Instructional Implementation The fourth component of instructional design is concerned with the effects of instructional implementation in the short and in the long run – effects that occur immediately in the context of instruction and effects that persist in terms of long-term transfer, generalized patterns of behavior, and ability for further learning. One requirement for this purpose is to break away from the tradition of norm-referenced measurement to measurement more concerned with identifying the nature of competent performance (Glaser, 1963; Glaser & Nitko, 1971). For effective instructional design, tests will have to be criterion referenced in addition to being norm referenced. They will have to assess performance attainments and capabilities that can be matched to available educational options in more detailed ways than can be carried out with currently used testing and assessment procedures. This will be an important part of the development of a psychology of instruction. It is mandatory that testing not stand out as evaluative devices that are an extrinsic and external adjunct of instruction. Tests need to be interpreted in terms of performance criteria so that the learner and the teacher are informed about an individual’s

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progress relative to developing competence. In this way, information is provided for deciding upon appropriate courses of instruction. The performance measured by tests designed to facilitate instruction needs to be related to processes identified as components of competence. For this purpose, some interesting endeavors can be envisioned. One example is work going on in analyzing the processes involved in the comprehension of written language, stimulated by the work in psycholinguistics and cognitive psychology (e.g., Carroll & Freedle, 1972). This development should be juxtaposed with the fact that there has been a great deal of work on the development of tests of reading comprehension. As we begin to analyze comprehension tasks and relate them to theories of semantic memory, imagery, and so forth, we should be able to develop tests that provide us with diagnostic information about component processes that contribute to performance and that can be influenced through instruction. This kind of activity should change the nature of assessment procedures and provide us the kind of information required for maximizing instructional outcomes. Another area of investigation that is beginning to provide significant evaluative information about the conditions under which learning takes place in school contexts should be mentioned. This is the growing sophistication in the study of the nature of classroom processes. In the past, we essentially attempted to describe school learning by relating the nature of student input to the quality of student output; but the process intervening between the two, the independent variable, was only generally described. Detailed information was rarely obtained about differences between effective and less effective classroom processes. There are now a number of attempts to research these details. I am especially impressed by the model for such research being developed by my colleague, William Cooley, in conjunction with Paul Lohnes (Cooley & Lohnes, in press). Their model is derived from Carroll’s 1962 model of school learning and consists of six components: (a) initial ability, which reflects the basic incoming skills and general intellectual development of children in a classroom; (b) opportunity, which describes the relative proportion of classroom activities (the dominant classroom subject-matter themes) that are directly related to the assessed outcomes of instruction; (c) motivation, which reflects a student’s tendency to engage in learning activities when the opportunity exists, and operationally defined (in elementary school classrooms) as the fit between the learning situation and the child’s needs, and the relative incidence of teacher praise and encouragement and their antitheses for particular pupil behaviors; (d) structure and placement, which reflect the extent to which the curriculum is structured by specifying objectives, sequences of instruction, particular methods used in differentiating students or in individualizing instruction, and, in general, the organization of instruction and teaching materials; (e) instructional events, which reflect the relative incidence of teacher-pupil instructional interaction and observed, for example, through the extent of teacher acknowledgment of, and feedback with

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respect to, a student’s task-related activity; (f ) criterion ability, which reflects end-of-year student performance, for example, on standardized achievement and intellectual ability tests. After obtaining information on these components of instruction, a multivariate analysis procedure is used to determine the regression of criterion ability on the other five components of the instructional model. This permits an analysis of the total variance represented in the criterion variable that is explainable in terms of the other components – (a) variance due to incoming ability independent of classroom process variables, (b) variance uniquely due to the classroom process variables independent of initial ability factors, and (c) variance due to the interaction or overlap between initial ability and instructional processes. In this way, detailed information is obtained on the kind of classroom implementation of an instructional system that is effective or ineffective in producing school outcomes. What is of particular interest in research of this kind is that we can begin to relate the effectiveness of school implementation procedures to psychological dimensions of learning theory and to a theory of the acquisition of competence. Each endeavor can reinforce or challenge the findings of the other. To conclude: A speculative outline of a psychology of instruction as a science of design has been presented. Directions in which it might develop and what some of its substantive components might be have been suggested. There is much to be done, but many promising leads are now offered for testing fundamental theories of human learning and cognition and for contributing strongly to educational practice.

Reference Note 1. Schadler, M., & Pellegrino, J. W. Maximizing performance in a problem solving task. Unpublished manuscript, University of Pittsburgh, Learning Research and Development Center, 1974.

Note 1. Dewey is quoting Hugo Munsterberg, Psychology and life, p. 138. (New York: Houghton, Mifflin & Co., 1899.)

References Atkinson, R. C., & Paulson, J. A. An approach to the psychology of instruction. Psychological Bulletin, 1972, 78, 49–61. Bandura, A. Vicarious- and self-reinforcement processes. In R. Glaser (Ed.), The nature of reinforcement. New York: Academic Press, 1971. Bruner, J. S. Some theorems on instruction illustrated with reference to mathematics. Theories of learning and instruction. The Sixty-third Yearbook of the National Society for the Study of Education, Part I, 1964, 63, 306–335.

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Carroll, J. B., & Freedle, R. O. (Eds.). Language comprehension and the acquisition of knowledge. Washington, D. C.: V. H. Winston & Sons, 1972. Cooley, W. W., & Lohnes, P. R. Evaluative inquiry in education. New York: Irvington Publishers, in press. Dewey, J. Psychology and social practice. The Psychological Review, 1900, 7, 105–124. Estes, W. K. Reward in human learning: Theoretical issues and strategic choice points. In R. Glaser (Ed.), The nature of reinforcement. New York: Academic Press, 1971. Estes, W. K. Learning theory and intelligence. American Psychologist, 1974, 29, 740–749. Gagné, R. M. The acquisition of knowledge. Psychological Review, 1962, 69, 355–365. Gagné, R. M. The conditions of learning (2nd ed.). New York: Holt, Rinehart & Winston, 1970. Gilbert, T. F. Mathetics: The technology of instruction. Journal of Mathetics, 1962, 1, 7–73. Glaser, R. Instructional technology and the measurement of learning outcomes: Some questions. American Psychologist, 1963, 18, 519–521. Glaser, R. Individuals and learning: The new aptitudes. Educational Researcher, 1972, 1, 5–13. Glaser, R. Educational psychology and education. American Psychologist, 1973, 28, 557–566. Glaser, R., & Nitko, A. J. Measurement in learning and instruction. In R. L. Thorndike (Ed.), Educational measurement (2nd ed.). Washington, D. C.: American Council on Education, 1971. Greeno, J. G. Cognitive objectives of instruction: Theory of knowledge for solving problems and answering questions. In D. K lahr (Ed.), Cognition and instruction. Hillsdale, N. J.: Lawrence Erlbaum Associates, in press. Groen, G. J., & Atkinson, R. C. Models for optimizing the learning process. Psychological Bulletin, 1966, 66, 309–320. Holzman, T. G. Process training as a test of computer simulation theory. Unpublished master’s thesis, University of Pittsburgh, 1975. Hunt, E., Frost, N., & Lunneborg, C. Individual differences in cognition: A new approach to intelligence. In G. H. Bower (Ed.), The psychology of learning and motivation (Vol. 7). New York: Academic Press, 1973. Kotovsky, K., & Simon, H. A. Empirical tests of a theory of human acquisition of concepts for sequential patterns. Cognitive Psychology, 1973, 4, 399–424. Resnick, L. B. Task analysis in instructional design: Some cases from mathematics. In D. K lahr (Ed.), Cognition and instruction. Hillsdale, N. J.: Lawrence Erlbaum Associates, in press. Resnick, L. B., & Glaser, R. Problem solving and intelligence. In L. B. Resnick (Ed.), The nature of intelligence. Hillsdale, N. J.: Lawrence Erlbaum Associates, in press. Resnick, L. B., Wang, M. C., & Kaplan, J. Task analysis in curriculum design: A hierarchically sequenced introductory mathematics curriculum. Journal of Applied Behavior Analysis, 1973, 6, 679–710. Simon, H. A. The sciences of the artificial. Cambridge, Mass.: MIT Press, 1969. Simon, H. A., & Chase, W. G. Skill in chess. American Scientist, 1973, 61, 394–403. Simon, H. A., & Kotovsky, K. Human acquisition of concepts for sequential patterns. Psychological Review, 1963, 70, 534–546. Suppes, P., & Groen, G. J. Some counting models for first-grade performance data on simple addition facts (Tech. Rep. 90). Also in J. M. Scandura (Ed.), Research in mathematics education. Washington, D. C.: National Council of Teachers of Mathematics, 1967. Thorndike, E. L. The psychology of arithmetic. New York: Macmillan, 1922. Thurstone, L. L., & Thurstone, T. G. Factorial studies of intelligence. Chicago: University of Chicago Press, 1941. Woods, S. S., Resnick, L. B., & Groen, G. J. An experimental test of five process models for subtraction. Journal of Educational Psychology, 1975, 67, 17–21.

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26 The Emergence of Cognitive Psychology Robert R. Holt

O

ne of the great unmistakable trends in psychology during the past decade and a half has been the emergence of a new and vigorous interest in cognition. This last term itself has experienced a revival of currency and respectability; once a scholastic term for knowledge, encountered in the classical threefold division of human function into cognition, affection, and conation, it has now shaken off such dusty and facultative connotations and is used boldly by neo-behaviorists who still feel a trifle shy about admitting an interest in thinking as a subject matter for psychology. The scope of the term is, to be sure, a good deal broader than thought processes: it comprises perceiving, judging, forming concepts, learning (especially that of a meaningful, verbal kind), imagining, fantasying, imaging, creating, and solving problems. One might try to summarize all this by saying that cognition deals with all aspects of symbolic behavior, in the broad sense, if it were not for the fact that the study of language is traditionally separated off into linguistics. Such boundaries are, of course, artificial, but the attempt to draw them helps us to recognize that cognitive psychology is growing actively at its peripheries as well as in its core area of thinking. The appended bibliography gives some of the evidence for the declaration of a renaissance starting in 1951. It does not, of course, show the preceding decades of dearth, but they could be easily documented. Other evidences of the trend are the establishment, during these years since 1951, of a number of institutes such as the Center for Cognitive Studies at Harvard University and the Research Center for Mental Health at New York University, and the appearance of such new publications as the Journal of

Source: Journal of the American Psychoanalytic Association, 12 (1964): 650–665.

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Verbal Learning and Psychological Issues. Indeed, a substantial part of the appended bibliography comes from this last-mentioned monograph series. Of the nine titles that had appeared by the end of 1962, seven are listed here, and several other major contributions to cognitive psychology are announced for early release, notably works by I. Kohler, G. S. K lein, C. Fisher and W. Dement. Elsewhere1 I have set forth some hypotheses about historical reasons for the decline of interest in cognition, which was the central problem of psychology before 1910, and its recent recrudescence. Briefly, it seems plausible that the two greatest revolutionary influences in psychology of the twentieth century both helped to turn attention away from the subjective phenomena of consciousness: behaviorism and psychoanalysis. Conscious processes were downgraded in these simultaneous and seemingly antithetical movements for seemingly quite different reasons, but I believe that there was an underlying unity of outlook that is beginning to emerge. During the polemical decade 1915–1925, both the psychoanalysts and the behaviorists were impatient with studying the contents of consciousness as misleading and epiphenomenal, Freud because he was so much more impressed by the importance of unconscious motivations, Watson because he wanted to focus on objectively observable behavior. As each of these schools became entrenched and attained the status of an orthodoxy instead of a struggling radicalism, it slowly became apparent that there were legitimate and important problems left unsolved by the neglect of the subjective, cognitive realm. And both those who strove for depth and those who valued objectivity most highly began to realize that they did not have to betray their basic values to study these problems. Learning had always been a central concern of the behaviorists; gradually, they began to take an interest in the acquisition of meaningful cognitive structures – concepts as well as motor performances, words as well as nonsense syllables. Ego psychology began to come to the fore in psychoanalysis; thanks largely to Hartmann, Erikson, and Rapaport, interest was turned to the nature of reality and man’s adaptation to it. Another trend that impresses me as underlying the burgeoning of concern with cognition is the gradual emergence of model-building as a major theoretical objective in almost all schools of psychology, including psychoanalysis. During the past two decades, we have begun to appreciate the proper role of psychological theory as an abstract model or simulacrum of man capable of predicting his behavior (in the broadest sense). This point of view implies that we shall not truly understand how and why people do, feel, and think as they do until we can build an imitation that will accurately simulate these human activities, and that a good theory is an abstract blueprint for such a working model. The advent of the high-speed digital computer, which can acquire information, store it in a memory, process it in such a way as to solve problems and output the answers, has been a powerful stimulant to the

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imagination of model-builders – of whom Freud was one of the first, long before the thinking machines. But whether the model is as concrete as a computer or as abstract as Hullian “behavior theory” or Freud’s “psychology for neurologists” (the Project), it has to provide for some internal processing of symbols – that is, coded information; and this is what cognitive psychology essentially deals with. In his lucid survey of trends in cognitive psychology, which ends the Colorado symposium (33) (see below), Fritz Heider comments on the emergence of this field and draws attention to some of the immediate influences within psychology that helped bring it about. Shortly after World War II there was a movement informally called the New Look, which brought together perception and thought processes into relation with emotions, needs, and other aspects of personality (3); it was in a sense an experimental exploration of instances in which perception and thinking lose their primary autonomy. One source of this revived interest in perception, Heider goes on, was the strong interest in the structure and dynamics of thinking engendered by the rise of projective techniques and (more generally) diagnostic psychological testing, particularly as conceptualized by Rapaport and his co-workers.2 The other source pointed out by Heider is information theory, which may be only indirectly related to cognition, “since it does not concern itself with meanings,” but it “suddenly achieved high status through being clothed in a magnificent mathematical theory” (p. 204). Because all these movements led to a good deal of laboratory experimentation, it became possible for academic psychology to readmit thought processes as a legitimate focus of scientific interest. A single book essay of reasonable length can only sample the flood of textbooks and monographic literature that has appeared in the last decade. The five books rather haphazardly chosen for consideration here are in a number of ways not a very representative sampling – most glaringly omitted are anything by Piaget (5, 10, 11, 19, 30, 39, 44; see also 67), any representative of information theory and related work (17, 25, 34, 42, 45, 74), and the work of the Russians (78). Nevertheless, they do illustrate a number of important trends in this new literature, about which psychoanalysts should be informed. Moreover, one of the books, the Colorado symposium, is itself quite a good sample of major developments in cognitive psychology, containing essays by a half dozen of the most important workers in this field, each of whom is represented by a book in the bibliography. Let us begin with it, therefore. Contemporary Approaches to Cognition (33) is highly recommended to analysts who wish to learn something about this whole area. The contributions vary considerably in their level of difficulty and the amount of prior contact with academic cognitive psychology they presuppose, yet all are worthy of close study, and the chapter on “Cognitive Structures” by Rapaport – the one most immediately accessible to psychoanalysts, and the only

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one that considers data from psychopathology – is a small classic of ego psychology, one of Rapaport’s important statements about the neglected concepts of psychic structure and states of consciousness. The book begins with a characteristically difficult but important essay by the late Egon Brunswik. His major work (28) is more widely known and respected by psychologists than it is read, for Brunswik not only had an original and highly abstract set of ideas but clothed them in a jargon of barbaric density. He alludes, for example, to “problems of textural ecology,” by which he means problems of the psychologically significant structure of man’s environment – all of which are swallowed, for psychoanalysis, by the blandly encompassing term, “reality.” Moreover, he seems at first to be dealing with problems of little concern for analysts: details of how we achieve perception of distance, for example, and just how correct it usually is under everyday conditions. Yet his ideas are of fundamental importance to any theory that wants even grossly to cover the problems of adaptation, for the considerations he advances are at the heart of man’s relation to reality. Without a mastery of his conceptual apparatus, we come up against insoluble problems in the study of cognitive development, for example. Yet I hesitate to try to expound Brunswik here; he is too condensed, and his relevance not readily elucidated. Let me only indicate how he locates the problems of cognition. For Brunswik, the real objects that consensual validation assures us surround the percipient organism are distal; they give rise to patterns of radiant energy which at the moment of their impact on the sense organs comprise the proximal object, an objective state of affairs that changes constantly while the distal object remains constant. Consider the mother moving about the nursery and cooing to her child; she is a relatively stable distal object. To the baby’s retina, however, she presents an ever-changing pattern of light waves, which undergoes radical transformation as she approaches a step nearer and as the infant moves his head or eyes. But this is only the beginning of ambiguity; there are two further cognitive stages: the peripheral events in the organism, the configuration of retinal excitations and auditory stimulations in the sense organ proper, which are transmitted and transformed by various way stages until they attain central representation in the projection areas of the cortex. In this system, there are problems of what Heider calls mediation (how information is transmitted at various points between distal object and central process), and of what Brunswik calls “achievement” or “functional validity”: “the over-all correspondence between a certain distal and a certain central variable, so that the former could be considered successfully mapped into the latter.” More simply, it is the basic epistemological problem: how does man attain veridical knowledge of the world? And Brunswik shows a number of ways in which it can be fruitfully studied. It is no news to psychoanalysts that, by and large, they have neglected the study of reality in their fascination with inner dynamics; but it is interesting to hear Brunsrvik charge psychology with a similar neglect: “Both historically

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and systematically psychology has forgotten that it is a science of organismenvironment relationships, and has become a science of the organism.” There is one notable exception, Fritz Heider (48), the pioneer who has taken more seriously than perhaps any other psychologist the problems of mediation and of the structure of the environment, and who first brought them to Brunswik’s attention. In one place, Brunswik mentions psychoanalysis (toward which he was generally sympathetic): “Psychoanalytic mechanisms [of defense, that is] are an expression of vicarious functioning,” he writes ( p. 22). The significance of this statement depends first on a realization that he believed “vicarious functioning is one of the most fundamental principles, if not the most fundamental principle, of behavior.” This asserts that man is not a stimulus-response machine, but that many external events may have the same significance for a person (thus are vicarious substitutes one for another) and he may respond by a great variety of specific acts, all having the same meaning and readily interchangeable. In line with its tendency to put as much as possible within the organism, making hypothetical energic transformations the fundamental explanatory principle, psychoanalysis conceptualizes as displacements or transformation of instincts events that can be simply accounted for in terms of the looseness of coordination between stimulus cues, with their limited validities as indicators of what is the real state of affairs in the distal object, and centrally evoked meanings. Just because there is this ambiguity of relation between aspects of reality and proximal events (eg., retinal), adaptation demands that man be able to use, in a flexibly vicarious way, a great variety of cues; and to use them not at random but in a hierarchical order following their actual ecological validities. It would miss the point entirely and locate in the realm of primary process something that is an essential part of secondary process and contact with reality, if we considered such substitutions to be displacements. At this point, it may be meaningful to digress a bit and take up a book by Sarbin, Taft, and Bailey (64), Clinical Inference and Cognitive Theory, for it professes to be much influenced by Brunswik. It is an unfortunate example of how much easier it is to copy mannerism than to absorb substance; the worst features of Brunswik’s writing are here together with much of his idiosyncratic vocabulary, but without his underlying clarity of thought. Taking off from the controversial attack on clinical thinking in Paul Meehl’s influential Clinical vs. Statistical Prediction (University of Minnesota Press, 1954), these authors restate the epistemological problem for the clinician, and set up a hypothetical analysis of the ways he proceeds from observations to inferred propositions about the distal object: the patient. Their answer, briefly, is that all cognition is the ordering of data into categories, which it pleases them to call “modules.” The process of statistical inference, too, is one of ordering data into categories, and the book’s principal conclusion is “that, in principle, clinical inference is only a special form of statistical

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inference” (p. 267). Only a few lines later, as well as in many other places in the book, we read that major premises often used by clinicians “are developed through non-inductive processes, namely, analogy and construction” – that is, from theory. If so, they are not empirical generalizations or summaries of statistical experience, and it puzzles me how Sarbin and his collaborators can square this with their main conclusion. What is statistical about statistical inference is not the syllogistic form but the way the major premises are formed. Thus, it is misleading and incorrect to claim that clinical inference is a “special form” of the statistical. There is much in the book that I agree with, yet the tone, the slant, the interpretation, continually rub me the wrong way, and in fact seem inconsistent with much of the argument. It is enough of a contribution to show the clinician that much of what he does is in fact based on statistical reasoning, and to urge the use of more exact and formalized experience tables for decisions of this kind. Why not admit, however, that there is such a thing as a creative or a synthesizing cognitive act, and that clinical judgment involves a good deal that is qualitatively different from statistical inference? The need to deny these obvious facts gets the authors into a couple of fallacies. They show that nonstatistical forms of reasoning can lead to error, giving them only the unimportant status of flaws in the inferential system, which may help the authors to ignore their existence. This argument overlooks the phenomena implied by the concept of regression in the service of the ego: in creating new ideas, new forms, new theories, we can use primitive, illogical, even magical kinds of thought processes. A single case is obviously not a reliable base for a hypothesis, yet the hypothesis may prove true. The fact that the primary process often disregards logic and reality does not mean that it necessarily leads to error: it only means that hypotheses must be tested. But these authors seem unaware that anything like the primary process exists; if they had to recognize it, they would undoubtedly dismiss it as only a faulty and thus unimportant variant of logical (for us, secondaryprocess; for them, statistical) thinking. Sarbin et al. try to deal with the sticky problem of creativity essentially by denying that it exists except within a narrow range. Since their model for thought involves only the manipulation of empirically built-up premises, it is embarrassed by the problem of novelty; therefore, in trying to refute Meehl’s argument that clinical intuition involves creative cognitive acts, they minimize the extent to which novelty actually exists and stress the fact that “no creative act emerges from the void without knowable antecedents” – obviously a strawman argument. Of course there are antecedents; of course nothing is wholly new; but how do we account for the part that is new? Only through the “development and employment of previously undiscovered or unutilized species or classes,” we are told (P. 82). Although this is stated in a grudging way that seems to imply that it doesn’t amount to much, it is a considerable admission; but how about novel configurations of concepts? There is not a word on this

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issue, because this aspect of clinical work is ignored in a procrustean restriction of clinical inference to processes that can be fitted to a syllogistic model. Finally, we read the curious, truistic statement that the creative acts of the clinician “are predictable with a probability p.” Does this, then, deny that they are creative? Is the value of p irrelevant, no matter how small? This is a variant of an argument that pervades the book: a false dichotomy is set up between statistical inference, on the one hand, and intuition defined as a mystical bolt from the blue, a revelation not amenable to empirical analysis, on the other. The latter is not hard to refute, and so the (specious) demonstration is apparently complete that nothing is left but statistical inference. Despite the apparent promise in its title that a psychoanalyst who is interested in thought processes will find Clinical Inference and Cognitive Theory especially rewarding, quite the opposite is the case. The book’s merits are far overshadowed by the fallacies mentioned, by the preposterous difficulties of its style, and by a pervasively static outlook to which psychodiagnosis is mere pigeonholing. All of this is a far cry from Brunswik, to whom this book was dedicated, as was that of Bruner, Goodnow, and Austin (27). Next in the Colorado symposium is a characteristically graceful and interesting paper by Jerome s. Bruner (director of the already-mentioned Center for Cognitive Studies), on “Going Beyond the Information Given.” In this chapter, as in his book, A Study of Thinking (27), the Harvard psychologist is so literate and persuasive that one may easily miss the fact that his contribution is more a synthesizing than a very deep or original one. These remarks are largely a response to what strikes me as overpraise of his book in some early reactions: sound, thorough, suggestive, insightful, and valuable it certainly is, but hardly a “magnificent book . . . a revolution in the psychology of thought,” as Jean Piaget was incautious enough to comment for the dust jacket. It covers far more intelligibly much of the same territory as Sarbin’s book – the organization of knowledge by means of conceptual categories – and is well worth reading, at least its first few, general chapters; most psychoanalysts will be less interested in the bulk of the book, which reports specific experiments. To a reader who is already familiar with the extensive treatments of conceptual thinking in Rapaport’s Diagnostic Psychological Testing and Organization and Pathology of Thought (6), much of the general discussion will seem like familiar thoughts put into slightly different words (though the book is quite without reference to either of these books and does not mention Rapaport’s name). In the Colorado essay as well as in his own book, Bruner has interesting things to say about the adaptive utility of concepts, which enable man to go beyond the information given him; he ingeniously brings together a wide variety of sources and shows a common thread of relevance to his point. But in the end it is difficult to say just what was specifically Bruner. Perhaps I am overvaluing originality, and being overcritical of this gifted, widely learned, highly productive, and eminently useful middleman of ideas.

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One of Bruner’s contributions is his ability to point out relevance of an idea in many diverse fields, or to see the point that many seemingly illassorted sources have in common. He is in this respect a kind of virtuoso of the very process he and his collaborators studied: concept attainment. One of the fields he brings into relation to his thesis is linguistics; and here he has the doughty assistance of Roger Brown, who contributed a meaty, 65-page appended chapter on “Language and Categories.” Brown is one of those interesting new scientific hybrids, the psycholinguists; Charles Osgood, who contributed the next chapter in the Colorado symposium, is another. Osgood declares his allegiances with his chapter title: “A Behavioristic Analysis of Perception and Language as Cognitive Phenomena.” He is indeed a behaviorist, but one who has been induced to expand his purview by an attempt to take “verbal behavior” seriously. As psychologists have lifted their eyes from the simplified artificial situation of the association test, in which one word calls forth one other, and have caught sight of the intricate beauty and highly organized complexity of ordinary, connected, meaningful discourse, they have begun to see that these woods are something more than rows of trees. Moreover, they have discovered there a hardy band of woodsmen, the structural linguists, already possessed of a great deal of lore about this awesome realm. Linguists have developed a formidable body of rigorous method and theory for searching out the order within language and conceptualizing it quite without any concern for its psychological origins. One of the saving virtues the behaviorists have always had has been a genuine respect for rigor. Even though it has led many of them to reject psychoanalysis out of hand for its conspicuous lack of such properties, this value enabled behavioristically trained psychologists such as Osgood and Mowrer (61), of Illinois, and Russell and Jenkins of Minnesota (to name only two well-known university centers of this kind) to see the merits of structural linguistics and the possibility of a fruitful collaboration of disciplines. Out of such unions, in which nonbehaviorists also took part, the field of psycholinguistics was born. It is showing all the signs of hybrid vigor, and much contribution to cognitive psychology may be expected from it (18, 20, 34, 40). Osgood’s paper (which I shall not attempt to summarize, since it is a technical and detailed analysis, partly in neurophysiological terms) introduces his ingenious technique of studying connotative meaning, the semantic differential, which he describes in a great deal more detail in his recent book (38). The essence of the device is to get people to rate any word or concept on a variety of rating scales, which may be related to it only tangentially or physiognomically. People agree that the term HERO is “fast” rather than “slow,” “heavy” instead of “light,” and more “clean” than “dirty.” StatisticaI studies show that three and only three independent dimensions of connotative meaning, sampled by the terms just quoted, recur again and again. This flexibly useful tool has won a far broader acceptance than the theory, in which coding and mediation are key conceptions.

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With the aid of these concepts, Osgood attains a level of sophistication rather characteristic of neo-behaviorists in recent decades. It is no longer possible for psychoanalysts simply to brush their work aside as “obviously fallacious” or “inherently too limited to be taken seriously.” David Rapaport’s discussion, which follows the paper, is quite respectful, yet points out several deficiencies from the vantage point of ego psychology: the theory’s lack of any concept of or explanation for autonomy, the instability of conditioning as a theory of learning on which the structural aspects of Osgood’s theory are based, and its neglect of the problem of motivation. One of Bruner’s principal concepts, which played something of an economic role in his theory, was cognitive strain and the need to minimize it. In his essay on “The Relation between Behavior and Cognition,” Leon Festinger introduces the apparently similar conception of cognitive dissonance, which has, however, had a much more massive impact. Like so many of the other contributors, he set his ideas forth at greater length in a contemporary book (35); he and his students have published extensively on his theory and experiments growing out of it since then. A new book by Brehm and Cohen (73) is the most recent addition to a fast-growing literature. Obviously, there are some productive ideas here. The underlying conception is very simple. Festinger postulates “that, if a circumstance should arise such that some cognitive elements do not fit or are not in line with a person’s actions, there will arise pressures directed toward changing these dissonant cognitive parts” (p. 128). In short, he has discovered intrasystemic conflict and the synthetic function of the ego, operating on cognitive materials in a way Breuer and Freud nicely described in 1893. But they did not discover what philosophers have called the strain for consistency, either; it is an ancient conception. Festinger’s merit lies in having observed sharply some of the kinds of behavior that are so motivated, and in having set up a theory about it from which a number of nonobvious predictions can be derived and tested. As Bruner points out in his discussion, some of them are obvious and Aesop-like enough: the theory predicts that the inaccessible grapes will be judged sour. And in the brief presentation that was possible here, in the course of which three experiments are described, Festinger could not go into some of its more interesting and less elementary aspects. His success has been such that it may be desirable to point out the theory’s limitations: cognitive dissonance does not try to account for all of cognition, but deals only with a range of problems in the realm of the synthetic function. At least, however, his work shows that this single aspect of autonomous ego functioning can have quite powerful motivating effects on behavior. Rapaport’s paper on “Cognitive Structures” is, except for Fritz Heider’s wise concluding remarks, the last chapter in the Colorado book. Rapaport never made a more richly concrete presentation of his ideas than in the present paper. Particularly fascinating are his detailed descriptions and structural

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analyses of sequences of his own images, reveries, fantasies, and dreams – the yield of an extraordinary feat of self-discipline. He trained himself in a kind of near-automatic writing so that he was able to make a running record of the contents of experience as he passed from drowsiness into sleep, a series of states of consciousness accompanied by demonstrable changes in many detailed formal features of thought. Altogether, it constitutes an extremely valuable clinical exposition of primary-process ideation (cf. also his paper in [2]). The material of self-observation is well supplemented by clinical observations of an amnestic case and test data from schizophrenics, all interpreted to support the propositions “that varieties of consciousness are themselves organized means of cognition . . . [and] that we are dealing here with quasistable cognitive organizations that use different tools or mechanisms of cognition, and are themselves organized means of cognition” (p. 180). The concept of states of consciousness has had a curious history in psychoanalysis. Breuer, of course, expounded the idea that there were special hypnoid states, with their own unique structural characteristics and contents in poor communication with those of other states of mind – all this was central to his understanding of hysteria. The origins of the conception are plain to see in the contemporary French psychopathology, impressed as it was ever since Mesmer by the phenomena of that special state of consciousness, hypnosis. For a youthful Freud, fired with his first dynamic hypotheses and insights into the defensive nature of hysterical symptoms, what was valid in the older views was much less impressive than the fact that they could be used as a conservative resistance against his own bold propositions. His intellectual development required first the mastery of the French approach through his apprenticeship with Charcot and then the rejection of hypnosis and most of the ideas associated with it. It has apparently required the long years during which the dynamic point of view reigned supreme in psychoanalysis to make it possible for analysts to turn serious attention to such structural considerations as states of consciousness without feeling it an act of disloyalty or resistance. So we have seen, on the one hand, the general growth of ego psychology, and on the other, the rise of interest in hypnosis (cf. the important book by Rapaport’s colleagues Gill and Brenman [47]), what Fliess has aptly called The Revival of Interest in the Dream,3 and the remarkable revival of attention to the work of Pötzl, Allers and Teler (62) on subliminal phenomena, with which the names of Charles Fisher and George S. K lein are prominently associated. Important parallel and recent developments outside psychoanalysis have also turned scientific attention to states of consciousness and correlated cognitive phenomena: the physiologically oriented approach to sleep and dreams growing out of the work of K leitman and Dement (and related work on the cognitive effects of sleep deprivation and dream deprivation), and the enormously influential work of Magoun, Lindsley, Jaspers, and others on the structure and function of the reticular activating systems of the brainstem.

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From this last line of work has come the important physiological concept level of arousal, which clearly seems coordinated with the psychological phenomena Rapaport called states of consciousness. An exciting field of collaborative work is emerging in which such teams as the physiologist Dement and the psychoanalyst Fisher can bring together the insights and methods of these two independent traditions on phenomena of this type. Dreams, subliminal influences on thought, altered states of consciousness – all these are at the center of the stage for the non-Freudian anaIysts Tauber and Green, and are summed up as forms of Prelogical Experience (53). The range they try to cover is even broader, taking in all forms of symbols, and phenomena as diverse as countertransference and extrasensory perception – truly a fascinating set of off-beat phenomena. One approaches the book with hopes that a Sullivanian approach to this underworld may provide some fresh insights about the primary process. The authors have a commendable striving for breadth; they read widely, quote many authors, pick up and handle many concepts, and cite quite a few experimental studies. Somehow, however, nothing much seems to come of it all. What tries to be breadth finishes as footless eclecticism; when they quote an interesting set of observations, it ends up as an isolated datum for lack of any conceptual framework within which diverse contributions might have been integrated. One has only to recall Organization and Pathology of Thought to realize that it is perfectly possible to piece together ideas and facts from diverse sources in a useful way; but these authors lack not only Rapaport’s synthetic powers but his theory. The difficulty goes beyond the lack of a stout theory to give the book backbone. The authors always fail to come to grips with a problem fully: they open it up, talk around it, and occasionally throw out an original observation or insight – for they are intelligent men – but then it somehow slips through their fingers in a lather of frothy phrases about the richness of human experience, or the like. The key to the difficulty may be found, I believe, in the point of view expressed in such a passage as this: “The rational process is actually at its best when it is dealing with past established structural entities; but when applied to emerging material, to emerging facts, it may actually introduce an irrational note by virtue of the fact that it is not applicable as such to emerging material” (p. 73). The argument is, in effect, that the secondary process cannot really grasp the primary process: therefore, we should abandon the attempt to be clear, logical, and rational when discussing what is intrinsically unclear, irrational, and inchoate. This is the logic of “set a thief to catch a thief” – which is plausible until you start to argue that all good policemen must be practicing criminals. It is the attitude of the notorious group of psychopharmacologists who recently proclaimed that it was fallacious to try to study the effects of LSD unless the experimenter was under the influence of the drug himself at the same time as the subjects. In this extreme example, we can see the fallacy: empathy and intuition play an indispensable role when one studies the primary process, but they are not identical with losing oneself

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in it. Fortunately for man’s survival, it is perfectly possible for the secondary process to deal with data obtained during moments of controlled regression; otherwise we should have no science of psychoanalysis at all. At the risk of getting beyond my sphere of competence, I cannot resist pointing out the dangerous implications of this point of view when applied to technique. This same kind of failure to see the limits and the proper role of empathy leads Tauber and Green to advocate acting out in the countertransference as a therapeutic strategy. They have a valid point, that the analyst may at times be unable to grasp the unconscious significance of the patient’s behavior except through seeing its effect on his emotions (what H. A. Murray called recipathy); but it is perfectly possible for the analyst to let only enough of this kind of affect develop to recognize and use it, without burdening the patient by countertransference confessions, all done in the self-justifying name of “spontaneity” and being honest with the patient. The links of this outlook to the anti-intellectualism of Zen and the oldfashioned verstehende psychology are apparent enough. It is sad to see interesting data on fringe experiences misused in an attempt to justify the abandonment of controls by invoking fidelity to the “prelogical” sources of what is creative and spontaneous in human behavior. It is even sadder to reflect on the consequences for the patient of the therapeutic approach advocated here. For the reader who turns from this slight book to Rapaport’s paper in the Colorado symposium, there could be no more convincing refutation of the Tauber-Green hypothesis: their attempt gets them nowhere, while the application of a sharp critical intelligence to the same kinds of twilight phenomena greatly enriches our understanding. The last book to be considered here also is devoted to the realm of imagery, creative and abnormal thought, dreams and the products of lowered states of vigilance. In his Imagination and Thinking, Peter McKellar (36) draws on a British tradition of gentlemanly curiosity about odd mental phenomena that goes back to Sir Francis Galton. With a very British kind of urbanity, he converses with us informally yet elegantly about neglected aspects of experience, into which he has delved over a period of years. He begins with a distinction between A-thinking and R-thinking, which turns out to be exactly Freud’s differentiation of primary and secondary process under another name. Oddly enough, though McKellar shows a pretty good grasp of a few of Freud’s basic works, he never refers to these concepts and fails to integrate his own observations with the psychoanalytic tradition. Nevertheless, the very fact that so many of the facts gathered together in this small and pleasantly written book do not come from psychoanalytic sources may make it worth the while of analysts to read McKellar’s contribution. It adds a little to the study of the primary process, and is quite easy to absorb into a psychoanalytic conceptualization. One of the principal merits of Imagination and Thinking is its meticulous attention to the form-varieties of mental imagery, a much neglected and

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fascinating topic. Analysts who have been impressed by Isakower’s observations concerning hypnagogic imagery will be interested by this report of McKellar’s large-scale studies of these images occurring “between wakefulness and sleep” (one of his chapter headings), though perhaps a bit disappointed by his failure to do much with the ramifications of such phenomena into the realm of unconscious motivation. He is a natural historian with a touch of statistics, very much after Galton’s model, rather than a seeker for functional relatedness. He misses, therefore, the fact that “A-thinking” is not merely associative and uninhibited but has laws of its own – those of the primary process. Cognitive psychology has many faces, as even this cursory survey shows. To change the metaphor, it is growing rapidly at several separate points. Equally healthy and promising growth has been taking place in sectors of the field that are informed with a knowledge of psychoanalysis and others in which the term itself is anathema. Yet little of the old-fashioned ignorant and prejudiced rejection of psychoanalytic contributions is heard any more. Ours is a more sophisticated era, one in which Gestalt psychology (once a stronghold of cognitive psychology [cf. Wertheimer, 55]) is disappearing as such, and behaviorism is mellowing, having won its point about the need for objectivity. Now that workers of so many persuasions can deal with many of the same cognitive problems, there is renewed hope that research in this field may stimulate a truly embracing theory, a synthesis of academic and psychoanalytic psychologies that will be more powerful and more comprehensive than either alone.

A Bibliography of Recent Books on Thinking and Cognitive Psychology Note: The coverage of the core topic is relatively complete for the dozen years under consideration, but books in the following tangential areas have been included only rather unsystematically if at all: testing, intelligence, hypnosis, parapsychology, perception, learning, dreams, psycholinguistics, communication and information theory, prejudice and other attitudes, forcible indoctrination, computer simulation of cognitive processes, and works centered on neurological or physiological approaches.

Notes 1. In a paper, “Imagery: The Return of the Ostracized.” Amer. Psychologist, 19:254–264, 1964. 2. Rapaport, D., Gill, M. M., & Schafer, R. Diagnostic Psychological Testing, 2 Vols. Chicago: Yearbook Publishers, 1945–1946. 3. New York: International Universities Press, 1953.

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References 1951 1. Abramson, H. A., ed. Problems of Consciousness: Transactions of the First Conference (1950). New York: Josiah Macy, Jr. Foundation. 2. Abramson, H. A., ed. Problems of Consciousness: Transactions of the Second Conference (1951). New York: Josiah Macy, Jr. Foundation. 3. Blake, R. R. & Ramsey, G. V., eds. Perception: An Approach to Personality. New York: Ronald Press. 4. Humphrey, G. Thinking. London: Methuen. 5. Piaget, J. Play, Dreams and Imitation in Childhood (1945). New York: Norton. 6. Rapaport, D., ed. Organization and Pathology of Thought. New York: Columbia University Press. 1952 7. Abramson, H. A., ed. Problems of Consciousness: Transactions of the Third Conference (1952). New York: Josiah Macy, Jr. Foundation. 8. Ashby, W. R. Design for a Brain. New York: Wiley. 9. French, T. M. The Integration of Behavior, Vol. 1. Chicago: University of Chicago Press (Vol. 2, 1954; Vol. 3, 1958). 10. Piaget. J. The Child’s Conception of Numbers (1941). New York: Humanities Press. 11. Piaget, J. The Origins of Intelligence in Children (1936). New York: International Universities Press. 12. Vinacke, W. E. The Psychology of Thinking. New York: McGraw-Hill. 1953 13. Abramson, H. A., ed. Problems of Consciousness: Transactions of the Fourth Conference (1953). New York: Josiah Macy, Jr. Foundation. 14. Price, H. H. Thinking and Experience. Cambridge: Harvard University Press. 15. Walter, W. G. The Living Brain. New York: Norton. 1954 16. Abramson, H. A., ed. Problems of Consciousness: Transactions of the Fifth Conference (1954). New York: Josiah Macy, Jr. Foundation. 17. Jackson, W., ed. Communication Theory. London: Butterworths Sci. Publ. 18. Osgood, C. E. & Sebeok, T. A., eds. Psycholinguistics: A survey of theory and research problems. J. Abnorm. Soc. Psychol., 203 pp. suppl. 19. Piaget, J. The Construction of Reality in the Child (1937). New York: Basic Books. 20. Revesz, G., ed. Thinking and Speaking: a symposium. Amsterdam: North-Holland. 21. Witkin, H. A. et al. Personality Through Perception. New York: Harper. 1955 22. Allport, F. H. Theories of Perception and the Concept of Structure. New York: Wiley. 23. Johnson, D. M. The Psychology of Thought and judgment. New York Harper. 24. Kelly, G. A. The Psychology of Personal Constructs, 2 Vols. New York: Norton. 25. Quastler, H., ed. Information Theory in Psychology. Glencoe, Ill.: Free Press. 26. Patrick, C. What Is Creative Thinking? New York: Philosophical Library. 1956 27. Bruner, J. S., Goodnow, J. J., & Austin, G. A. A Study of Thinking. New York: Wiley. 28. Brunswik, E. Perception and the Representative Design of Experiments, 2nd ed. Berkeley: University of California Press. 29. Odier, C. Anxiety and Magic Thinking (1948). New York: International Universities Press.

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30. Piaget, J. & Inhelder, B. The Child’s Conception of Space (1948). New York: Humanities Press. 31. Russell, D. H. Children’s Thinking. Boston: Ginn. 32. Smith, M. B., Bruner, J. S., White, R. W., Aberle, D. F., et al. Opinions and Personality. New York: Wiley. 1957 33. Bruner, J. S., Brunswik, E., Festinger, L., Heider, F., Muenzinger, K. F., Osgood, C. E., & Rapaport, D. Contemporary Approaches to Cognition. Cambridge: Harvard University Press. 34. Cherry, C. On Human Communications. New York: Wiley. 35. Festinger, L. A Theory of Cognitive Dissonance. Evanston, Ill.: Row, Peterson. 36. McKellar, P. Imagination and Thinking. New York: Basic Books. 37. Olsen, F. et al. The Nature of Creative Thinking. New York: New York University Press. 38. Osgood, C. E., Suci, G. J., & Tannenbaum, P. H. The Measurernent of Meaning. Urbana: University of Illinois Press. 39. Piaget, J. Logic and Psychology (1953). New York: Basic Books. 40. Skinner, B. F. Verbal Behavior. New York: Appleton-Century-Crofts. 1958 41. Bartlett, F. C. Thinking. New York: Basic Books. 42. Broadbent, D. E. Perception and Communication. New York: Pergamon Press. 43. Kubie, L. S. Neurotic Distortion of the Creative Process. Lawrence: University of Kansas Press. 44. Inhelder, B. & Piaget, J. The Growth of Logical Thinking from Childhood to Adolescence (1955). New York: Basic Books. 1959 45. Attneave, F. AppIications of Information Theory to Psychology. New York: Holt. 46. Gardner, R., Holzman, P. S., Klein, G. S., Linton, H., & Spence, D. P. Cognitive Control: A Study of Individual Consistencies in Cognitive Behavior [Psychological Issues, Monogr. No. 4]. New York: International Universities Press. 47. Gill, M. M. & Brenman, M. Hypnosis and Related States. New York: International Universities Press. 48. Heider, F. On Perception, Event Structure, and the Psychological Environment [Psychological Issues, Monog. No. 3]. New York: International Universities Press. 49. Luchins, A. S. & Luchins, E. H. Rigidity of Behavior. Eugene: University of Oregon Books. 50. Paul, I. H. Studies in Remembering: The Reproduction of Connected and Extended Verbal Material [Psychological Issues, Monogr. No. 2]. New York: International Universities Press. 51. National Physical Laboratory. Mechanization of Thought Processes, 2 Vols. London: Her Majesty’s Stationery Office. 52. Schachtel, E. G. Metamorphosis. New York: Basic Books. 53. Tauber, E. S. & Green, M. R. Prelogical Experience. New York: Basic Books. 54. Thomson, R. The Psychology of Thinking. Baltimore: Penguin Books. 55. Wertheimer, M. Productive Thinking, enlarged ed. New York: Harper. 1960 56. Abercrombie, M. L. J. The Anatomy of Judgment. New York: Basic Books. 57. Berlyne, D. E. Conflict, Arousal and Curiosity. New York: McGrarv-Hill. 58. Gardner, R. W., Jackson, D. N., & Messick, S. J. Personality Organization in Cognitive Controls and Intellectual Abilities [Psychological Issues, Monogr. No. 8]. New York: International Universities Press.

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59. Harms, E. & Guilford, J. P., eds. Fundamentals of psychology: the psychology of thinking. Trans. N. Y. Acad. Sci., 91:1–158. 60. Miller, G. A., Galanter, E., & Pribram, K. H. Plans and the Structure of Behavior. New York: Holt. 61. Mowrer, O. H. Learning Theory and the Symbolic Processes. New York: Wiley. 62. Pötzl, O., Allers, R., & Teler, J. Preconscious Stimulation in Dreams, Associations, and Images: Classical Studies [Psychological Issues, Monogr. No. 7]. New York: International Universities Press. 63. Rokeach, M. The Open and Closed Mind. New York: Basic Books. 64. Sarbin, T. R.. Taft, R., & Bailey, D. E. Clinical Inference and Cognitive Theory. New York: Holt, Rinehart & Winston. 65. Shands, H. Thinking and Psychotherapy. Cambridge: Harvard University Press. 66. Solley, C. M. & Murphy, G. The Development of the Perceptual World. New York: Basic Books. 67. Wolff, P. H. The Developmental Psychologies of Jean Piaget and Psychoanalysis [Psychological Issues, Monogr. No. 5]. New York: International Universities Press. 1961 68. Church, J. Language and the Discovery of Reality: A Developmental Psychology of Cognition. New York: Random House. 69. Fiske, D. W. & Maddi, S. R. Functions of Varied Experience. Homewood, Ill.: Dorsey Press. 70. Harvey, O. J. Conceptual Systems and Personality Organization. New York: Wiley. 71. Restel, F. Psychology of Judgment and Choice. New York: Wiley. 72. Schwartz, F. & Rouse, R. O. The Activation and Recovery of Associations [Psychological Issues, Monogr. No. 9]. New York: International Universities Press. 1962 73. Brehm, J. W. & Cohen, A. R. Explorations in Cognitive Dissonance. New York: Wiley. 74. Garner, W. R. Uncertainty and Structure as Psychological Concepts. New York: Wiley. 75. Getzels, J. W. & Jackson, P. W. Creativity and Intelligence. New York: Wiley. 76. Gruber, H., Terrell, G., & Wertheimer, M., eds. Contemporary Approaches to Creative Thinking. New York: Atherton Press. 77. Messick, S. & Ross, J., eds. Measurement in Personality and Cognition. New York: Wiley. 78. Vygotsky, L. S. Thought and Language (1934). New York: Wiley.

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27 The Advancement of Learning Ann L. Brown

Neither the hand nor the mind alone would amount to much without aids and tools to perfect them. (Bacon, Novum Organum, 1623)

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his loosely translated quotation is taken from Francis Bacon’s Novum Organum, not from Vygotsky, as one might well imagine. In this article, I argue that designing aids and tools to perfect the mind is one of the primary goals of educational research. In this spirit, the major themes of the article are that:

• Instruction is a major class of aids and tools to enhance mind. • To design instruction, we need appropriate theories of learning and development. • Enormous advances have been made in this century in our understanding of learning and development. • School practices in the main have not changed to reflect these advances. • The question posed is, Why? My title, The Advancement of Learning, is also taken from Bacon (1605). The title is a metaphor, as I will view the advancement of learning particularly during the 30 years or so since the cognitive revolution. Contemporary theories, unlike those of the past, concentrate on the learning of complex ideas as it occurs in authentic situations including, but not limited to, schools. In keeping with Bacon, I will paint a general picture of progress but at the same time add a cautionary note concerning the infanticide rate of our profession. We repeatedly throw out babies along with bathwater, when we Source: Educational Researcher, 23 (1994): 4 –12.

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should build cumulatively. No community can afford to lose so many valuable offspring in the service of progress. I will begin with a personal odyssey. In rereading the Presidential Addresses from the past 10 years or so, I realized that this genre, the odyssey, is a popular one. Indeed, the metaphor of an odyssey was the leitmotif of Eliot Eisner’s 1993 address. Pivotal to this narrative genre is the retelling of the myriad interesting life experiences of those who subsequently went on to become President of AERA. Now here’s my problem. I am a psychologist. I have always been a psychologist of sorts. I started my academic career as an undergraduate studying learning, and I am still doing that today, in my fashion. But what I did then and what I do now are as distinct as night and day. I was well prepared for my career as a learning theorist. In high school, I specialized in 18th century literature and 19th century history, and was on my way to study history in college. Why switch? I saw a television program on animal learning, on how animals learn naturally in their environments, an introduction to ethology. The heroes of this piece were Huxley, Lorenz, Thorpe, and Tinbergen. Fascinated, I looked up animal learning in my handy guide to universities and found that to study learning you needed a degree in psychology. Thus prepared I set out for an interview, having seen one television program on ethology and having read Freud’s Psychopathology of Everyday Life on the train getting there. By chance the head of department was an expert in 18th century literature. We discussed poetry for 2 hours. I got a scholarship to study psychology! So in the early 60s I started out for London to study animal learning. I arrived in Iowa, or maybe it was Kansas, feeling a little like Dorothy in The Wizard of Oz. The cognitive revolution had not yet come to London. What followed was 3 years of exposure to behaviorist learning theory. Rather than learning about animals adapting to their natural habitats, I learned about rats and pigeons learning things that rats and pigeons were never intended to learn. Pan-Associationism. Experimental psychologists in England (and Iowa) at that time were enthralled with a certain form of behaviorism. Dominating the field were the all-encompassing learning theories of Hull/Spence, Tolman, and Skinner.1 These theories shared certain features that limited to a greater or lesser extent their ability to inform educational practice. All derived their primary data from rats and pigeons learning arbitrary things in restricted situations. They shared a belief that laws of learning of considerable generality and precision could be found. These basic principles of learning were thought to apply uniformly and universally across all kinds of learning and all kinds of situations. The principles were intended to be species-, age-, domain-, and context-independent. Pure learning was tested in impoverished environments where the skills to be learned had little adaptive value for the species in question. Paul Rozin (1976) argued that by studying the behavior

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of pigeons in arbitrary situations, we learned nothing about the behavior of pigeons in nature, but a great deal about the behavior of people in arbitrary situations. I will illustrate with a surely apocryphal tale related by Mary Catherine Bateson (1984). Her father Gregory Bateson’s favorite tongue-in-cheek psychologist anecdote was the following: It occurred to a thoughtful rat-runner after many years of running rats that as rats do not usually live in mazes, mazes were perhaps less than optimal testing grounds for learning. Therefore, he bought a ferret, a species that in nature does hunt in mazes – rabbit warrens. He baited a maze with fresh rabbit meat and set the ferret to find it. On the first day, the ferret systematically searched the maze and found the rabbit quicker than a rat. But what happened on the second day? The rat, as expected, searched the maze and found the bait more quickly than on the original trial. Learning was said to have occurred. But not so for the ferret. It searched the maze and came to the route that had previously led to the reward, but didn’t go down it. Why? He’d eaten that rabbit yesterday. What the ferret had learned was colored by its expectation of how the world works – for ferrets. (anecdote adapted from pp. 170–171)

How did this dominance of certain forms of behaviorism come about? Psychology as a nascent science didn’t start out that way. One of the few female pioneers in the early part of the century, Mary Calkins (1915), criticized the overwhelmingly male establishment by arguing that psychology started out as the study of consciousness and then set about to explain it away, even to deny its existence. Throughout her career she argued, in the wilderness, that psychology should be the study of “conscious interacting social selves in relation to other selves and objects.” Vygotsky, perhaps, but a far cry from Thorndike, Watson, and Hull. Animal Learning. The dominance of behaviorism in the mid part of the century has often been blamed on the increasing dependence on animals as experimental subjects. Animals are not known for their introspection, and few investigators were concerned whether animal thought was imageless or not, or whether they entertained theories of mind. This argument does not follow through, however, as early work with animals had a distinctly mentalistic flavor. Leonard Hobhouse, in his delightful book, Mind in Evolution (1901), studied a variety of animals, albeit somewhat informally: One reads that the subjects were: “a dog, a cat, an otter, and an elephant” or “a rhesus monkey called Jimmy and a chimpanzee named Professor.” Using a variety of puzzle-like, meaningful situations (a dog opening a gate to escape its own yard, rather than playing in a Thorndikian puzzle box), Hobhouse found evidence for such mental-sounding entities as purpose, planning, cunning, and deceit, mental entities again being studied today (Griffin, 1992). So too,

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during the first world war, Kohler’s chimpanzees, such as the famous Sultan, were also seen to be insightful as they set about building towers of boxes to reach fruit hanging out of reach, or combining short sticks into long ones to reach outside cage bars. This mentalism was almost stamped out, but with notable exceptions, such as Lashley’s rats on the jumping stand experiencing vicarious (mental) trial and error, or Tolman’s rats buried in thought at the start box of a maze, troubled by ideas, hypotheses, and mental maps. Lashley and Tolman were atypical, however; Lashley was trained as an ethologist, and Tolman was always a closet cognitivist, and a self-proclaimed cryptomentalist.2 But to the dyed-in-the-wool behaviorist, learning did not imply conscious intent but rather was seen as the autonomous outcome of the formation of S-R bonds stamped in or out by reinforcement contingencies with no need for conscious intent. This position had powerful implications for education, whose residual clings today. Developmental Psychology. Child psychology underwent a similar history. Although at the beginning of the century we saw ingenious studies of children’s thought (witness those of Binet, Baldwin, Piaget, and Darwin for that matter), they were forgotten, and a large part of the field became imprinted on behaviorism. The Zeitgeist affected not only the theories of learning that were tested but also the methods by which they were examined. What were children asked to learn? Some were asked to stack boxes or use sticks to obtain objects out of reach, just like Sultan the chimp (Sobel, 1939). It did not seem to occur to anyone that a set of boxes more readily affords climbing to an ape than to a less agile human toddler. Others were asked to run mazes! They were “run” through a child-size maze of darkened runways where they had to complete routes to reach goal boxes in a similar inferential pattern to that shown by rats. It was not until well into the school years that children performed as well at this as did rats (Maier, 1936)! Again, the fact that running in a darkened maze may be a task suitable to no organism, but better suited to rats than preschoolers, did not seem to be open to debate. Children were tested in cages – well, almost – specifically, a Wisconsin General Test Apparatus designed by Harlow3 for use with monkeys that bit. I assume children in the 1960s were not rabid, and, therefore, the physical protection of the experimenter could not have been a prime motivation for this odd practice, engaged in, I might add, by myself and many of my closest friends. The prime motivation was in fact to minimize social or verbal interactions with the child. Deliberately, the child could not see the experimenter’s facial expressions behind a one-way mirror, and hence could not be influenced by them. The fact that a great deal of learning is inherently social was not a topic of discussion; indeed, we explicitly controlled for such undesirable influences. The point of this little walk down memory lane is not only to amuse you, but also to make the point that it was on the basis of studies like these that

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children below 7 or so were deemed incapable of inferential reasoning, insightful learning, and all kinds of logical operations, a position later reinforced by simplistic interpretations of Piaget.

Impact on Education These developments in psychology impacted educational practice. The dominant learning theories for many years encouraged educational psychologists to concentrate on such external factors as reward schedules and transfer gradients. Transfer could be expected only if identical elements of external situations were held constant, thereby capturing the mind willy-nilly. Even though Thorndike, the originator of much of this, gave up on his position concerning learning and transfer in the late 20s (Thorndike & Gates, 1929), the theories, albeit somewhat disguised, are still alive today. Equally important was the model of the child that emerged. It was received wisdom that young children had limited attention spans. They got bored easily in those boxes, mazes, and cages. So it was assumed that the young bore easily in any learning situation. Similarly, young children performed abysmally in settings designed to exploit animal wit. As a result they were deemed incapable of inferential reasoning, of performing certain types of classification, of insightful learning and transfer in general. Because of these assumed problems of immaturity, it was believed that children in school should work to mastery on simple decontextualized skills for short periods of time under appropriate reinforcement schedules. Despite this pessimistic legacy, behaviorist theories of learning of the midcentury had their clear value. They were in fact remarkably successful at explaining the range of phenomena they set out to explain. For example, Skinnerian theory gave us token economies, fading, scaffolding, and today, valuable clinical methods, such as those used to control nausea during chemotherapy. Tolman was a clear forerunner of cognitive psychology, lending a legitimacy to mental models and states. And Hullian theory has much to say to contemporary connectionism. And in defense of psychologists, those concerned with educational practice were only too ready to adopt these theories in the absence of viable alternatives that did include concerns for context, content, and developmental status. Behaviorist conceptions of learning and development postulated 30 years ago had important implications for instruction, both positive and negative. The theories permeated the language of schooling – and are still in evidence. Lauren and Dan Resnick (1991) have made this point forcibly regarding the state of the art in standardized testing, where the design of tests still reflects behaviorist theories of the past. Cognitive learning theory is only now beginning to have an effect on classroom practice and the testing industry. The vocabulary is slowly changing. The practices lag behind. Where we once had

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behavioral objectives, we now have cognitive objectives, although it is sometimes a challenge to find the differences.

New Learning Theory So what’s new in learning theory? Slowly, the cognitive revolution did come to town and upset many accepted beliefs. A dramatic change occurred in what “subjects” were required to learn, even in laboratory settings, accompanied by a dawning awareness that real life learning is intrinsically entangled with situations. One cluster of such situations is the classroom. The model of the human learner, including the child, was transformed. Learners came to be viewed as active constructors, rather than passive recipients of knowledge. Learners were imbued with powers of introspection, once verboten. One of the most interesting things about human learning is that we have knowledge and feelings about it, sometimes even control of it, metacognition if you will. And, although people are excellent all-purpose learning machines, equipped to learn just about anything by brute force, like all biologically evolved creatures, humans come predisposed to learn certain things more readily than others. We know now that small children understand a great deal about basic principles of biological and physical causality. They learn rapidly about number, narrative, and personal intent. They entertain theories of mind. All are relevant to concepts of readiness for school, and for early school practices. Those interested in older learners began to study the acquisition of disciplined bodies of knowledge characteristic of academic subject areas (e.g., mathematics, science, computer programming, social studies, and history). Higher order thinking returned as a subject of inquiry. Mind was rehabilitated. Psychologists also began considering input from other branches of cognitive science: anthropology, sociology, linguistics, and they began to consider learning settings outside the laboratory, or even the classroom walls. Clearly a strictly laboratory-based psychological theory of learning is, and always was, a chimera.

Community of Learners I now turn to my current work in urban classrooms, where my colleagues and I are attempting to orchestrate environments to foster meaningful and lasting learning in collaboration with inner-city grade school students and teachers. We refer to this as the Community of Learners (COL) project (Brown & Campione, 1990, 1994).

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How did I get here from there? How did I make the journey from testing kids in cages to designing learning communities? To me the journey felt seamless. From studying rote memory for words and pictures, and strategies to enhance it, I progressed to studying memory for stories, narrative, and expository text. As the human mind does not resemble a tape recorder, memory for texts involves seductive simplification and inadvertent elaboration well documented by Bartlett (1932) at the early part of the century. Inferences and strategies abound, and their development in the young interested me. Texts are understood and re-created in the telling. Understanding admits of degree, monitoring one’s understanding of texts requires far more subtle judgment than monitoring if one can recall lists of words or sentences. It was this move away from rote learning of discrete stimuli to understanding text that led me down the slippery slope toward an area of research with obvious educational implications: reading comprehension and comprehension monitoring. Children have difficulty in recruiting strategies to help them understand lengthy texts. So too the subjective judgment required to monitor whether or not one has understood presents the developmentally young with difficulty, not surprising given the problems college students have with calibrating their attention to avoid the illusion of comprehension. So, my colleagues and I began a series of studies to help children learn from texts, training individual strategies such as questioning, clarifying, and summarizing to help them monitor their progress (Brown, Bransford, Ferrara & Campione, 1983). This was the precursor to the next step, the design of a reading comprehension instructional intervention that would combine these activities in an effort after meaning. Reciprocal teaching, designed by Annemarie Palincsar and me (Palincsar & Brown, 1984) became that intervention, and, as we will see, it is still a central part of the COL. Reciprocal teaching involved the development of a minilearning community, intent not only on understanding and interpreting texts as given, but also on establishing an interpretive community (Fish, 1980) whose interaction with texts was as much a matter of community understanding and shared experience as it was strictly textual interpretation. It was to capture this influence of common knowledge, beliefs, and expectations that the notion of a community of learners was developed. For the past 10 years or so, my colleagues and I have been gradually evolving learning environments that would deliberately foster interpretive communities of grade-school learners.

Engineering of a Community of Learners The fundamental engineering principle behind the design of a COL is to lure students into enacting roles typical of a research community. I take this

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metaphor seriously. The COL classrooms feature a variety of activities that are essentially dialogic in nature, modeled after research seminars, that when working well facilitate interchange, reciprocity, and community. Theoretically, I imagine such classrooms as enculturating multiple zones of proximal development, to use the now popular Vygotskian (1978) term. A zone of proximal development defines the distance between a child’s current level of learning and the level she can reach with the help of people, tools, and powerful artifacts – tools and aids to perfect mind, in Bacon’s terms. Within these multiple overlapping zones, students navigate by different routes and at different rates. But the push is toward upper, rather than lower, levels of competence. These levels are not immutable, but rather constantly changing as participants become increasingly independent at successively more advanced levels. Practically I imagine classrooms as learning communities that have extensions beyond the classroom walls. I will share with you a few essential components (for fuller details, see Brown & Campione, 1990, 1994). One is that we feature students as researchers and teachers, partially responsible for designing their own curriculum. A variety of collaborative activities encourage this. I will discuss just two of them: reciprocal teaching learning seminars and jigsaw teaching sessions. Reciprocal Teaching. Reciprocal teaching began as a method of conducting “reading group,” once an established ritual of the grade-school class. Reciprocal teaching seminars can be led by teachers, parents, peers, or older students. Six or so participants form a group with each member taking a turn leading a discussion about an article, a video, or other materials they need to understand for research purposes. The leader begins the discussion by asking a question and ends by summarizing the gist of the argument to date. Attempts to clarify any problems of understanding take place when needed, and a leader can ask for predictions about future content if this seems appropriate. These four activities were chosen because they are excellent comprehensionmonitoring devices. Quite simply, if you cannot summarize what you have just read, you do not understand, and you had better do something about it (for more details, see Palincsar & Brown, 1984). Reciprocal teaching was designed to provoke zones of proximal development within which readers of varying abilities could find support. Group cooperation, where everyone is trying to arrive at consensus concerning meaning, relevance, and importance, helps ensure that understanding occurs, even if some members of the group are not yet capable of full participation. Because thinking is externalized in the form of discussion, beginners can learn from the contributions of those more expert than they. So, unlike many decontextualized skills approaches to reading, skills here are practiced in the context of actually reading. Collaboratively, the group, with its variety of expertise, engagement, and goals, gets the job done; usually the text gets understood. The integrity of the task, reading for meaning, is maintained throughout.

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Jigsaw. This idea of learning with a clear purpose in mind is a mainstay of all the components of the Community of Learners. In particular it carries over to our version of Aronson’s (1978) jigsaw classroom. Students are asked to undertake independent and collaborative research. As researchers, they divide up units of study and share responsibility for learning and teaching their piece of the puzzle to each other. How does this work? Classroom teachers and domain area specialists together decide on central abiding themes visited at a developmentally sensitive level. Each theme (e.g., changing populations) is then divided into five or six subtopics (endangered species, rebounding populations, introduced species, etc.), dependent in part upon student age and interest. Each group of students conducts research on one subtopic, and then shares its knowledge by teaching it to others. As a concrete example, recent classes of second graders chose to study animal/habitat interdependence. Some children studied how animals protect themselves from the elements or from predators. Others became experts on animal communication or reproductive strategies. Still others studied predator/prey relations. Design teams were then formed that create habitats for an adopted animal or invent an animal of the future. These design teams were configured so that each member had conducted research on part of the knowledge. In each group someone knew about predator/prey relations, someone could talk wisely on the strengths and weaknesses of possible methods of communication, and so forth. All pieces are needed to complete the puzzle, to design the habitat, hence jigsaw. By these methods, expertise is distributed deliberately. Majoring. Expertise is also distributed by happenstance. Variability in expertise arises naturally because of the different research paths followed by groups and individuals. We refer to this phenomenon as majoring. Children are free to major in a variety of ways, free to learn and teach whatever they like within the confines of their subtopic. Some become experts on disease and contagion, some concentrate on bizarre reproductive strategies; others major in pesticides or pollution. All contribute their specific knowledge, thereby enriching the intellectual resources of the community. Let us consider just one example of majoring: delayed implantation. This is a reproductive strategy whereby fertilized eggs lay dormant inside the female until environmental conditions are suitable for the survival of offspring, at which point the eggs begin to develop. This principle was discovered by some fifth graders last year, but not by previous cohorts. At least 9 months after their discovery, a group of now sixth graders told me about another example of the principle, the Minnesota Mink, that they had seen in a television program. According to my informants (my commentary in brackets): • Minks breed aggressively in late winter because their thick coats will protect them from bites and scratching.

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• [This was an inference. On the program, we learned only that mink shed their valuable heavy winter coats for light summer ones. And the mating minks did look like they were engaged in strenuous activity. The inference was actually an example of transfer of prior knowledge from an animal these students had previously studied, the sea otter, with a heavy coat and notably rough mating habits.] • The females mate with as many males as possible, and subsequent litters consist of pups that are fathered by more than one male. The students argued that this increased the variability of the gene pool [a biologically appropriate inference]. • The last male to mate has more pups, because, the students argued, if he could still mate at the end of the season, he must be pretty strong [inference based on a Spencerian/Darwinian notion of survival of the fittest]. • The fertilized eggs just sit there, another child corrects, lie dormant, until it is spring, and then start to develop. • Pups are partly “acquarian.” [I think they meant aquatic]. The point about my story is not the demonstration of long-term retention of facts, or the assimilation of new facts about a complex biological mechanism, or even the inferential powers the students displayed. It is their excitement about what they are learning sustained over considerable time, and at their own expense (they were no longer accountable for this topic). I was impressed by their confidence in their own developing knowledge and their belief that this is something that the community will respect and value. And by way of metaphorical extension, delayed implantation is what we do with ideas – plant them in the community and hope they come to fruition when the time is ripe. The Role of Performance. In telling their story, these students were putting on a performance, for my benefit. Everyone in the community is at some stage an actor and an audience. Regular exhibitions to a variety of audiences are an important component of the community. The sense of audience for one’s research efforts is not imaginary, but palpable and real. Audiences demand coherence, push for higher levels of understanding, require satisfactory explanations, request clarification of obscure points, and so on. Students do not have to deal only with a single audience, the teacher, as they often do in school. These opportunities to display provide an element of reality testing, also an important feature of many of the school activities such as dramatic plays put on by boys’ and girls’ clubs (Heath & McLaughlin, in press). Such groups typically engage in seasonal cycles of planning, preparing, rehearsing, and finally performing. There are deadlines, discipline, and most important, reflection on performance. So, too, in the COL we have cycles of planning, preparing, practicing, and teaching others. Deadlines and performance demand the setting of priorities – what is important to know? What is important to teach? What of our newfound knowledge do we display?

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The Classroom Teacher. The classroom teacher is not absent from these proceedings. She learns along with the children as well as assists their efforts. In addition, she periodically calls the whole class into conference to consider the main theme and the relation among the research activities. The aim is to lead the students to higher levels of thinking and to help them set goals for future research. These whole-class discussions provide a reflection period in which to take stock of where they are and where they want to be.

Extending the Learning Community Inside the School For the program to run optimally, adults other than the classroom teacher are needed to guide the learning activities. But we have to live with the feasible. How many extra bodies can there be? Parenthetically, I note that at its peak, Dewey’s (1936) Laboratory School had a 4:1 child/adult ratio, not counting adult experts. Because this is unrealistic, the COL relies heavily on the expertise of the children themselves. We use cross-age teaching, both face-to-face and via electronic mail. We use older students as discussion leaders guiding the reciprocal teaching or jigsaw activities of younger students. Such tutoring extends the teaching “capital” available to our students, but it is also a formative aspect of community building.

Outside the School Any learning community is limited by the combined knowledge of its members. Within traditional schools, members draw on a limited knowledge capital if the faculty and students are relatively static. Or they face jarring discontinuity if there is rapid turnover, as is the case in many inner-city schools. In addition, both teachers’ and students’ expectations concerning excellence, or what it means to learn and understand, may be limited if the only standards are local. Schools are not islands. They exist in wider communities, and we rely on them. For example, experts coaching via electronic mail provide us with an essential resource, freeing teachers from the sole burden of knowledge guardian and allowing the community to extend in everwidening circles of expertise.

Principles of Learning A major part of my personal effort in the design experiment (Brown, 1992) of creating community is to contribute to a theory of learning that can capture

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and convey the core essential features. The development of theory is critical for two reasons, conceptual understanding and practical dissemination. The development of theory has always been necessary as a guide to research, a lens through which one interprets, that sets things apart and pulls things together. But theory development is essential for practical implementation as well. It is for these reasons that we have been concerned with the development of a set of first principles of learning to guide research and practice. But in this light, it is a sobering thought that for decades the Progressive Education Association of America produced sets of principles (usually 9) every few years, principles that were so vague that they could not lead to a convergence in practice of any kind (Graham, 1967). They included: freedom to develop naturally; work guided by interest; cooperation between home and school; community building; teacher as guide, not taskmaster. All these are principles that I would agree with and will probably reiterate. But what does developing naturally mean? How does one follow interest and guide learning while at the same time helping chart legitimate pathways of intellectual inquiry? Without more specificity, more models, more documentation, more evaluation, these principles become part of a common vocabulary, but influence practice little. Descriptions of current “innovative” programs also share a family resemblance in rhetoric, but again one might ask, do they result in any consensual practice? My own rhetoric in describing principles of learning is far from safe from these criticisms. And the problem of dissemination is a real one. As a cautionary tale, consider the fate of reciprocal teaching. The program has enjoyed widespread dissemination. It has been picked up by researchers, teachers, and textbook publishers, and has become part of the discourse of the educational community. But too often something called reciprocal teaching is practiced in such a way that the principles of learning it was meant to foster are lost, or at best relegated to a minor position. The surface rituals of questioning, summarizing, and so forth are engaged in, divorced from the goal of reading for understanding that they were designed to serve. These “strategies” are sometimes practiced out of the context of reading texts. Quite simply, if one wants to disseminate a program on the basis of principles of learning rather than surface procedures, one must be able to specify what those principles are in such a way that they can inform practice. Adaptation and modification are an organic part of any implementation process. When working with new teachers, we encourage implementation as evolution (Majone & Wildavsky, 1978) constrained by first principles. Here, by way of illustration, we will discuss a few of these first principles of learning. A more complete list is given in Brown and Campione (1984).

Steps Toward Learning Principles of the COL Program 1. A great deal of academic learning, though not everyday learning, is active, strategic, self-conscious, self-motivated, and purposeful. Effective learners

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operate best when they have insight into their own strengths and weaknesses and access to their own repertoires of strategies for learning. For the past 20 years or so, this type of knowledge and control over thinking has been termed metacognition (Brown, 1978). Interest in things metacognitive is, of course, not new; it is just that a concentrated period of research has reaffirmed what was already known but not established very well. And that is progress. A little recognized progenitor of this position was actually Binet, known in this country primarily for the introduction of intelligence testing. Binet was also interested in the education of the child-like mind. True to the newfound confidence in testing, Binet designed tests of what he called autocriticism to root out metacognitive lacunae. For example, what is wrong with these sentences? • An unfortunate cyclist fractured his skull and died at once; he has been taken to the hospital and we are afraid he won’t be able to recover. • Yesterday we found a woman’s body sliced in 18 pieces; we believe she killed herself. Gruesome Victoriana indeed, but as Binet pointed out, “You would be surprised at how many of the thoughtless young are quite happy with this nonsense.” “Apres le mal, le remede.” Binet believed diagnosis to be of little use if it were not followed by remediation. “If it is not possible to change intelligence, why measure it in the first place?” Given this philosophy, not shared by many in the early part of the century who began to believe in the immutability of IQ, Binet developed a remedial curriculum for the “thoughtless young.” The curriculum, called Mental Orthopedics, was intended to strengthen the child’s “unreflective and inconsistent mind.” As the thoughtless child “does not know that he does not understand,” he needs help “to observe, to listen and to judge better.” The curriculum was specifically designed to train, in Binet’s terms, “habits of work, effort, attention, reasoning and self-criticism,” leading to the “pleasures of intellectual selfconfidence” (all quotations from Binet, 1909). Unfortunately for us, he was more than a little vague about how we might do this. Actual descriptions of the training or its outcomes do not survive, a problem in general for past innovative programs. One might argue that all this talk of strategies and metacognition is silly. Who indeed would want passive, unmotivated, purposeless, indeed mindless, learning? There is certainly a place for mindlessness in human learning; a great deal of learning does occur incidentally, and humans have reasoning biases that allow them to get by on this most of the time (Bartlett, 1958; Tversky & Kahneman, 1974). But scholarship, the domain of schools, demands intentional learning (Bereiter & Scardamalia, 1989). In this context, who could possibly argue against mindful learning? My point is not that peopled

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argued against mindful learning; rather, that they did not campaign actively for it. Remember, a belief that rote learning trains the mind has been around for a long time. Advocates of fact acquisition, in and of itself and by whatever means, still stalk the land. One legacy of behaviorism was a concern with capturing the mind in spite of itself. Understanding and reflection were not prominent features of the psychological learning theories of the mid-century. The need for a resurgence of interest in mind and its uses was overdue. 2. Classrooms as Settings for Multiple Zones of Proximal Development. I take it as given that learners develop at different rates. At any time they are ripe for new learning more readily in some arenas than others. They do not come “ready for school” in some cookie-cutter fashion. The central Vygotskian notion of zones of proximal development is one of learning flowering between lower and upper bounds of potential, depending on environmental support. Bacon’s aids, tools, and guides to perfect mind serve to push as much as possible toward the upper bounds of competence. This is also a position that needed to be reinvented. The set of influential contrasting theories that has influenced American schools include errorless learning, mastery learning, skill building, and so on: All attempt to aim instruction at the child’s existing level of competence, often interpreted as lower levels of performance. Indeed, many interpret Dewey as suggesting emphasis on lower bounds when he argued in favor of teaching to the child’s level. I argue that an essential role for teachers is to guide the discovery process toward forms of disciplined inquiry that would not be reached without expert guidance, to push for the upper bounds. 3. Legitimization of Differences. A central principle of COL is that individual differences be recognized and valued. I borrowed the term from studies of out-of-school learning (Heath, 1991), but I also see reflections in Howard Gardner’s (1983) concern for fostering multiple intelligences in school and Lave and Wenger’s (1991) description of multiple ways into communities of practice. Can we do this in schools, can we rejoice in diversity? What if classrooms were designed explicitly to capitalize on varieties of talent to provide multiple “ways in” – through art, drama, technological skills, content knowledge, reading, writing, teaching, social facilitation, and so forth? Indeed, it is very much our intention to increase diversity in COL classrooms. Traditionally, school agendas have aimed at just the opposite, decreasing diversity. This tradition is based on the false assumption that there exist prototypical, normal students who, at a certain age, can do a certain amount of work, or grasp a certain amount of material, in the same amount of time (Becker, 1972). In our program, although we assuredly aim at conformity on the basics (everyone must read, write, think, reason, etc.), we also aim at nonconformity in the distribution of expertise and interests so everyone can benefit from the subsequent richness of available knowledge. The essence of teamwork is pooling expertise. Teams composed of members with homogeneous ideas and skills are denied access to such richness.

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4. A Community of Discourse. It is a common belief that higher thought is an internalized dialogue. To foster this we create the active exchange and reciprocity of a dialogue in our classrooms, which are intentionally designed to foster interpretive communities (Fish, 1980). The sociologist Wurthnow (1989) argued that changes in communities of discourse led the way to powerful movements in society – the Reformation, the Enlightenment, and European Socialism. At a less grandiose level, our baby COLs foster change by encouraging newcomers to adopt the discourse structure, goals, values, and belief systems of the community. Ideas are seeded (or implanted) in discussion. Sometimes these ideas migrate throughout the community via mutual appropriation and negotiated meaning, sometimes they lie fallow, and sometimes they bloom. These interpretive communities (Fish, 1980) give place to multiple voices in Bakhtin’s (1986) sense of voice as the speaking personality. 5. Community of Practice. Learning and teaching depend heavily on creating, sustaining, and expanding a community of research practice. Members of the community are critically dependent on each other. No one is an island; no one knows it all; collaborative learning is not just nice, but necessary for survival. This interdependence promotes an atmosphere of joint responsibility, mutual respect, and a sense of personal and group identity. These five principles are closely intertwined, forming as they do a system. Multiple zones of proximal development presuppose distributed expertise, distributed expertise presupposes legitimization of differences, and so on. Two final pairs of principles form systemic clusters: (a) the need for deep conceptual content that is sensitive to the developmental level of the students; and (b) the need for assessment procedures that are authentic, transparent, and aligned with the curriculum (Frederiksen & Collins, 1989). I have space to discuss just the first set.

Need for a Theory of Development I am reminded of a story told by Jerry Bruner in his book Actual Minds, Possible Worlds (1986). After he had given a presentation, a member of the audience stood up and said she had a question about his claim that any subject could be taught to a child at any age in some intellectually honest way. Bruner was expecting the usual question about calculus in the first grade. But no, the question was much more thoughtful: “How do you know what’s honest?” Now that really is the pivotal question. It is not an easy question to answer. Most contemporary school reform projects finesse the problem by adopting a “one-size-fits-all” philosophy. The principles and structure of the program are the same, independent of age. The developmental model is missing. Of course, from some theoretical stances, learning and development are synonymous: learning = development; development is simply the outcome of learning, a truly Skinnerian argument.

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Implicit developmental assumptions are governing school practices nonetheless. We teach the young social studies in reference to their own neighborhood. Why? Because someone decided this was developmentally appropriate? A unit on boats was thought suitable for third graders at the Lincoln School, and 6-year-olds in the Chicago Lab School studied “occupations serving the household.” Why do we teach fractions (American history, biology) when we do? It is traditional in educational circles to make up developmental theory. My favorite example is that of G. Stanley Hall, sometimes called the father of developmental psychology. Brushing aside the need for empirical validation, Hall (1881) championed a developmental-stage theory made up of cultural epochs, a notion subsequently picked up by Dewey. Hall argued that a curriculum should mimic the history of mental evolution. Young children at the “savage” stage should study material from the corresponding historical epoch, that is, ancient myths and fables. High school boys should study the knights of the feudal period because, developmentally, they were in the period of chivalry and honor. Young women were not accorded a corresponding period! There existed no scientific justification for these developmental stages whatsoever. This story is not just one of historical curiosity. In contemporary curriculum design, in both science and history, a simplistic interpretation of Piagetian theory has led to the consistent underestimation of young students’ capabilities. This slant on Piagetian theory encourages sensitivity to what children of a certain age cannot do because they have not yet reached a certain stage of cognitive operations. The “theory” still prevails in the face of 30 years of ingenious work by developmental psychologists emphasizing the impressive cognitive abilities that children do possess. Especially relevant to the design of, for example, science curricula is the painstaking documentation of children’s evolving knowledge about biological and physical causality. Similarly, we know a great deal about children’s impressive reasoning processes within contexts that they do understand. Again my point is that the design of school practice is influenced by theories of development more typical of the 1950s than the 1990s. It is essential to the philosophy of the COL that the students be engaged in research in an area of inquiry that is based on deep disciplinary understanding, and that follows a developmental trajectory based on research about children’s developing understanding within a domain. Deep Disciplinary Understanding. Although it is surely romantic to think of young children entering the community of practice of adult academic disciplines, awareness of the deep principles underlying disciplinary understanding enables us to design academic practice for the young that are stepping stones to mature understanding, or at least are not glaringly inconsistent with the end goal. For example, in the domain of ecology and environmental science, a contemporary understanding of the underlying biology would necessitate a ready

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familiarity with biochemistry and genetics, not within the grasp of the young. Instead of watering down such content, we invite young students into the world of the 19th-century naturalist, scientists who also lacked modern knowledge of biochemistry and genetics. Ideally, by the time students are introduced to contemporary disciplinary knowledge, they will have developed a thirst for that knowledge, as indeed has been the case historically. Developing Understanding Within a Domain. I take seriously the fact that a scientific understanding of the growth of children’s thinking in a domain should serve as the basis for setting age-appropriate goals. As we learn more about children’s knowledge and theories about the biological and physical world (Carey & Gelman, 1991), we are better able to design a spiraling curriculum such as that intended by Bruner (1969). Topics are not just revisited willy-nilly at various ages at some unspecified level of sophistication, as is the case in many curricula that are self-described as spiraling, but each revisit is based on a deepening knowledge of that topic, critically dependent on past experience and on the developing knowledge base of the child. It should matter what the underlying theme is at, say kindergarten and Grade 2; it should matter that the sixth-grade students have experienced the secondgrade curriculum, and so on. In designing the ecology/environmental science/ biology strand, we seek guidance from developmental psychology concerning students’ evolving biological understanding (Carey, 1985; Hatano & Inagaki, 1987). We know that by age six, children can fruitfully investigate the concept of a living thing, a topic of great interest that they refine over a period of years, gradually assimilating plants into this category. Second graders concentrate on design criteria for animal/habitat mutuality and interdependence. Sixth graders study the effect of broad versus narrow niches, and by eighth grade the effect of variation in the gene pool on adaptation and survival is not too complex a research topic. Whereas second graders begin to consider adaptation and habitats in a simple way, sixth through eighth graders come to distinguish among structural, functional, and behavioral adaptations, biotic and abiotic interdependence, and so forth. Similarly, a consideration of extant research governs our approach to reasoning within a domain. Again in biology, we permit teleological reasoning (Keil, 1992) and an overreliance on causality, but then we press for an increasingly sophisticated consideration of chance, probability, and necessity that underlies mature disciplinary thinking. Let us not forget domain-general scientific reasoning (Brown, 1990) if such exists. Do children understand the difference between hypothesis and evidence? What is their understanding of “the scientific method”? Indeed, what should it be? Francis Bacon’s or Karl Popper’s? Dare we share with them the insights of Peter Medawar that scientists as human beings do what everyday people do? They are not omniscient. They tell good stories, they create imaginary worlds. Indeed, the scientific method itself

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like any other explanatory process is a dialogue between fact and fancy, the actual and the possible, between what could be true and what is in fact the case – it is a story of justifiable beliefs about a possible world. (Medawar, 1982, p.111)

And then there is the age-old problem for a developmental psychologist – transition mechanisms. What triggers conceptual change? In short, the amount of work involved in mapping a spiraling curriculum that is truly developmentally sensitive is quite overwhelming. But it would be more so if we fail to capitalize on the impressive amount we already know by throwing out the bathwater and the babies.

Conclusion There is a conundrum running throughout this article. I have argued that: • School practices are influenced by outmoded theories of learning and development that are relics of psychology’s behaviorist past; • Contemporary theories are better suited to inform the design of schooling because they take as their data base the learning of complex systems of knowledge characteristic of what we want schools to enculturate; and • The new theories are making little headway at influencing school practices. To quote Bacon again, “All things change, but nothing perishes.” Why? I argue that this is because what the new theories ask is so hard. It is easier to organize drill and practice in decontextualized skills to mastery, or to manage 164 behavioral objectives, than it is to create and sustain environments that foster thought, thought about powerful ideas. We are asking a great deal from everyone in the learning community. But we know a great deal more about how to do it now than a century ago. Advancement in our understanding of learning is slow but real. So, I conclude with a paraphrase of quotations from John F. Kennedy, Lee Shulman, and Jerry Bruner, to show my catholic tastes: “We choose to do this, not because it is easy, but because it is hard.” (Kennedy, 1962) “Those that understand, teach honestly.” (Shulman, 1986, p. 14) Those that teach honestly teach ideas that are “lithe and beautiful and immensely generative.” (Bruner, 1969, p. 21)

I believe that a century of research has helped us know what these ideas are and better prepared us to design instruction in the form of aids and tools to perfect hand and mind.

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Notes This article was the presidential address at the AERA Annual Meeting in April 1994. The work reported in this article was supported by grants from the James S. McDonnell Foundation, the Andrew W. Mellon Foundation, the Evelyn Lois Corey Research Fund, and Grant HD-06864 from the National Institute of Child Health and Human Development. But the preparation of the article was supported principally by the Spencer Foundation, whom I would like to thank for giving me time to think. I would like to thank my many colleagues and friends who contributed to the research agenda in this article, but notably I thank my husband and colleague, Joseph C. Campione, for contributions too deep for telling. 1. For descriptions and retrospectives on the major psychological learning theories of the mid century, see Koch (1959). 2. Koch, 1959. 3. Koch, 1959.

References Aronson, E. (1978). The jigsaw classroom. Beverly Hills, CA: Sage. Bacon, F. (1605). The advancement of learning. Oxford: The Clarendon Press. Bacon, F. (1623). Novum organum. Oxford: The Clarendon Press. Bakhtin, M. M. (1986). Speech genres and other late essays (C. Emerson & M. Holquist, Eds., V. W. McGee, Trans.). Austin, TX: University of Texas Press. Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge: Cambridge University Press. Bartlett, F. C. (1958). Thinking: An experimental and social study. New York: Basic Books. Bateson, M. C. (1984). With a daughter’s eye: A memoir of Margaret Mead and Gregory Bateson. New York: Morrow. Becker, H. (1972). A school is a lousy place to learn anything in. American Behavioral Scientist, 16, 85–105. Bereiter, C., & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Closer, (pp. 361–392). Hillsdale, NJ: Erlbaum. Binet, A. (1909). Les idees modernes sur les infants. Paris: Ernest Flammarion. Brown, A. L. (1978). Knowing when, where, and how to remember: A problem of metacognition. In R. Glaser (Ed.), Advances in Instructional Psychology, 1 (pp. 77–165). Hillsdale, NJ: Erlbaum. Brown, A. L. (1990). Domain-specific principles affect learning and transfer in children. Cognitive Science, 14, 107–133. Brown, A. L., (1992). Design experiments: Theoretical and methodological challenges in creating complex interventions in classroom settings. The Journal of the Learning Sciences, 2(2), 141–178. Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering, and understanding. In P. H. Mussen (Series Ed.) & J. H. Flavell & E. M. Markman (Vol. Eds.), Handbook of child psychology: Vol. 3. Child development, (4th ed., pp. 77–166). New York: Wiley. Brown, A. L., & Campione, J. C. (1990). Communities of learning and thinking, or A context by any other name. In D. Kuhn (Ed.), Contributions to Human Development, 21, 108–125.

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Brown, A. L., & Campione, J. C. (1994). Guided discovery in a community of learners. In K. McGilly (Ed.), Classroom lessons: Integrating cognitive theory and classroom practice, (pp. 229–270). Cambridge, MA: MIT Press/Bradford Books. Bruner, J. S. (1969). On knowing: Essays for the left hand. Cambridge, MA: Harvard University Press. Bruner, J. S. (1986). Actual minds, possible worlds. Cambridge, MA: Harvard University Press. Calkins, M. W. (1915). The self in scientific psychology. American Journal of Psychology, 26, 495–524. Carey, S. (1985). Conceptual change in childhood. Cambridge, MA: Bradford Books, MIT Press. Carey, S., & German, R. (1991). The epigenesis of mind. Hillsdale, NJ: Erlbaum. Dewey, J. (1936). The theory of the Chicago experiment. In K. C. Mayhew & A. C. Edwards (Eds.), The Dewey School: The laboratory school of the University of Chicago, 1896 –1903 (pp. 463–477). New York: Appleton-Century. Fish, S. (1980). Is there a text in this class? The authority of interpretive communities. Cambridge: Harvard University Press. Frederiksen, J., & Collins, A. (1989). A systems approach to educational testing. Educational Researcher, 18(9), 27–32. Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. New York: Basic Books. Graham, P. S. (1967). Progressive education, from Arcady to Academe: A history of the Progressive Education Association, 1919–1955. New York: Columbia University, Teachers College. Griffin, D. R. (1992). Animal minds. Chicago: University of Chicago Press. Hall, G. S. (1881). The contents of children’s minds. Princeton Review, 11, 249–272. Hatano, G., & Inagaki, K. (1987). Everyday biology and school biology: How do they interact? The Newsletter of the Laboratory of Comparative Human Cognition, 9, 120–128. Heath, S. B. (1991). “It’s about winning!” The language of knowledge in baseball. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on socially shared cognition, (pp. 101–126). Washington, DC: American Psychological Association. Heath, S. B., & McLaughlin, M. W. (in press). Learning for anything every day. Journal of Curriculum Studies. Hobhouse, L. T. (1901). Mind in evolution. London: Macmillan. Keil, F. C. (1992). The origins of autonomous biology. In M. R. Gunnan & M. Maratsos (Eds.), Minnesota symposium on child psychology: Modularity and constraints on language and cognition, (pp. 103–137). Hillsdale, NJ: Erlbaum. Kennedy, J. F. (1962). Televised address from Rice University, September 12. Koch, S. (Ed.) (1959). Psychology: A study of a science: General systematic formulations, learning, and special processes. Vol. 2. New York: McGraw-Hill. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press. Maier, N. R. F. (1936). Reasoning in children. Journal of Comparative Psychology, 21, 357–66. Majone, G., & Wildavsky, A. (1978). Implementation as evolution. In H. E. Freeman (Ed.), Policy studies review annual. Vol. 2 (pp. 103–117). Beverly Hills: Sage Publications. Medawar, P. (1982). Pluto’s republic. Oxford: Oxford University Press. Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition and Instruction, 1(2), 117–175. Resnick, L. B., & Resnick, D. P. (1991). Assessing the thinking curriculum: New tools for educational reform. In B. R. Gifford & M. C. O’Connor (Eds.), Future assessment: Changing views of aptitude, achievement and instruction. Boston: Academic Press. Shulman L. S. (1986). Those who understand teach: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.

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Sobel, B. (1939). The study of the development of insight in preschool children. Journal of Genetic Psychology, 55, 381–385. Thorndike, E. L., & Gates, A. I. (1929). Elementary principles of education. New York: Macmillan. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. John-Steiner, S. Scribner, & E. Souberman, Eds. and Trans.). Cambridge, MA: Harvard University Press. Wurthnow, R. (1989). Communities of discourse: Ideology and social structure in the Reformation, the Enlightenment, and European socialism. Cambridge, MA: Harvard University Press.

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28 Paradigms of Knowledge and Instruction S. Farnham-Diggory

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nyone who tries to review a corpus of literature for this journal must grapple with an extraordinarily difficult problem: identifying exactly what a set of studies has in common. Common terminology is not enough. One person’s motivation may be another’s cognitive strategy. Methodological differences often crucially differentiate studies that go by the same names (e.g., mastery learning). Figuring out what the studies really have in common means delving well below terminology to underlying assumptions. Once identified, such assumptions should constitute categories that can be strongly defended on theoretical grounds. They could even be true. People who write textbooks, as compared to people who undertake such limited projects as writing review articles, face categorization problems that are practically insurmountable. That’s why textbook authors so often clump and list research findings in ways that are essentially arbitrary. And that’s why their hapless students (and I speak as an instructor of hundreds of the hapless) have no alternative but to try to memorize arbitrary lists of research findings and hope for the best on examinations. The first textbook I wrote, Cognitive Processes in Education, was published in 1972. The revision was published in 1992. Normally, a revision is accomplished in 3 years. It took me 20 years because the field of educational psychology was changing so rapidly. I produced three completely revised drafts in manuscript before the fourth draft finally went to press. This rapid change forced me to try to figure out, once and for all, how the instructional literature could be classified logically. Source: Review of Educational Research, 64(3) (1994): 463–477.

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I have concluded that there are exactly three instructional paradigms and that, within the framework of these paradigms, exactly five types of knowledge can be acquired. I believe this categorization system provides solid stepping stones through research swamps. I believe it also provides guidelines for curriculum design. I believe that my system helps resolve controversies – between types of reading instruction, for example. Above all, I believe that logical categorization of instruction and learning, as compared to political rhetoric, is the first step in genuine school reform. However, in this article, I will discuss only research issues, especially as they relate to literature reviews. I’ll first provide a very brief, introductory summary. I will then. go into details and address issues that the summary raises.

The Three Core Instructional Paradigms Every instructional program – those designed for laboratory research, written up in curriculum guides, observed in schools, and informally practiced by communities – can be classified as fitting one of three mutually exclusive models that I am calling behavior, development, or apprenticeship models, although I am using these terms in limited ways. As shown in Table 1, the nature of the model is determined by two factors: how the model distinguishes novices from experts and what the mechanism of transformation is. I use the term expert relatively, to refer to whatever level of competence a program may set. I also use the term novice relatively, to refer to whatever beginning level a program may specify. Mechanism of transformation refers to what happens inside the head that turns a novice into an expert. I now turn to details and issues.

The Behavior Model In the behavior model, novices and experts are on the same scale(s), and transformation is accomplished through the mechanism of incrementation. A novice is low, and an expert is high. A novice gets to be an expert by accumulating something, getting better, getting faster, getting more, and so forth. Table 1: Instructional model criteria Instructional paradigm

Expert-Novice distinction

Key mechanism of transformation

Behavior Development

Quantitative differences on same scale(s) Differences in qualitative models (personal beliefs)

Incrementation Perturbation

Apprenticeship

Sociological differences in the culture of practice

Acculturation

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A founding father of this model was, of course, Edward Thorndike (Farnham-Diggory, 1992, pp. 485–491; Joncich, 1968; Thorndike, 1913– 1914). Anything that exists, Thorndike used to say, exists in some amount. (The original version of this often-paraphrased statement was in an essay by Thorndike that appeared in the Seventeenth Yearbook of the National Society for the Study of Education, 1918, p. 16). What is taught, how often it is reviewed, and so on can be measured. What is learned, how well it is learned, and so forth, can be measured. Education, being thus quantifiable, can therefore be a science, and Thorndike turned it into one. My own most recent experience with this type of instructional model has concerned reading. For several years at the University of Delaware, I directed a remedial reading center that introduced a program called Intensive Literacy (Farnham-Diggory, 1992, appendix to chap. 9, pp. 296–310). This was a modification of a tutorial program originated by neurologist Samuel Orton (1925) and adapted for whole-class instruction (with modifications of her own) by Romalda Spalding (Spalding & Spalding, 1986). In this program, students are taught letter-sound correspondences called phonograms, as well as rules such as “ y not i is used at the end of English words.” Eventually, students learn about 70 phonograms and about 30 rules. Once an initial set of phonograms has been mastered, students begin spelling and reading words. They will progress through a 1700-word list, arranged by frequency. They study between 20 and 30 words per week – writing word lists from dictation, reading them back, reviewing previous lists, and so on. Students are repeatedly assessed on a test keyed to the word list, so the teacher will know how far up the list the student has traveled. Each student builds a personal wordbook annotated according to rules. By the end of a year, with daily instruction, students will have compiled a personal glossary of over 700 words. There is much more to the program than I have described here, but the principles of countability and incrementation should be clear.

The Development Model In the development model, novices and experts are distinguished on the basis of their personal theories and explanations, sometimes called qualitative models, of events or experiences. The novice’s model is different in many complex ways from the expert’s model (an example is given below). Instruction begins with probes of the student’s personal theory. By questioning, contradicting, and challenging that theory (the process that I have called perturbation), the student is pushed to revise it. The result is essentially a whole new way of thinking, a wide-ranging qualitative shift. I presented a detailed example in my book, Schooling (Farnham-Diggory, 1990, pp. 96–01), taken from the “Shadows” curriculum designed by Deborah Smith (1989; Smith is now at Michigan State University). The curriculum is

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a science unit for first or second graders. Through interviews, Smith determined (in line with Piaget’s findings) that young children believe that a shadow is a substance projected from the front of the body, exists in the dark, and is something like a mirror image. Smith designed a series of daily projects that perturbed these beliefs. For example, the children collected data on shadows of nonhuman objects, on shadow movements, on double shadows (from double light sources), and so forth. Eventually, through interviewing the children again, Smith demonstrated that they had constructed a new theory – that shadows result when light is blocked, that light is emitted in all directions, that mirror images result from bouncing light, not from blocked light, and so on. A founding father of the development instructional model was, of course, Jean Piaget (see his chap., “The Problem of Shadows,” in The Child’s Conception of Physical Causality, 1951). Interest in this type of instruction has been more recently revived under the name of conceptual change (Posner, Strike, Hewson, & Gertzog, 1982).

The Apprenticeship Model In the apprenticeship model, novices and experts are from different worlds, and a novice gets to be an expert through the mechanism of acculturation into the world of the expert. Actual participation in this world is critical for two reasons: (a) much of the knowledge that the expert transmits to the novice is tacit, and (b) the knowledge often varies with context. To illustrate: My own most recent experience with this model of instruction has been in the field of archaeology – literally, out in the field as a novice member of excavation teams. It has become clear that I am, to begin with, an outsider, but it is considerably less clear (to me) what I am outside of. The experience, as a whole, I would describe as coming to understand shapes emerging from a fog. For example, I cannot spot flakes – artifacts that have been chipped from core rocks by human hands. I have walked across fields with experts who spot flakes, exclaim at their beauty, and explain to me, time and time again, exactly what the criteria are (a curve here, a point there). What they cannot explain to me is how they spot the flakes in the first place. We are walking across a newly plowed field after a rain. Rocks, crop stubble, flakes, and heaven knows what else have been raised to the surface and washed clean. It is in the midst of all this perceptual rubble, that experts spot the flakes. I remain befogged. But I am hopeful that eventually I will somehow acquire their tacit knowledge. The acquisition of such knowledge, through membership in a culture, is what apprenticeship is mainly about. Another example: as an archaeology apprentice, it is necessary for me to learn the lines of intellectual allegiance and emnity. I mention the name of a wellknown authority. “Oh, him,” my apprentice master says dismissively.

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Now what do I do? Ask “What’s wrong with him?” The answer is going to be largely uninterpretable (to an apprentice), since it is linked to unknown histories, affinities, and styles. There are, of course, published materials that explicate controversies and critically review them. But nowhere is it explained which of these materials should be taken seriously and which constitute just another set of “Oh, hims.” I am again acquiring tacit knowledge through immersion in a culture. As a final example, there is the problem of learning what constitutes scientific rigor. You find a bit of bone or pot. What can you arguably extrapolate from these bits about the nature of the ancient culture? In one excavation, it was discovered that treasures were found only in the grave of a child. The archaeologists concluded that the culture must have been relatively advanced – because status was ascribed. That is, status (as indexed by accompanying treasures) obviously could not have arisen simply from having lived a long time. The presence of something like royal families, wherein even young members are given burial honors, is an indication of a relatively advanced culture. As an apprentice, I must learn how it got decided that this interpretation is scientifically justifiable. What I think is that maybe some kid discovered a treasure hoard, tried to dig for it, and got buried when the dirt caved in on him. But I have learned to keep such ruminations to myself. Little by little, out of the fog, the principles of this particular scientific culture will emerge. That is what it means to be an apprentice – what it has meant since all that flaking began. Recent interest in cognitive apprenticeship and situated cognition was sparked by Collins, Brown, and Newman (1989); Brown, Collins, and Duguid (1989); and Rogoff (1990). Two useful books on craft apprenticeships are by Coy (1989) and Rorabaugh (1986). Regarding school programs, I reviewed eight apprenticeship projects in science and the humanities, including an entire secondary school built around marine studies, in Schooling (FarnhamDiggory, 1990, chaps. 4 and 5). In Cognitive Processes in Education (FarnhamDiggory, 1992), I devoted a whole chapter to a detailed example of a multidisciplinary curriculum called “Whale” (Watterson, Rendell, & Bell, 1988). I was surprised to discover that there doesn’t appear to be a cognitive instructional model, as such. Cognitive processes, strategies, knowledge bases, and so forth can be taught within behavior, development, or apprenticeship frameworks. The fact that cognitive procedures are being explicitly taught is not a defining characteristic of a core instructional paradigm. In fact, it is impossible to teach anything in any format without activating cognition. However, cognitive science has a major role to play in my overall classification system, as I will shortly explain.

Exclusivity and Inclusivity The three core models are defined as mutually exclusive. Definitional criteria were deliberately selected so that a form of instruction fitting one model

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would not fit the others. For example, both the behavior model and the development model are defined as culture free, while the apprenticeship model is not. Both the development and the apprenticeship models define expertise as qualitative changes in cognition and/or life styles, while the behavior model is defined as not doing so. I recognize that more than one model may appear to be operating simultaneously, but close analysis will show that one or two models are functioning as modules within a parent instructional paradigm. For example, my apprenticeship training included behavior modules on sorting pottery shards and some major developmental shifts in beliefs about archaeology. Issues of instructional hierarchies and interfacing of modules are, of course, important but are beyond the scope of this essay.

The Five Core Learning Paradigms Within the framework of the three instructional paradigms, five types of knowledge may be acquired – declarative, procedural, conceptual, analogical and logical. I formulated this typology through processes that mathematicians aptly call brute force. I was trying to find connections between knowledge as cognitive scientists currently study it and learning as experimental psychologists used to study it. For example, the declarative knowledge studied by cognitive psychologists got into heads somehow. But cognitive psychologists weren’t concerned with acquisition. The psychologists who were concerned with acquisition used to be called verbal learning theorists. They produced a huge experimental literature prior to the onset of the cognitive revolution in the 1960s. As it turned out, I discovered that five distinct types of knowledge, as defined by cognitive psychologists, could be linked to five distinct experimental paradigms of learning psychology, as conducted from the 1930s through the 1960s. Below, I present very brief, simplified summaries of what I mean. (For more details and references, see Farnham-Diggory, 1992, pp. 77–80, chap. 4, and pp. 139–152).

Declarative Knowledge This is knowledge that can be declared, usually in words, through lectures, books, writing, verbal exchange, braille, sign language, mathematical notation, and so on. Acquisition of declarative knowledge has been studied primarily in the field of verbal learning, and the direction of the field, over the years, has been toward understanding the crucial role of meaning. The field began with heroic attempts to extrude meaning from verbal learning and only gradually devised ways of defining and quantifying meaning units (Ebbinghaus, 1964; Underwood, 1982; van Dijk & Kintsch, 1983).

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Procedural Knowledge Procedural knowledge is in the form of action sequences. It is knowledge that must be demonstrated. (One may also have verbal knowledge of an action sequence, but the part that can be talked about is declarative knowledge.) Acquisition of procedural knowledge has been extensively studied and reported in the skill-learning literature. Additionally, there is a vast literature on skill learning in everyday life – reports of athletes, composers, business entrepreneurs, and many others. Despite differences in terminology, there turn out to be three phases of skill development that everyone, in and out of experimental psychology laboratories, talks about: (a) analysis, (b) practice to the point of automaticity, and (c) attention management. These are mutually exclusive phases. Analyzing football strategies (by watching, e.g., video replays) isn’t practicing. Automaticity, by definition, precludes analysis. Attention management (usually referred to by performers as concentration) requires strictly controlled sequences of thought and crucially avoids on-line analysis of automatized behaviors (Bilodeau, 1969; Russell & Branch, 1979; Shiffrin & Schneider, 1977).

Conceptual Knowledge Concepts are of two types: (a) categories and (b) schemata. Categories are defined usually as lists of attributes – dogs have tails, ears, four legs, bark. Schemata add in spatial and temporal attributes. They are map-like and/or scriptlike. The acquisition of conceptual knowledge was extensively studied in the concept-learning (sometimes called concept-attainment or conceptformation) experimental literature. Concepts come into existence through repeated exposure to examples that are similar in some respects and dissimilar in others. When one acquires a concept, one has, in effect, learned to extract commonalities. This extraction must be performed by the learner, or a concept – category or schema – will not, by definition, have been acquired. If a student is told outright: “This is the principle . . . ,” only a bit of declarative knowledge will have been imparted. If a student repeatedly rehearses the same experience, only a bit of procedural knowledge will have been acquired (Hilgard, 1948; Klausmeier & Harris, 1966; Reber, 1967; Reed, 1973).

Analogical Knowledge Analogical knowledge, sometimes called imagery, preserves specific correspondences between what is outside in the world and what is inside the head. It may come into existence through a single exposure. The senses were stimulated, and the memory of that sensory pattern remains.

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This type of learning was studied within the framework of what used to be called the one-trial learning paradigm. Dogs, given one severe shock, would never go into that red box again. In more humane versions of the paradigm, pictures that subjects had seen only once were mixed in with unfamiliar pictures. Subjects were astonishingly accurate at picking out the pictures they had seen before. This type of learning is, in effect, something like a sensory imprint (Brush & Overmier, 1985; Standing, 1973).

Logical Knowledge Logical knowledge is a system of causal implications, a mental model of what is connected to what and what leads to what. The implications and connections may or may not be true by objective scientific standards, but they are characterized, as Piaget pointed out, by a compelling feeling of necessity. This is because they have come into existence by the exercise of one’s own reasoning. The early experimental literature on the acquisition of logical knowledge came from the field called problem solving and also from the so called naive theories introduced by social psychologists (Heider, 1958).

Learning and Instruction At first glance, one might think that the acquisition of certain forms of knowledge could occur only within certain instructional paradigms (e.g., declarative or procedural knowledge within the behavior paradigm, logical knowledge within the development paradigm). That’s not actually the case. All five types of knowledge can be acquired within the framework of all three types of instruction. However, particular instructional paradigms dictate different knowledge acquisition strategies and objectives. This is summarized in Table 2. I will not go into further detail here because that would lead into issues of curriculum design, which is not what this essay is about.

Teaching Tactics It may be surprising (it surprised me) that methods of teaching are not defining characteristics of instructional paradigms or of knowledge acquisition. There are, in my experience, exactly four teaching methods, and they can, and usually do, appear whenever a teacher is present. Much learning, of course, occurs in the absence of a teacher, but in those cases self-instruction is usually occurring. That, too, may take one or more of the following forms:

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Table 2: Paradigms of knowledge and instruction Knowledge paradigms Instruction paradigms

Declarative, Procedural, Conceptual, Analogical, Logical

Behavior

Experts and novices are on the same measurement scales, and instruction enables novices to systematically accrue all five types of knowledge, until they reach expert levels.

Development

Experts and novices have different beliefs, and instruction enables novices to acquire all five types of knowledge in ways that challenge them to reconstruct their beliefs.

Apprenticeship

Experts and novices are in different worlds, and instruction enables novices to acquire all five types of knowledge (often tacit) in ways that facilitate their entry into the culture of expertise.

(a) Talking – lecturing, telling, reading from notes, presenting information verbally, talking back and forth (including dialectic), questioning, and so on, including talking to oneself. (b) Displaying – modeling, showing, demonstrating. (c) Coaching – pointing out cues, suggesting changes, guiding (all this while the student is doing something). (d) Arranging the learning environment – setting up a self-instructing situation (e.g., arranging for students in a geology lab to classify rocks by checking them against a chart). All other teaching tactics – reinforcement, use of media, and so forth – can appear in all four categories. Involvement of students as co-teachers can also occur in all four categories, as can the degree of social interaction generally. Different combinations and proportions of teaching methods may occur in different instructional and learning paradigms. For example, there may be more lecturing where declarative knowledge is being imparted, but not necessarily. Arranging for students to browse through a library may impart more declarative knowledge than lecturing does. My point is that, while it is important to be aware of teaching tactics, these tactics do not uniquely characterize the paradigms under discussion here.

Implications for Research The foregoing paradigms carry a number of implications for research in instruction and learning. For example, they help clarify calls for alternative forms of research, and they provide parsimonious criteria for classifying research that is currently being conducted. I will briefly discuss a few of these implications and then conclude.

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Calls for Alternative Research An excellent example is the presidential address to the American Educational Research Association delivered by Elliot Eisner in 1993. Eisner presents a personal odyssey of his own intellectual growth – from painting, through teaching painting to children in the ghettos of Chicago, through graduate school at the University of Chicago, through specialization in formal educational evaluation, and eventually to a confrontation (still unresolved, as he describes it) with discrepancies between educational research as it is traditionally practiced and the true ways that (in Eisner’s experience) human minds grow and work. As Eisner sums up his thesis: If there are different ways to understand the world, and if there are different forms that make such understanding possible, then it would seem to follow that any comprehensive effort to understand the processes and outcomes of schooling would profit from a pluralistic rather than a monolithic approach to research. How can such a pluralism be advanced? What would it mean for the way we go about our work? I hope that questions of these kinds will become an agenda for our research in the future. (Eisner, 1993, p. 8)

The paradigms that I have described provide practicable ways of specifying the agenda for which Eisner is calling. The “monolithic” approach could be described as a behavior instructional paradigm that primarily fosters the acquisition of certain types of declarative and procedural knowledge. The “pluralistic” approach could be described as development or apprenticeship paradigms (the latter being especially familiar to art students) that would foster conceptual, analogical, and logical knowledge, as well as a wider variety of declarative and procedural knowledge. The question then becomes: Just how monolithic is current educational research?

The Nature of Current Research I decided to approach this question by analyzing a volume of the Journal of Educational Psychology (1992, Vol. 84; see appendix). This is, of course, a very different task from observing in classrooms and laboratories. I had to depend on what authors said they did, and, in view of journal page constraints, experimenters and subjects must have done a great deal more than authors could describe. So I have doubtless made some errors, but I think the general picture will be clear. Method. As listed in the appendix, there were 43 articles altogether. A few were omitted because they were commentaries, reprints from earlier years in the history of the journal, or had foreign language ambiguities.

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Listed first in each appendix entry are authors, titles, and page numbers. Listed second are the core instructional models that explicitly or implicitly influenced subject responses – behavior, development, apprenticeship, or unspecified. Only 11 of these articles explicitly manipulated instruction. These are coded El (for experimental instruction). However, because all subjects had attended school, I also attempted to classify the nature of relevant school instruction (SI). Unspecified SI means that no portion of any school instruction (e.g., arithmetic, reading) was described in any terms by the author(s), and the school instruction’s possible relevance to the theory and procedure of the research project was therefore never addressed. Listed third in the appendix entries is the type of knowledge activated and/or enhanced by experimental manipulations – declarative, procedural, conceptual, analogical, logical, or unclassifiable composite (UC). This last was usually an achievement or aptitude test battery. I asked myself what kind of knowledge was primarily activated by procedures described in the methods section of the research report? Declarative knowledge was activated by reading, writing, talking, filling out questionnaires, drawing lines under words, and so forth. Procedural knowledge was activated by strategy training, rehearsing, eye movements, and so on. (I recognize that doing anything activates some procedural knowledge, but my concern here was with explicit skills of theoretical interest to the investigator.) Conceptual knowledge was activated by requirements to formulate schemata or categories. Analogical knowledge was activated by specific sensory stimuli – for example, the cartoon cat, Garfield; diagrams; and so forth. Logical knowledge was activated by reasoning about people (attribution theory), mental models of mechanical contrivances, belief systems, and problem solving. Results. As will be immediately evident from scanning Table 3, the most outstanding result of my analysis of these articles is that the nature of school instruction was seldom specified in them. Of the four papers that did mention (if only in a sentence or two) the nature of the school program, two were behavior models, and two were apprenticeship models. Of the latter, one was described as a play-based kindergarten program, and the second was a language arts program that required outside reading. Because both programs implied that instruction involved some degree of acculturation, I classified them as apprenticeships. Of the 11 experimental instructional programs, 6 were behavior models, 1 was a development model, and 4 were apprenticeship models. In three of the apprenticeship studies, researchers simply administered questionnaires about activities (e.g., participation in community sports) that involved acculturation. The remaining apprenticeship study examined the acculturation of mainstreamed special education students. In the single development study, students were challenged with controversial questions, and older students were expected to give responses that qualitatively differed from those of younger students.

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Table 3: Number of instruction and knowledge paradigms in Journal of Educational Psychology, Vol. 84, 1992 Paradigm Behavior Development Apprenticeship Unspecified Declarative Procedural Conceptual Analogical logical Unclassifiable Composite

School instruction (N = 43)

Experimental manipulation (N = 11/43)

Instruction paradigms 2 0 2 39 Knowledge paradigms (in 43 studies) 38 26 8 8 19 14

6 1 4 0

The lower part of Table 3 lists the knowledge categories. A number of the studies activated more than one type of knowledge (see appendix), so the knowledge category list total exceeds the article total (43). It is clear from the table that declarative and procedural knowledge occur most frequently. A close third is logical knowledge – mostly arising from problem solving (e.g., algebra) and from belief systems (e.g., judgments of peers). Fourth in frequency is the unclassifiable composite of standardized test scores, performance ratings, and so on. There were only eight studies that activated conceptual and/or analogical knowledge.

Discussion and Conclusions I want first to emphasize that I did not conduct a reliability study and that some of my classifications can be (and undoubtedly will be) disputed. However, the classification of school instruction as unspecified is not disputable. Description of school instruction is either present in the article, or it isn’t. Mostly, it isn’t. I found this result surprising and disturbing. The majority of researchers who are conducting studies of reading, writing, arithmetic, and other school subjects or classroom activities are not presenting any information about their subjects’ school training in these areas. The standard justification is that school programs constitute randomized variation. While this may make sense statistically, the question is does it make sense logically? A 5-year period of school training in a particular subject, such as reading, surely constitutes a powerful treatment effect. Have researchers perhaps trapped themselves into a quintessential catch-22? Are they alleging that rigorous educational research can be conducted only if

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major treatments (school training) and avowed objectives (improvements in school training) are defined as random errors? Surely this is not the case. Surely most researchers avoid describing school programs because it is often very difficult to map classroom instruction onto theories that drive laboratory research. One specific purpose of my classification system has been to facilitate such mappings. School and laboratory programs can both be classified within the framework of three instructional paradigms and five knowledge paradigms. Once that is done, the links between school and laboratory can be much more easily identified. Reviews, as well, should begin with an analysis of the core instructional and knowledge paradigms that underlie the research programs of interest. It is this underlying logic that cuts through fashionable, but often confusing, terminology; that identifies the lines of research that the program is truly linked to; and that reveals the degree to which educational science as a whole is (or is not) advancing.

Appendix Classification of research reports in the Journal of Educational Psychology, Vol. 84, 1992 Stader, S. R., & Licht, B. G. Effects of questionnaire administration condition on children’s achievement-related beliefs, 28–34; behavioral SI; declarative, procedural, logical. Marsh, H. W. Content specificity of relations between academic achievement and academic self-concept, 35–42; unspecified SI; declarative, logical; UC. Vaughn, S., Haager, D., Hogan, A., & Kouzekanani, K. Self-concept and peer acceptance in students with learning disabilities: a four- to five-year prospective study, 43–50; unspecified SI; declarative, analogical, logical; UC. Benbow, C. P. Academic achievement in mathematics and science of students between ages 13 and 23: are there differences among students in the top one percent of mathematical ability? 51–61; unspecified SI; UC. Low, R., & Over, R. Hierarchical ordering of schematic knowledge relating to area-of-rectangle problems, 62–69; unspecified SI; declarative, procedural, conceptual, analogical, logical. Hegarty, M., Mayer, R. E., & Green, C. E. Comprehension of arithmetic word problems: evidence from students’ eye fixations, 76–84; unspecified SI; declarative, procedural, conceptual, logical. Verschaffel, L., De Corte, E., & Pauwels, A. Solving compare problems: an eye movement test of Lewis and Mayer’s consistency hypothesis, 85–94; unspecified SI; declarative, procedural, conceptual, logical. Herdman, C. M., & LeFevre, J. Individual differences in the efficiency of word recognition, 95–102; unspecified SI; declarative, procedural. Bisanz, G. L., Das, J. P., Varnhagen, C. K., & Henderson, H. R. Structural components of reading time and recall for sentences in narratives: exploring changes with age and reading ability, 103–114; unspecified SI; declarative, procedural, conceptual.

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Woloshyn, V. E., Pressley, M., & Schneider, W. Elaborative-interrogation and prior-knowledge effects on learning of facts, 115–124; unspecified SI; declarative, procedural. Gustafsson, J., & Undheim, J. O. Stability and change in broad and narrow factors of intelligence from ages 12 to 15 years, 141–149; unspecified SI, apprenticeship El; UC. Hong, E., & O’Neil, H. F. Instructional strategies to help learners build relevant mental models in inferential statistics, 150–159; unspecified SI, behavior EI; declarative, procedural, analogical, logical. Imai, M., Anderson, R. C, Wilkinson, I. A. G., & Yi, H. Properties of attention during reading, 160–173; unspecified SI, behavior EI; declarative, procedural. Treiman, R., & Weatherston, S. Effects of linguistic structure on children’s ability to isolate initial consonants, 174–181; apprenticeship SI; declarative, procedural. Haenggi, D., & Perfetti, C. A. Individual differences in reprocessing of text, 182– 192; unspecified SI; declarative, procedural. Breznitz, Z., & Share, D. L. Effects of accelerated reading rate on memory for text, 193–199; unspecified SI; declarative, procedural. Maki, R. H., & Share, M. Role of practice tests in the accuracy of test predictions on text material, 200–210; unspecified SI; declarative, procedural. Rabinowitz, M., Freeman, K., & Cohen, S. Use and maintenance of strategies: the influence of accessibility to knowledge, 211–218; unspecified SI; declarative, procedural. Hall, V. C, & Edmondson, B. Relative importance of aptitude and domain knowledge on immediate and delayed posttests, 219–223; unspecified SI, apprenticeship EI; declarative; UC. Nicholls, J. G., & Nelson, J. R. Students’ conceptions of controversial knowledge, 224–230; unspecified SI, development EI; declarative, logical. Pressley, M., Shuder, T., Bergman, J. L., & EI-Dinary, P. B. A research-educator collaborative interview study of transactional comprehension strategies instruction, 231–246; behavior SI; declarative. Duda, J. L., & Nicholls, J. G. Dimensions of achievement motivation in schoolwork and sport, 290–299; unspecified SI; apprenticeship EI; declarative, logical. Pierson, L. H., & Connell, J. P. Effect of grade retention on self-system processes, school engagement, and academic performance, 300–307; unspecified SI; declarative; UC. Costanzo, M. Training students to decode verbal and nonverbal cues: effects on confidence and performance, 308–313; unspecified SI; behavior EI; declarative, procedural, analogical, logical. Juvonen, J. Negative peer reactions from the perspective of the reactor, 314–321; unspecified SI; declarative, logical. Juvonen, J., & Bear, G. Social adjustment of children with and without learning disabilities in integrated classrooms, 322–330; unspecified academic SI; apprenticeship EI; declarative, logical; UC. Fantuzzo, J. W., King, J. A., & Heller, L. R. Effects of reciprocal peer tutoring on mathematics and school adjustment: a component analysis, 331–339; unspecified SI; behavior EI; declarative, procedural, logical; UC. Sawyer, R. J., Graham, S., & Harris, K. R. Direct teaching, strategy instruction, and strategy instruction with explicit self-regulation: effects on the composition

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skills and self-efficacy of students with learning disabilities, 340–352; unspecified SI; behavior EI; declarative, procedural, conceptual, logical. Spector, J. E. Predicting progress in beginning reading: dynamic assessment of phonemic awareness, 353–363; unspecified SI; declarative, procedural. Torgesen, J. K., Morgan, S. T., & Davis, C. Effects of two types of phonological awareness training on word learning in kindergarten children, 364–370; unspecified SI; behavioral EI; declarative, procedural. Weaver, C. A., & Kintsch, W. Enhancing students’ comprehension of the conceptual structure of algebra word problems, 419–428; unspecified SI; declarative, procedural, conceptual, logical. Schommer, M., Crouse, A., & Rhodes, N. Epistemological beliefs and mathematical text comprehension: believing it is simple does not make it so, 435–443; unspecified SI; declarative, logical. Mayer, R. E., & Anderson, R. B. The instructive animation: helping students build connections between words and pictures in multimedia learning, 444–452; unspecified SI; declarative, procedural, analogical, logical. Patterson, M. E., Dansereau, D. F., & Newbern, D. Effects of communication aids and strategies on cooperative teaching, 453–461; unspecified SI; behavior EI; declarative, procedural, analogical. Tirre, W. C, & Pena, C. M. Investigation of functional working memory in the reading span test, 462–472; unspecified SI; declarative, procedural; UC. Swanson, H. L. Generality and modifiability of working memory among skilled and less skilled readers, 473–488; unspecified SI; declarative, procedural, analogical, conceptual; UC. Allen, L., & Cipielewski, J. Multiple indicators of children’s reading habits and attitudes: construct validity and cognitive correlates, 489–503; apprenticeship EI; declarative, procedural, analogical; UC. Goldman, S. R., & Murray, J. D. Knowledge of connectors as cohesion devices in text: a comparative study of native-English and English-as-a-second-language speakers, 504–519; unspecified SI; declarative, conceptual, logical. Wang, A. Y., Thomas, M. H., & Ouellette, J. A. Keyword mnemonic and retention of second-language vocabulary words, 520–528; unspecified SI; declarative, procedural. Lobel, T. E., & Bempechat, J. Socialization of achievement: influence of mothers’ need for approval on children’s achievement cognitions and behavior, 529–536; unspecified SI; declarative, logical. Goff, M., & Ackerman, P. L. Personality-intelligence relations: assessment of typical intellectual engagement, 537–552; unspecified SI; UC. Marsh, H. W. Extracurricular activities: beneficial extension of the traditional curriculum or subversion of academic goals, 553–562; unspecified SI; UC. Cashin, W. E., & Downey, R. C. Using global students rating items for summative evaluation, 563–572; unspecified SI; UC. Note. Articles are listed in the order of their appearance in the Journal of Educational Psychology, rather than alphabetically. Articles of a certain type may have been grouped together by the journal editor, and I didn’t want to disrupt those patterns. SI = school instruction. EI = experimental instruction. UC = unclassifiable knowledge composite.

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References Bilodeau, E. A. (Ed.). (1969). Principles of skill acquisition. New York: Academic Press. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. Brush, F. R., & Overmier, J. B. (Eds.). (1985). Affect, conditioning, and cognition: Essays on the determinants of behavior. Hillsdale, NJ: Erlbaum. Collins, A., Brown, J. S., & Newman, S. E. (1989). Cognitive apprenticeship: Teaching the crafts of reading, writing, and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 453–494). Hillsdale, NJ: Erlbaum. Coy, M. W. (Ed.). (1989). Apprenticeship: From theory to method and back again. New York: SUNY. Ebbinghaus, H. (1964). Memory. New York: Dover. Eisner, E. W. (1993). Forms of understanding and the future of educational research. Educational Researcher, 22(7), 5–11. Farnham-Diggory, S. (1972). Cognitive processes in education (1st ed.). New York: Harper & Row. Farnham-Diggory, S. (1990). Schooling. Cambridge, MA: Harvard University Press. Farnham-Diggory, S. (1992). Cognitive processes in education (2nd ed.). New York: HarperCollins. Heider, R. (1958). The psychology of interpersonal relations. New York: Wiley. Hilgard, E. R. (1948). Theories of learning. New York: Appleton-Century-Crofts. Joncich, G. (1968). The sane positivist: A biography of Edward L. Thorndike. Middletown, CT: Wesleyan University Press. Klausmeier, H. J., & Harris, C. W. (Eds.). (1966). Analyses of concept learning. New York: Academic Press. Orton, S. T. (1925). Word-blindness in school children. Archives of Neurology and Psychiatry, 14, 582–615. Piaget, J. (1951). The child’s conception of physical causality. London: Routledge & Kegan Paul. Posner, G., Strike, K., Hewson, P., & Gertzog, W. (1982). Accommodation of a scientific conception: Toward a theory of conceptual change. Science Education, 66, 211–227. Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior, 6, 855–863. Reed, S. K. (1973). Psychological processes in pattern recognition and categorization. Cognitive Psychology, 3, 382–407. Rogoff, B. (1990). Apprenticeship in thinking. New York: Oxford University Press. Rorabaugh, W. J. (1986). The craft apprentice: From Franklin to the machine age in America. New York: Oxford University Press. Russell, B., & Branch, T. (1979). Second wind. New York: Ballantine. Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing II: Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190. Smith, D. C. (1989). The role of teacher knowledge in teaching conceptual change science lessons. Unpublished doctoral dissertation, University of Delaware. Spalding, R., & Spalding, W. (1986). The writing road to reading. New York: Morrow. Standing, L. (1973). Learning 10,000 pictures. Quarterly Journal of Experimental Psychology, 35, 207–222. Thorndike, E. L. (1913–1914). Educational psychology (Vols. 1–3). New York: Teachers College Press. Thorndike, E. L. (1918). The nature, purposes, and general measurements of educational products. In G. M. Whipple (Ed.), The measurement of educational products. Seventeenth

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Yearbook of the National Society for the Study of Education, Part II (pp. 16–24). Bloomington, IL: Public School Publishing Co. Underwood, B. J. (1982). Studies in learning and memory: Selected papers. New York: Praeger. van Dijk, T. A., & Kintsch, W. (1983). Strategies of discourse comprehension. New York: Academic. Watterson, P. R., Rendell, F., & Bell, A. (1988). Whale: Computer-assisted topic. Glasgow: Jordanhill College of Education, Sales and Publications.

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29 Health Promotion by Social Cognitive Means Albert Bandura

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am deeply honored to be a recipient of the Healthtrac Award. It is a special honor to be recognized by a foundation that promotes the betterment of human health in the ways I value highly. In comparing myself to the figure Larry so generously described, I feel like a Swiss yodeler following Pavarotti. The field of health is changing from a disease model to a health model. It is just as meaningful to speak of levels of vitality and healthfulness as of degrees of impairment and debility. Health promotion should begin with goals, not means.1 If health is the goal, biomedical interventions are not the only means to it. A broadened perspective expands the range of health-promoting practices and enlists the collective efforts of researchers and practioners who have much to contribute from a variety of disciplines to the health of a nation. The quality of health is heavily influenced by lifestyle habits. This enables people to exercise some measure of control over their health. By managing their health habits, people can live longer and healthier and retard the process of aging. Self-management is good medicine. If the huge health benefits of these few habits were put into a pill, it would be declared a scientific milestone in the field of medicine.

Supply-Side versus Demand-Side Approaches Current health practices focus heavily on the medical supply side. The growing pressure on health systems is to reduce, ration, and delay health services to Source: Health Education & Behavior, 31(2) (2004): 143–164.

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contain health costs. The days for the supply-side health system are limited. People are living longer. This creates more time for minor dysfunctions to develop into chronic diseases. Demand is overwhelming supply. Psychosocial factors partly determine whether the extended life is lived efficaciously or with debility, pain, and dependence.2,3 Social cognitive approaches focus on the demand side. They promote effective self-management of health habits that keep people healthy through their life span. Aging populations will force societies to redirect their efforts from supply-side practices to demand-side remedies. Otherwise, nations will be swamped with staggering health costs that consume valuable resources needed for national programs.

Social Cognitive Theory This article focuses on health promotion and disease prevention by social cognitive means.4,5 Social cognitive theory specifies a core set of determinants, the mechanism through which they work, and the optimal ways of translating this knowledge into effective health practices. The core determinants include knowledge of health risks and benefits of different health practices, perceived self-efficacy that one can exercise control over one’s health habits, outcome expectations about the expected costs and benefits for different health habits, the health goals people set for themselves and the concrete plans and strategies for realizing them, and the perceived facilitators and social and structural impediments to the changes they seek. Knowledge of health risks and benefits creates the precondition for change. If people lack knowledge about how their lifestyle habits affect their health, they have little reason to put themselves through the travail of changing the detrimental habits they enjoy. But additional self-influences are needed for most people to overcome the impediments to adopting new lifestyle habits and maintaining them. Beliefs of personal efficacy play a central role in personal change. This focal belief is the foundation of human motivation and action. Unless people believe they can produce desired effects by their actions, they have little incentive to act or to persevere in the face of difficulties. Whatever other factors may serve as guides and motivators, they are rooted in the core belief that one has the power to produce desired changes by one’s actions. Health behavior is also affected by the outcomes people expect their actions to produce. The outcome expectations take several forms. The physical outcomes include the pleasurable and aversive effects of the behavior and the accompanying material losses and benefits. Behavior is also partly regulated by the social reactions it evokes. The social approval and disapproval the behavior produces in one’s interpersonal relationships is the second major class of outcomes. This third set of outcomes concerns the positive and negative selfevaluative reactions to one’s health behavior and health status. People adopt

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personal standards and regulate their behavior by their self-evaluative reactions. They do things that give them self-satisfaction and self-worth and refrain from behaving in ways that breed self-dissatisfaction. Motivation is enhanced by helping people to see how habit changes are in their self-interest and the broader goals they value highly. Personal goals, rooted in a value system, provide further self-incentives and guides for health habits. Long-term goals set the course of personal change. But there are too many competing influences at hand for distal goals to control current behavior. Short-term attainable goals help people to succeed by enlisting effort and guiding action in the here and how. Personal change would be easy if there were no impediments to surmount. The perceived facilitators and obstacles are another determinant of health habits. Some of the impediments are personal ones that deter performance of healthful behavior. They form an integral part of self-efficacy assessment. Self-efficacy beliefs must be measured against gradations of challenges to successful performance. For example, in assessing personal efficacy to stick to an exercise routine, people judge their efficacy to get themselves to exercise regularly in the face of different obstacles: when they are under pressure from work, are tired, feel depressed, are anxious, face foul weather, and have more interesting things to do. If there are no impediments to surmount, the behavior can be easy to perform and everyone is efficacious. The regulation of behavior is not solely a personal matter. Some of the impediments to healthful living reside in health systems rather than in personal or situational impediments. These impediments are rooted in how health services are structured socially and economically.

Primacy of Efficacy Belief in Causal Structures Self-efficacy is a focal determinant because it affects health behavior both directly and by its influence on the other determinants. Efficacy beliefs influence goals and aspirations. The stronger the perceived self-efficacy, the higher the goals people set for themselves and the firmer their commitment to them. Self-efficacy beliefs shape the outcomes people expect their efforts to produce. Those of high efficacy expect to realize favorable outcomes. Those of low efficacy expect their efforts to bring poor outcomes. Self-efficacy beliefs also determine how obstacles and impediments are viewed. People of low efficacy are easily convinced of the futility of effort in the face of difficulties. They quickly give up trying. Those of high efficacy view impediments as surmountable by improvement of self-management skills and perseverant effort. They stay the course in the face of difficulties. Figure 1 shows the paths of influence in the posited sociocognitive causal model. Beliefs of personal efficacy affect health behavior both directly and by their impact on goals, outcome expectations, and perceived facilitators and impediments.

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Outcome Expectations Physical Social Self-Evaluative

Self-Eficacy

Goals

Behavior

Sociostructural Factors Facilitators Impediments

Figure 1: Structural paths of influence wherein perceived self-efficacy affects health habits both directly and through its impact on goals, outcome expectations, and perception of sociostructural facilitators and impediments to health-promoting behavior

Overlap in Health Belief Models There are many psychosocial models of health behavior. They are founded on the common metatheory that psychosocial factors are heavy contributors to human health. For the most part, the models include overlapping determinants but under different names. In addition, facets of a higher order construct are often split into seemingly different determinants, as when different forms of anticipated outcomes of behavioral change are included as different constructs under the name of attitudes, normative influences, and outcome expectations. Following the timeless dictum that the more the better, some researchers overload their studies with a host of factors that contribute only trivially to health habits because of redundancy. Figure 2 shows the factors the various health models select and their overlap with determinants in social cognitive theory. Most of the factors in the different models are mainly different types of outcome expectations. Perceived severity and susceptibility to disease in the health-belief model are the expected negative physical outcomes. The perceived benefits are the positive outcome expectations. In the theory of reasoned action and planned behavior, attitudes toward the behavior and social norms produce intentions that are said to determine behavior. Attitude is measursed by perceived outcomes and the value placed on those outcomes. As defined and operationalized, these are outcome expectations, not attitudes as traditionally conceptualized. Norms are measured by perceived social pressures and one’s motivation to comply with them. Norms correspond to expected social outcomes for a given behavior. Goals may be distal ones or proximal ones. Intentions are essentially proximal goals. I aim to do x and I intend to do x are really the same thing. Perceived control in the theory of planned behavior overlaps with perceived self-efficacy.

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SelfEfficacy Physical

Social

SelfEvaluative

Outcome Expectations

Proximal

Goals

Distal

Personal & Situational

Health System

Impediments

Figure 2: Summary of the main sociocognitive determinants and their areas of overlap in different conceptual models of health behavior

Protection Motivation Theory

Theory of Planned Behavior

Theory of Reasoned Action

Health Belief Model

Social Cognitive Theory

Theories

Psychosocial Determinants of Health Behavior

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Regression analyses reveal substantial redundancy of predictors bearing different names.6 For example, after the contributions of perceived self-efficacy and self-evaluative reactions to one’s health behavior are taken into account, neither intentions nor perceived behavioral control add any incremental predictiveness. Most of the models of health behavior are concerned only with predicting health habits. But they do not tell you how to change health behavior. Social cognitive theory offers both predictors and principles on how to inform, enable, guide, and motivate people to adapt habits that promote health and reduce those that impair it.4

Threefold Stepwise Implementation Model The social utility of health promotion programs can be enhanced by a stepwise implementation model. In this approach, the level and type of interactive guidance is tailored to people’s self-management capabilities and motivational preparedness to achieve desired changes. The first level includes people with a high sense of efficacy and positive outcome expectations for behavior change. They can succeed with minimal guidance to accomplish the changes they seek. Individuals at the second level have self-doubts about their efficacy and the likely benefits of their efforts. They make halfhearted efforts to change and are quick to give up when they run into difficulties. They need additional support and guidance by interactive means to see them through tough times. Much of the guidance can be provided through tailored print or telephone consultation. Individuals at the third level believe that their health habits are beyond their personal control. They need a great deal of personal guidance in a structured mastery program. Progressive successes build belief in their ability to exercise control and bolster their staying power in the face of difficulties and setbacks. Thus, in the stepwise model, the form and level of enabling interactivity is tailored to the participants’ changeability readiness. The following sections are devoted to a more detailed consideration of how to enable people at these various levels of changeability to improve their health status and functioning.

Public Health Campaigns Societal efforts to get people to adopt healthful practices rely heavily on public health campaigns. These population-based approaches promote changes mainly in people with high perceived efficacy for self-management and positive expectations that the prescribed changes will improve their health. Meyerowitz and Chaiken7 examined four possible mechanisms through which health

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communications could alter health habits: by transmitting information on how habits affect health, by arousing fear of disease, by increasing perceptions of one’s personal vulnerability or risk, or by raising people’s beliefs in their efficacy to alter their habits. They found that health communications foster adoption of healthful practices to the extent that they raise beliefs in personal efficacy. To help people reduce health-impairing habits by health communications requires a change in emphasis from trying to scare people into health to enabling them with the self-management skills and self-beliefs needed to take charge of their health habits. In longitudinal analyses of community-based health campaigns, Rimal8,9 found that perceived self-efficacy governs whether individuals translate perceived risk into a search for health information and whether they translate acquired health knowledge into healthful behavioral practices. Those of low self-efficacy take no action even though they are knowledgeable about lifestyle contributors to health and perceive themselves to be vulnerable to disease. Maibach and colleagues10 found that both people’s preexisting self-efficacy beliefs that they can exercise control over their health habits and the selfefficacy beliefs instilled by a community health campaign contributed to adoption of healthy eating habits and regular exercise (Figure 3).

Overprediction of Refractoriness Our theories overpredict the resistance of health habits to change. This is because they are developed by studying mainly refractory cases but ignoring successful self-changers. For example, smoking is one of the most addictive substances. It is said to be intractable because it is compelled by biochemical and psychological dependencies. Each puff sends a reinforcing nicotine shot to the brain. Prolonged use is said to create a relapsing brain disease.

.35 (.32)

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.3 5

(.4 4)

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Community Health Campaign

.16 (.16)

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Note: The initial numbers on the paths of influence are the significant path coefficients for adoption of healthy eating patterns; the numbers in parentheses are the path coefficients for regular exercise.10

Figure 3: Paths of the influence of perceived self-efficacy on health habits in community-wide programs to reduce risk of cardiovascular disease

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The problem with this theorizing is that it predicts far more than has ever been observed. More than 40 million people in the United States have quit smoking on their own. Where was their brain disease? How did the smokers cure the disease on their own? Superimposed Superimposed on the 40 million self-quitters, the dismal relapse curves that populate our journals are but a tiny ripple in the vast sea of successes. Carey and his colleagues verified longitudinally that heavy smokers who quit on their own had a stronger belief in their efficacy at the outset than did continuous smokers and relapsers.11 Successful self-changers combine efficacy belief with outcome expectations that benefits will outweigh disadvantages of the lifestyle changes. The same is true for alcohol and narcotic addiction. Lee Robins12 reported a remarkably high remission for heroin addiction among Vietnam veterans without the benefit of treatment. Vaillant13 has shown that a large share of alcoholics eventually quit drinking without treatment, assistance from selfhelp groups, or radical environmental change. Granfield and Cloud14 put it well when they characterized the inattention to successful self-changes in substance abuse as “the elephant that no one sees.”

Enhancement of Health Impact by Interactive Technologies The absence of individual guidance places limits on the power of one-way mass communication. The revolutionary advances in interactive technology can increase the scope and impact of health promotion programs. On the input side, health communications can now be personally tailored to factors known to affect health behavior. Tailoring communications does not necessarily guarantee better outcomes. The benefits of individualization will depend on the predictive value of the tailored factors. If weak or irrelevant factors are targeted, individualization will not provide incremental benefits. Development of measures for key social cognitive determinants known to affect health behavior can provide guidance for tailoring strategies. On the behavioral adaption side, individualized interactivity further enhances the impact of health promotion programs. Social support and guidance during early periods of personal change and maintenance increase longterm success. Here, too, the impact of social support will depend on its nature. Converging evidence across diverse spheres of functioning reveals that the social support has beneficial effects only if it raises people’s beliefs in their efficacy to manage their life circumstances.15 If social support is provided in ways that foster dependence, it can undermine coping efficacy. Effective enablers provide the type of support and guidance that is conducive to self-efficacy enhancement for personal success.5 Interactive computer-assisted feedback provides a convenient means for informing, enabling, motivating, and guiding people in their efforts to make lifestyle changes. The personalized feedback can be adjusted to participants’

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efficacy level, the unique impediments in their lives, and the progress they are making. The feedback may take a variety of forms, including individualized print communications, telephone counseling, and linkage to supportive social networks. I shall describe shortly a self-management system that encompasses these various enabling features.

Socially Mediated Pathways of Influence There is another way in which the power of population-based approaches to health promotion can be strengthened. There is only so much that largescale health campaigns can do on their own, regardless of whether they are tailored or generic. There are two pathways through which health communication can alter health habits (Figure 4). In the direct pathway, media promote changes by informing, modeling, motivating, and guiding personal changes. In the socially mediated pathway, the media link participants to social networks and community settings. These places provide continued personalized guidance, natural incentives, and social supports for desired changes. The major share of behavioral changes is promoted within these social milieus.16 Psychosocial programs for health promotion will be increasingly implemented via interactive Internet-based systems. People at risk for health problems typically ignore preventive or remedial health services. For example, young women at risk of eating disorders resist seeking help. But they will use Internet-delivered guidance because it is readily accessible, convenient, and provides a feeling of anonymity. Studies by Taylor and colleagues17 attest to its potential. Through interactive guidance, women reduced dissatisfaction with their weight and body shape, altered dysfunctional attitudes, and rid themselves of disordered eating behavior. Interactive technologies are a tool, not a panacea. They cannot do much if individuals cannot motivate themselves to take advantage of what they have to offer. These systems need to be structured in ways that build motivational and self-management skills as well as guide habit changes. Otherwise, those who need the guidance most will use this tool least. Dual Paths of Influence

Media Influence

Connections to Social Systems

Behavior Change

Figure 4: Paths of influences through which mass communications affect psychosocial changes both directly and via a socially mediated pathway by linking viewers to social networks and community settings

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Promoting Society-Wide Changes by Serial Dramas The social-linking function via the media is illustrated in global applications of serial television dramas founded on social cognitive theory that address some of the most urgent global problems.18 They include the soaring population growth and transmission of AIDS. Hundred of episodes in these long-running serials get people deeply involved in the lifestyle changes being modeled. The serials dramatize the everyday problems people struggle with, model solutions to them, and provide people with incentives and strategies for bettering their lives. The story lines model family planning, women’s equality, environmental conservation, AIDS prevention, and a variety of life skills. It is of limited value to motivate people to change if they are not provided with appropriate resources and environmental supports to realize those changes. The dramatizations, therefore, link people to community resources where they can receive a lot of continued supportive guidance. Worldwide applications in Africa, Asia, and Latin America are raising people’s efficacy to exercise control over their family lives, enhancing the status of women, and fostering the adoption of contraceptive practices to lower the rates of childbearing. A controlled study in Tanzania compared changes in family planning and contraception use in half the country that received a dramatic series with the rest of the country that did not.19 Compared to the control region, more families in the broadcast area went to family planning clinics and adopted family planning and contraceptive methods (Figure 5). The dramatic series produced similar changes later, when they were broadcast in the former control region of the country. Some of the story lines centered on safer sexual practices to prevent the spread of AIDS. Infection rates are high among long-distance truckers and prostitutes at truck stops. The dramatic productions focused on self-protective and risky sexual practices and modeled how to curb the spread of HIV infection. Compared with residents in the control region, those in the broadcast region increased belief in their personal risk of HIV infection through unprotected sexual practices, talked more about HIV infection, reduced the number of sexual partners, and increased condom use.20,21 The greater the exposure to the modeled behavior, the stronger the effects on perceived efficacy to control family size and risky sexual practices.

Self-Management Model Health habits are not changed by an act of will. It requires motivational and self-regulatory skills. Self-management operates through a set of psychological subfunctions. People have to learn to monitor their health behavior and

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Mean Number of New Family Planning Adopters Per Clinic

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Note: The period 1990 to 1992 is the prebroadcast baseline. The values for 1993 to 1994 are the family planning adoption levels in the broadcast region (solid line) and the control region (dotted line). The values for 1995 to 1996 are the adoption levels when the serial was aired in the previous control region (solid line).20

Figure 5: Mean number of new family planning adopters per clinic in the Ministry of Health Clinics in the broadcast region and those in the control region

the circumstances under which it occurs, and how to use proximal goals to motivate themselves and guide their behavior. They also need to learn how to create incentives for themselves and to enlist social supports to sustain their efforts. DeBusk and his colleagues22 have developed a self-management model for health promotion and disease risk reduction founded on the self-regulatory mechanisms of social cognitive theory. This self-management model combines self-regulatory principles with computer-assisted implementation (Figure 6). It includes exercise programs to build cardiovascular capacity, nutrition programs to reduce dietary fat to lower risk of heart disease and cancer, weight reduction programs, and smoking cessation programs. For each risk factor, people are provided detailed guides on how to improve their health functioning. They monitor their health habits, set themselves short-term goals, and report the changes they are making. The computer mails personalized reports that include feedback of progress toward subgoals. The feedback also provides guides on how to manage troublesome situations and new subgoals to realize. Efficacy ratings identify areas in which self-regulatory skills must be developed if beneficial changes are to be achieved and maintained. A single implementer, assisted with a computerized implementation system, provides intensive, individualized guidance in self-management to large numbers of people.

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Self-Regulatory Delivery System Progress Reports

Physician

Program Implementor

Phone Contact

Patient

Computerized System Data Base Self-Regulatory Change Programs

Figure 6: Computer-assisted self-regulatory system for altering health habits

In tests of the preventive value of this self-management system, employees in the workplace lowered elevated cholesterol by altering eating habits high in saturated fats (Figure 7). They achieved even larger reductions if their spouses took part in the program. The more room for dietary change, the larger the reduction in plasma cholesterol. A single nutritionist implemented the entire program at minimal cost for large numbers of employees. Nonadherence to drug therapies is a pervasive, serious problem. It worsens health conditions and raises medical costs. Moreover, it may lead physicians to prescribe stronger medications or more drastic interventions in response to the seeming failure of the prescribed treatment. A major public health nightmare is that excessive use of drugs and erratic compliance will breed hardier strains of pathogens that render existing medications ineffective. The success of the self-management system in promoting adherence is shown in a program by West and his colleagues23 to reduce sodium intake in patients suffering from heart failure (Figure 8). It strengthened patients’ efficacy to adhere to a low-sodium diet. They achieved substantial reduction in sodium intake and maintained it during a 6-month period. At each time point, the higher the perceived self-efficacy, the greater the sodium reduction. Haskell and his associates24 used the self-management system to promote lifestyle changes in patients suffering from coronary artery disease. This places them at high risk of heart attacks. At the end of 4 years, those receiving medical care by their physicians showed no change or they got worse. In contrast, those aided in self-management by nurse implementers achieved big reductions in multiple risk factors: They lowered their intake of saturated fat, lost weight, lowered their bad cholesterol, raised their good cholesterol, exercised more, and increased their cardiovascular capacity (Figure 9). The program also altered the physical progression of the disease. Those receiving the self-management program had 47% less buildup of plaque on their artery walls (Figure 10). They also had fewer coronary events, fewer hospitalizations, and fewer deaths.

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Note: The panel on the left summarizes the mean cholesterol reductions achieved in applications in the workplace by participants who used the self-management system either by themselves or along with their spouses, or did not receive the system to provide a control baseline. The right panel presents the mean cholesterol reductions achieved with the self-management system by participants whose daily cholesterol and fat intake was high or relatively low at the outset of the program.

Figure 7: Levels of reduction in plasma cholesterol achieved with the self-regulation system

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Figure 8: Enhancement of perceived self-regulatory efficacy and reduction of sodium intake through the aid of the self-management system

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Percent Change

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Dietary Fat

Weight

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Cardiovascular Framingham Capacity Risk Index

Figure 9: Reduction in multiple risk factors by patients with coronary atherosclerosis depending on whether they received the usual care from their physicians or training in self-management of health habits

Triglycerides

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Source: Plotted from data of Haskell et al.24

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Figure 10: Differences in the number of cardiac deaths, hospitalizations for nonfatal myocardial infarction, and other cardiac events for patients who received the usual care from their physician or training in self-management of health habits

The success of the self-management system has been compared in five hospitals to the standard medical postcoronary care in patients who have already suffered a heart attack. At the end of the 1st year, the self-management system is more effective in reducing risk factors and increasing cardiovascular functioning than the standard medical care. The self-management system is well received because it is individually tailored to people’s needs. It provides continuing personalized guidance that enables people to exercise control over their own change. It is a home-based program that does not require any special facilities, equipment, or group meetings plagued with high drop-out rates. It can serve large numbers of people simultaneously under the guidance of a single implementer. It is not constrained by time and place. It combines the high individualization of the clinical approach with the large-scale applicability of the public health approach. It provides valuable health promotion services at low cost. In the present applications, the computer is used as a coordinating and mailing system to guide self-directed change and to provide feedback of progress. By linking the interactive aspects of the self-management model to the Internet, one can vastly expand its availability to people wherever they may live, at whatever time they may choose to use it.

Health Promotion in Children Through Interactive Media The interactive capabilities of electronic technologies are beginning to be creatively enlisted for health promotion. A company in Silicon Valley is developing interactive video games that raise children’s perceived self-efficacy and enable them to manage chronic health conditions.25

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In a role-playing video game for diabetic children, they win points depending on how well they understand the diabetic condition and regulate the diet, insulin, and blood sugar levels of two wacky diabetic pachyderms, Packy and Marlon. They set out to retrieve the food and diabetes supplies snatched by pesky enemy critters in a diabetes summer camp. To succeed, children have to boost the elephants’ health by managing their diabetes as they fight off the pesky critters using their trunks as water cannons and peanut launchers. The better the children manage the meals, blood glucose, and insulin dosage of the pachyderm duo to stay in the safe zone, the more points the children win. Children love the video game. They quickly become experts in how to manage diabetes (Figure 11). In assessments conducted 6 months later,26 the interactive role playing raises the children’s self-care efficacy. They talk more freely about their diabetes and their feelings about it. They adopt dietary and insulin practices to keep their blood sugar level under control. They reduce urgent doctor visits for diabetes emergencies by 77%. Control children who played a video game unrelated to health decreased their self-care and increased emergency doctor visits by 7%. Communication 100 80 Percent Change

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Figure 11: Changes exhibited in a 6-month follow-up in perceived self-efficacy to manage different aspects of diabetes, child-initiated discussions about diabetes, level of diabetes self-care, and number of emergency doctor visits by children who had the benefit of the role-playing video game and diabetic control children who played other entertainment video games26

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Asthmatic children learn how to manage their condition by helping an asthmatic dinosaur named Bronchiasaurus stay strong and healthy while on a risky mission in an environment riddled with allergens. In the interactive game, children learn how to avoid asthma triggers, to keep the air free of respiratory irritants, to track peak flow, and to take medication. The video game improves knowledge about asthma, enhances perceived efficacy to avoid things that trigger asthma attacks, and improves use of emergency medications.27 Children with cystic fibrosis are taught how to deal with their lung problem by using medications and physical therapy to keep the lungs of a virtual puppy clear. Another interactive video game discourages children from smoking promoted by the Blackburn Tobacco Company. A daring surgeon enters the body in microscopic size with lasers to repair the damage done by smoking to save the smoker’s life. He clears phlegm from the bronchial tubes, removes tar deposits and precancerous cells from the throat and lungs, removes plaque and a deadly blood clot in the arteries, and enters the brain to conquer nicotine addition. The children become experts in the harmful effects of smoking. They lose any appetite for it. These health-promoting videos are being widely distributed to families by pediatricians. This is but the beginning in the creative use of the interactive video technology to promote childhood health.

Childhood Health Promotion Models Many of the lifelong habits that jeopardize health are formed during childhood and adolescence. For example, unless youngsters take up the smoking habit in their teens, they rarely become smokers in adulthood. It is easier to prevent detrimental health habits than to try to change them after they become deeply entrenched as part of a lifestyle. Prevention should be given priority but rarely is. Health habits are rooted in familial practices. But schools have an important role to play in promoting the health of a nation. This is the only place where all children can be easily reached. It is a natural setting for promoting healthful eating and exercise habits, discouraging smoking and other types of substance abuse, and building generic self-management skills. An effective preventive program includes four major components. The first component is informational. It informs children of the health risks and benefits of different lifestyle habits. The second component develops the social and self-management skills for translating informed concerns into effective preventive practices. The third component builds a resilient sense of efficacy to support the exercise of control in the face of difficulties and setbacks that inevitably arise. The final component enlists and creates social supports for desired personal changes. Educational efforts to promote the health of youths usually produce weak results. They provide factual information about health. But they usually do little

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to equip children with the skills and efficacy beliefs that enable them to manage the emotional and social pressures to adopt detrimental health habits. Managing health habits involves managing social relationships, not just targeting a specific health behavior for change. Health promotion programs that include the essential elements of the self-management mastery model prevent or reduce injurious health habits. Health knowledge can be conveyed readily, but changes in values, self-efficacy, and health habits require greater effort. The more behavioral mastery experiences provided, the greater the beneficial effect.28 The more intensive the program, and the better the implementation, the stronger the impact.29 Comprehensive approaches that integrate guided mastery health programs with family and community efforts are more successful in promoting health and in preventing detrimental habits than are programs in which the schools try to do it alone.30 Schools are inadequately equipped with the resources, training, and incentives to undertake health promotion and early modification of habits that jeopardize health. As in other social systems, schools focus on areas in which they are evaluated. They are not graded for health promotion. When preventive programs are grudgingly allowed in schools, they try to do too much, with too little, in too short a time, with fitful quality of implementations to achieve much. Such efforts often do more to discredit psychosocial approaches through deficient implementation than to advance the health of youths. Health promotion must be structured as a part of a societal commitment that makes the health of its youth a matter of high priority. A serious commitment must provide the personnel, incentives, resources, and the operational control needed to do the job well. The programs should be in the school, but not of the school. New school-based models of health promotion should operate together with the home, the community, and the society at large. Schools’ health-related practices need changing as well. Schools that are provided with a brief health promotion curriculum and encouraged to lower the fat content of their lunch offerings and enhance their physical activity offerings produce lasting improvements in children’s eating and exercise habits.31 It is the height of irony to strive to promote healthful habits in schoolchildren while schools promote in their lunch program fast foods and house vending machines that dispense sodas and candy in return for substantial payments to schools by commercial enterprises.

Self-Management of Chronic Diseases The weight of disease is shifting from acute to chronic maladies. The selfmanagement of chronic diseases is another example of the use of self-regulatory and self-efficacy theory to develop cost-effective models with high social

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utility. Biomedical approaches are ill-suited for chronic diseases because they are devised mainly for acute illness. The treatment of chronic disease must focus on self-management of physical conditions over time. Holman and Lorig32 devised a generic self-management program in which patients are taught pain control techniques, self-relaxation, and proximal goal setting combined with self-incentives as motivators to increase level of activity. Participants are also taught problem-solving self-diagnostic skills and how to take greater initiative for their health care in dealings with health personnel. These skills are developed through modeling of self-management skills, guided mastery practices, and informative feedback. In the self-management of arthritis, the program is implemented in groups in community settings by leaders who lead active lives despite their arthritis (Figure 12). A 4-year follow-up with arthritic patients reveals that it retards the biological progression of diseases, raises perceived efficacy, reduces pain, markedly decreases the use of medical services by 43%, and improves the quality of life. Both the baseline efficacy beliefs and the efficacy beliefs instilled by the self-management program predict the health benefits 4 years later.

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Figure 12: Enduring healthful changes achieved by training in self-management of arthritis as revealed in a follow-up assessment 4 years later

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The self-management approach provides a generic model that can be adapted with supplementary components to different chronic diseases. Indeed, the self-management program produces similar health benefits for people suffering from other types of chronic diseases, such as heart disease, lung disease, stroke, and arthritis.33

Socially Oriented Approaches to Health The field of health has been plagued by a contentious dualism. It gets politicized in battles between individualist approaches and structuralist approaches to health. The individualist proponents argue that people can exercise a good deal of control over their health. So it is their responsibility to maintain it. The structuralist proponents argue that health is largely the product of social, environmental, political, and economic conditions, over which individuals have little control. In actuality, health promotion needs both approaches, not contentious debates. The quality of health of a nation is a social matter, not just a personal one. It requires changing the practices of social systems that impair health rather than just changing the habits of individuals. We do not lack sound policy prescriptions in the field of health. What is lacking is the collective efficacy to realize them. The main focus of a social approach is on collective enablement for changing social, political, and environmental conditions that affect health.4 Socially oriented approaches seek to raise public awareness of health hazards, to educate and influence policy makers, to build community capacity to change health policies and practices, and to mobilize the collective citizen action needed to override vested political and economic interests that benefit from existing unhealthful practices. Social cognitive theory extends the conception of human agency to collective agency.34,35 People do not operate as isolates. They work together to improve the quality of their lives. Their shared beliefs in their collective efficacy to accomplish social change play a key role in the policy and public health approaches to health promotion and disease prevention. For example, cigarette smoking is the most personally preventable cause of death. People got smoke-free workplaces, restaurants, public buildings, and airliners through their own collective action, not through the governmental agencies with the responsibility to protect national health. Lobbyists get legislators to block tobacco regulation (Figure 13). The more tobacco money the legislators get, the more dutifully they vote against tobacco regulation. The political impediments to legislative initiatives take the form of the obstructive triad – defeat, defang, and deregulate. The obstructive strategy is to defeat legislative initiatives, preferably in congressional committees, to spare legislators public votes that may be unpopular with their constituents. Laws provide the general guidelines. Congressional staff must convert them

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Figure 13: Relationship between the amount of campaign money legislators receive from the tobacco industry and their likelihood of voting against legislation to regulate tobacco products

into operational regulations. If you cannot defeat the legislation, defang it by translating the law into regulations that circumvent the intent of the legislation. If you cannot defang it, deregulate the regulators to undermine the monitoring and implementation of the legislation. With industry lobbyists and legislators erecting protective barriers, the social battles over health shift increasingly to grassroots initiatives at local levels.

Enablement for Community Self-Help While collective efforts are made to change unhealthful social practices, people need to improve their current life circumstances over which they have some control. We need to devote more attention to psychosocial models on how best to enable people to work together to improve their health at local levels. The approaches that work best promote community self-help. But people need to be given the necessary resources and enabling guidance to help themselves. Otherwise, simply to tell people with intractable problems to fend for themselves is an evasion of societal responsibility. Unsupported prescription of local self-help can be easily used as a political subterfuge for civic neglect. A community effort to reduce infant mortality resulting from unsanitary conditions in poor Latino neighborhoods provides one example of effective collective enablement.36 The community was fully informed of the impact of unsanitary conditions on children’s health through the local media, churches, schools, and

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neighborhood meetings conducted by influential persons in the community. The residents were taught how to install plumbing systems, sanitary sewerage facilities, and refuse storage. They were also taught how to secure the financing needed from different local and governmental sources. This enabling self-help program greatly improved sanitation and markedly reduced infant mortality.

Components of Psychosocial Models for Social Change There are three major components in the social cognitive theory for promoting psychosocial changes society-wide.16,18 The first component is a sound theoretical model that specifies the determinants of psychosocial change and the mechanisms through which they produce their effects. This knowledge provides the guiding principles. The second component is a translational and implementational model that converts theoretical principles into an innovative operational model by specifying the content, strategies of change, and their mode of implementation. The third component is a social diffusion model on how to promote adoption of psychosocial programs in diverse cultural milieus. It does so by making functional adaptations of the programs to different sociostructural circumstances, providing enabling guidance, and enlisting the necessary resources to achieve success. We construct theories and clarify how they work. But we do not profit from our successes because we fail to develop effective translational and social diffusion models. If we are to contribute significantly to the betterment of human health, we must broaden our perspective on health promotion and disease prevention beyond the individual level. This calls for a more ambitious socially oriented agenda of research and practice. We can further amplify our impact on human health by making creative use of evolving interactive technologies that expand the scope and impact of health promotion efforts. But this is another story. And I have come to the end of this one. As you venture forth to promote your own health and that of others, may the efficacy force be with you.

Note A major portion of this article was presented as the Healthtrac Foundation Lecture at the convention of the Society for Public Health Education in Philadelphia, November 9, 2002.

References 1. Nordin I: The limits of medical practice. Theor Med Bioeth 20:105–123, 1999. 2. Fries JF, Crapo LM: Vitality and Aging: Implications of the Rectangular Curve. San Francisco, Freeman, 1981. 3. Fuchs V: Who Shall Live? Health Economics and Social Choice. New York, Basic Books, 1974.

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4. Bandura A: Self-Efficacy: The Exercise of Control. New York, Freeman, 1997. 5. Bandura A: Psychological aspects of prognostic judgments, in Evans RW, Baskin DS, Yatsu FM (eds.): Prognosis of Neurological Disorders (2nd ed.). New York, Oxford University Press, 2000, pp. 11–27. 6. Dzewaltowski DA, Noble JM, Shaw JM: Physical activity participation: Social cognitive theory versus the theories of reasoned action and planned behavior. J Sport Exerc Psychol 12:388– 405, 1990. 7. Meyerowitz BE, Chaiken S: The effect of message framing on breast self-examination attitudes, intentions, and behavior. J Pers Soc Psychol 52:500–510, 1987. 8. Rimal RN: Closing the knowledge-behavior gap in health promotion: The mediating role of self-efficacy. Health Commun 12:219–237, 2000. 9. Rimal RN: Perceived risk and self-efficacy as motivators: Understanding individuals’ long-term use of health information. J Communic 8:633–654, 2001. 10. Maibach E, Flora J, Nass C: Changes in self-efficacy and health behavior in response to a minimal contact community health campaign. Health Commun 3:1–15, 1991. 11. Carey MP, Kalra DL, Carey KB, Halperin S, Richards CS: Stress and unaided smoking cessation: A prospective investigation. J Consult Clin Psychol 61:831–38, 1993. 12. Robins LN: The Vietnam drug user returns. Special Action Office Monograph. Ser. A, No. 2. Washington, DC, Government Printing Office, 1974. 13. Vaillant GE: The Natural History of Alcoholism Revisited. Cambridge, MA, Harvard University Press, 1995. 14. Granfield R, Cloud W: The elephant that no one sees: Natural recovery among middleclass addicts. J Drug Iss 26:45–61, 1996. 15. Bandura A: Social cognitive theory in cultural context. J Appl Psychol 51:269–290, 2002. 16. Bandura A: Social cognitive theory of mass communications, in Bryant J, Zillman D (eds.): Media Effects: Advances in Theory and Research (2nd ed.). Hillsdale, NJ, Lawrence Erlbaum, 2001, pp. 121–153. 17. Taylor CB, Winzelberg A, Celio A: Use of interactive media to prevent eating disorders, in Striegel-Moor R, Smolak L (eds.): Eating Disorders: New Direction for Research and Practice. Washington, DC, American Psychological Association, 2001, pp. 255–270. 18. Bandura A: Environmental sustainability by sociocognitive deceleration of population growth, in Schmuck P, Schultz W (eds.): The Psychology of Sustainable Development. Dordrecht, the Netherlands, Kluwer, 2002, pp. 209–238. 19. Rogers EM, Vaughan PW, Swalehe RMA, Rao N, Svenkerud P, Sood S: Effects of an entertainment-education radio soap opera on family planning behavior in Tanzania. Stud Fam Plann 30:1193–1211, 1999. 20. Vaughan PW, Rogers EM, Swalehe RMA: The Effects of “ Twende Na Wakati,” an Entertainment-Education Radio Soap Opera for Family Planning and HIV/AIDS Prevention in Tanzania. Unpublished manuscript, University of New Mexico, Albuquerque, 1995. 21. Vaughan PW, Rogers EM, Singhal A, Swalehe RM: Entertainment-education and HIV/ AIDS prevention: A field experiment in Tanzania. J Health Communic 5:81–100, 2000. 22. DeBusk RF, Miller NH, Superko HR, Dennis CA, Thomas RJ, Lew HT, Berger WE III, Heller RS, Rompf J, Gee D, Kraemer HC, Bandura A, Ghandour G, Clark M, Shah RV, Fisher L, Taylor CB: A case-management system for coronary risk factor modification. Ann Intern Med 120:721–729, 1994. 23. West JA, Bandura A, Clark E, Miller NH, Ahn D, Greenwald G, DeBusk RF: Self-Efficacy Predicts Adherence to Dietary Sodium Limitation in Patients With Heart Failure. Unpublished manuscript, Stanford University, Stanford, CA, 1999. 24. Haskell WL, Alderman EL, Fair JM, Maron DJ, Mackey SF, Superko HR, Williams PT, Johnstone IM, Champagne MA, Krauss RM, Farquhar JW: Effects of intensive multiple

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risk factor reduction on coronary atherosclerosis and clinical cardiac events in men and women with coronary artery disease. Circulation 89:975–990, 1994. Lieberman DA, Brown SJ: Designing interactive video games for children’s health education, in Morgan K, Satava RM, Sieburg HB, Mattheus R, Christensen JP (eds.): Interactive Technology and the New Paradigm for Healthcare. Amsterdam, IOS Press and Ohmsha, 1995, pp. 201–210. Brown SJ, Lieberman DA, Gemeny BA, Fan YC, Wilson DM, Pasta DJ: Educational video game for juvenile diabetes care: Results of a controlled trial. Med Inform 22:77–89, 1997. Lieberman DA: Interactive video games for health promotion: Effects on knowledge, self-efficacy, social support, and health, in Street RL, Gold WR, Manning T (eds.): Health Promotion and Interactive Technology: Theoretical Applications and Future Directions. Hillsdale, NJ, Lawrence Erlbaum, 1997, pp. 103–120. Bruvold WH: A meta-analysis of adolescent smoking prevention programs. Am J Public Health 83:872–880, 1993. Connell DB, Turner RR, Mason EF: Summary of findings of the school health education evaluation: Health promotion effectiveness, implementation, and costs. J School Health 55:316–321, 1985. Perry CL, Kelder SH, Murray DM, Klepp K: Communitywide smoking prevention: Long-term outcomes of the Minnesota heart health program and the class of 1989 study. Am J Publ Health 82:1210–1216, 1992. Luepker RV, Perry CL, McKinlay SM, Nader PR, Parcel GS, Stone EJ, Webber LS, Elder JP, Feldman HA, Johnson CC, Kelder SH, Wu M: Outcomes of a field trial to improve children’s dietary patterns and physical activity: The child and adolescent trial for cardiovascular health (CATCH). JAMA 275:768–776, 1996. Holman H, Lorig K: Perceived self-efficacy in self-management of chronic disease, in Schwarzer R (ed.): Self-Efficacy: Thought Control of Action. Washington, DC, Hemisphere, 1992, pp. 305–323. Lorig K, Sobel DS, Stewart AL, Brown BW, Bandura A, Ritter P, Gonzalez VM, Laurent DD, Holman HR: Evidence suggesting that a chronic disease self-management program can improve health status while reducing hospitalization: A randomized trial. Med Care 37:5–14, 1999. Bandura A: Exercise of human agency through collective efficacy. Curr Dir Psychol Sci 9:75–78, 2000. Bandura A: Social cognitive theory: An agentic perspective. Annu Rev Psychol 52:1–26. Palo Alto, CA, Annual Reviews Inc., 2001. McAlister AL, Puska P, Orlandi M, Bye LL, Zbylot P: Behaviour modification: Principles and illustrations, in Holland WW, Detels R, Knox EG (eds.): Oxford Textbook of Public Health (2nd ed.), Vol. 3. Applications in Public Health . Oxford, UK, Oxford University Press, 1991, pp. 3–16. Lorig K: Self-Efficacy: Its Contributions to the Four Year Beneficial Outcome of the Arthritis Self-Management Course. Paper presented at the meeting of the Society for Behavioral Medicine, Chicago, April 1990. Public Citizen Health Research Group: Health Letter 9(11), 1993.

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30 Models of the Learner Jerome Bruner

T

opics, including the topics of keynote addresses to learned societies, have a hermeneutic history. The hermeneutic history of a topic, we are cautioned, must be taken into account if we are fully to interpret its meaning. The topic of my paper, Models of the Learner, is no exception. It has such a history and has a proximal origin in a set of exchanges – first as a conversation and then as the topic of a more formal learned discussion. Let me set forth the beginning narrative of that hermeneutic circle (or spiral) and continue it in the discussion that follows. The setting was an international conference in the not very Orwellian summer of 1984, a conference ostensibly on the vexed subject of how to improve the quality of education. Sponsored jointly by the Van Leer Jerusalem Foundation and the Aspen Institute, it took place in a handsome mansion overlooking one of the scenic lakes on the outskirts of Berlin – a mansion that had been reconstructed on the ruins of the residence of the infamous Goebbels, Hitler’s Minister of Culture, or was he the Minister of Propaganda? The participants were appropriately distinguished: some Deans of famous faculties of education, more than a sprinkling of great names in what everybody would agree is educational research, and a handful of psychologists and associated behavioral scientists whose work bore that tangential relation to the process of education that excites the optimism of educators with respect to the relevance of “pure” research. We were perhaps two dozen in number, and it was a convivial company. After a day and a half of discussion on topics of great generality, all conducted at a level of striking knowledgeability, someone proposed that we

Source: Educational Researcher, 14 (1985): 5–8.

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could really not get to the heart of the matter unless we had more clearly in mind some working model of what a learner was, how he or she operated, and above all, what we thought to be an adequate learning environment for our putative learner. It was proposed to the plenary session that we give over the next morning to these issues. I was among those asked to prepare some sort of statement on the matter. The discussion that ensued was lively. What it left behind in my mind and what several of us discussed later was the flat-footed impossibility of ever settling institutional questions of education without first making a decision – yes, a political decision – on the nature of learning and learners. Yet for all that, the decision about learning and learners was perforce a decision about an ideal, about how we conceived what a learner should be in order to assure that a society of a particularly valued kind could be safeguarded. There is no completely naturalistic way of resolving the question about what model of the learner we want to enshrine at the center of our practice of education. For there are many ways to learn and many ways of encouraging different forms of learning with different ends in view. At the heart of the decision process there must be a value judgment about how the mind should be cultivated and to what end. While I wish to consider alternate models of the learner, I have no illusion that I can do so just in the spirit of a naturalist or as a student of the learning process. In fact, models of the learner that are on offer in the psychological literature, in the cognitive sciences, or in AI are themselves constructions based on a selection not simply of data, but of the conditions under which learning is studied. As I tried to say a few years ago, it is possible to construct not only experimental studies but “real life” situations that make people (or pigeons, for that matter) look stupid or clever, generative or passive, combinatorial or rote (Bruner, 1982). Then the theoretical model that is constructed becomes, as it were, the text of the culture, and life is made to imitate text in the same subtle ways in which, in another closely related domain, life imitates art. Please do not interpret what I am saying in the relativistic sense that all theories or models of the learner are equally true or even equally right. Rather, what I wish to say is that any model of learning is right or wrong for a given set of stipulated conditions, including the nature of the tasks one has in mind, the form of the intention one creates in the learner, the generality or specificity of the learning to be accomplished, and the semiotics of the learning situation itself – what it means to the learner. This is not to say that a new or different model of the learner is needed for every task or situation in which learning takes place. To put it in the current jargon, it is absurd to insist that each and every theory of learning is utterly domain specific, that nothing general can be said about learners or learning or learning environments. You do not quite need a different model of a learner to talk about learning how to play chess, learning how to play the flute, learning mathematics, and learning to read the sprung rhymes in the verse of Gerard

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Manley Hopkins. Even if I do have to say it in folk psychology rather than in programming talk, all of them will involve attention and memory and courage and even, pace AI, some heuristics for maintaining frustration tolerance. The issue, as we shall see yet again, is that learning is indeed context sensitive, but that human beings, given their peculiarly human competence, are capable of adapting their approach to the demands of different contexts. But I am tipping my hand, for it is only later that I wanted to talk about a general model of a learner as one equipped to discriminate and deal differentially with a wide variety of possible worlds exhibiting different conditions, yet worlds in which one can cope. Let me now take a fast gallop through the landscape we surveyed that day from the phoenix nest on the site of Goebbels’ house in the exurbs of Berlin when we got down to our formal discussion of “models of the learner.”

Models of the Learner Tabula rasa. The first, and perhaps the most ancient is really based on the Aristotelian notion of mimesis. In its 18th century version, it rested on the premise that experience writes on the wax tablet of the mind. One learns from experience (rather than through divine revelation or through received texts). Or as Locke put it, nothing gets into the mind save through the senses – but as Leibniz countered, nothing except mind itself. This view takes as a central premise that such order as there is in mind is a reflection of the order that exists in the world, and that is why the concept of association is always so central to empiricist theories. Things that are together in space and time in the world succeed, under the sway of this principle, in being together in the mind. I need not go into the troubles of empiricism. They have been raked over historically by everybody from Aristotle (whose sensus communis was something of a constructivist takeover bid) through the Schoolmen, from Kant through Wittgenstein and Chomsky. I want, rather, to take it as a given, a cultural text in Geertz’s sense, to be examined for its cultural significance in shaping our practices. I want to note only that, given belief in associationist empiricism, we adopted ideas about learning procedures to fit and constructed learning environments that in fact made people look like little empiricists–averting our eyes from all instances where it didn’t, as for example in the acquiring of a language. And when we were forced to look at that, we concocted Augustinian theories about it and devised nonsense syllable research in support of them. The formula for success in empiricism is to have experience. Hypothesis generator. There is a class of learner models that represents a reaction against the rather passive view of empiricist, tabula rasa notions. They have in common a notion of intentionality at their center. The learner, rather than being the creature of experience, selects that which is to enter.

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The principle of selection varies from theory to theory: from the sensus communis of Aristotle and the vis integretiva of Aquinas that sorted the associated input of experience in the light of the principles of reason, to the principles of wish-fulfillment and ego defense of Freud that permitted us to experience (or interpret) only those parts of experience that were adequate compromises between the demands of conflicting needs. What exactly generates hypotheses or programs the filter, which selects and organizes what gets through the senses into the mind, varied widely and was always seriously underdefined. Even such towering learning theorists as Edward Tolman, Lev Vygotsky, and John Dewey, all of whom took the view that experience came shaped by hypotheses rather than by the world, were grandly vague in their specification of how hypotheses came into being – though Dewey and Vygotsky gave special pride of place to the role of language as a hypothesis-generator, a place that promised more than it delivered. It was never altogether clear how to extrapolate an educational posture from hypothesis theories, save in one respect. Emphasis was on an active curiosity guided by self-directed projects – a feature of Progressivism in America and in the unrealized pedagogy of Vygotsky’s followers in the Soviet Union, unrealized save in the discipline of “defectology.” The formula for success in learning, according to the hypothesis formulation, is to have a good theory. Nativism. At least three forms of muted nativism have shaped our models of the learner. One derives from Immanuel Kant. A second comes from Gestalt theory. The third, derivative of Descartes, is still with us in Chomsky’s theory of mind. In a deep historical sense, they are all inheritors of the tradition of Platonism. All share one central concept: Mind is inherently or innately shaped by a set of underlying categories, hypotheses, forms of organizing experience. The task of the learner is to work his or her way through the cluttered surface structure of sense to an underlying or ideal or deep organization that provides a richer or righter or more predictive or more generalizable representation of reality. Where evolutionism entered this view (as with ethnologists and, in a shriller form, in sociobiology) it is assumed that the fit between the categories or hypotheses of mind and the world that they represent is a product of natural selection. For all their disagreements on details, Nativist theories have one big thing in common: The opportunity to use and exercise the innate powers of mind is all. That is the formula for success as well. Constructivism. Probably the most powerful expression of this view comes from Jean Piaget, although a more rigorous and considered expression of it can be found in the writings of the philosopher Nelson Goodman. The tenet of Piaget’s constructivism is that the world is not found, but made, and made according to a set of structural rules that are imposed on the flow of experience. By structural rules it is intended to emphasize that knowledge is not local but derived from a structure of the whole – that local operations reflect universal

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operations of the system as a whole. Learning is bound within the limits of the rules of the system; it consists of realizations of the general rules in application to particulars. Development consists of a series of stage-like progressions, stage change consisting of a change in the rules of the system and later rule systems absorbing earlier ones as special cases. The learning dynamic of the system at any stage is provided by an unstable equilibrium or dialectic between assimilating experience to the rules and accommodating the rules to experience. When the equilibrium becomes unstable enough, the structure changes. The constructivist model of the learner places strong emphasis on selfpropelled operations on the world as the way to mastery – a pretty wideband conception. Its formula for success is that nothing succeeds like a theoretical system, and one succeeds supremely only by going to a higher system that subsumes it. Novice-to-Expert. This view of the learner has so recently emerged that is hard to characterize. It is very practical, in some respects highly anti-theoretical. It operates within domains, almost at times denying the utility of a general theory – or perhaps that is a sign of its immaturity. It begins with the premise that if you want to find out about learning, ask first about what is to be learned, find an expert who does it well, and then look at the novice and figure out how he or she can get there. To aid in this task, simulate the novice’s performance and the expert’s in a computer program, and see what transformations and heuristics will get you from the one to the other. You may even be helped by studying and simulating some typical mid-stages. Such generality as may be present in learning different tasks will eventually show up in the simulations. The immediate challenge is to get the novice to be an expert as quickly and as painlessly as possible, and never mind high theory. The formula for success is “be specific and be explicit.” Or, a computer programmer is a better friend than a philospher of mind. Or, it is more important to get through the keyhole than to see the sky. Or, and perhaps more seriously, subordinate the learner to the steps he must take to attain expertise. In sketching these views about the model of the learner, I have omitted an important issue, one that had better be treated independently of each, for it is curiously extrinsic to all of them. It is the issue of the carrot and the stick – the role of motivation in learning. It has been a source of embarrassment in the history of the subject from the Stoics to Skinner. Let me state its dilemma in the starkest way. How can knowing something be affected by whether the knowledge gained leads to reward or to punishment? If the theory of reinforcement related to the acquisition of knowledge, God would not have had to expel poor Adam and Eve from the garden for eating of the tree of knowledge. He would have arranged, Huck Finn style, for them to have developed a very bad stomach ache from the consumption of green apples. Instead, He knew that knowledge, once attained, is irreversible and for better or for worse. And so, if I may be Miltonian, he had to condemn them to a new way of life where that knowledge could be put to use.

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It is the use of knowledge rather than knowledge itself that is affected by the nature of its consequences. Use implies performance; performance entails action. The carrot and the stick are instruments for affecting action, not thought. Thus the degree to which models of the learner feature reinforcement is the degree to which they concentrate on the behavior of the learner rather than on his or her mind. It is not surprising, then, that even in the heyday of the Empiricists (who thought of themselves as philosophers of mind) virtually nothing was said about carrots and sticks. Indeed, as Crane Brinton reminded us a generation ago in his classic Anatomy of Revolution, the precepts of Empiricism (particularly in John Locke) were designed to justify man’s freedom from the authority of King and Clergy. He was, in this new dispensation, his own knowledge getter. Thus Jonathan Edwards could preach to his flock in Northampton on the frontier of the Massachusetts Bay Colony in the late 17th century that they too, like Isaac Newton, could by their own efforts of mind unlock the secrets of God. It is interesting, then, that most theories depicted the learner, either implicitly or explicitly, as self-motivated – at least while they concentrated on learning as a means of acquiring knowledge. Indeed, we can say that the carrot and the stick – reinforcement – have to do not with learning but with morality: how one acts on the basis of what one knows. Even then, the connection between reward and punishment on the one side and virtuous action on the other remains as obscure as ever. The debate over the effectiveness of, say, prisons rages as incoherently as ever. And the thought controls imposed by dictators are much more concerned with censorship and other means of stopping the flow of information than they are with tinkering with schedules of reinforcement.

A Model of Models I have already tipped my hand, as I confessed in passing. There is no reason, save ideology and the exercise of political control, to opt for a single model of the learner. We do learn from experience, when that is all we have to go on. On occasion we act like induction machines, though it is rarely so dark out that we can’t do better than that. Indeed, given half a chance, we generate hypotheses that take us way beyond the information given – often with good effect, and always with some risk, which requires courage and the buffering of a support system. There is every reason to believe that a nervous system evolved in nature and more latterly and swiftly in culture endows us with a set of useful presuppositions about both nature and culture. How else can we account for the swift mastery of language and other symbolic forms to which we take so easily and with insufficient knowledge for proper induction? How can we doubt that a culture that regulates its moods and acts according to such abstract inventions as interest rates, social slights,

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gross national products, and loyalty to Alma Mater is made up of people who not only construct the world in which they live but share it as an ontological given? It is even true that if you want to be a postman or a trust officer, you would do well to look closely at how they go about their business and then try to simulate them as a clever clone, hopefully keeping your tongue in your cheek and your powder dry the while. What it amounts to, as I have already hinted, is treating all models of the learner as stipulative, and then inquiring into the conditions under which they might be effective or useful or comforting. If you genuinely believe that it improves a nation’s confidence in its control over things to keep children in schools for a good part of the day, then do so. Or if you think formal schooling is structurally inevitable in a society with more disensus than consensus, again keep them in school. These are reasons of politics, and they plainly have a place in any debate, for education is political too. But if you see children learning mathematics by rote, you can also say (this time on more naturalistic yet practical grounds) that somebody got confused about models and slipped in an empiricist one in place of a constructionist one. In a word, the best approach to models of the learner is a reflective one that permits you to “go meta,” to inquire whether the script being imposed on the learner is there for the reason that was intended or for some other reason. There is not one kind of learning. It was the vanity of a preceding generation to think that the battle over learning theories would eventuate in one winning over all the others. Any learner has a host of learning strategies at command. The salvation is in learning how to go about learning before getting irreversibly beyond the point of no return. We would do well to equip learners with a menu of their possibilities and, in the course of their education, to arm them with procedures and sensibilities that would make it possible for them to use the menu wisely. Here the hermeneutic circle ends. You cannot improve the state of education without a model of the learner. Yet the model of the learner is not fixed but various. A choice of one reflects many political, practical, and cultural issues. Perhaps the best choice is not a choice of one, but an appreciation of the variety that is possible. The appreciation of that variety is what makes the practice of education something more than a scripted exercise in cultural rigidity.

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31 Child’s Talk: Learning to Use Language Jerome Bruner

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his is a short but fascinating book which sums up Jerome Bruner’s work on children’s language over a ten-year period. As anyone who has read any of his earlier work would expect, there is a major emphasis in the book on the child’s ability to USE the linguistic structures he acquires to communicate needs, wishes, and intentions, and to ‘conduct joint action with another’ (p. 7). Learning to talk is not simply learning the words, or the syntax, but learning how to do things with them. It follows, for Bruner, that an exclusive focus on grammatical structure is unlikely to be fruitful in explaining the mystery of language development in the child, because the functions which the linguistic forms serve are ignored. In his preface Bruner is explicit about his lack of interest in the earliest phase of developmental linguistics, in the 1960s, when the field was dominated by the study of syntax. It was his move to Oxford as Watts Professor of Experimental Psychology in 1972, to an intellectual climate sympathetic to his views on language, which stimulated a research programme which forms the basis for his book. Bruner and his collaborators recorded conversations between young children and their mothers in their homes, and used these data to try to answer the question of how children are assisted by their linguistic community to develop the language they use. The claim put forward in the introduction to the book is that a crucial factor in the child’s acquisition is the ‘transactional format’: [Language acquisition] begins when the mother and infant create a predictable format of interaction that can serve as a microcosm for communicating and for constituting a shared reality. The transactions that occur in Source: Child Language Teaching and Therapy, 1 (1985): 111–114.

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such formats constitute the input from which the child then masters grammar, how to refer and mean, and how to realize his intentions communicatively (p. 18).

The formats are a principal feature of what Bruner calls the Language Acquisition Support System, a framework for interaction which inducts the child into the language to be learned via its appropriate uses. In the first part of the book Bruner examines some early formats which he claims ‘provide a type case for the framing of early communication’. These forms are games like peekaboo, hide and seek, and object exchange, and they each provide a highly ritualized setting in which joint action by mother and child takes place, regulated by (relatively restricted though variable) language. In examining these formats in his longitudinal studies, Bruner was able to document the child’s developing mastery of roles, and of the language of transactions in the game. He also noted that children become able to generalize the game formats to contexts in which they had not occurred before. Bruner moves on from these early game routines to discuss the growth of reference, which, it is again argued, develops within strictly constrained routines or formats. One of these is the ‘reading format’ (studied by Bruner and Anat Ninio) in which mother and child are looking at picture books together. It turns out that in this format there are a restricted range of utterance types by the mother, and that their order in the discourse is stable and predictable. The mother’s utterances direct the child’s attention towards easily identifiable objects, ask questions about them, label them, and provide feedback for the child’s attempts at labelling. Again a context shared between mother and child, in which the child can gradually become familiar with a restricted range of linguistic possibilities, serves as the framework within which a crucial aspect of language is learned. The linguistic possibilities cannot be TOO restricted, however, otherwise change cannot take place. It is in addressing this issue that Bruner uses the metaphor (familiar to poker players) of ‘raising the ante’. Within a familiar framework the mother will be stricter about what she will accept as appropriate behaviour as the child’s linguistic ability improves. Bruner gives as an example a mother’s changing response to her child’s vocalisations in the reading game, once he had started to use his first words. She responded by treating him as someone who was linguistically more capable, and required him to produce identifiable lexical item in his responses, and would not accept babbles that she could not understand. The child had to repeat or modify his utterance. ‘She became much firmer in her demands’ (p. 83). The demands that are made are however fine-tuned to the child’s current capacities. This maternal raising of the ante is seen, then, as an adaptation of these shared contexts to the child’s developing linguistic abilities, which is presumably crucial to their constructive role in development.

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The third important topic in the book is that of procedures of request – invitations, requests for objects, and requests for assistance in action. Once again early development is found to depend on the scaffolding provided by familiar formats. The ‘specification’ for requests in English is made clear for the child by his participation in routines structured for him by the adult community. The book presents a well-worked-out and coherent view of the Language Acquisition Support System, not only as a necessary framework for the child learning to talk, but also as a way for adults to induct the child into the culture which he is entering. Readers of this journal, particularly those concerned with very young children, or with older children who for some reason are just starting to talk, will find it thought-provoking and challenging. It is not a practical book, in any sense, but in its attempt to face head-on some of the most central and difficult questions about language development, from the perspective of the child as a language user in a cultural setting, it cannot fail to be instructive. There are however caveats to be entered, which concern the extent to which the notion of format can be generalized. First, the data which are carefully considered in the book are from children from middle-class homes. One is aware of other modes of acculturation, in different homes, which could well not involve some or many of the formats which emerge in Bruner’s data. For the theory to hold, it would be necessary to identify, in these alternative environments, similar routines which did the work of peekaboo, reading picture books, etc. More seriously, if the substitute formats were not available, we would expect that the functions they underpin would simply not develop. The second problem of generalization is with respect to language development later than that addressed by Bruner. He largely restricts himself to the first 18 months of life, and so it might be regarded as unreasonable to raise issues that concern children older than this. But once we start to think about language development even up to the age of three, it is a little unclear what the explanatory role of formats is, perhaps because as the child’s linguistic abilities improve dramatically, it seems likely that identifying and isolating formats will prove a problem. There is between two-and-a-half and three years of age a fairly dramatic influx of various kinds of verb modification into the English-speaking child’s language. The modals can and will, for example, are likely to make their appearance, most often in sentence-initial position in utterances which are requests of various kinds. Now if it is possible to identify a ‘steady format’ (p. 127) for requesting, that persists up to the age of three (and one would need convincing that procedures could be specified for format identification), the weight of explanation for the changes in the child’s request language (from pointing, to single word, to multi-item declarative, to can-initial structure) has to fall somewhere else – perhaps on the mother’s linguistic fine-tuning within the steady format and not on the

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format at all. And if we consider other verb-forms, like the tense markers which typically appear at the same time, what is/are the format/s which assist the child to their appropriate use? (Bruner in fact suggests right at the end of the book that children may ‘develop primitive concepts of aspectual time’ (p. 134) from the sequential structure that formats have. But this is an aside which is not particularly illuminating.) The reader will find that the book stimulates thought on these and other issues central to language acquisition studies. The book is well produced and has been carefully copy-edited. A paperback version is available.

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32 The Reflexivity of Cognitive Science: The Scientist as Model of Human Nature Jamie Cohen-Cole

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n 1963, Bernard Berelson edited a collection of essays by leading scholars in the behavioral sciences. The essays found their origin in a series of radio broadcasts for Voice of America, the radio-based propaganda arm of the United States Information Agency. These programs aimed both to cover the immediate topic at hand and to carry out the general mandate of the Voice of America – showing the virtues of the American way to people around the world (on Voice of America see Heil, 2003; Krugler, 2000; Shulman, 1990). To accomplish this dual aim, the programs’ more specific goal was to explain how the various behavioral sciences operated. Their broader goal – unstated but very real – was to demonstrate the connection between American democracy and the objective and scientific study of society. At the conclusion of his address on psycholinguistics, George Miller remarked: ‘the scientist is Everyman, looking just as you and I. We go and look for the things we want, and when we find them we find part of ourselves’ (1963: 150). These comments raise several issues worth close attention. First, Miller collapsed the distinction between the scientist and ‘Everyman’. From this perspective, the psychology of the scientist is the same as the psychology of the human subject. Second, the salient feature of human nature (or of the scientific process) is the process of searching. To Miller, searching provides both knowledge of the world and knowledge of oneself. Clearly, Miller’s image of objectivity was not one that required the scientist to stand apart from the object of knowledge.

Source: History of the Human Sciences, 18(4) (2005): 107–139.

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Miller’s conclusion engaged in a double reflexivity, linking the scientist’s self to the human subjects (Ss) studied and, at the same time, connecting self-knowledge to knowledge of the world. While Miller may be unusual in his ability to engage two forms of reflexive argument in the space of two sentences, the mere fact that he engaged in reflexivity at all should not be particularly surprising. Several genres have suggested the importance of seeing the interconnection of psychologists’ own selves with the human selves they seek to describe. First, history of psychology literature has noted how psychologists have engaged in reflexivity by linking their topics of study to their own subjectivity (Capshew, 1999; Danziger, 1990; Morawski, 1992, 2000; Richards, 1987). Second, work in science studies has argued for analyzing science by breaking down the analytic boundaries between the natural world and the social world (Latour, 1993). If applied to history of psychology, this work would imply the analytic value of breaking down distinctions between the natural world described by psychology (the human mind) and the minds and social worlds of psychologists themselves. Even if one does not accept the value of engaging in reflexive practices in the human sciences, there remain good reasons to believe that human nature consistently leads human scientists to engage in reflexive practices. If human selves are socially constructed or if people are ‘made up’ as Ian Hacking has argued (1986), then one would expect to find that human scientists have often used their self-knowledge as a generative feature in their work. Science made from such self-knowledge would then have helped bring into being an external psychological and social world that, to some extent, mirrored the human scientist’s understanding of himself or herself. Historians have shown us numerous examples of this phenomenon, from Sigmund Freud, to William James, to Gordon Allport (Anzieu, 1986; Nicholson, 1998; Richards, 1987; Shorske, 1981; Toews, 1991). While it might not be surprising to find such reflexivity (some might say ‘lack of objectivity’) in social psychology and psychoanalysis, the same interplay between self-knowledge and scientific psychological knowledge has pervaded even those parts of experimental psychology that have been regarded as the most ‘objective’ and methodologically rigorous. Neo-behaviorists Ernest Tolman, B. F. Skinner, Clark Hull, and Edwin Guthrie regularly worked between their senses of themselves and their scientific investigations (Hilgard and Bower, 1966: 104; Smith, 1986; 1990: 237–66). Likewise, the arch-operationist S. S. Stevens took the normative rules of data collection that he prescribed for psychologists and translated them into his studies of audition. By the time he was done, Stevens had produced a theory of hearing in which the brain acts as if it were a scientist following operationist rules of method by making measurements of the cochlea’s electrical output to calculate the loudness of a particular sound (Stevens, 1936). Although it may be the case that the possibility of ‘making up’ people and the nature of the subject matter make it difficult, if not impossible, for human

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scientists to escape reflexive practices, this article concerns itself with another reason for the pervasiveness of reflexivity in the human sciences. Because of their history and the history of the cultures within which they emerged, human scientists have lacked sufficient authority as experts to maintain their autonomy and insulate the objects of knowledge they produce from lay audiences. The issue for the human sciences extends beyond the fact that nonexperts invariably possess some form of folk psychology (or folk sociology, or folk anthropology, etc.). For just as all (or most) adults possess some form of theory of mind, so too do we find that all (or most) people possess naïve physics. If people did not possess folk physics then Jean Piaget’s studies on children’s construction of time, space, matter, and volume would have looked quite different; moreover, college students, including 70 per cent of engineering students, would not retain a belief in Aristotelian physics after taking a course in introductory mechanics.1 The difference I want to highlight here is that while physicists have had the authority to hold forth on topics such as quantum mechanics and, for the most part in recent history, remain unchallenged,2 human scientists have not often been accorded a similar authority by their societies. This lack of autonomy has meant that the barriers between professional human science and folk psychology, sociology, and anthropology have been both low and permeable. Not only have the human sciences generously borrowed and shared ideas, facts, categories, and ways of thinking with other parts of the societies within which they developed, they have also been particularly answerable to non-experts (Barber, 1952; Merton and Wolfe, 1995; Rose, 1996).3 Consequently, the human sciences have been largely unable to establish a reliable epistemic or social distinction between the intrinsic aspects of their fields and their extrinsic social, cultural, political, and institutional contexts. The porous nature of the boundaries surrounding their disciplines has opened possibilities for human scientists to engage in reflexive work. It has provided them with the opportunity to turn a wide variety of ideas (whether popular or expert) about human nature on themselves and their colleagues. Human scientists have been able to deploy categories, methods and arguments drawn either from the human sciences or from folk knowledge to legitimate their own endeavors or attack foes within their own disciplines. This article focuses on a particular moment in the history of human sciences in which this sort of reflexivity played a significant role: the early days of revolution in which cognitive science supplanted behaviorism as the hegemonic science of human nature.4 In the struggle that marked the cognitive revolution we see little use of Jamesian or Freudian deep and thorough selfexamination in efforts to make a science of the human. Rather, behaviorists and their foes regularly traversed the boundary between scientific and folk psychology as a strategy for legitimating their work. Reflexivity provided the combatants with weapons to attack their foes and also methods and concepts to form their respective sciences of human nature.

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To enhance their public standing, they sought to make their own thought processes match folk ideas of scientific thinking. They applied the same categories of selfhood found in popular culture and social psychology to themselves. They collapsed distinction between normative rules for scientific thinking and the actual processes of human thinking. As cognitive scientists like George Miller and Herbert Simon (1966a; 1966b; 1980) crossed back and forth between scientific descriptions of the human and normative discussions of the best way for scientists to think, they borrowed from the folk and social psychological image of right thinking to inform their own personal and public images. These very same scientific self-images would form the basis for the image of human nature that cognitive science produced.

The (Disciplinary) Politics of Psychological Theory In the years after the Second World War, intellectuals and social scientists developed a language with which to discuss social and political issues in terms of categories of thought. They produced normative accounts of mind, characterizing some forms of thinking as better than others. Academics valued flexible, interdisciplinary minds in their colleagues and students (Cohen-Cole, 2003: ch. 3). A wide range of Americans joined them in assigning political and social meaning to open-mindedness, equating it with democratic values. To social critics, personal autonomy and freedom of thought distinguished the United States from the Soviet Union and also offered hope to those who bemoaned the disappearance of individualism under the pressures of mass society, suburbanization, and social conformism.5 This psychologization of politics appeared, for instance, in two different landmark works in 1950, The Authoritarian Personality (hereafter cited as TAP) and The Lonely Crowd. The latter work examined how, under the influence of mass society and late capitalism, Americans had lost their traditional individualism to social conformity. TAP argued that democratic people exhibit openminded, tolerant, and flexible minds while authoritarian people are closed-minded and rigid, leaving them insensible to empirical realities. This particular characterization of the cognitive attributes of democratic and undemocratic minds was part of and followed a much larger research tradition in social psychology that examined the psychological structures connected to certain political orientations (Crutchfield, 1955; Maslow, 1943; Rokeach, 1948, 1950, 1951b, 1951c; Stanger, 1936a, 1936b). While these psychological works approached the politics of mind from the position of psychological expertise, others examined the problem from the side of political analysis. In his ‘Long Telegram’ of 1946, the work in which he articulated the containment doctrine that would define America’s cold war strategy and form the basis of much of the country’s foreign policy for the subsequent 50 years, George Kennan used essentially the same psychological categories to

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describe Soviets as TAP used to describe authoritarians. Importantly, Kennan’s telegram preceded the appearance of TAP by several years. Later on, Pulitzer prize-winning historian Arthur Schlesinger, Jr, an advisor to John F. Kennedy, likewise noted how communism’s rigid ideology ‘obscured reality’.6 There were those at the time who believed that priority for explaining the mind lay not with human scientists but with politicians. For instance, in 1953 Homer Ferguson, Republican Senator from Michigan, attacked Harvard’s Russian Research Center for wasting taxpayers’ money by usurping Congress’ role and priority in defining, identifying, characterizing, and locating the communist mind. Gesturing to the work led by his colleague, Joseph McCarthy, Ferguson noted: ‘there has been research, by the Congress, in the way the communist mind works…’ He added that Congress had done a better job in understanding communist mentality than Harvard’s Russian Research Center (Kelso, 1953). This widespread practice of treating politics in terms of thinking styles had implications for the disciplinary structure of psychology and for research programs within the field. Because of the low and permeable boundaries between expert and folk psychology, the choice to pursue one kind of psychology rather than another was filled with political meaning. Moreover, again and again psychologists would engage in a casual form of reflexivity, investing not only their models of human nature, but also their own thought processes with political meaning. To many psychologists, pursuit of political change could come about through the development of the right kinds of psychological theories. For instance, in 1950, Theo Lentz articulated a typical political argument for pursuing psychological study of specific forms of human subjectivity (1950: 213–14). In this paper, Lentz’s argument relied on a common juxtaposition of claims. In particular, he linked disciplinary reform (the advancement of social psychology) with political reform (development of world government) with reconceiving human nature (by making world-mindedness a facet of the human mind) with the call for psychologists to be imaginative. In a 1953 grant proposal, Jerome Bruner articulated a similar argument for how the ‘world crisis’ required the development of cognitive psychology.7 Although there were certainly variations in the political goals (not everyone called for world government), norms of thought (not everyone called for world-mindedness), models of human nature, and disciplinary goals (not everyone called for social psychology), a wide range of psychologists, social scientists, public figures, and foundation officials made these sorts of links. They tied the promotion of certain ‘better’ forms of human nature to particular scientific models of human nature and to the disciplinary reconstruction of the human sciences (Bryson, 1948; Cantril, 1949, 1950; Frank, 1951; Kluckhohn, 1952; Maslow, 1946, 1948; Mooney, 1954; Tolman, 1948). Although social scientists used their tools of psychological analysis to critique specific social groups or specific modes of thinking, they also regularly

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used these techniques to talk about their own discipline. In one instance, Milton Rokeach’s discussion of psychological ideas indicates the way in which those theories could carry political significance. Rokeach had devoted substantial effort to understanding and explaining the emotional and cognitive deficits of the closed-minded person and the corresponding virtues of the open-minded person (Rokeach, 1948, 1949, 1950, 1951a, 1951b, 1951c, 1951d). His analysis of these categories, much like the arguments in TAP, associated openmindedness to democracy and closed-mindedness with racism, ethnocentrism, or authoritarianism. On the basis of this work, Rokeach’s description of behaviorism and psychoanalysis as presenting a model of the closed-minded person, and of Gestalt psychology as presenting a model of the open-minded person, was laden with political implications. Specifically, the model of human thinking presented in Gestalt theory was that of the democratic citizen, while the model presented in behaviorism and psychoanalysis was appropriate to people who were subjects of totalitarian states (Rokeach, 1960: 65).8 As the language and arguments of psychologists like Bruner, Lentz, and Rokeach and folk psychologists like Kennan, Schlesinger, and Ferguson (and McCarthy) indicates, political and cultural values were embedded in the effort to develop understanding of the open, autonomous mind. Their arguments involved contentious disciplinary politics in which the overtly political categories of open, rational, and democratic thinking were contrasted with those of the closed, prejudiced, authoritarian mind and deployed in a disciplinary struggle to define what constituted scientific psychology.

Psychology, the Science of Behavior or the Science of Mind? In the 1950s at the center of psychology’s struggle was the question of whether the study of mind could properly be understood as a part of scientific psychology. In a 1958 invited address to the American Psychological Association, the philosopher of science Herbert Feigl noted: ‘Intuition,’ ‘insight,’ ‘understanding,’ and ‘empathy’ have been key words in the strife of psychological movements. These terms are used honoriflcally by one party, but they are suspect (if not on the index verborum prohibitorum) with the other party. (1959: 118)

These two parties struggled over whether intuition, insight, and understanding belonged in psychology.9 Feigl’s sense of two parties was by no means an outsider’s idiosyncratic reading of the field. George Miller, Eugene Galanter, and Karl Pribram, for instance, divided the field into ‘optimists’ who believed that human behavior was determined by the environment and could be described completely by stimulus-response chains and ‘pessimists’ who believed that other things (such as mental processes) were necessary to explain human nature.10

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At the end of the Second World War, and for the following 10 years, Miller, Galanter and Pribram’s optimists controlled the discourse on what was and what was not scientific psychology. At that time, psychology was anything but the science of mind.11 The ‘fundamental’ and scientific center of the discipline was experimental psychology, which was dominated by the behaviorist and operationist concerns that made mind an improper subject for scientific study.12 The central importance of ‘learning theory’ in the scientific end of the discipline gives another indication of how many in psychology deemed the study of mind to be unscientific.13 Learning theorists sought to explain how people and other organisms act differently in different circumstances. The main branch of learning theorists followed in the footsteps of J. B. Watson and Edward Thorndike. These psychologists included E. R. Guthrie, Clark Hull, Kenneth Spence, B. F. Skinner, and their followers. They, their students, and their theories dominated experimental psychology, at least in numbers.14 Although it is important to recognize the differences among these psychologists, they shared a fundamental perspective on the nature of human (and animal) subjectivity. For them, most, or all, of what one needed to know about psychology could and should be explained on the basis of the environment’s effects on the subject. From this perspective, organisms ‘learned’ to solve problems not from ‘understanding’, but through random trial and error and the association of particular behavioral responses with a reward or other stimulus. Although there were certainly other approaches to learning represented by Gestalt theorists or Edward Chance Tolman or David Krech (né Krechevesky) that emphasized the importance of insight, hypothesis, and cognition in learning, these were distinct minority positions.15 It is certainly the case that large sections of psychology – particularly clinical and educational psychology (the fastest-growing components) – were concerned with mind.16 But social and clinical psychology had, at best, a marginal status as a scientific endeavor. Those who studied mind may have been psychologists, but their status as scientists was questionable in their own community. The more psychologists were concerned with mind, the less they qualified as scientists within the discipline (Johnson, 1956: 712; Kelley, 1955; McGuire, 1956: 153; Rogers, 1955; Skaggs, 1945: 234–48; Strupp, Castore, Lake, Merrill and Bellak, 1956: 153–7). As the psychologist E. Parker Johnson put it in 1956: Practically everyone who is not a psychologist knows that [psychology] is the science or study of the mind, and anyone with a dictionary may easily confirm this. . . . But, oddly enough, many modern psychologists refuse to accept this definition. Why? … The word mind … [is] by its very definition beyond the ken of science which, by its very definition, is built on the observation of observable events. . . . . Many protest, indeed, that scientifically speaking there is no such thing as the mind to be studied! (1956: 712)

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Two critical assumptions were necessary for this argument, the definition of mind and the definition of science. With these two definitions taken as given, there was little compatibility between mind and science. But those two definitions were under attack even as this paper was published.

Attacks on Behaviorism At the end of the Second World War a broad range of academics called for developing a science that could account for the autonomous, the creative, and the rational aspects of human nature. This effort involved a struggle with proponents of behaviorism and (somewhat less) of operationism and positivism – those who saw humans as (mere) products of their environment and their basic drives (such as hunger). Drawing on the politics of thinking, a primary strategy for advocates of the science of mind was to attack the thought processes of behavioristic psychologists. These attacks made use of the normative categories of thinking drawn from social and folk psychology. Specifically, the features of the closed-minded, conformist person appeared as characteristics of the behavioristic psychologists. In these critiques, the center of scientific psychology (which was primarily behavioristic) appeared as uncreative, narrow-minded, rigid (Solomon, 1955: 170), and dogmatic (Allport, 1940; Ericson, 1941: 76; Harlow, 1958: 674); in short, it appeared to be governed by an ideology that confused methodological rigor with true (i.e. creative) science (Brower, 1949: 326, 328, 330, 332; Bruner, 1957a: 156). The primary reason offered for using epithets such as ‘narrow’ and ‘ideological’ to describe operationist, behaviorist, and positivist psychology was because of its reported aversion to and slighting of the study of the mind. Some psychologists, for instance, argued that ‘narrow operationism’ had limited the ‘freedom’ of psychology to pursue its ‘ultimate purpose, the scientific understanding of man’s cognitive behavior’ (Gruber, Hammond and Jessor, 1957: v). Just like ethnocentric, closed-minded people described in The Authoritarian Personality or Gordon Allport’s Studies in Prejudice, behavioristic psychologists were pictured as conformists, intolerant of difference. Of operationists, one psychologist noted, ‘their discussions and criticisms have produced a social climate in which the psychological theorist may hesitate to present theories which contain non-operational definitions’ (Prentice, 1946: 247). Another commented: ‘it has been noted that psychologists seem over conscientious and even compulsive in their efforts to be simon-pure [sic] and scientific almost to the point of fetish. Colleagues suspected of indiscretions are ostracized and avoided’ (Thorne, 1956: 152). The philosopher Alain Locke, an important advocate of social pluralism, noted that ‘in the cause of scientific objectivity’ positivism and behaviorism had become dogmatic and had ‘squeezed values and ideals out completely in a fanatical cult of “fact” ’ (1942: 197).

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Critics of behavioristic psychology regularly suggested that it was a religious phenomenon. One of the standard procedures of behavioristic psychology – the assumption that rats could stand in for humans or other organisms as experimental subjects17 – came under attack as religious dogma (Bitterman, 1960: 705, 711).18 Donald Hebb participated in this critique, noting that psychologists deviating from the formula of stimulus and response theory could place themselves in a ‘larger demonology’. ‘As for “insight,” “purpose,” “attention” ’, Hebb continued, ‘any one of these may still be an invocation of the devil, to the occasional psychologist’ (1949: 4).19 This characterization of behaviorism was by no means a simple matter of internal disciplinary debate. The picture of behaviorism as rigid, religious ideology spilled outside of the discipline of psychology into academic journals outside the field and into popular non-fiction (Birnbaum, 1955: 15, 30; Koestler, 1964: 560–1 ).20 Sigmund Koch, editor of a three-volume survey of scientific psychology commissioned by the National Science Foundation (NSF), devoted his epilogue to an extended critique of the reigning methodology of behavioristic psychology. In Koch’s eyes, both behaviorism and the use that psychologists made of philosophy of science (operationism and logical positivism) could be subsumed under a single heading, the ‘Age of Theory’. Koch commented again and again on the ‘ideology’ (1959a: 732, 734–5, 769, 776–7, 786) or ‘code’ (1959a: 783) of the ‘age of theory’, its ‘reigning stereotypes’ (1959a: 783), ‘lack of realism’ (in contrast with ‘increase in realism’ as the age waned) (1959a: 748, 770),21 and its ‘narrow’ approach (1959a: 769–70). Koch noted the age of theory’s ‘hypothetico-deductive prescription’ (1959a: 776–8), its ‘doctrine’ (1959a: 785–6), its ‘programmatic’ thinking style and attachment to ‘facile’ mythology of perfection (1959a: 786), as well as its ‘autism’ and ‘autisms’ (1959a: 770, 785). In the parlance of the psychological theory of the time, autism meant lack of connection with reality and often implied lack of creativity (see McKellar, 1957). Noting that psychology sought security and respectability in following ‘fashionable theory of proper science’, Koch argued that ‘the dependence of the Age of Theory on prescription from extrinsic sources is but the most recent chapter in a consistent story of such extrinsic determination of ends and means’ (1959a: 783). With the waning of the Age of Theory, Koch saw reason to be hopeful. He noted that ‘for the first time in its history, psychology seems ready – or almost ready – to assess its goals and instrumentalities with primary reference to its own indigenous problems’ (1959a: 783). Koch continued: [Psychology] seems ready to think contextually, freely, and creatively about its own refractory subject matter, and to work its way free from dependence on simplistic theories of correct scientific conduct, (ibid.)

In other words, psychology’s ideological subscription to an external vision of science had narrowed its range and restricted its creativity (see also Hunt,

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1956: 6; Koch, 1959a: 748, 786). Koch continued by pointing out that this new independence had led psychology to be more open-minded. Here Koch highlighted psychology’s maturity and autonomy and contrasted it with its past history (during the Age of Theory) in which it depended upon an outdated model of science derived from external authority framed, and subsequently rejected, by logicians of science. Thus behavioristic psychology had relinquished its own autonomy to a philosophy of science that philosophers themselves no longer believed in (1959a: 787–8). Koch’s assessment of the Age of Theory operated by a similar system as that the social psychologists used in their analysis of the prejudiced or authoritarian mind.22 Both critiques pointed out a lack of autonomy, a tendency to follow rules imposed by others, a lack of realism, and narrow, stereotyped thinking. Koch added that with psychology’s independence from external rules, it had also recently matured from focusing on rigor alone. ‘From the beginning, some pooled image of the form of science was dominant [in psychology]: respectability held more glamour than insight, caution than curiosity, feasibility than fidelity or fruitfulness’ (1959a: 784). To Koch this transition had enabled a more modern ‘open and liberated conception of psychology’ which allowed for ‘the role of creativity in all aspects of the scientific enterprise’ (1959a: 785–6).

Philosophy of Science, Natural Science, and Their Use in Psychology Philosophy of science was at the center of this debate within psychology. Whether following a formal school or a more informal understanding of science, psychologists were highly attuned to philosophical issues (Green, 1992; Winston and Blais, 1996). Casual references to modern philosophers and philosophical issues that peppered articles in psychology journals indicate the depth of familiarity psychologists had with philosophy of science (for example, see MacCorquodale and Meehl, 1948: 96). In fact, psychology paid more attention to philosophy than did the other sciences. As Herbert Feigl put it in an invited lecture to the American Psychological Association, ‘The majority of physicists want to unmuddle themselves without the aid of philosophical clarifiers. But I have found psychologists and social scientists much more hospitable’ (1959: 115). There was symmetry between behaviorists and anti-behaviorists. While behaviorists such as Kenneth Spence commonly welcomed Feigl’s positivist brand of philosophy of science, anti-behaviorists subscribed to an anti-positivist vision of science (Bergmann and Spence, 1941; Bruner, 1957a: 155–7; Spence, 1957). The normative account of scientific practice the anti-behaviorists adopted was one that emphasized the insightful aspects of science. Rather than functioning in the role of collating data, to anti-behaviorists the scientist’s mind had to be active and creative.

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The anti-positivist vision of science was articulated by men vested with enormous cultural authority to speak for the nature of the scientific endeavor. These men included leaders of the scientific establishment such as James Bryant Conant (1950, 1951a, 1951b, 1952), J. Robert Oppenheimer (1956, 1958), Warren Weaver,23 and Jerrold Zacharias.24 They were joined by best-selling science writers such as Jacob Bronowski and Arthur Koestler (Bronowski, 1956; Koestler, 1964). Sociologists such as Bernard Barber and Talcott Parsons articulated similar anti-positivist visions of science (Barber, 1952).25 Likewise, historians of science including Thomas Kuhn (who was a protégé of Conant) attacked the positivist vision of science (Conant, 1947; Kuhn, 1962).26 All of these figures argued that science was a process that involved creativity, insight, ideas, and invention as much as the collection of data.27 Jacob Bronowski’s vision of science indicates just how closely the critiques of behaviorism and positivism were bound. For him, positivist and operationist philosophers failed to grasp the creative nature of scientific work. Attacking the behaviorist flavor of operationist philosophy and the rigidity of logical positivism, Bronowski argued: These accounts of science seem to me to be mistaken, on two counts. First, they fly in the face of historical evidence. . . . And second, both schools fly in the face of contemporary evidence.

Scientists know, Bronowski argued, ‘that science is not something which insects or machines can do. What makes it different is a creative process. . . . and this has sadly tiptoed out of the mechanical worlds of the positivists and the operationalists, and left them empty.’ From this point about the nature of science, Bronowski appended an argument about the nature of human thought. The world which the human mind knows and explores does not survive if it is emptied of thought. And thought does not survive without symbolic concepts. The symbol and the metaphor are as necessary to science as to poetry. (1956: 48–9)

In Bronowski’s eyes then, all thinking and science rely on the creative use of symbolic concepts. Moreover he took his vision of cognition and proper science to be opposed to that advocated by positivist philosophers of science. Oppenheimer concurred with Bronowski. ‘Truth’, he proclaimed in a 1955 invited address to the American Psychological Association, ‘is not the whole thing; certitude is not the whole of science. Science is an immensely creative and enriching experience; and it is full of novelty and exploration’ (1956: 130). Oppenheimer cautioned psychologists against borrowing from obsolete classical physics the mistaken view that the physical world is determinate (ibid.: 134). Even more, he warned psychologists against quantification for

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its own sake, noting that such fascination with number had been typical of and appropriate for Babylonian prophecy and magic. More modern sciences, Oppenheimer suggested, should be pluralistic enough to value descriptive naturalistic approaches. Tellingly, the cognitive-developmentalist Jean Piaget was Oppenheimer’s candidate as someone who deserved respect despite his lack of statistically robust results (1956: 135). Oppenheimer’s arguments would have been useful to the anti-behavioristic psychologists. His argument against rigor for its own sake, suggestion that zeal for quantification was superstitious, and call for methodological pluralism gave ammunition to anti-behavioristic psychologists.28 Jerome Bruner, for instance, echoed many of Oppenheimer’s points in a hostile review (1957a) of a book by Kenneth Spence (Spence, 1956). The negative characterization of behavioristic psychology adopted the categories of analysis that social psychologists had used to describe rigid, closedminded, ideological people. Critics of behaviorism framed a positive counterpart to behaviorism’s narrowness. This positive version of psychology would value and reward creative insight among its practitioners rather than seeing merit in rigorous methodology alone. In this version of psychology, psychologists would be autonomous of narrow positivist philosophy of science, independent of external influence, open-minded, flexible, realistic, interdisciplinary, and creative (Koch, 1959a: 784 – 6).

Scientific Thinking as the Content of Cognitive Psychology In this section I turn to a direct challenge to the content of behaviorism, a challenge right on behaviorism’s home turf: the scientific study of normal (and universal) human nature. At stake was whether human nature could or could not be completely accounted for by stimulus-response connections. In the 1950s, an array of scientists joined forces from several fields to critique behaviorism by arguing for the existence of thought or behavior that was autonomous from stimulus. Central to this endeavor of creating a cognitive rather than behavioral psychology was proving that human behavior was creative and was not simply the product of experience.29 To those who insisted that thinking could not be explained solely by conditioning, the project extended beyond the claim that cognition was an irreducible aspect of normal human nature. Instead, there were very specific modes of thought that they ascribed to the normal human: human cognition was supposed to operate much like the thinking of a particular sort of person – the good scientist. But as discussed above, there was not unanimity in America about how to conduct proper science. When cognitivists compared human thinking to the scientific process, they were quite selective in their choice of the model of science. They adopted a vision of science that emphasized the creative and insightful nature of the scientific process.

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From the earliest days of cognitive science, studies of human mental processes treated thinking, perception, and language as relying on scientific methods such as hypothesis formation and theory construction. The 1956 A Study of Thinking by Bruner, Goodnow, and Austin explained everyday cognitive processes by comparing them to scientific thinking. The authors wrote: ‘the development of formal categories is, of course, tantamount to sciencemaking’ (1956: 6; hereafter cited as BGA): Let us take as an example of concept attainment the work of a physicist who wishes to distinguish between substances that undergo fission under certain forms of neutron bombardment from substances that do not. . . . This kind of problem is hardly unique. The child seeks to distinguish cats and dogs by means other than the parent’s say-so, the Army psychiatrist seeks out traits that will predict ultimate adjustment to and performance in the Army, (ibid.: 233)

While these psychologists saw science in everyday cognition, their metaphor was more focused than merely linking one to the other. Not just any form of science would be the model of human thinking. They selected only certain aspects of the scientific process to compare to thinking: inference, invention, problem-solving, making hypotheses, and model construction (BGA, 1956: 10, 14, 17, 19, 31, 37–8, 54, 56, 92, 233, 244, 246). BGA saw their account of science as opposing the dogma of ‘naive realism’ in which science is a ‘voyage of discovery’ that sought to ‘discover the islands of truth’. In contrast with this vision, they argued, ‘science and common-sense inquiry alike do not discover the ways in which events are grouped in the world; they invent ways of grouping’ (1956: 7; emphasis added). This emphasis on the inventive nature of science drew support from both the biological and the physical sciences. BGA cited Ernest Mayr’s point that ‘species are not “discovered” but “invented”‘ (1956: 19; Mayr, 1952).30 The physical sciences taught ‘the revolution of modern physics is as much as anything a revolution against naturalistic realism in the name of a new nominalism’ (7). From the perspective of this nominalism, they asked: in what sense do the categories ‘such as tomatoes, lions, snobs, atoms, and mammalia exist’? The answer was that ‘they exist as inventions, not as discoveries’ (ibid.). According to this account, the categories atoms and tomatoes are both invented, not discovered, by people. Atoms are invented by scientists and tomatoes by everyone – but neither is discovered. The nominalistic lessons of modern physics had two sorts of implications. On the one hand, these lessons were relevant to the argument about which sorts of scientific thinking were appropriate as metaphors for everyday cognition. In this regard, the claim was that nominalistic philosophy of science was better than realist philosophy of science as a model for human nature. On the other hand, the reference to nominalism could also serve as a critique of behaviorism. This was because stimuli were equivalent to people not when

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they were objectively, measurably equivalent to the experimenter, but when people constructed psychological categories that grouped the stimuli together (8). This point could make meaningless the behaviorist effort to relate observable and measurable stimuli and responses.31 As Bruner put it in 1951, Let us begin by stating a heuristic theory of perception. We shall assume that the organism is always set or tuned or expectant; he is, in short, ready for certain classes of stimulus events to occur. The tuning of the organism, and we shall discuss its determinants presently, we shall call an hypothesis. It is a predisposition to organize and classify the perceptual field in a certain way at a certain moment. Stimulus information enters the prepared organism. We use the term stimulus information rather than stimuli for what we wish to denote here is not the energy characteristics of stimulation, but the cue characteristics provided by stimulation – its signaling value. . . . The data of the scientist are not the raw cues of stimulation, but the perceptions of the scientist which occur when those cues confirm perceptual hypotheses which he has acquired. In this important sense, then, the scientist’s data are not found, but created.32

On this account, data, stimuli, and responses do not exist independently of expectations. Both the psychologist and the people he or she studies do not experience the world in ‘raw’ form. Human perception is so thoroughly laden by prior hypotheses and theory that it is impossible for any scientist to make purely objective pointer-readings.33 While the implications of nominalism for behaviorism were not explicitly drawn in A Study of Thinking, Bruner did make this last point explicit in his review of a book by the behaviorist Kenneth Spence only a year later (1957a). In this sense, nominalism was not merely the proper model of human thinking, it was also a better model for psychological research. The argument, then, was that it is human nature to think nominalistically, and that it is good for psychologists (and other scientists) to think nominalistically. Thus there was an equivalence of good scientific thinking and normal human thinking. An implication here is that the only people who held to the dogma of realism were naïve philosophers and behaviorists. And realism, since it was dogma, was abnormal or ill – or, in Koch’s terms, autistic. While A Study of Thinking focused on categorizing the different ways that people go about understanding the world, Eugene Galanter and Murray Gerstenhaber’s 1956 article ‘On Thought’ drew on the conceptual modelbuilding aspects of science. This article argued that thinking and understanding were much like building an internal model of the world. These models would be either like maps or like the three-dimensional scale models scientists and inventors constructed to understand and represent large-scale physical phenomena. Galanter and Gerstenhaber extended the technical nature of the model analogy of thinking by suggesting ‘the environment will be a “machine,” or “mechanism.” … The process by which the behavior of the mechanism is predicted is called “thinking” ’ (1956: 219).34

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Whether they came to the study of mind from psychology or other disciplines, for the early cognitive scientists although creative thinking was praiseworthy, it was not exceptional. Instead theory construction and creative problem-solving was the cognitive scientists’ model of everyday thinking and problem-solving. Learning was not so much a process of acquiring facts about the world as of developing a skill or acquiring proficiency with a conceptual tool that could then be deployed creatively (BGA, 1956: 6–7; Chomsky, 1959a; Galanter and Gerstenhaber, 1956; Miller, Galanter and Pribram, 1960; Newell, Shaw and Simon, 1958; Newell, Shaw and Simon, 1962; Simon, 1966a; Simon, 1967). For instance, according to the MIT linguist Noam Chomsky, the acquisition and use of language was an active and creative process. In his eyes, a child learning a language was not acquiring specific words so much as operating like a scientist by actively developing a theory of how to speak properly. Such theories are nothing other than the grammar of the language in question. Chomsky also held that adults require similar theories in order to produce and comprehend sentences (1956: 113, 116; 1957). This view of language users and learners as scientists was not neutral with respect to either linguistic theory or philosophy of science. According to Chomsky the linguist or psychologist looking to account for the ‘actual behavior of speaker, listener, and learner’ would fail if he or she followed the purely empiricist rules of scientific method as described and advocated, for instance, by B. F. Skinner (1950, 1956). Indeed, the ‘mechanisms’ or ‘theories’ of grammar that Chomsky saw each individual as possessing could never be observed directly, but only be inferred from their behavior. While Chomsky’s view of language set constraints on the proper method for scientists seeking to account for language, it also framed humans as following certain forms of method as well. Specifically, native ‘hypothesis-forming’ abilities enable children to rapidly learn grammar, a process which Chomsky described as constructing an ‘abstract deductive theory’ or ‘an extremely complex mechanism’ for producing or recognizing proper sentences (1959b: 56–7). Chomsky supplemented his claims for the active and thoughtful nature of learning by citing the neurologist Roger Sperry’s argument that even simple conditioning requires insight (cited in Chomsky, 1959b: 44; Sperry, 1955). Chomsky’s views of language, learning, thinking and the ways to study them both typified early cognitive science and catalyzed much later work in the field (see, for instance, Galanter and Gerstenhaber, 1956; Miller, Galanter and Pribram, I960).35 Practitioners in the field discussed the human mind as if it were a complex machine or computer capable of reasoning, hypothesis formation, and insight. These cognitive processes were taken as innate human abilities that were necessary for both learning and perception. To many cognitive scientists the computer provided a useful metaphor to combat stimulus-response behaviorism. George Miller, for instance, recalls that the metaphor allowed cognitive psychologists the opportunity to have a

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mechanism to support their views (Baars, 1986: 212). If the computer could demonstrate higher thinking, then surely it would not be pure speculation to attribute those thought processes to people. There could, therefore, be a defensible science of thinking. The computer metaphor was also used to make an anti-behaviorist point and emphasize the way in which human nature is creative and (partly) autonomous of the environment. Hebb, for instance, argued that the ‘computer analogy’ developed by Miller, Galanter and Pribram (1960), and Donald Broadbent (1958), ‘can readily include an autonomous central process as a factor in behavior’ (1960: 740; emphasis added). Although Hebb made a common, cognitively oriented interpretation of the computer analogy, it is worth noting its paradoxical framing.36 Specifically, why did he believe that human autonomy could be illustrated by a machine? As Miller, Galanter and Pribram argued, the mechanism that psychologists choose as a model of humanness does not necessarily force a particular vision of that nature (1960: 41). It may have been the case that computers did things that could not be predicted, but it seems unlikely that committed stimulus-response behaviorists would have concluded that human cognition is autonomous from that result. Certainly prior failures by psychologists to perfectly predict human behavior had not convinced behaviorists that organisms are autonomous. Early cognitive scientists and artificial intelligence researchers selected quite specific features of human nature to model with computer programs. For the cognitive scientists Newell, Shaw and Simon, and Miller, Galanter and Pribram, there was a clear route to convincing their audiences that mind did in fact exist and that it was possible to study it scientifically. This was to build models of the forms of human thought that Americans most widely saw as requiring higher reasoning. The cognitive scientists thus selected quite specific and widely recognized problems to model. For instance, Herbert Simon and Allen Newell built a program that could solve a logic problem that had recently been featured on a television show (1962). The computer models that cognitive scientists built used heuristic (rather than strictly logical or deterministic) methods to play chess, re-derive the proofs in Russell and Whitehead’s Principia Mathematica, or produce novel proofs of the theorems in Euclid’s Elements (Miller, Galanter and Pribram, 1960; Newell, Shaw and Simon, 1958; Newell, Shaw and Simon, 1962). The picture of human thinking that cognitive scientists inscribed in their computer models depended on the accounts of science provided by Henri Poincare (1952), Michael Polyani (1958), and George Polya (1945; 1954),37 and shared much with the anti-positivist one developed by men such as Conant, Oppenheimer, Kuhn, Bronowski, and Zacharias. Notably, Herbert Simon opined in 1958 that Bruner, Goodnow and Austin’s use of ‘strategies’ to describe thinking was ‘the nearest thing in the psychological literature to the use of programs to describe behavior’.38 In the time since these early works, cognitive science has continued to use computer models to ascribe such scientific methods as

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hypothesis-making, theory construction, and inference to everyday thought (for instance, Johnson-Laird, 1988). There is, however, the possibility that the computer would have been used to model not the rational, autonomous, cognitive, creative version of human nature, but the reactive, reflex-driven version of the human nature. Consider the models of human thinking proposed by Clark Hull, E. G. Boring (1946), Saul Gorn (1959), and Howard Hoffman (1962). In every one of these cases, psychologists developed models that strengthened the behaviorist vision of human nature. Clark Hull, for instance, compared learning to making a series of connections linking stimulus and response on a telephone switchboard and thereby emphasized its non-cognitive, non-insightful aspects (Smith, 1990: 249–54). Gorn designed a computer program and Hoffman built an electrical device (an ‘analogue lab’) to simulate stimulus-response learning. In both cases, the models indicate that the behaviorist vision of human nature, rather than being at stake, was assumed to be true. E. G. Boring’s robot model of human nature was likewise dependent on the stimulus-response model. Having failed Norbert Wiener’s challenge to give a single example of any human mental function that computers could not perform, Boring proceeded to outline all of the characteristics that the computer should have if it were to be a good model of human nature (1946: 178).39 The remarkable feature of Boring’s mechanical model is the specific characteristics of humanness that the computer was supposed to mimic. To Boring, a computer or robot would be a good model of human nature if it exhibited the properties of stimulus-response learning developed by behaviorists (1946: 183–4). Unlike Newell, Simon and Shaw, Boring did not argue that a computer could be considered a good model of human nature if it could produce novel solutions to mathematics problems or play chess (Newell, Shaw and Simon, 1959).40 In other words, unlike cognitive scientists, Boring used the computer model of mind to argue against mentalism. Miller, Galanter and Pribram pointed out that behaviorist, anti-cognitive implications that Hull drew from his analogy between a telephone switchboard and organisms were not necessitated by the model itself. They noted that until shortly before they wrote, switchboards needed human operators to work (1960: 41). Thus, they showed that the switchboard metaphor of thinking and learning could be used to imply that human thought processes were necessary to properly link a stimulus to a response. Of course, Hull did not take this option. His model emphasized the non-thinking aspects both of telephone switching and of learning. Although Miller, Galanter and Pribram noted that the telephone switchboard did not force behaviorist conclusions, they did not also point out a related conclusion: much as Boring had demonstrated, the computer model does not require a cognitive vision of the human mind. Set the task of designing something like a Turing test, behaviorists and cog-nitivists framed very different sorts of questions to put to the computer. Boring looked to see if the computer could follow stimulus-response rules of

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learning. The cognitivists looked to see if the computer could play chess or solve problems according to heuristic methods. The way that these two groups of human scientists understood themselves and their own thinking was at the center of this difference. That Boring, Hull, Gorn, and Hoffman looked at machines and saw a way to support behaviorists’ claims about organisms says more about these psychologists than it does about the organisms or the machine models they worked with. Likewise, that Hebb, Miller, Galanter, Pribram, Newell, Simon, and Shaw looked at computers and saw a model of autonomous human cognitive processes says more about these scientists than about either computers or people. Whether behaviorist or cognitivist, the meaning that scientists read into mechanical metaphors depended on highly value-laden visions of human nature. It turned on what they already knew about thinking, human nature, themselves, and the scientific process.

Conclusion This article has examined the elision of a normative vision of ‘right thinking’ with a descriptive account of ‘thinking’. Cognitive scientists inscribed a highly political and value-laden notion of proper thinking into their descriptive accounts of human thinking. In their struggle with behaviorism, contests over the structure of the world (i.e. human nature and the nature of mind) were often contests over proper scientific methodology at the same time. Conversely, statements about whether rats or computers exhibited insight were also claims about the kind of thinking in which scientists themselves ought to engage. Psychologists who ascribed insight to rats, computers, or people were likely to see insight as an important component of the scientific process. In opposition, behaviorists who denied rodent insight and focused on learning through trial and accumulated experience were more likely to frame the empirical experience as the foundation of proper scientific method – of which they were practitioners.41 Making a science of the mind involved developing a cognitive psychology – an image of normal human nature that was universal, independent (at least in part) from the environment and instinct, creative, and autonomous. This move involved, for the most part, dropping the normative descriptions of better and worse kinds of people and personality types described in political discourse and social psychology. Rather than contrasting the open and closed mind, the democratic and authoritarian, the creative and conformist personality, cognitive scientists looked at computers and saw the open-minded, creative, flexible, and heuristic thinking processes that they deemed to be characteristic of human nature. Although lacking language that identified better and worse forms of thinking, this science of autonomy substituted normalizing for normative

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terminology. Instead of identifying better and worse forms of humanness, it identified as universally human the specific forms of human nature that political discourse, social psychology, and anti-positivist philosophy of science had already marked as good. Thus, the better forms of human thinking constructed by social psychology (democratic, broad, open, flexible, creative) became the only forms of human thinking. The person portrayed by this psychology was fundamentally the same regardless of social situation, personality, or culture. By implication, that which had been marked as other forms of humanness were consigned to a pathology worse than illness – to non- or sub-humanness. The permeable boundary between expert and non-expert knowledge of human nature afforded cognitive scientists a collection of tools that, used reflexively, could further their research programs. The politics and social psychology of right thinking in cold war American culture gave anti-behaviorists techniques to turn on their own discipline and with which to mark themselves as open-minded and behaviorists as authoritarian ideologues. Antipositivist philosophy of science offered cognitive scientists not only a defense of their own kind of thinking, but also a model for how humans in general think. As George Miller put it in the Voice of America address discussed at the beginning of this article: A scientist … searches through ideas as well as through objects in order to find what he seeks. And he does not look indiscriminately – always he carries an image of what he seeks. . . . He is looking for something that matches up to his image of what the world must be, something that meets a test he himself imposes, something that has meaning only in terms of the standards he lives by.

Miller and his fellow cognitive scientists did just that – they looked for human nature by holding an image of what they were looking for in their minds. The image they held was none other than their own self-image. Concluding his address Miller remarked, ‘the scientist is Everyman, looking just as you and I. We go and look for the things we want, and when we find them we find part of ourselves’ (1963: 149–50).

Notes 1. On the difficulty of teaching physics see Clement (1982: 66–71); Gardner (1991: 152–8). 2. One example of invasion of the physicists’ domain was the case of Aryan physics (Beyerchen, 1992). 3. Of course the same could be said about the modern physical sciences. See, for instance, Galison (2003); Wise (1988; 1993). 4. In an examination of a later period and somewhat different vision of mind than that considered in this article, Gigerenzer (1991; 1992) has shown how cognitive psychologists’ own ways of thinking and working became the model for human cognition. For general discussion of the cognitive revolution see Baars (1986); Dupuy (2000); Gardner

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7. 8.

9.

10.

11.

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(1985); Greenwood (1999); Mandler (2002). Robins, Gosling, and Craik have provided an empirical study of how cognitive psychology supplanted behaviorism (1999). Thomas Leahey has argued that the cognitive revolution was not actually a ‘real’ revolution (1992). On the other hand, historians and psychologists continue to use the term. Without seeking to judge the validity of the term, this article follows actors’ categories in adopting the term ‘cognitive revolution’. For discussion of the deficiencies in how communists thought, see Allen (1949) and Shils (1954). ‘Ideology vs Democracy’: text of a speech by Arthur Schlesinger, Jr, before the Indian Council on World Affairs, New Delhi, 15 February 1962; papers of J. Robert Oppenheimer, Box 65, Folder: Schlesinger, Arthur, Manuscript Division, Library of Congress. Schlesinger (1949). Archives of the Ford Foundation, Grant Files, Reel R-004, Grant 53–78, Project Proposal, Jerome S. Bruner to Ford Foundation, 1/9/53. For a similar, but slightly less politically charged, argument, see Tolman (1948: 207–8). For similar discussion in regards to theory in the social sciences in general, see Dunlop, Gilmore, Kluckhohn, Parsons and Taylor (1941). There is a double sense in which the place of insight and intuition in psychology was contested. First, there was the question of whether the discipline should study insight and intuition. Second, there was the question of whether psychologists themselves display insight and intuition in their work. I address the relationship of these two questions later in this article. For interesting examination of the connection between the subject matter of psychology and the psychological states of psychologists, see Morawski (1992) and Richards (1987). The split was noted by many leaders of psychology. The social psychologist Gordon Allport parsed the division in psychology similarly. To Allport, psychology was split between ‘Lockeans’ and ‘Leibnizians’. Allport’s behavioristic Lockeans believed that humans could be completely explained by (and were determined by) their experiences and the stimuli that impinged upon their senses. According to Allport, Leibnizians believed that the human mind was, at least in part, autonomous (Allport, 1955). Others who saw a similar division in psychology included three presidents of the APA (Tolman, 1937; Hebb, 1960; Rogers, 1947; Hilgard, 1949; Harlow, 1958; and Miller, 1969), the editor of The Journal of Experimental Psychology (Melton), and the author of one of the most widely read textbooks in psychology (Hilgard). See Harlow (1956: 274); Hebb (1960); Hilgard (1948: 9); Kahn (1955: 171–2); Kelley (1955: 172–3); Melton (1956); Miller, Galanter and Pribram (1960: 7–8); Rogers (1955); Tolman (1948). Hilgard’s book is particularly significant due to its wide readership. In surveys conducted in 1953– 4 and 1958–9 Hilgard’s book was recommended for graduate education by over half of surveyed departments. Hilgard’s text was one of only six books to rate so highly (Sundberg, 1960). David McClelland noted that ‘psychologists used to be interested in what went on in people’s heads. . . . [B]ut with the rise of modern scientific psychology we lost interest in ideas, by and large’ (McClelland, 1955: 297). Positivist, operationist and behavioristic psychology had such claim on being the ‘fundamental’ part of psychology that it was a notable event when social and clinical psychologists characterized their own work as ‘fundamental’ (Koch, 1959b: 5). In his presidential address to the American Psychological Association, Donald Hebb remarked that learning had been ‘the fundamental issue in psychology’ ever since the work of J. B. Watson and Edward Thorndike in the early 20th century (Hebb, 1960: 736). ‘[B]etween 1944 and 1950, 70 per cent of the articles on learning and motivation in the Journal of Experimental Psychology and the Journal of Comparative and Physiological Psychology cited Hull’ (Logue, 1985: 180). Logue cites evidence collected in Spence (1952).

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15. A useful overview of learning theory, including both perspectives, is Hilgard and Bower (1966). Discussion of the marginality of Gestalt theory may be found in Ash (1985); Sokal (1984). On understanding and cognition see Krechevesky (1932a, 1932b); Tolman (1948). 16. In descending order of size of APA membership, the top ten psychological specialties in 1952 were clinical, educational, experimental, industrial, vocational, social, general, developmental, personality, and physiological (Sanford, 1952; cited in Hunt, 1956: 14). For excellent analysis of the changing composition of the discipline cf. Capshew (1999). 17. John Dollard and Neal Miller articulated the primary reason for using rats to study people. ‘The basic facts and concepts can best be introduced by the discussion of a simple experiment on albino rats. In using results from an experiment of this kind we are working on the hypothesis that people have all the learning capacities of rats so that any general phenomenon of learning found in rats will also be found in people, although, of course, people may display additional phenomena not found in rats. Even though the facts must be verified at the human level, it is often easier to notice the operation of principles after they have been studied and isolated in simpler situations so that one knows exactly what to look for’ (Dollard and Miller, 1950: 63). For a similar discussion about the inter-species applicability of laws of behavior see Skinner (1956: 221–33). 18. For other attacks on this methodological assumption see Beach (1950) and Dukes (1960). 19. For other examples of psychologists noting the religious nature of behaviorism see Bruner (1957b: 341); Harlow (1953: 23); Hunt (1956: 7). 20. This image of religion and particularly Catholicism as dogmatic and anti-scientific (and anti-democratic) appeared particularly strongly during the Second World War and afterwards in the writings of Sidney Hook (1945; 1991). For further discussion of the religion versus science polarization see Cohen-Cole (2003: ch. 2, sec. 1); Hollinger (1995); Purcell (1972). 21. Koch also suggested a contrast between the ‘realities of science’ and its converse the ‘facile myth’ advanced by the Age of Theory (1959a: 786). 22. For discussion of conformity and other features of the closed mind see Cohen-Cole (2003). 23. Warren Weaver, ‘Science and Faith’, delivered on Layman’s Sunday in the Congregational Church of New Milford, Connecticut, 16 May 1954. Papers of Vannevar Bush, Box 117, Folder: Weaver, Warren (1948–1954), Manuscript Division, Library of Congress. 24. Oppenheimer was known as the ‘father’ of the Atomic Bomb because he had been scientific director of the Manhattan Project. Conant, in some sense, was the grandfather of the bomb because of his work in OSRD, the government agency that oversaw scientific development for the Second World War. On the cultural authority of Manhattan Project alumni after the Second World War, see Kevles (1987). Weaver served as a grants officer for the Rockefeller Foundation. On Weaver, see Kohler (1991). Zacharias had worked in the Rad Lab during the Second World War and in MIT’s Research Laboratory of Electronics afterwards. He organized PSSC, the first of numerous NSF-funded secondary school science curricula. On Zacharias, see Goldstein (1992); Rudolph (2002). 25. On Parsons’s philosophy of science see Camic (1991). For discussion of how these two and other social scientists followed Conant’s lead in defining science, see Cohen-Cole (2003: ch. 3). 26. On the relationship between Conant and Kuhn see Fuller (2000). 27. Although Kuhn is well known for his anti-positivist stance, he did not join his contemporaries in seeing creativity as the most important component of scientific thought. For instance, as a participant in a series of conferences sponsored by the

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29.

30.

31.

32.

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34.

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National Science Foundation, devoted to characterizing and improving creativity in science, in 1959 Kuhn scolded his audience for focusing too much on ‘divergent’ and ‘flexible’ thinking scientific work to the exclusion of the forms of cognition that characterize normal science (Kuhn, 1959). The psychologist Abraham Maslow had made similar arguments about the proper structure of science almost a decade earlier (1946). Focused on the general problems with ‘means-centering’ in all sciences, Maslow did not explicitly point to behavioristic psychology as the object of his critique. The terms of his criticism of ‘means-centering’, however, were so typical of charges leveled at behavioristic psychology (narrowness, over-concern with rigor, etc.) that Maslow was almost certainly intending his article as a thinly veiled critique of main-line experimental psychology. Several years later, Maslow published an article making the direct link between ‘means-centering’ and psychology (1949: 261). Surveying the field in his 1960 presidential address to the APA, Donald Hebb remarked: ‘though this opposition of aims may seem over-simplified, I believe it is fundamentally sound. How [should we] understand otherwise the learning theorist’s [i.e. the behaviorist’s] bland refusal even to discuss attention or purpose, or the cognitive psychologist’s happy preference for phenomena he cannot explain – so long as the other cannot explain them either?’ (1960: 737). Six years earlier, the psychologist Hadley Cantril, with whom Bruner had worked during the Second World War, made similar points. He noted that ‘variables scientists use do not exist in their own right. They are only aspects abstracted out of the total situation by scientists as inquiring human beings endowed with the capacity to manipulate ideas.’ Citing Einstein and Infeld, Cantril also noted the ‘creative imagination’ necessary in science (Cantril, 1950: 491; Einstein and Infeld, 1942: 95). On this point, Koch argued: ‘If stimuli and responses are acknowledged to depend for their identification on the perceptual sensitivities of human observers, then the demand for something tantamount to a language of pointer readings, whether as simple energysource or movement descriptions, or as disjunctions of fixed-stimulus “indicators” and response “measures,” must be given up. And if this demand must be given up, then much time-worn argumentation as to the intrinsic ambiguity of experiential language, or in fact any language the end-terms in which are not S and R, become idle and beside the point. If, further, the requirement is asserted that S be specified in a way which includes its inferred meaning for the organism then any basis for a difference in epistemological status between an S-R language and what has been called “subjectivistic” language is eliminated’ (1959a: 768–9; original emphasis). For a similar argument see also Bruner (1957a: 156; 1957b: 340–58); Hebb (1954: 404); Miller, Galanter and Pribram (1960: 21–5). ‘Cognition and the Limits of Scientific Inquiry’, unpublished paper read at the Institute for the Unity of Science at the American Academy of Arts and Sciences, 1951. Jerome S. Bruner Papers, Harvard University Archives, HUG 4242.28. Bruner based this argument on a series of experiments he conducted on how expectation determines what people see. For historians of science the most notable of these experiments is one that examines how individuals experience Gestalt switches in their perception when viewing mis-colored playing cards such as black hearts (Bruner and Postman, 1949). This paper was one of Thomas Kuhn’s central examples of Gestalt switching in The Structure of Scientific Revolutions. Bruner later made a similar argument. ‘I am inclined to think of mental development as involving the construction of a model of the world in the child’s head, an internalized set of structures for representing the world around him’ (Bruner, 1979[1962]: 103). Subsequently, Chomsky collaborated with the Harvard psychologist George Miller in developing a cognitively oriented field of psycholinguistics (Chomsky and Miller,

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1958; Chomsky and Miller, 1963a and 1963b; Miller and Chomsky, 1963). In addition to these co-authored works, Miller and the cognitive psychologists he trained spent much time investigating the psychological reality of Chomsky’s understanding of language (Harvard University Center for Cognitive Studies, 1961–9; Miller, 1967). For more discussion of Chomsky’s linguistics work, its anti-behaviorist implications, and the ways that psychologists adopted it, see Cohen-Cole (2003: chs 5–6). For discussion of this paradox, see Crowther-Heyck (1999). Cited in Miller, Galanter and Pribram (1960: 87, 160, 167–9, 179, 180, 183, 191). For additional references to Poincaré and Polya, see Newell, Shaw and Simon (1962). ‘Bibliography – Miscellaneous Comments on Starred Items’, Commentary – 1958 [bundled] (Consulting – Social Science Research Council – Conferences and Seminars – Summer Research Training Institute on Simulation of Cognitive Processes – 1958). Herbert Simon Digital Archive. http://diva.library.cmu.edu/ Simon/index.html[.] See also Newell, Shaw and Simon (1958: 153, note 2); Newell and Simon (1959: 10). For discussion of the exchange between Boring and Wiener, see Galison (1994: 247, 251–2). Cited in Miller, Galanter and Pribram (1960: 189). Lawrence Smith has noted the tendency of neo-behaviorists to see the mode of animal thinking described in their work as functioning in essentially the same fashion as their announced prescriptions (philosophy of science) for their own thinking (1986).

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33 History, Culture, Learning, and Development Patricia M. Greenfield, Ashley E. Maynard and Carla P. Childs

W

e feel very privileged to be part of the special issue honoring and remembering Dr. Ruth Munroe. In the history of our field, she was a pioneering figure who introduced a cross-cultural approach to all aspects of human development into the field of cross-cultural psychology. With her husband, Dr. Robert L. Munroe, she blazed a path for fruitful collaboration between members of anthropology and psychology departments. There are a number of important ways in which the research that we will present in this article can be considered the fruit of intellectual and empirical seeds planted by Dr. Munroe, in collaboration with her husband. First, the Munroes carried out longitudinal research, making connections between two different parts of the life cycle in Kenya. Second, they made cross-cultural investigations of children’s work, highlighting the importance of work as an important shaper of children’s development and the sensitivity of children’s work to larger ecological forces. Third, they realized the potential impact of economic factors on cognitive performance and were able to test this relationship through controlled cross-cultural study. The research presented in this article stands on the shoulders of Ruth Munroe’s collaborative research program in all three respects: It is longitudinal, it centers on children’s work, and it connects larger economic forces with pathways of socialization and human development. In human history, there have been three major ecological adaptations: hunting and gathering, agriculture, and commerce with advanced technology. Like the Munroes, we hypothesize that each ecology emphasizes a different Source: Cross-Cultural Research, 34(4) (2000): 351–374.

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set of skills, different developmental pathways, and different processes of socialization or informal education. Human development is an adaptation to two types of characteristics: the characteristics of the surrounding ecology, such as the climate and type of land available, and the characteristics of cultural practices that arise as adaptations to those ecologies (Weisner, 1984). It follows that different socialization patterns are necessary to prepare children for a changing environment or for an environment that is different from the one in which parents themselves were raised. In this first diachronic study of the impact of ecocultural changes on the practices of informal education, we demonstrate how the distal variable of historical epoch affects proximal variables in the teaching of weaving in a Zinacantec Maya community, resulting in changed teaching practices from one generation to the next. We also investigate a closely related change from a small, closed stock of woven patterns to a new variety of woven patterns, with constant innovation. Last, we show how ecocultural variability in subsistence patterns affects the representation of cultural artifacts.

Informal Education Processes of informal education have been documented by many researchers in recent years (e.g., Greenfield, 1984; Greenfield & Lave, 1982; Lave & Wenger, 1990; Rogoff, 1990). We have come to understand informal education as an apprenticeship process that expresses cultural goals. Past studies of informal education have focused on scaffolding processes from parent to child in the teaching of everyday tasks (Rogoff, Mistry, Göncü, & Mosier, 1993), and the apprenticeship of crafts such as carpentry, tailoring (Lave & Wenger, 1990), and weaving (Childs & Greenfield, 1980). No study has yet considered how processes of apprenticeship change with changes in the ecocultural environment. The research presented in this article investigates the historical transition from agriculture to commerce, focusing on the implications of this transition for learning and development. We focus on three areas of learning and development: the creation of artifacts, apprenticeship, and the symbolic representation of those artifacts. The data are taken from research conducted with two generations of participants in Nabenchauk, a Zinacantec Maya hamlet in the highlands of Chiapas, Mexico.

History, Culture, and Socialization Our investigation also relates to larger questions concerning the relationship between history, culture, and the socialization of the individual. This relationship is central to the field of cultural psychology and, particularly,

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to the sociohistorical approach. Culture at any given moment is the product of historical change, as well as a reflection of cultural constancy and conservatism. The process of cultural transmission from one generation to another links culture at one historical moment with culture at another historical moment. What is called cultural transmission from the point of view of society is called socialization from the point of view of the family, and development from the point of view of the individual. Socialization is intrinsically future oriented; it prepares children for an adulthood that has not yet arrived. It follows that changing socialization patterns should be a key component of the psychological adaptation to social change. However, an important question in conditions of ecocultural change is, do parents merely repeat the socializing process that they underwent as children? Or do parents develop new methods and processes as societal conditions – in this case, economic conditions – change? And what, if any, are the consequences of such changes in socialization for the development of children? The sociohistorical research tradition, derived from Vygotsky (1962,1978), emphasizes that development is constructed through social interaction, cultural practices, and the internalization or cognitive appropriation of symbolic tools (Saxe, 1990). Although the historical dimension of cultural practices and symbolic tools is emphasized – that is, we understand how the practices and tools fit with the development of the culture itself over time – the developmental implications of historical change for those cultural practices and symbolic tools have not been studied directly. To do this, diachronic evidence comparing the development and socialization of one generation with that of the next is required. In taking up these issues of the connection between history and individual development, it is important to consider how, methodologically, to connect macro conditions on the societal level to the micro level of individual development and socialization. We use both quantitative and qualitative analyses to demonstrate the relationship of the macro conditions of a society undergoing ecocultural change to the micro level of individual development and behavior.

Results from Our Study of the First Generation of Weavers In the first video study of informal education in a nonindustrial society, Childs and Greenfield (1980) looked at the interactional processes involved in the transmission of weaving skill from one generation to the next in Zinacantán. This study was a sequel to another in which the authors compared the cognitive consequences of weaving, the most complex skill acquired by Zinacantec girls, with those of formal schooling, received predominantly by boys at the time the data were collected (Greenfield & Childs, 1977).

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In 1969 and 1970, weaving instruction in Zinacantán was characterized by a relatively error-free scaffolding process based on observation of models, obedience to developmentally sensitive commands, and use of help when needed (Childs & Greenfield, 1980; Greenfield, 1984). This mode of informal instruction was well adapted both to the superordinate Zinacantec goal of preserving the baz’i, or “true” (i.e., traditional Zinacantec) way of life (Greenfield & Lave, 1982), and to the innate nature of Zinacantec children (Brazelton, Robey, & Collier, 1969). In terms of developmental theory, weaving apprenticeship followed a Vygotskian model of learning, with its emphasis on guidance by a more skilled “other” (Vygotsky, 1978). In weaving, the “true” way involved learning to construct the repertoire of only four traditional Zinacantec patterns. Pattern innovation and the creation of new patterns were simply not a part of the culture or the transmission process. In 1969 and 1970, the transmission of weaving skill was a relatively error-free, scaffolded process. Teachers stayed close to their pupils and prevented errors before they happened. In the intervening two decades since the first weaving data were collected, profound social change has occurred in Zinacantán. Many Zinacantecs have become entrepreneurs, joining the modern Mexican economy (Cancian, 1990). The community is in the process of a transition from agriculture and a subsistence economy to commerce, entrepreneurship, and cash. Both men and women have become involved in the new cash economy. Some men who formerly farmed are now involved in the transport business, running a van service back and forth to the former colonial city of San Cristobal de las Casas. Some girls and women weave and embroider servilletas, pieces of cloth suitable for use as placemats by tourists and other outsiders who buy them. Whereas the method of apprenticeship practiced in 1969 and 1970 was adapted to transmitting a tradition intact, another method of apprenticeship, trial-and-error learning, with its emphasis on the learner’s own discovery process, should foster the development of an ability to innovate. If innovation had, in fact, entered the culture as a value orientation in response to or as part of commercial entrepreneurship, we thought that weaving education would make a corresponding shift. Earlier, the teacher had carefully built a scaffold of help for the learner, providing help before the learner had an opportunity to make a serious error. Because the learner, in this situation, was afforded very little opportunity to make a mistake, let alone to explore, we predicted that the methods of teaching and learning would change to a more independent trial-and-error approach. From the point of view of developmental theory, this is the model of learning emphasized and valued by Piaget (1965/1997). Independence could also come from having a mother engaged in her own commercial activity. A mother might, for example, use her time to create a commodity to sell, assigning another daughter to teach the younger one how to weave.

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Our study of the effects of cultural change on developmental processes has three parts. In the first part, we present our qualitative data on the changes in the woven artifacts. We predicted that the shift toward entrepreneurship would engender greater innovation in Zinacantec woven artifacts, and this prediction was confirmed. We also predicted that greater innovation would be preceded by a different socialization pattern, one not oriented to the maintenance of tradition. This prediction was confirmed by the results of the second part of our study: We show, using qualititative and quantitative data, how informal weaving apprenticeship at home moved from a more controlled, interdependent style to a more independent, trial-and-error style. In the third part, we present both qualitative and quantitative findings from studies of Zinacantecs’ representations of woven patterns, linking changes in representation to economic movement away from subsistence and toward commerce.

From Tradition to Innovation: The Creation of Artifacts Innovation was in sharp contrast to the values and practices observed in Zinacantán in 1969 and 1970. At that time, tradition, rather than innovation, was valued; there was but a single baz’i, or “true way,” to do everything, from speaking to dressing. In 1969 and 1970, woven artifacts, like other parts of the culture, were stable and unchanging, limited by tradition. Woven patterns were limited to two red-and-white striped configurations, one multicolor stripe, and one gray-and-white basket-weave pattern. Figure 1 shows two Zinacantec boys dressed alike in 1970. Indeed, in 1970, all males dressed virtually alike; for example, these two boys are wearing red-and-white striped pon-

Note: The ponchos are red-and-white striped, with white predominating (see Figure 6, top). Photo courtesy of Sheldon Greenfield.

Figure 1: Two brothers wearing the Zinacantec Poncho, 1970

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Note: Figure 2a shows the whole garment with embroidered flowers on two sides and a bottom band of brocade-woven deer. Figure 2b is a detail from another poncho showing a bottom band of brocade-woven flowers. The background is a red-and-white stripe. Note the high ratio of red to white in the fringe of each poncho. Photos courtesy of Lauren Greenfield.

Figures 2a and 2b: Two different Zinacantec Ponchos, 1991

chos in the only available pattern with no distinctive embroidery. By 1991, each poncho had unique, innovative elements of design. Two examples, each with different brocaded designs along the lower edge (Figures 2a and 2b) and elaborate embroidered designs on each side (Figure 2a), reflect a contemporary trend of pattern innovation. No two pieces of clothing or other woven items were alike in their brocaded and embroidered designs. We saw both new motifs and new recombinations of old motifs. Although the garments were now unique, one element remained the same – the configuration of the red-and-white background stripe (see Figure 2b, top). However, the ratio of red to white had increased dramatically. (The red and white background stripe of the poncho was an important stimulus in the pattern representation experiment described in the last section.)

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From Interdependence to Independence: Apprenticeship Methods Based on our research in 1969 and 1970, we concluded that the goal of Zinacantec education and socialization was the intergenerational replication of tradition: Learning to weave meant learning to weave a few specific patterns. Because tradition was maintained by a more controlled apprenticeship process, with the teacher guiding the pupil very closely, we predicted that innovation would be the result of a less controlled, less guided apprenticeship process, in other words, a more independent process. Our focus for studying changes in apprenticeship methods was on the learning processes involved in the important cultural technology of weaving, the most complex skill in the culture, a skill acquired by virtually all Zinacantec females. Recall that the particular way in which weaving was taught in 1969 and 1970 fostered the goal of maintaining tradition: The learning process was a relatively error-free one in which the teacher, usually the mother, sensitively provided help, models for observation, and verbal direction in accord with the developmental level of the learner. The mother provided a scaffold of help that allowed the learner to complete a weaving she could not have done by herself. There were no failures; every young girl successfully learned to weave. Because the process was highly structured by the older generation and did not allow room for learner experimentation and discovery, the method of informal education (or apprenticeship) was, as mentioned earlier, well adapted for the continuation of tradition and the status quo. In the 1991 and 1993 data, we expected to see more trial-and-error learning, with the learner spending more time weaving unassisted and having to ask for help herself.

Method Participants The participants were 72 Zinacantec girls, ranging in age from 3 to 19 (mean = 11.8, median = 12). The first generation of girls was observed learning to weave in 1970. The second generation, virtually all daughters, nieces, and goddaughters of the first generation, was mainly observed in 1991. Two descendents of the original sample, too young to weave in 1991, were observed in 1993. The girls had varying experience in learning to weave. For some girls, the videotaped session in our protocol was their very first weaving session. Others had woven various items before, ranging in difficulty from very low to very high. Participants were recruited in two ways: by a Zinacantec research assistant, Xun Pavlu, who visited people he knew in the community and asked them to participate, and by word of mouth, as people in the community began to know the researchers and feel comfortable with them.

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Procedure Participants were videotaped for one hour in front of their homes (or inside if it was raining). Participants and their mothers were interviewed about the girls’ experience in weaving. A more extensive description of the procedures is presented in Greenfield, Maynard, and Childs (1999). The medium of communication between researchers and participants was the Maya language of Tzotzil.

Coding of the Videotapes The videotapes were extensively coded by Childs. One major variable of interest in this article is the proportion of time in which learner and teacher were engaged in collaborative weaving activity. This was defined as the proportion of time the learner and teacher/helper were observed working together. For this article, we measured collaborative activity during two segments of the weaving process that are relatively difficult to carry out: attaching the endstick (for woven items that do not have fringe and must therefore be woven to the end of the warp threads), and the first cycle of weaving (the first time a weft thread is inserted into the warp). For learners who attached an endstick during their video observation, we used this segment for our measure; for learners who were making fringed items and therefore did not attach an endstick, we used the first cycle of weaving for our measure. A second measure of interest was the extent of the learner’s observational activity; this was defined as time spent observing the teacher demonstrate some aspect of the weaving process. Interrater reliability for these measures had been established by Childs and Greenfield (1980), based on the 1970 study. However, for the historical comparison presented in this article, Childs recoded all of the 1970 videotapes, as well as coding the 1991/1993 data; this recoding of the old data prevented “historical drift” in the coding process and ensured that the old and new videos were coded in exactly the same way. New interrater reliability was also established. Interrater reliability for collaborative weaving activity and learner observation was based on a random sample of eighteen weaving segments from eighteen learners. Greenfield served as the reliability coder. The correlation between the two coders for proportion of collaborative weaving activity was .8872 ( p = .000); for proportion of learner observation, the correlation was .9703 ( p = .000). Another variable of interest in the current article was the generational status of the teacher. This was a 4-point scale: no helper, helper younger than the learner, helper in same age cohort as learner, helper in older generation than learner. Generational status of the teacher/helper was coded from the video record aided by notes taken at the time of the observation, familiarity with the weavers and their families, and family tree records.

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Textile Commerce Scale To assess mothers’ and daughters’ experiences in textile commerce, we used our own interview data supplemented by access to a survey of the community carried out in 1991 by the Stanford Medical Project. From these data sources, we created a textile-commerce scale. Mother-daughter textile commerce scores are an additive composite of various binary items: for example, whether either mother or daughter sold her weavings, whether daughter wound balls of thread for wages, or whether mother or daughter worked in a family retail store selling thread.

Results Qualitative Results The 1970 video data reveal a highly structured apprenticeship method. Katal Pavlu, age 9, is one example of a girl learning to weave. In the videotape of Katal’s learning session, we first see Katal there by herself. Very soon after the tape starts, her mother enters to help on her own initiative, without being summoned by her daughter. Her mother is very much there, continuously helping or doing part of the weaving for her daughter. This type of involved participation is illustrated in a frame from the videotape, shown in Figure 3. In the video frame, four hands on the loom symbolize the closely assisted style of weaving apprenticeship typical of the era. Katal grew up and had daughters of her own. In the video of her daughter, Loxa Santis, learning to weave 21 years later, also at about age 9, we see a style of teaching geared more toward independent learning (see Figure 4). In the video of Loxa learning to weave, her mother is not there

Note: Nabenchauk (1970), video by Patricia Greenfield.

Figure 3: Four hands on the loom: Xunka’ helps her daughter, Katal Pavlu

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Note: Loxa is about the same age as her mother was in Figure 3. Her teacher is her older sister, Xunka’ Santis. Nabenchauk, 1991, video by Patricia Greenfield.

Figure 4: Katal Pavlu’s daughter, Loxa Santis, learns to weave 21 years later

at all to help her. This might be because she is busy embroidering a blouse sold on order to one of the researchers; this is an example of textile commerce. In addition, Figure 4 illustrates that the teacher no longer comes from the older generation; it is Loxa’s older sister, Xunka’, a member of the peer generation. Although Xunka’ is the teacher, she is paying little attention to the learner (note the direction of the teacher’s visual attention in Figure 4, away from the learner). Indeed, Loxa, the learner, has to call Xunka’ several times, taking the initiative to get her attention. We see, in the diachronic study of one family over two generations, how learning has moved from a more interdependent style of apprenticeship to a more independent style of apprenticeship.

Quantitative Results But how general is this historical case study? Its generality was confirmed by the quantitative analysis of our entire sample of weaving learners from both historical periods. Using structural equation modeling as our primary means of statistical analysis, we demonstrated the predicted relationship between historical period and learner independence-interdependence. Moreover, as predicted, this relationship was mediated by mother and daughter’s participation in textile-related commerce. Figure 5 shows how participation in textile-related commerce creates a pathway by which historical period, a very distal variable, influences collaboration, a proximal variable in cultural apprenticeship, through the mediation of involvement in textile commerce. The model shows that from one historical period to the next, participation in textile-related commerce increases significantly (a positive link of .38 between

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Historical period 0.38∗∗

Textile commerce –0.28∗

Collaboration Note: *Parameter is significant at the .05 level. **Parameter is significant at the .01 level. Using EQS (Bentler, 1980, 1995) with maximum likelihood estimation, we found a good fit between model and data. The comparative fit index (CFI) for the tested model was 1.000, and the model chi-square was nonsignificant, χ2 = .029, p = .8659. (For the CFI and chi-square test, good fit is indicated by a value greater than .90 and by nonsignificant results, respectively. A CFI of 1 is the maximum possible). The model includes all weaving participants videotaped attaching the endstick or weaving the first weft thread (N = 69).

Figure 5: Path diagram of relationship among the variables of historical period, mother-daughter involvement in textile commerce, and teacher-learner collaboration

historical period and textile commerce, significant at the .01 level). More involvement in textile-related commerce led, in turn, to less collaborative activity between learner and teacher (a negative link of .28 between textile commerce and collaboration, significant at the .05 level); in other words, it led to a decrease in learner-teacher interdependence. In line with our diachronic case study, the relationship between the generational status of the teacher/helper and the amount of collaborative activity also generalized to the sample as a whole. In other words, just as Katal’s mother provided more collaborative help to Katal in 1970 than Loxa’s older sister provided to Loxa in 1991, so too there was an overall significant correlation between the generational status of the teacher and amount of collaborative weaving between teacher and learner.1 This result indicated that older teacher/helpers (who were also more skilled weavers) provided more collaborative assistance to weaving learners than younger teacher/helpers did. In other words, on the average, a mother would provide more collaborative help to a weaving learner than an older teenage sister, who would, in turn, provide more collaborative help to the learner than a younger cousin. There was additional evidence of an increase, from 1970 to the 1990s, in learner independence and trial-and-error learning. Between the first and second generation of learners, we found a significant increase in the proportion of time learners spent working independently (attaching the endstick without help from the teacher)2 and a significant decrease in the proportion of time learners spent watching their teachers demonstrate this part of the weaving process.3 Together, these findings paint a picture of an increase in learner independence and trial-and-error experimentation from 1970 to the 1990s.

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Discussion These findings indicate that changes in apprenticeship accompany changes in the ecocultural milieu. Our path model demonstrates the relationships among the variables, from the distal variable of historical period to the experiential variable of mother-daughter participation in textile commerce to the most proximal variable of teacher-learner collaboration. Other analyses showed that in the 1990s, weaving learners spent more time working independently and less time observing a model provided by the teacher than their mothers, aunts, and godmothers had when they learned to weave in 1970. On a theoretical level, our findings indicate that processes of scaffolded guidance (the processes emphasized by Vygotsky, 1978) are emphasized more when cultures are in a more stable, traditionmaintaining state. In contrast, processes of independent trial-and-error experimentation (the processes emphasized by Piaget, 1965/1977) are used more when cultures are in a more dynamic, innovation-oriented state.4 As the Zinacantecs moved from one state to the other in our time slice of two decades, the emphasis in their modes of cultural learning changed accordingly. However, change was uneven. As predicted, it was most concentrated in those families who had made the greatest shift to a commercial way of life.

From Specificity to Abstraction: Symbolic Representation Subsistence involves exchanges and contributions of very specific items. In sharp contrast, a cash economy involves the abstraction of money, which is a totally generalized medium of exchange. Our study of the shift from specificity to abstraction focuses on participants’ ability to represent Zinacantec woven patterns. Our hypothesis was that the historical increase in commerce and use of money would lead to an increase in abstract (as opposed to detailed) representation of woven patterns. We also thought that this historical shift would be mediated by commercial involvement.

Method Participants Participants were 202 Zinacantec children and young adults, ranging in age from 3.5 to 22, with a mean age of 11.54 years. Participants were recruited by the same Zinacantec assistant as in the study of weaving apprenticeship, Xun Pavlu.

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Materials Materials included a wooden frame and sticks that could be arranged in striped patterns inside the frame. The sticks were available in three widths – narrow, medium, and broad. In each width, sticks came in several colors, including pink, orange, red, and white. In addition, participants were provided examples of Zinacantec woven items, one male poncho and one female shawl; they were asked to use the sticks to represent the poncho and the shawl. The poncho (such as was shown in Figure 1) and shawl each had a distinctive configuration of stripes. The male pattern contains a simple red-and-white stripe, whereas the female pattern contains a more complex red-and-white stripe. Examples of each configuration, circa 1969, are shown in Figure 6. The particular items to be represented were whatever the participant was wearing (shawl if a girl, poncho if a boy), plus another contemporary standard example from the clothing of the opposite sex.

Procedure Participants came to the home of a Zinacantec family to be individually tested on the pattern representation procedure. Each participant was asked to use the colored sticks to represent two patterns, the pattern for the men’s poncho and the pattern for the women’s shawl (see Figure 6). (Additional pattern representation tasks are analyzed in Greenfield, Maynard, & Childs, 1999.)

Note: Two examples of each red-and-white stripe pattern are shown (photo by Carla Childs).

Figure 6: Striped configuration for male Poncho (upper left), striped configuration for female Shawl (lower right)

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Family Commerce Scale Because we had both boys and girls in the data set, we made a scale of family participation in nontextile commerce. Like the textile commerce scale, this scale was derived from interview and census data. Almost all items could equally apply to boys or girls. The scale included such items as the family owning a television, working in a local shop, and selling peaches.

Results Styles of Representation in 1969 and 1970 Detailed representation. Skilled weavers often produced an accurate analysis of the configuration of stripes (Greenfield & Childs, 1977). Their analytic representations were always specific or detailed: Each thread in a broad stripe was represented by a separate, thin stick, just as a weaver would construct a broad stripe out of putting together several individual threads (see Figure 7). The accuracy of the pattern analysis can be seen by comparing the configurations of red-and-white stripes constructed in the experiment (see Figure 7) with the actual woven patterns (see Figure 6). Abstract representation. Figure 8 shows an abstract representation of the same woven patterns. This is a style of representation never used by the 1969 and 1970 Zinacantec weavers but used by U.S. college students (Greenfield & Childs, 1977). Like Zinacantec weavers, these college students carried out an accurate, analytic representation of the configuration of stripes (compare Figure 8 with the actual woven patterns in Figure 6). However, this representation uses a single broad stick for a broad stripe rather than combining several narrow ones into a single stripe. As a representation of the two patterns, this strategy is equally accurate. However, it is less specific or detailed, thus more general or abstract. Poncho

Shawl

KEY: RED WHITE

Figure 7: Detailed representation of Poncho and Shawl

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Shawl

KEY: RED WHITE

Figure 8: Abstract representation of Poncho and Shawl

Styles of Representation in 1991 Detailed representations showing a line-by-line or thread-by-thread construction of the patterns remained in 1991. However, many participants used the abstract style, as depicted in Figure 8. Abstraction had been added to analysis of the woven patterns. Our hypothesis was that it was participation in the money economy, with its abstract medium of exchange, that caused this change to a more abstract and less detailed style of representation.

Quantitative Analysis The historical change toward increasingly abstract representation and the role of commercial involvement in this shift were tested by means of structural equation modeling. The structural equation model (Greenfield, Maynard, & Childs, 1999) confirmed our hypotheses that there was a historical shift from detailed to abstract representation of the woven patterns and that this shift was mediated by involvement with commerce. For the purpose of creating a quantitative variable that could be used in a structural equation model, abstract representation was based on the number of medium and broad sticks (as opposed to thin sticks) used to represent stripes in the poncho and shawl. The model showed that from one historical period to the next, participation in nontextile commerce increased and that this increase led, in turn, to a more abstract mode of representation. Although we tend to associate both formal schooling and maturational age with the development of abstraction, the Wald statistical test indicated that neither of these variables contributed to mediating the historical increase in abstract visual representation.

Discussion Our results indicate that ecocultural patterns affect the cognitive representation of cultural artifacts. That is, those Zinacantec children whose families were more involved in commercial activity tended to show a more abstract style of

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representing the woven patterns they were presented. Representational style is not a static quality of a given population. Instead, representational style can change and adapt in the face of changing ecocultural conditions.

General Conclusions The diachronic study of ecocultural change has reaped rich rewards: It reveals theoretical and empirical links between individual processes of cultural apprenticeship and societal processes of cultural change. Our qualitative and quantitative findings point to a process of reciprocal change in which societal conditions provide an ecological push toward new modes of cultural apprenticeship, as new modes of apprenticeship create a younger generation with the skill profile appropriate to the changed societal conditions. Our findings indicate that processes of cultural learning and cultural transmission change as cultures change over time. Based on our earlier analysis, we predicted that socioeconomic changes in the culture would be accompanied by a change in the cultural goals of socialization – that innovation would begin to replace conformity to tradition and, most important for this study, that informal education would reflect the changing value system by coming to rely more on trial-and-error methods, less on demonstration and help (Greenfield, Brazelton, & Childs, 1989). We have found that the Zinacantecs used scaffolded guidance in weaving apprenticeship when they were in a more stable, tradition-maintaining state. In contrast, they used more independent, trial-and-error learning when they moved to a more dynamic, innovation-oriented state. As predicted, the teaching style associated with innovation and independence was used more in those families who had made a greater shift from agriculture to commerce. Sociocultural forces on the macro level affected the process of cultural apprenticeship on the micro level. Changes in processes of cultural apprenticeship produced a new generation that was well adapted to the changed ecological niche. In other words, there was a tight relationship between a changing ecological niche and a changing developmental niche (Super & Harkness, 1986). One aspect of this adaptation was changes in the creation of cultural artifacts. With the shift from a more interdependent to independent style of weaving apprenticeship, girls had the independence to be more creative in their weaving, going outside the traditional frameworks of what a woven piece of clothing should look like and innovating with new designs and colors. At the same time, commerce itself was a socializing force that affected cognitive representation. As the economy moved from subsistence to money-based commerce in our window of two decades, styles of representing textiles became more abstract and less tied to the detailed way

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in which the textiles were created. Our quantitative analysis showed that this change in representational style was mediated by participation in the commercial economy. Our diachronic study is a kind of longitudinal study on the family, rather than individual, level. This new methodology was able to demonstrate links between cultural change, variability in the production and representation of cultural artifacts, and the apprenticeship process by which people learn to produce those artifacts. When the Zinacantecs were in a more homogeneous, agriculture-based ecocultural pattern, socialization processes fostered a continuance of tradition and a more specific style of representation. As many families moved to a more commercial, money-based ecocultural pattern, socialization processes changed to stimulate independent learning, innovation, and abstraction.

Notes 1. r = .3152; p = .026, two-tailed test; n = 50. This correlation is based on all weaving learners who had teachers or helpers. 2. One-way analysis of variance, F(1, 58) = 5.0793, p = .0280. 3. One-way analysis of variance, F(1, 58) = 11.1965, p = .0014. 4. The link to Piaget and Vygotsky was suggested by R. Gelman (personal communication, 1991).

References Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology, 31, 419–456. Bentler, P. M. (1995). EQS: Structural Equation Program Manual. Encino, CA: Multivariate Software. Brazelton, T. B., Robey, J. S., & Collier, G. A. (1969). Infant development in the Zinacantecan indians of southern Mexico. Pediatrics, 44, 274–383. Cancian, F. (1990). The Zinacantan cargo waiting list as a reflection of social, political, and economic changes, 1952–1987. In L. Stephen & J. Dow (Eds.), Class, politics, and popular religion in Mexico and Central America (pp. 63–76). Washington, DC: American Anthropological Association. Childs, C. P., & Greenfield, P. M. (1980). Informal modes of learning and teaching: The case of Zinacanteco weaving. In N. Warren (Ed.), Studies in cross-cultural psychology ( Vol. 2, pp. 269–316). New York: Academic Press. Greenfield, P. M. (1984). A theory of the teacher in the learning activities of everyday life. In B. Rogoff & J. Lave (Eds.), Everyday cognition (pp. 117–138). Cambridge, MA: Harvard University Press. Greenfield, P. M., Brazelton, T. B., & Childs, C. P. (1989). From birth to maturity in Zinacantan: Ontogenesis in cultural context. In V. Bricker & G. Gossen (Eds.), Ethnographic encounters in Southern Mesoamerica: Celebratory essays in honor of Evon Z. Vogt (pp. 177–216). Albany: Institute of Mesoamerican Studies, State University of New York.

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Greenfield, P. M., & Childs, C. P. (1977). Weaving, color terms, and pattern representation: Cultural influences and cognitive development among the Zinacantecos of southern Mexico. Inter-American Journal of Psychology, 11, 23–48. Greenfield, P. M., & Lave, J. (1982). Cognitive aspects of informal education. In D. Wagner & H. Stevenson (Eds.), Cultural perspectives on child development (pp. 181–207). San Francisco: Freeman. Greenfield, P. M., Maynard, A. E., & Childs, C. P. (1999). Historical change, cultural learning, and cognitive representation in Zinacantec Maya children. Manuscript submitted for publication. Lave, J., & Wenger, E. (1990). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press. Piaget, J. (1977). Developments in pedagogy. Reprinted in H. E. Gruber & J. J. Vonèche (Eds.), The essential Piaget: An interpretive reference and guide (pp. 696–719). New York: Basic Books. (Original work published 1965) Rogoff, B. (1990). Apprenticeship in thinking: Cognitive development in social context. New York: Oxford University Press. Rogoff, B., Mistry, J. J., Göncü, A., & Mosier, C. (1993). Guided participation in cultural activities by toddlers and caregivers. Monographs of the Society for Research in Child Development, 58, (7, Series No. 236). Saxe, G. (1990). Culture and cognitive development: Studies in mathematical understanding. Hillsdale, NJ: Lawrence Erlbaum. Super, C., & Harkness, S. (1986). The developmental niche: A conceptualization at the interface of society and the individual. International Journal of Behavioural Development, 9, 545–570. Vygotsky, L. S. (1962). Thought and language. Cambridge, MA: Harvard University Press. Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press. Weisner, T. S. (1984). Ecocultural niches of middle childhood: A cross-cultural perspective. In W. A. Collins (Ed.), Development during middle childhood: The years from six to twelve (pp. 334–369). Washington, DC: National Academy Press.

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34 Biology and Cognition Jean Piaget ( Translated by Martin Faigel )

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his article is a summary of the conclusions of a work in progress on “Biology and Cognition;” from this stems the rather general character of the following observations. In order to compare cognitive and biological mechanisms, we must first state that the former are an extension and utilization of organic auto-regulations, of which they are a form of end-product. To demonstrate this, one can begin by noting the close parallels between the major problems faced by biologists and those faced by theoreticians of the intelligence or of cognition. Secondly, one can analyze the functional analogies and especially the structural isomorphisms between organic life and the means of cognition: “nested” structures, structures of order, multiplicative correspondence, etc. One can also attempt a sort of comparative epistemology of the different levels of behavior (the “logic” of the instincts or of the learning processes, etc.). Finally, one can examine the explanations current among biologists to account for the formation of intelligence. But if these various analyses bring into relief the continuity between organic life and cognitive mechanisms, on the other hand it still remains to be seen that the latter constitute differentiated and specialized organs for reacting physiologically to the external world. Or in other words, that at the same time that they are an elaboration of organic structures in general, they fulfill particular functions, although still of a biological nature. The following pages are based on this premise, but it should be understood that it is not a question of contrasting cognition with organic behavior but rather of placing the functions of the former within the framework of the latter. Source: Diogenes, 14 (1966): 1–22.

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A. The Functions Specific to Cognition In studying the functional relationships and the partial structural isomorphisms between cognitive and organic functions, one notes the existence of a remarkable number of similarities but also a certain number of differences which show that cognition also has specific functions. Moreover the contrary would be unthinkable since if organisms were self-sufficient – without instincts, acquired ability, or intelligence – it would indicate a radical duality of kind between life and cognition, since cognitive mechanisms do in fact exist. This in turn would raise inextricable difficulties for an epistemology simply trying to explain how science is able to arrive at objective knowledge. I. Behavior, the extension of the environment and the closing of the “open system.” To begin with the basic facts of ethology, the majority of perceptions characteristic of animals are of a utilitarian and practical kind. Instinct is always at the service of the three fundamental needs of nutrition, selfdefense, and reproduction. If with migrations or different types of social organization it seems to pursue derivative ends, they are derivative only in the sense that these interests, grafted onto the three principal ones, are still based on them and are ultimately subordinate to the survival of the species and to the possible survival of the individual. The elementary forms of perceptual or sensory-motor learning fall within a similar functional structure, and it is the same for a very large part of routine or sensory-motor intelligence. Nevertheless, in this latter area one must admit that with mammals and especially Anthropoids there is some development of activity which remains functional but involves comprehension for its own sake: we know that young mammals play and that this, despite K. Groos, is not just an exercise of the instincts, but a general exercise of the activities possible at a given level, without present utility or without being put into use. Now, play is but one pole of the functional processes operating in the course of individual development, the other pole being non-playful exercise, where the young subject “learns to learn” (Harlow) in a context of cognitive adaptation and not solely of play. One of our children, aged about one year, chanced to pass through the bars of his play-pen a toy which he wanted but which, being too long, had to be placed vertically in order to make the passage possible. He was not satisfied by his chance success, but he put it outside again and repeated his efforts until he “understood.” This beginning of disinterested knowledge is without doubt equally accessible to chimpanzees. But whether exclusively utilitarian or involved in this transition from “know-how” to “understanding,” animal cognition thus already quite clearly demonstrates a specific function, in comparison with survival, nutrition, or reproduction in their purely organic aspects: this is the function of extension of the environment. To search for food instead of drawing it from the earth or from the atmosphere like a plant, is already to enlarge the environment.

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To search for the female and to care for offspring is to assure to reproduction more spatial-temporal control than that of the purely physiological function. And to explore for the sake of exploration (like the rats described by Blodgett), without immediate utility, to the point of learning for its own sake, as this already appears within the realm of sensory-motor intelligence, is to extend even further the part of the environment that is actually put to use. It is clear that during later development the mere existence of instruments for intelligent cognition, even if it pursued only utilitarian ends at the start, creates a new functional situation, since every organ tends to develop and maintain itself for its own sake: from this stem the fundamental cognitive needs of comprehension and invention; but they in turn lead to an evergrowing extension of the environment, this time as an object of consciousness. One can express biologically this slow extension, later to become more and more accelerated with man, of the accessible environment to needs at first biological and later more specifically cognitive, by relating it to the fundamental traits of the living system. An organism, according to Bertalanffy, is an “open system” precisely in the sense that it retains its form only through a continuous flow of exchanges with the environment. Now, an open system is a constantly threatened system, and it is not for nothing that the basic concerns of survival, food and reproduction lead to behavior which results in the extension of the usable environment. This extension must then be translated into terms which express its actual function: it is essentially an attempt to close the system and this precisely because it is too “open.” From the point of view of probability (and it is the only one suitable here) the particular risk to the open system is that its immediate environment or its frontiers will not supply the necessary elements for its survival. To close the system would instead be to circumscribe an area capable of ensuring survival. One sees at once that the closing of the system is a goal constantly pursued but never achieved. It is not that the initial needs of food, protection, or reproduction are infinite, far from it. Rather it is that, as soon as various actions serving to satisfy these needs are developed, thanks to a slight enlargement of the initial environment, the cognitive controls of these actions lead sooner or later to an unlimited extension of the system, and this for two reasons. The first is related to the probability of encounter with desired elements (food and sex) or feared ones (protection). So long as a living creature does not have differentiated sensory organs, exterior events affect it only through immediate contacts and cease to exist as soon as the immediacy disappears. There exist then only momentary needs which disappear as soon as they are satisfied and reappear later, according to a periodic cycle of varying length. However, as soon as a cognitive control develops and olfactory or visual organs indicate food or danger some distance away, the needs are modified by this extension itself: even if the appetite is momentarily satisfied, the absence of visible nourishment or its odor becomes a disturbing modification of the

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possibilities of recurrence and creates a new need in the form of the need to search, although there may be no immediate desire to be satisfied. Similary, awareness of enemies, even a safe distance away, engenders a new need for vigilance and watchfulness. In other words, the appearance of a cognitive control leads to its alteration as a consequence of function, and this change involves an enlargement of the environment without the possibility, on this elementary level, of ever closing the “open system.” Moreover we should note that a similar general extension of the environment begins already on an organic scale previous to sensory controls. This is the dissemination of seed in the sexual reproduction of plants, a good example of spontaneous extension without cognitive control. What would happen if a cognitive control permitted the plant to be informed by feedback of the relative insuccess of this manner of propagation? II. Behavior and cognitive controls. The second reason for the enlarging of the environment which aims at closing the “open system” but which constantly pushes back the limits of this closure is progress in the internal mechanisms of cognitive regulation. Here we reach an essential point about the nature of the cognitive process and the way it develops. Let us take an ordinary physiological cycle (A × A′)→(B × B′) →… (Z × Z′) → (A × A′) →, where A, B … Z are the elements of the organism and A′, B′… Z′ are the elements of the milieu with which they are in basic interaction. One can then schematize the intervention of a developing cognitive mechanism as a control which reacts to the presence of some external element or other, A′, informs the relevant organs, A, and thus participates in the process A B, facilitating its development. From the beginning therefore, cognitive response has a role of control and leads to compromise, intensification, change, compensation or other regulation of the physiological process. But it goes without saying that this elementary response, which can take the form of tropisms or of only slightly differentiated reflexes, precisely because it is a regulating mechanism involves the possibility of, and even requires, indefinite development, for it is in the nature of a regulating agency to be able to correct itself through the control of controls. In the case of our elementary scheme the chain or feedback leading from A′ to A, which comprises a signal from A′, or afference, and an effect on A, or effection, results in two kinds of possible improvements or controls of behavior to the second power, while internal or physiological regulation affect the process A→B: (1) there can be refinements in the recording of A′ in the form of various conditionings which assimilate new signals or cues within the initial set of perceptive schemata and thus constantly enrich the perceptive keyboard with controls differentiating the initial total stimulus; (2) above all there will be refinements in the reactive systems affecting A, and it is here that new controls show their possibilities in an uninterrupted sequence, of which sensory-motor development in man’s growth pattern gives a particularly striking example: on the basic reflex schemata such as suction,

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grasping, or ocularymotor reflexes a succession of more and more complex behaviors is built, whose two general principles are the accommodation of assimilatory schemata leading to their differentiation, and above all the reciprocal assimilation of schemata (vision and touch, etc.), leading to their coordination. Now from the point of view which concerns us here, the double basic significance of this development, which produces sensory-motor intelligence, is (a) that the progress we have observed is due to a control of controls which results in the exercise of cognitive functions for their own sake, independently of utilitarian or strictly biological basic needs (nutrition, etc.), and (b) that consequently this pushes further and further back the “closure” of a system open to the environment. That this progress is due to a control of controls is evident, to begin with, in the differentiation by accommodation of the assimilatory systems. For on one hand this accommodation is carried out by trial and error, and this is typical of feedback systems where the action is corrected according to its results. But on the other hand, this trial and error control does not develop from nothing, but from within a previous framework of reflexes or acquired assimilatory schemata, and these initial schemata are the basic controls whose differentiation is elicited by a superimposed regulation. The coordination of schemata by reciprocal assimilation also involves the control of previous regulations by new ones, and these secondary regulations are especially important since they are related to actions. For the coordination of schemata is a process which simultaneously moves forwards and backwards, since it arrives at a new synthesis which modifies in its turn the schemata thus coordinated. The internal progression of the mechanism of cognitive control then implies its exercise, that is to say, the formation of a series of new interests no longer subject to the initial interests which are activated by the functioning per se of the system. These interests are the functional expression of the mechanism of cognitive assimilation itself but, again we see, as a direct extension of the initial sensory process. The resulting enlarged environment is therefore both the environment, in the biological sense of all the stimuli which affect the organism in its physiological cycle, and the cognitive milieu, considered as all the objects of interest to the consciousness. But this new extension of the environment is unable to close the “open system” since it remains subject to probabilities of occurrence or, in other words, to the chance experiences of the subject. It is only with imagination or thought, which multiplies at an accelerating rate the spatial-temporal distances characterizing the field of action and comprehension of the subject, that the closing of the open system becomes a possibility. But this requires inter-individual or social exchanges as well as individual exchanges with the environment, and we shall return to this problem later. III. Organic equilibrium and cognitive equilibrium. If the first essential function of cognitive mechanisms is thus the progressive closing of the “open system”

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of the organism thanks to an indefinite extension of the environment (and this function is indeed an essential part of the process even if, or above all if, it never reaches complete stability), this function leads to a series of others. The second one to remember is of equally fundamental importance, for it relates to the system’s mechanisms of equilibrium. Living systems are essentially self-regulating. If what we have discussed is correct, the development of cognitive functions is clearly, in accordance with our hypothesis, the creation of specialized organs of control for the regulation of exchanges with the exterior, at first of a physiological type, directed at materials and forces, and later purely functional, that is to say, bearing essentially on the functioning of actions and of behavior. But once differentiated organs come into being, are their controls identical to those of the organism? Or in other words, are the forms of equilibrium the same? The body of known facts leads to the reply, yes and no. They are the same regulations or the same forms of equilibrium in the sense that cognitive organization is an extension of living organization and therefore introduces an equilibrium in the sectors where the organic equilibrium is inadequate – in its particular sphere (as we have seen) and in its accomplishments. But the controls and the cognitive equilibrium differ from the organic equilibrium precisely in that they succeed where the latter is incomplete. The evolution of organized life appears as an uninterrupted sequence of assimilations of the environment to more and more complex forms, but the very diversity of these forms shows that none of them has been able to put this assimilation in a state of lasting equilibrium. If each group or species is in equilibrium, their succession demonstrates a perpetual beginning anew. It is therefore first of all in the relationship between assimilation and adjustment that the cognitive functions introduce something new. To begin with the development of knowledge, it seems at first sight that we are in the presence of a completely comparable phenomenon. Not to mention the diversity of instincts or of elementary learning processes, the evolution of the human sciences does not always give us a picture of coherent development easily able to introduce new adjustments required by experience into a permanent assimilatory framework by enlarging or simply differentiating it. But there is an exception, and this is the major one of logico-mathematical structures, important enough by itself but notably increased in significance by the fact that these structures provide the principal assimilatory schemes used by the experimental sciences. In effect, logico-mathematical structures present the unique example of a continuously evolving development, such that no new structurization has had to eliminate its predecessors. Of course these can be poorly adapted to an unforeseen situation but only in the sense that they are unable to resolve a new problem and not that they are contradicted by the very terms of this problem, as it can happen in physics. Thus, in the relationship between assimilation and accommodation, logico-mathematical structures involve a sui generis type of equilibrium.

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On one hand they can be viewed as the continuous construction of new schemes of assimilation – the assimilation of previous structures in a new, integrated one, and the assimilation of experimental data in the structures thus created. But on the other hand, they show a permanent accommodation in the sense that they are not modified by the newly created structures (except to be amplified) or by the experimental data which the latter are capable of assimilating. Certainly, new data on physical experience can pose unexpected problems for mathematicians and lead to the creation of theories which can absorb them; but the creation in this case is not drawn from an accommodation in the manner of the concepts of physics. On the contrary it is derived from previous structures or schemata at the same time that it is adapted to the new reality. One can then propose an interpretation which might appear to be rash but which seems to have a true biological foundation if one agrees, as everything seems to suggest, that the primary source for the coordination of actions, out of which come mathematics, can be found in the general laws of the system: it is that the equilibrium between assimilation and accommodation reached by logico-mathematical structures constitutes the simultaneously flexible, or dynamic, and stable state vainly sought after by the succession of forms, at least in the realm of behavior, during the evolution of organized life. While this evolution is marked by a continuous series of disequilibriums and equilibriums, logico-mathematical structures achieve a permanent equilibrium despite the new additions which characterize their evolution. This brings us to the problem of “vection” or of “progress” raised by many present-day biologists. Vection, which seems to be proved by organic evolution, is characterized by the remarkable union of two apparently antithetical qualities, whose cooperation is necessary for the major accomplishments of adaptation. One has been especially stressed by Schmalhausen: this is an increasing integration which makes the processes of development more and more autonomous with regard to the environment. The other, stressed by Rensch and by J. Huxley, is the increasing “widening” of possibilities for influencing the environment, and by consequence penetration into environments which become more and more extended. It goes without saying that these two aspects in combination can be found in the development of the sciences. It is to the extent that human intelligence has found in logico-mathematical structures an instrument of integration increasingly independent of experience that it has made a greater conquest of the experienced environment. But once again, because of the very nature of their equilibrium, the cognitive structures develop from the organic ones through extension. They have a similar nature but, as we have seen, in the case of cognition it has developed into forms which are inaccessible to the organic equilibrium. With regard to vection, the difference appears in the following way. The process of integration pointed out by Schmalhausen involves only a certain type of integration, which can be described as current or synchronous,

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and it therefore has to reconstitute itself in every new group without being able to integrate the entire phyletic past as a sub-system both retained and developed (to put it concretely, mammals have lost some of the characteristics of reptiles by becoming mammals, etc.). The unique character of the integration characteristic of cognitive evolution is, on the contrary, as we have seen, that it is more than temporary and integrates previous structures as subsystems of the current integration. This integration, surprisingly both diachronic and synchronous, occurs without conflict in mathematics (whose “crises” are only those of growth with but momentary contradictions); however, in the experimental sciences a new theory can contradict previous ones. It remains notable though that a new theory always aims at a maximum of integration of the past, so that the best theory is the one which integrates previous results, adding necessary retroactive corrections. IV. The dissociation and conservation of forms. But this achievement is due to another specific character of the cognitive functions in contrast with organic life: this is the possible dissociation of form and content. An organic form is inseparable from the matter which it organizes, and in any particular case it is suited only to a limited and well-defined group of substances, whose modification necessitates a change in form. Once again we find a similar situation (given the continuity between the living system and the cognitive one) in elementary forms of consciousness such as sensory-motor or perceptual schemes, although they are already more generalized than the innumerable forms of biological organization. But with the development of intelligence, operative systems become still more generalized, although at the level of concrete operations (classes and relations) they may still be related to their contents, just as structurization is to the structured matter when it can proceed only step by step without sufficient deductive mobility. Finally, with the hypothesizing-deductive activity which proportional combination permits, it becomes possible to elaborate a formal logic, in the sense of an organizing structure applicable to any kind of content whatever. This is what makes it possible to create “pure” mathematics, viewed as an assemblage of organized forms prepared to organize anything, but ceasing temporarily to act according as it is dissociated from application. Once again we find a biological situation impossible on an organic level, where micro-organisms are capable of “transduction” of genetic messages from one species to another, but only as content or matter, and where genetic “transduction” of an organization understood as a form dissociated from all substance has not yet been observed! But on the cognitive level, this refining of form leads to accomplishments constantly sought after, one might say, in the organic domain but never fully achieved. It is possible to establish certain analogies between the conservation of biological forms (so evident in the regulatory self-conservation of the chromosome) and the exigencies of conservation characteristic of different forms of intelligence, from sensory-motor intelligence (a system for the

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permanency of objects) to operative conservation. In this respect it might seem that an artificial comparison is being made between quasi-physical systems on the one hand and normative or ideating ones on the other. But once one is aware of the basic nature of regulation characteristic of elementary cognitive functions and the sequence from regulation to action, the comparison becomes more natural, for organic conservation is in fact the outcome of regulatory mechanisms. But the analogies thus touched upon nonetheless run into an important difference, and this is what concerns us here: organic conservation is never more than approached. Moreover, this is also true for preliminary cognitive forms (perceptual constants), while only the operative conservations of intelligence are rigorous and “necessary,” on account of the dissociation of form and content. Conservation is closely related to operative reversibility, which is its source and which, in addition, demonstrates the particular form of equilibrium reached by logico-mathematical structures. We must then be at the very heart of the difference which, deep within their similarity, distinguishes the constructive work of intelligence from that of organic transformations. The basic analogy is that both have to struggle incessantly against the irreversibility of events and the deterioration of energy and information. And both systems deal with the problem by elaborating organized and balanced systems whose principle is to compensate for deviation and error. Thus, beginning with controls of a homeostatic1 nature – genetically as well as physiologically – there is a fundamental tendency towards reversibility of which the attempted conservation of the system is the result. Whatever may be the eventual explanations, still to be worked out, used to resolve the problem of the anti-chance function necessary to the organization and evolution of life (exceptions to Carnot’s principle or various forms of conciliation) there remains however that an auto-regulatory system involves actions oriented in two opposed directions and that it is this partial reversibility whose progress we can follow in the development of cognitive controls. But as we have pointed out above, the result of the general interplay of reflective abstractions and of reconstructions converging with this evolution, is that the evolution which marks the progress of each level with respect to the preceding one is based more on the regulation of regulations, and so on a reflexive refining of the system or on superimposed controls, rather than on a simple horizontal extension. This is why the mechanism of the “operations” of thought represents more than an extension of previous controls and constitutes a sort of limiting process towards the point where strict reversibility establishes itself as soon as the retroactive action of feedback becomes an “inverse operation,” thus ensuring the exact functional equivalence of the two possible directions of the construction. V. Social life and the general coordination of action. But the most remarkable aspect of human knowledge in its mode of formation, as compared with the evolutionary transformations of organisms and the forms of knowledge

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achieved by animals, is its collective as well as individual nature. One can of course observe the outlines of a similar characteristic in a number of animal species, especially the chimpanzee. Nevertheless, the novelty with man is that external or educative transmission (as opposed to the hereditary or internal transmission of the instincts) has led to an organization capable of fathering civilizations. We should first note that, if it is necessary to distinguish between two types of development, one organic (characteristic of a single organism) and the other genealogical (comprising lines of descent, whether social or genetic), the history of human science combines these two developments in a single whole: ideas, theories, and schools of thought develop genealogically, and one can construct for them genealogical trees representing the relationship of structures. But they are so well integrated into a single intellectual organism that the succession of thinkers is comparable, to quote Pascal, to a single man endlessly learning. Now, human societies have been described, in turn, as the result of individual initiative propagating itself by imitation, as totalities acting from the outside on individuals, or as systems of complex interactions producing individual action, which is always in conjunction with a more or less important part of the group, as well as producing the entire group defined as the system of these interactions. In the area of cognition, it seems evident that the individual operations of intelligence and the operations that ensure the actual exchange in cognitive cooperation are one and the same thing, since the “general coordination of the actions,” which is the source of logic, is an inter-individual as well as intra-individual coordination, inasmuch as these “actions” are collective as well as individual. It is therefore a meaningless question to ask if logic or mathematics are essentially individual or social. The epistemic subject which creates them is both an individual, placed off-center with respect to his specific “me,” and the sector of the social group, off-centered with respect to the constraining idols of the tribe; and these two types of displacement show the same intellectual interactions or general coordination of action which is constitutive of cognition. The result is thus (and this is the final basic difference which we shall point out between biological and cognitive organization) that the most general forms of thought, since they are capable of being dissociated from their content, are because of this the medium for cognitive exchange or inter-individual regulation, at the same time that they arise out of common functions characteristic of all living systems. Certainly, from a psychogenetic point of view, these inter-individual or social (and not hereditary) regulations form a new element with respect to individual thought, which if deprived of them is exposed to all kinds of egocentric deformation, and they are a necessary condition for the constitution of a decentralized, epistemic subject. But from a logical point of view, these higher controls are still dependent on the conditions of all general coordination of action and so have the same biological origins.

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B. Organic Regulation and Cognitive Regulation This collective re-elaboration of forms already built out of elements pertaining to biological organization also helps to locate the remaining observations within their true framework. Our hypothesis is thus that cognitive functions are a specialized organ for the regulation of exchanges with the external world, although they derive their instruments from biological organization in its general forms. I. Life and truth. It might seem that the necessary existence of a differentiated organ is self-evident, since the specific character of knowledge is to attain truth, while it is specific of life only to seek its persistence. But if we do not know exactly what life consists of, we know even less about cognitive “truth” There is general agreement that it is something other than a faithful copy of reality, for the good reason that such a copy is impossible, since only the copy could provide the knowledge of the model to be copied and since this knowledge, on the other hand, is necessary for the copy! To attempt it leads to a simple phenomenism, where subjectivity constantly interferes with the perceived datum, which itself demonstrates an inextricable connection between subject and object. If truth is not a copy, it is then an organization of reality. But organized by what subject? If we take the human subject, the risk in this case is expanding egocentrism intro anthropocentrism – which will also be sociocentrism – and the gain is minimal. Consequently philosophers concerned with the absolute have had recourse to a transcendental subject which goes beyond man and especially “nature” so as to place truth outside spatial-temporal and physical contingencies and to make nature intelligible in a non-temporal or eternal perspective. But the question then is whether it is possible to leap over one’s shadow and to reach the “Subject” in se, without his remaining, in spite of all, “human, too human,” to quote Nietzsche. For the trouble is that from Plato to Husserl the transcendental subject has constantly changed shape, with no improvements other than those due to the progress of the sciences themselves, hence of the real model and not the transcendental one. Our intention then is not to run away from nature, since no one escapes nature, but to investigate it step in step with the effort of science because, whatever the philosophers may think, it has still not given up all its secrets and because, before putting the absolute in the clouds, it may be useful to look at the inside of things. Consequently, if truth is an organization of reality, the first question is to understand how one organizes an organization, and this is a question for biology. In other words, since the epistemological problem is to know how science is possible, we should exhaust the possibilities of immanent organization before having recourse to the transcendental. But if truth is not egocentric and should no longer be anthropocentric, is it then necessary to reduce it to a biocentric organization? If truth is more than man, is it necessary to look for it in protozoa, termites, or chimpanzees?

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If one defined it as a vision of the world shared in common by all living creatures, including man, the result would be a meager one. But the character of life is to surpass itself constantly, and if one seeks the secret of rational organization in the living system, including its own mechanisms of progress, the method then consists of trying to understand knowledge by its very construction, which is not the least bit absurd, since it is essentially construction. II. The deficiencies of the organism. From a cognitive point of view, these progressive evolutions, which are just as essential as the initial state, seem inherent to the living system itself. Its organization is that of the system of all exchanges with the environment; it tends then to spread out into the envire environment but it never completely succeeds. This is where cognition comes in to assimilate functionally the whole universe without being limited to material physiological assimilation. The living system creates forms and it tends to conserve them in as much stability as possible, but without success. And this again is where cognition comes in to extend material forms into forms of action or of operation which are then capable of conservation under their applications to the various contents from which they are dissociated. This living system is a source of homeostasis at every step; its regulations ensure equilibrium by the evolution of quasi-reversible mechanisms. However, this equilibrium remains fragile and resists the surrounding irreversibility during but transient stages, so that evolution appears to be a series of disequilibriums and of returns to equilibrium, partially giving way to a mode of structuring that comprises the integrations and reversible mobility which cognitive mechanisms only are able to accomplish completely by integrating control into the construction itself in the form of “operations” In short, the need for differentiated organs to regulate exchanges with the external world results from the inability of the living system to carry out its own program, implicit in the very laws of its organization. For on one hand, it involves genetic mechanisms which are formative and not merely transmittive; but their method of formation (as it is now understood) founded on the recombination of genes, ensures only a limited construction, bounded by the needs of hereditary programming which is necessarily restricted, as it is unable to conciliate construction and conservation within a single coherent dynamic (as cognition will do), and as it lacks sufficiently flexible information on the environment. On the other hand, phenotypes,2 that achieve a certain amount of interaction with the environment, fall within a norm of reactions in itself bounded; but above all their individual achievements remain both limited and without influence on the whole (for want of the social or external interactions which are made possible for man by cognitive exchanges) except through genetic recombinations, with their afore-mentioned limitations. This double deficiency of organisms in their material exchanges with the environment is partly compensated by the constitution of structured behaviors, created by the system as an extension of its internal program. For behavior is nothing more than the very organization of life, but applied or generalized to

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a larger sector of material and energy exchanges than those which are already ensured by the physiological organization. And functional implies that the emphasis is on the actions and forms or schemes of action that extend organic forms. Nonetheless, these new exchanges, like all the others, consist in adaptations to the environment, that take into account its events and their sequence; but above all they consist of assimilations which use the environment and often even impose shapes upon it through constructions or arrangements of objects satisfying the needs of the organism. Like all organization, this behavior involves regulations, whose function is to control constructive adaptations and assimilations by acting on information on the results received in the course of action or by the elaboration of anticipations which allow the forecasting of favorable events or of obstacles and the preparation of the necessary compensations. These regulations, which are differentiated with regard to the internal control of the organism (since we are concerned here with behavior) constitute the cognitive functions. And the problem then is to understand how they widen the scope of organic regulation to the point where they can carry out the internal program of the system without being subject to the deficiencies we mentioned. III. Instinct learning, and logico-mathematical structures. The basic facts here are in the first place, that cognitive controls begin by using only the instruments of organic adaptation in general, that is to say, heredity with its limited variations and phenotypic accommodation: from these stem the hereditary modes of cognition such as those that appear in instinctive behavior. But subsequently the deficiencies of the initial system that are corrected only slightly on the new behavioral level turn up at the level of this innate cognition. This is what causes, but only during the later stages of evolution, the final break-up of instinct and the separation of its two components, internal organization and phenotypic adaptation. What results then (and this is not immediately upon dissociation, but as an effect of complementary reconstructions in two opposite directions), is the double emergence of logico-mathematical structures and of experimental science, still undifferentiated in the practical intelligence of Anthropoids, who are geometers3 as much as they are technicians, and in the technical intelligence of the beginnings of humanity. The three fundamental types of knowledge are innate skill, whose prototype is instinct, knowledge of the physical world, which extends the learning process as a function of the environment, and logico-mathematical knowledge; and the connection between the first and the latter two seems essential to an understanding of the way in which higher forms are indeed an organ for controlling interchanges. We shall return to this point in conclusion. Instinct indeed already includes some cognitive controls as may be observed, for example, in the feedback system formed by Grassé’s “stigmergies.”4 But these controls remain limited and rigid, precisely because they develop within a framework of hereditary programming, and programmed controls are not capable of invention. Certainly it happens that animals are able to deal with

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unforeseen situations through readjustments which foretoken intelligence. The coordination of schemata that occurs on this occasion can be compared with the innate coordinations of the instinctual, trans-individual cycle, which gives an important indication of the possible functional relationship between instinct and intelligence, despite the difference of epigenotypic5 and phenotypic levels which characterizes them. But the phenotypic developments of instinct remain very limited and its deficiency thus remains tied to its nature, which demonstrates that a form of cognition that remains linked to the simple mechanisms of organic adaptation, despite some traces of cognitive regulation, scarcely approaches the achievements of intelligence. Though the area of learning stricto sensu, that which lies beyond the innate, begins with protozoa, it grows only very slowly until the cerebralization of the higher vertebrates, and however remarkable the exceptions that begin to appear with insects, it shows no systematic development until the primates. IV. The break-up of instinct. The fundamental phenomenon of this scission, or in other words, the almost total disappearance in the Anthropoids and man, of a cognitive organization which remained dominant throughout the entire evolution of animal behavior, is thus highly significant. This is not, as it is generally said, because a new mode of cognition, that is to say, intelligence considered en bloc, replaces a superseded one. More deeply, it is because a still quasi-organic form of cognition develops into new forms of control which take the place of the preceding form but do not replace it. Properly speaking, they inherit it, dissociating it and using its components in two complementary directions. What disappears with the dismemberment of instinct is hereditary programming, and this benefits two new types of cognitive self-regulation, that are both flexible and constructive. One might then say that this is in fact a replacement, and indeed a complete one. But one then forgets two essential factors. Instinct does not consist exclusively of hereditary mechanisms – such a concept is an extreme one, as Viaud has properly pointed out. On the one hand, instinct derives its programs and above all its “logic” from an organized activity which originates in the most general forms of the living system. On the other hand, it extends this programming by individual or phenotypic actions that contain an important element of adaptation and even of assimilation, in part learned and in certain cases almost intelligent. Now, what vanishes with the disappearance of instinct is only the central or median part, that is to say, programmed control, while the other two components – the origins of organization and the results of individual or phenotypic adjustment – remain. Intelligence therefore inherits instinct while it rejects the methods of programmed regulation in favor of constructive selfregulation. What it retains allows it to follow the two complementary directions of interiorization, towards sources, and of exteriorization, towards learned or experienced adjustments.

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The condition for this double evolution is naturally the construction of a new mode of control, and this must be remembered to begin with. These controls, which are no longer programmed but from now on are flexible, begin with the usual corrective activity, carried out as a function of the results of actions and of anticipations. But as participants in the construction of schemes of assimilation and in their coordination, under the combined influence of progressive and retroactive effects they end up moving in the direction taken by operations themselves, in as much as these are viewed as controls for precorrection and not just correction, and as the inverse operation is viewed as an action ensuring complete and not simply approximate reversibility. It is thanks to this new kind of control, that constitutes a differentiated organ for deductive verification as well as for construction, that intelligence can evolve simultaneously in the two directions of reflexive interiorization and experimental exteriorization we have just discussed. It is clear that this double orientation does not involve, and in fact has nothing in common with a sharing of the spoils of instinctual cognition. On the contrary, what remains of instinct is only its sources of organization and its end-products such as exploration and individual research. For intelligence to use the former and extend the latter, it must therefore turn to new constructions, of which some release the pre-conditions for general coordination of action through the use of reflective abstraction, and others absorb the experimental data into the operatory systems thus constructed. But it remains no less true that these two directions carry on the functions of two of the previous components of instinct. After the break-up of instinct, a new cognitive evolution begins and in fact it starts from scratch since the innate mechanisms of instinct have disappeared and, no matter how hereditary the cerebral nervous system and intelligence, seen as an ability to learn and invent, may be, the work to be done henceforth is phenotypic. Moreover, it is because this intellectual evolution starts from scratch that one generally finds it so difficult to relate it to the living system or above all to the structures, remarkable in their own right, of instinct. This is a good example of what one might call “convergent evolving reconstructions.” In the case of human intelligence, this reconstruction is in fact so complete that hardly any theoreticians of logico-mathematical knowledge have thought to explain it in the clearly necessary framework of biological organization. This was true at least before mechanophysiology showed the connection between logic, cybernetic models and the neurophysiological activity of the brain, or before McCulloch described the logic of neurons. V. Knowledge and society. But if such complete reconstruction is possible, it is because intelligence, by discarding the prop provided by hereditary structures and moving towards constructed and phenotypic controls, turns away from the trans-individual cycles of instinct only in order to engage in interindividual and social interaction. There does not seem to be any discontinuity here, since we already find group action in chimpanzees.

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One might say in this connection that from a cognitive point of view the social group plays the same role that “population” does from the point of view of genetics and therefore from that of instinct. In this sense society is the supreme unit, and the individual succeeds in inventing or in creating intellectual structures only to the degree that he is the seat of collective interactions whose level and value naturally depend on that of the society in general. The great man who seems to initiate new trends is only a point of intersection or of synthesis, of ideas elaborated by continuous co-operation, and even when he dissents from majority opinion he is responding to underlying needs of which he is not the source. This is why the social environment actually does for intelligence what genetic recombinations in the entire population did for evolutionary variation or the trans-individual cycle of the instincts. But society, however external and educative its methods of transmission and interaction may be in comparison with those of hereditary transmission or combination, is no less than the latter a product of life. And “collective representations,” as Durkheim called them, still presuppose the existence of a nervous system in the members of the group. This is why the important question is not to weigh the merits of the individual versus the group (like asking which came first, the chicken or the egg): it is to distinguish between logic, whether in the course of solitary reflection or co-operation, and errors or insanities in collective opinion or in the individual consciousness. For, despite Tarde, there are not two logics, one serving the group and the other, the individual. There is only one way of coordinating actions A and B in a nested relationship or in one of order, etc., regardless of whether these are the actions of various individuals, one or some for A and another or others for B, or the actions of the same person (who did not after all invent them alone, since he is a member of the whole society). It is in this sense that cognitive controls or operations are the same whether in a single brain or in a system of co-operations (which is the meaning in French of the word coopération). * In sum, and however banal the thesis might seem, it is worth stressing that cognitive functions are extensions of organic controls and that they constitute a differentiated organ for regulating exchanges with the external world, for this hypothesis implies far more than these few pages can suggest.

Notes 1. According to Cannon, homeostasis means the regulatory mechanism which maintains equilibrium as a physiological system, plus, as we have since discovered, the organic function which ensures hereditary transmission (genetic homeostasis). 2. By phenotypes we mean the form which individual organisms take with relation to the milieu, as opposed to the “genotype” or hereditary form.

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3. See the interesting experiments of I. Meyerson and P. Guillaume. 4. Grassé calls “stigmergies” certain hereditary behavioral regulations of termites. They form small pellets of matter in building their homes, and when these reach a specific volume, the pellets then become buildingused as supports, floors, etc., in accordance with a new set of laws, but without a particular order of succession. 5. The epigenotype is a structure (using the definition suggested by the work of Waddington) which includes both genotypic and epigenotypic structures, that is, related to an embryonic development interacting with the environment.

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35 Neural Bases of Intelligence and Training Mark R. Rosenzweig

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lthough research on biological bases of intelligence has not yet had much influence on special education, it may well have major impact over the next few decades. Opinions on the relevance and probable importance of biological research for special education vary, however, among members of different professional fields. Positive predictions come from many of those who are active in investigating the biological bases of intelligence and learning or who are acquainted with this research. But such optimistic forecasts may not be widely shared by professionals in special education, as we will discuss later. To provide readers with further bases for estimating possible developments in this field, this article will focus on research on neural bases of intelligence and training, stressing the advances that are being made but also acknowledging problems and limitations. Before entering on the review proper, let us note some dramatic predictions that the incidence of mental retardation will decrease markedly by the end of the century. Advances of science and technology, coupled with a low birthrate and improved medical services, could halve the incidence of biologically caused mental retardation in the United States by the year 2000, according to the U.S. President’s Committee on Mental Retardation (USPCMR, 1976b). Further, retardation caused by sociocultural-socioenvironmental factors can reasonably be expected to drop by one-third in the next 10 to 20 years, according to the same source (USPCMR, 1976b). The latter prediction is based on increasing educational opportunities and evolution of life styles among those segments of the population now in the poverty sector. Both alleviation

Source: The Journal of Special Education, 15(2) (1981): 105–123.

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and prevention of retardation can be aided by research on biological bases of intelligence. A number of biological causes of retardation have already been discovered. Growing knowledge of the neural mechanisms of learning and memory should permit a more systematic search for causal factors, whose isolation is an important step in finding treatments and methods of prevention for both biologically and socioenvironmentally induced retardation. In view of these prospects, those in the field of special education may wish to consider the progress of research on biological bases of learning and intelligence, both for its implications for their own work and for that of their students. The present article will review briefly some of the main lines of research.1 The main sections of this review are the following: (a) research on neural mechanisms of intelligence, concentrating on mechanisms of learning and memory as behaviors that are basic to intelligence; (b) effects of training and differential experience on the brain and behavior; and (c) a discussion of further predictions, including comment on divergent views of neuroscientists and professionals in special education.

Neural Mechanisms of Intelligence Intelligent behavior is considered by many investigators to depend upon both elaborate, orderly networks of neurons in the brain and upon the capacity for altering some aspects of these networks. Complex neural circuitry is required for such functions as processing both sensory information and internally generated signals, comparing percepts with memory stores, programming coordinated muscular responses, and monitoring ongoing bodily activity. Alteration of some aspects of the circuits is needed in order to store new information, to change existing patterns of response, and to elaborate new patterns. Such alterations may be achieved either by changing functional characteristics of existing synaptic functions between neurons or by formation of new connections (or removal of old ones). Deficits in intelligence may thus arise from several causes. Genetic defects may prevent the formation of the requisite complex and orderly sets of neural structures (Huttenlocher, 1975; Purpura, 1975), or they may prevent the normal plasticity of synaptic junctions. Inadequate early nutrition or inadequate early secretion of certain hormones (e.g., thyroid hormone) may also impair the growth of normally complex neural circuits. Diseases, vascular accidents, or mechanical injury may destroy or impair the functions of parts of the nervous systems needed for complex, adaptive behavior. Lack of adequate stimulation and experience during development may prevent both the full growth of important neural circuits and also the acquisition of knowledge needed for later intelligent behavior.2 It has been estimated that about half the cases of mental retardation have some type of neurological or genetic deficit (Mykleburst & Boshes, 1965).

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Let us now consider neural mechanisms of learning and memory, since these are basic for intelligence. Neural mechanisms of learning and memory have been topics of clinical investigation for a century, and they have been investigated with animal subjects since the beginning of the present century. The French psychologist T. Ribot published his influential Diseases of Memory, in 1882. In 1902 the American psychologist S. I. Franz published an important article in which he combined two experimental techniques – the new technique of E. L. Thorndike for studying animal learning, and the technique of making experimental brain lesions. These procedures allowed Franz to study the effects of localized lesions on learning and memory. Karl Lashley later worked with Franz and then continued this line of research. By the 1890s some investigators focused attention on the junctions between neurons as the likely site of plastic changes (Tanzi, 1893). This kind of junction did not have a name yet, but a little later in that same decade, Sherrington, in his chapter in Foster’s Neurophysiology (Foster & Sherrington, 1897), gave it the name “synapse.” Sherrington also stated that the synapse was likely to be strategic for learning. He put it in this picturesque way: Shut off from all opportunity of reproducing itself and adding to its number by mitosis or otherwise, the nerve cell directs its pent-up energy towards amplifying its connections with its fellows, in response to the events which stir it up. Hence, it is capable of an education unknown to other tissues. (p. 1117)

But investigators of the 1890s did not possess the techniques needed for detailed work at the level of the synapse. That had to await electron microscopy and intracellular recording of neural potentials; we will now examine the dramatic findings made in recent years by the use of these and other sophisticated techniques.

Neurochemistry of Learning and Memory By the 1960s several groups of biochemists had undertaken research on learning and memory. They were interested in the nucleic acids that hold the genetic instructions of cells and that direct the manufacture of enzymes and other proteins; the chemical agents that transmit signals from one neuron to the next across the synaptic gap were another focus of interest. Since biochemical contents and processes are closely similar in the brains of all mammals, the biochemists have worked chiefly with laboratory rats and mice. Often in collaboration with psychologists, these investigators employed two main strategies: Either they trained animals and looked for small chemical changes in the brain, or they attempted to see how various pharmacological agents would either improve or impair the formation of long-term memories in animals.

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Results of extensive research indicate strongly that synthesis of protein in the brain is required soon after training if long-term memories are to be formed (Dunn, 1980; Flood & Jarvik, 1976). If inhibitors of protein synthesis are given to animal subjects shortly before training in dosages that are effective for a few hours posttraining, learning proceeds normally and memory is present for a short period, but tests 24 hours or more later reveal that there is no long-term memory. Formation of memory can also be modulated by administering certain synaptic transmitters, excitant or depressant drugs, and certain hormones or hormone fractions (see Dunn, 1980). For example, mild doses of excitant drugs aid memory formation, whereas mild doses of depressants impair memory formation; neither of these effects works through alteration of protein synthesis (Flood, Jarvik, Bennett, Orme, & Rosenzweig, 1978). Acetylcholine is the transmitter agent at many central synapses as well as at peripheral synapses, and agonists or antagonists of cholinergic function have been reported, respectively, to improve or impair learning in studies with laboratory animals (e.g., Deutsch, 1971; Stratton & Petrinovich, 1963). Some of the research on neurochemistry of learning and memory done with laboratory rodents has proven applicable to human subjects. For example, serial verbal learning in normal human subjects was reported to be enhanced by arecholine, a cholinergic agonist, and by choline, a precursor of acetylcholine, but to be impaired by scopalamine, a cholinergic antagonist. Those subjects who showed poor scores under control conditions were also these who were more affected by both the enhancing and impairing drugs. In other words, the drugs may be useful in bringing individuals towards an optimal level of cholinergic activity, and may not be able to improve those who are already at that level (Sitaram, Weingartner, & Gillin, 1978). Both storage and retrieval of verbal material were enhanced in normal human subjects by physostigmine, which inhibits the enzyme acetylcholinesterase and thus prolongs activity of acetylcholine (Davis, Mohs, Tinklenberg, Pfefferbau, Hollister, & Kopell, 1978). Both Sitaram et al. and K. L. Davis et al. noted that in Alzheimer’s disease and other presenile dementias, the cortex shows a decrease in the enzyme that synthesizes acetylcholine, and both groups of investigators suggested that research should be done to see whether cholinergic agents might aid such patients. A subsequent pilot study with Alzheimer’sdisease patients has reported that while physostigmine alone did not cause improvement, there was facilitation of memory when it was coupled with lecithin, a precursor of acetylcholine (Peters & Levin, 1979). Perhaps these agents could also aid some kinds of retarded individuals. Vasopressin (antidiuretic hormone) has been known for several years to play an important part in regulation of fluid balance in the body. More recently vasopression has been shown to occur in the brain, and it may be a synaptic transmitter. Furthermore, administration of vasopressin has been shown to aid memory formation in rodents (e.g., de Wied, van Wimersma

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Greidanus, Bohus, Urban, & Gispen, 1976). Following up on this lead, a pilot study with four cases of amnesia (three caused by concussions and one by alcoholism) found that administration of vasopressin over a few days brought recovery of memory in each case (Oliveros, Jandali, Tinsit-Bethier, Remy, Benghezal, Audibert, & Moeglen, 1978). Further research on this topic can be expected in the near future. It is true that attempts to aid hyperkinetic children with learning problems or retarded children by pharmacological treatments have not demonstrated the value of such therapy (e.g., Adelman & Compas, 1977). Nevertheless, to call such attempts premature suggests that their time will come, and the favorable results mentioned in this section indicate that rapid progress is being made toward the identification of effective pharmacological treatments for certain types of learning disorders.

Synaptic Changes and Processes in Learning Two main kinds of synaptic changes have been found to occur during or after training. First, the effectiveness of already existing synapses may alter; that is, transmission at existing synapses may either increase or decrease. Second, the number of synapses in a region of brain may increase (or possibly decrease) after training has occurred. Let us take up change in the number of synapses first and then change in effectiveness of existing synapses. In a number of experiments since the early 1960s, laboratory rats have been given differential experience by assigning them to different living conditions – an enriched condition (EC), in which 10 to 12 animals are placed in a large cage with a variety of stimulus objects that are changed daily; the standardcolony condition (SC), with three animals housed together; or the impoverished condition (IC), with a single animal in a colony cage. Later, we will consider cerebral effects of these conditions more fully, but here we should note differences in numbers of synaptic junctions. Most of the synapses on neurons in the cerebral cortex are made on dendritic spines, small projections from the surface of the receptive branches (dendrites) of neurons (Figure 1). Globus, Rosenzweig, Bennett, and Diamond (1973), measuring numbers of healthy spines per unit of dendrite length in 40 EC and 40 IC rats, found significantly more spines in EC than in IC littermates. Subsequently, Greenough (1976) found significantly greater branching of dendrites in EC than in IC rats. Combining these effects, it is clear that rats develop greater numbers of cortical synaptic junctions in EC, where the opportunities for informal learning are greater than in IC. More recently, Chang and Greenough (1978) gave formal training to only one cerebral hemisphere in rats. To accomplish this, they first transected the corpus callosum. Then they gave rats daily maze training with a different maze pattern for each of 30 days. Each day during the maze training some

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Figure 1: Diagram of pyramidal neurons in the cerebral cortex. Each neuron has a single axon (1), which conducts nerve impulses away from the cell body (2); it also has several dendrites (3), which receive neural messages from the axons of other cells. To simplify the diagram, we show only a tiny fraction of the axons and synaptic junctions, since each pyramidal neuron receives thousands of contacts. Certain terminal boutons of axons contact the cell body or the surface of dendrites, but many of them end upon dendritic spines, which are little extensions of the dendrites. At the lower left is an enlargement of a synaptic junction: An axon (4) terminates in a bouton (5), which contains synaptic vesicles (6); the synaptic cleft (7) separates the bouton from a dendritic spine (8). The junction is magnified about 5,000 times, whereas the neuron at the right is magnified about 250 times. (After P. Mussen and M. R. Rosenzweig, Psychology: An Introduction. D. C. Heath, 1977.)

rats had an opaque contact lens placed over one eye so that visual information reached only one hemisphere. At the end of the 30-day training period, neurons in the occipital areas of the two hemispheres were analyzed for branching of dendrites. In the hemisphere that received input from the closed eye, branching was no greater than in control animals with no maze experience. But in the hemisphere that received input from the open eye, there was significantly greater branching, as in EC. Thus formal training as well as informal experience caused greater branching of dendrites and presumably a greater number of synaptic contacts. To study synaptic processes in learning in even greater detail, some investigators have worked with invertebrates that possess rather simple invariant nervous systems. In this field some of the best known research has been done

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on a large marine snail, Aplysia (Kandel, 1976, 1979). Such animals have rather limited learning abilities, but habituation (decrement in response to a repeated stimulus) proceeds in much the same way in Aplysia as in the human being. Moreover, Aplysia has the advantages for research that certain neural circuits have been traced out completely; the cells of these circuits can be identified and recognized from one Aplysia to the next, and electrical activity of these cells can be recorded by intracellular electrodes as habituation proceeds. With this preparation it has been possible to pinpoint the site of plasticity in habituation of the defensive gill-withdrawal reflex; it is at the synaptic junction between sensory and motor cells. Although the connections are fixed anatomically, the functional gain of the junctions varies. This occurs when an impulse in the sensory nerve causes release of fewer packets of synaptic transmitter than under control conditions. Sensitization also occurs in Aplysia; that is, increased response to a stimulus after strong stimulation of another input. The mechanism of this change has also been studied in some detail. The sensitizing impulses arrive at terminals on the presynaptic junctions and alter their state, so that impulses over the axons to the junction now release more synaptic transmitter than usual. To date, neurophysiological studies of learning in relatively simple invertebrates have been mainly confined to nonassociative learning – habituation and sensitization. Now ways are being found to train some of these animals associatively (Davis & Gillette, 1978; Mpitsos, Collins & McClellan, 1978; Walters, Carew, & Kandel, 1979), and we should soon be seeing results on synaptic mechanisms of associative learning. It is true that these animals look and behave quite differently from human beings, but their neurons are rather similar to ours, and the same synaptic transmitters are found in their nervous systems as in ours. Invertebrates also need to adapt, and the basic neural mechanisms by which they do so may be shared by human beings. Investigators working with these invertebrate preparations hope that they will provide keys to the fundamental cellular processes of learning. This would not be the first time that research with simple animals has revealed mechanisms that are important in human biology. Consider the revolution in molecular biology in the last 20 years that has led to a profound increase in knowledge about our hereditary mechanisms – the major experiments in this field were done on the bacterium E.coli.

Effects of Training and Experience on Brain and Behavior Giving animals formal training or allowing them to gain experience in differential environments brings about measurable changes in a number of aspects of the anatomy, chemistry, and electrophysiology of the brain and also in behavioral measures, as has been reported in many recent research

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publications and review articles (e.g., Bennett, Rosenzweig, Morimoto, & Hebert, 1979; Greenough, 1976; Rosenzweig & Bennett, 1976b, 1977, 1978; Rutledge, 1976). We have already noted changes in dendritic branching and in dendritic spines caused by giving animals experience in EC or IC environments. Now let us review briefly some of the other respects in which the nervous system can be altered by differential experience. Beneficial effects of enriched experience on later learning will be noted; in fact, enriched experience has been used as a therapeutic treatment for various kinds of impairment of the brain. We will consider later whether the cerebral changes are in fact related to learning and memory storage or whether they may be caused by other aspects of differential experience.

Effects on Brain Anatomy Both gross anatomy and microscopic anatomy have revealed differences induced in the brain by experience in EC or IC environments. The weight of standard samples of cerebral cortex is in greater in EC than in IC littermate rats. These differences amount to 9% or 10% in occipital cortex and 4% or 5% in total cortex. Significant differences are found after only 4 days of experience in the EC or IC environments, and the differences increase in magnitude as the period of differential experience is extended up to about 30 days. The age at which the animals are assigned to the differential environments has some effect on the magnitude of the cerebral differences; the differences are largest when the experience starts at or shortly after weaning (25 days of age), but significant effects are obtained even if rats are placed in the differential environments at 300 days of age. The difference in cortical weight probably reflects mainly the increased thickness of cortex of EC as compared with IC rats. It has also been found that the cerebral hemispheres of EC rats are both longer and wider than those of IC littermates. When rats under SC conditions (three per cage) have been compared in weights of brain regions with EC and IC littermates, it has been found that the EC animals exceed SC in cortical weight, whereas the IC have significantly lower weights than do SC. That is, enriched experience above the colony level increases cortical weight, while impoverished experience below the colony norm leads to a decrease in cortical weight. Similar relations among the groups have been found for cortical thickness and dendritic branching. Microscopic examination reveals other differences in addition to the numbers of dendritic spines and dendritic branching reported earlier. The average size of synaptic junctions has been found to be greater in EC than in IC rats (Diamond, Lindner, Johnson, Bennett, & Rosenzweig, 1975; West & Greenough, 1972). Altschuler (1976) reported that 80 days of combined nonspecific and specific training led to a doubling of synaptic density in the hippocampus of trained rats compared with that of control rats.

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Effects on Brain Chemistry An early finding in our laboratory was a small increase in total activity of the enzyme acetylcholinesterase (AChE) in EC as compared with IC rats. Further experiments showed the increase in total AChE to be rather small, but with the more effective enrichment of a seminatural outdoor environment (SNE), total AChE activity in the cortex is clearly and significantly greater in SNE rats than in IC littermates (Rosenzweig, Bennett, Hebert, & Morimoto, 1978). The less specific enzyme cholinesterase (ChE) has been found in numerous experiments to be greater in EC than in IC littermates. This difference suggested that EC brains might have greater numbers of glial cells than do IC brains, and we found such a difference (Diamond, Law, Rhodes, Lindner, Rosenzweig, Kreech, & Bennett, 1966). Nevertheless, we wish to be cautious about the interpretation of ChE as an index to glial number, because blood vessel walls as well as glial cells are rich in ChE activity. Glial cells play several roles in the nervous system – they form insulating sheaths around axons, bridge between neurons and capillaries, and remove dead tissue. It has been speculated that glial cells may also play active roles in learning. Several experiments have shown the RNA/DNA ratio to be a highly reliable chemical indicator of the EC-IC effect. In 90% of more than 550 EC-IC pairs, the RNA/DNA ratio in occipital cortex of the EC rat has been larger than that of the IC rat with an average difference of 8%. An increase in RNA can support heightened chemical synthetic processes in the brain. We have proposed that long-term increases in RNA with enriched experience represent the integrated effect of a continuing series of pulses of increased RNA synthesis and other biosynthetic processes resulting from a number of individual learning experiences. As is the case with tissue weight, somewhat greater effects in RNA are found when the animals are assigned to the differential environments at weaning, but highly significant effects are also found if the differential experience begins only at later ages. Significantly greater diversity of brain RNA has been found in EC than in IC rats in double-blind experiments using unique sequence molecular hybridization (Grouse, Schrier, Bennett, Rosenzweig, & Nelson, 1979). It is tempting to interpret this result as reflecting greater diversity of proteins in the brains of EC than in those of IC rats, but such a conclusion would be premature because of complexities in RNA functions.

Effects on Electrophysiology of Brain Latency of electrophysiological responses of the occipital cortex evoked by flashes of light was measured in EC and IC rats (Edwards, Barry, & Wyspianski, 1969; Mailloux, Edwards, Barry, Roswell, & Achorn, 1974). Latencies of responses were shorter in the visual cortex of EC rats as compared with those

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with standard colony experience. The authors noted that their finding was in agreement with reports that evoked potential latencies are longer in lowintelligence human beings. Differences in sleep patterns and in the electrophysiology of sleep have been reported between EC and IC animals. McGinty (1971) found that isolation-reared kittens spent less time sleeping than did kittens raised in a complex environment. When the previously isolated kittens were exposed to a complex environment, sleeping time increased. Tagney (1973) similarly reported that EC rats spent more time sleeping than did IC rats; there was apparently no difference between the percentages of time spent in fast-wave or slow-wave sleep. Lambert and Truong-Ngoc (1976) reported that not only did EC rats show more total sleeping time than did IC rats, but the EC rats also had a significantly higher proportion of fast-wave sleep. This finding may be related to the report of Bloch (1976) that formal training increases the percentage of fast-wave sleep in rats, and that preventing the occurrence of fast-wave sleep during the few hours following training impairs consolidation of memory.

Are the Cerebral Effects due to Training or to Other Factors? The differential experience studies were undertaken originally to investigate effects of different amounts of informal learning on the brain. The cerebral effects obtained could, however, be attributed to other aspects of the experimental situations. Thus the results might have been due to such factors as differential amounts of locomotion, handling, or stress. Each of these possibilities has been ruled out by control experiments (see Rosenzweig & Bennett, 1978). Walsh and Cummins (1975) have suggested that differential arousal may play an important role in causing the cerebral effects, but we have given reasons for rejecting this hypothesis (Rosenzweig, 1979; Rosenzweig & Bennett, 1978). Recent experiments support the hypothesis that learning as such can produce measurable changes in the brain, as in the Chang and Greenough (1978) study described earlier. In another study (Bennett et al., 1979), some individually caged rats ran self-paced maze trials between food and water stations, solving a different maze pattern each day for 30 days. They developed changes in weights and in RNA/DNA of brain regions similar to those of rats kept in groups in an enriched environment. But rats that ran self-paced trials through an enclosure without maze barriers did not develop changes from control rats kept in small individual cages. Thus the training itself seems to be the cause of the cerebral effects. In other words, training can produce measurable changes in the anatomy and biochemistry of the brain.

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Effects on Behavior The report by Hebb (1949) that rats reared as pets learned mazes more rapidly than did rats reared in laboratory cages provided the impetus for a major effort to investigate and understand the effects of differential experience on subsequent learning or problem-solving behavior. Among reviews on this subject are those by Davenport (1976), Greenough (1976), and Rosenzweig and Bennett (1977). Some of the behavioral differences reported appear to be specific to the species or even to the strain tested, to the ages at which differential experience is given, and to the behavioral test employed. The most consistent finding, although there are some exceptions even here, is that EC rats were significantly superior to SC or IC rats in performance on complex mazes such as the Hebb-Williams maze.

Enriched Experience as Therapy for Brain Damage The beneficial effects of enriched experience for learning and problem-solving behavior have led to investigation of the use of experience to alleviate behavioral deficits caused by brain damage. In several experiments, cortex was removed from the occipital area of rats; then the subjects were assigned to EC and either SC or IC environments, and several weeks later were tested on a series of problems in the Hebb-Williams maze. The deleterious effects of the lesions on performance were partially overcome by EC experience, regardless of whether the lesions were inflicted on neonatal rats (Schwartz, 1964; Will, Rosenzweig, & Bennett, 1976), on young postweanlings (Will, Rosenzweig, Bennett, Hebert, & Morimoto, 1977), or on adult rats (Will & Rosenzweig, 1976). In human beings also, there are indications that effects of early brain damage can be alleviated by subsequent enriched experience. Such evidence comes from a study by Holden and Willerman (1973) concerning development and retardation in children diagnosed as having a neurological abnormality at 1 year of age. (Children with Down’s disease were not included in this sample.) The children, part of a large national collaborative study, were evaluated medically at regular intervals. The families were rated on a socioeconomic index. Of the infants from lower-class homes who had been diagnosed as neurologically abnormal, 35% were found to be retarded at age 4 (IQ scores less than 80). Even among the lower-class children classified as neurologically normal at age 1, 14% had IQ scores below 80 at age 4. In contrast, among the children from upper-class homes only 5% of those with a neurological abnormality at 1 showed retarded IQ scores at 4, and none of the neurologically normal were found to be retarded. This finding – that the neurologically impaired upper-class children were less likely to show retarded IQ scores than were the neurologically normal children from lower-class homes – suggests the power of environmental enrichment.

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Effects on early insufficiency of thyroid secretion on the nervous system could also be counteracted by enriched experience. Davenport (1976) prepared “experimental cretin” rats by impairing thyroid function in utero. These animals performed poorly on the Hebb-Williams maze and on other tests, but enriched experience significantly lessened the degree of the behavioral deficits. Effects of early malnutrition on behavior may also be alleviated by enriched experience. Wells, Geist, and Zimmermann (1972) reported that the deleterious effects of early protein malnutrition in rats on Hebb-Williams scores could be largely overcome by EC experience. It should be noted, however, that many investigators have not found malnutrition by itself to impair intelligent behavior. It may be that heightened motivation masks deficits in previously malnourished animals (Katz, Rosett, & Ostwald, 1979). It has also been noted that in human beings malnutrition usually occurs in a context of poverty and inadequate social stimulation, and it has been suggested (e.g., Richardson, 1976) that it would be too simple to attribute mental retardation or impairment to severe malnutrition as such. Note that the additional background factors are the same ones that have been implicated as causes of sociocultural retardation. In children, effects of early malnutrition on later growth and on intelligence have been investigated in a study in which Korean infants were adopted into middle- and upper-class American homes. The children differed in their nutritional status at the time they entered the Korean orphanage; some were classified as severely malnourished (below the 3rd percentile for height and weight on Korean norms), some were moderately malnourished (between the 3rd and 25th percentiles), and the rest were classified as well nourished. Some were adopted before the age of 2 years and some later. In the children adopted before the age of 2, the effects of malnutrition on size or intelligence were almost entirely overcome (Winick, Meyer, & Harris, 1975). Although the previously severely malnourished group still had the lowest mean height and weight, all three groups were considerably above the Korean norms (but below U.S. norms). In both intelligence and school achievement, all three groups were above the U.S. means, and differences among the groups were small. For those adopted beyond the age of 2, the differences among groups in body size were larger, and the previously severely malnourished group did not quite reach the U.S. means in intelligence or achievement (Nguyen, Meyer, & Winick, 1977). Sustained environmental enrichment overcame much of the consequences of early malnutrition, and this treatment was most effective when it was begun before the age of 2.

Age, Neural Plasticity, and Training Given the findings that training or experience can act as therapy to overcome some consequences of brain damage, are such effects a function of the age of

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the individual? Is age a factor in overcoming other deficits, such as sensory handicaps? Is there a critical period in the life of an individual when experience is important in overcoming a deficit and outside of which it will not be effective? Experiments with animals have revealed critical periods for certain neural/behavioral systems but not for others, and these findings may be relevant to special education for different kinds of deficits in human beings. Sensory impairment or distorted sensory input affects development of neural pathways and subsequent behavior, but only during a critical period early in life. If one eye of a kitten or a monkey is kept closed or occluded during the first 2 months after birth, that eye will show gravely impaired acuity and is unlikely ever to gain normal acuity thereafter. On the contrary, closing the eye of an adult cat or monkey for a year will produce only a small transient effect on its acuity. In the case of children with congenital sensory deficits, special training can be of great help, but apparently only if it is begun during the first 2 years. This has been found with hard-of-hearing children (Wedenberg, 1954). Hearing aids are now being prescribed for hard-of-hearing infants, not only to aid programs of training but also so that they can benefit from ambient stimulation and informal experience. In the case of children with congenital strabismus, normal binocular vision can usually be restored if surgery is performed before the age of 2, but not thereafter (Banks, Aslin, & Letson, 1975). On the other hand, other brain systems mature considerably later than the sensory systems, and some may retain plasticity into adulthood. Harlow (1959) showed that while monkeys can learn some problems readily at the age of a few weeks, other problems cannot be learned with an adult level of speed and efficiency until 2 years of age, and some require 4 years or more of maturity. Goldman (1974) has confirmed that some brain systems in the monkey take several years to mature and that the same behavioral test may reflect activity of different brain structure in infant and adult monkeys. Earlier we saw that enriched experience could aid recovery of problemsolving ability in adult-operated as well as infant-operated rats. Evidence with monkeys indicates that training may benefit chiefly those that sustain brain lesions at an early age. It is not yet clear, however, whether there is a true species difference here or whether the results reflect different sites of brain lesions and/or different kinds of test problems (Goldman & Lewis, 1978). An unexpected finding is that training and testing that show no immediate benefit may nevertheless aid performance many months later. This was seen in a study by Goldman (1976). Lesions were made in orbital prefrontal cortex of monkeys at either 50 days of age or 18 months. At about 27 months of age they were tested for thousands of trials on a delayed spatial-alternation task, and both groups showed severe impairment as compared with controls. Nine to 12 months later, the animals were tested again. Now the early-operated monkeys showed considerable recovery, but the late-operated did not. A further experiment revealed that even the early-operated monkeys did not show

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improvement at 3 years of age unless they had been trained at an earlier period. Furthermore, the earlier training can exert a beneficial effect even if it is for quite a different task from that to be tested later. Obviously, these animal experiments can be controlled better than studies with retarded humans or brain-injured patients, and accordingly much more research is needed to advance our understanding of behavioral therapy for inadequate cerebral development or brain lesions. The results to date are encouraging, however, in several respects: They demonstrate that training or enriched experience can promote behavioral recovery after specific brain lesions, that training can be helpful even if given years after the lesion was sustained (although earlier treatment is usually better), and that training may have delayed beneficial effects even if no immediate benefits are seen.

Discussion The emphasis of this chapter has been on brain mechanisms of intelligence and learning and their relevance for future developments in special education. Some evidence suggests the importance of these data. For example, The 77th Yearbook of the National Society for the Study of Education concluded with a chapter on implications of brain research for education (Chall & Mirsky, 1978). Let us note briefly some of the points made in that chapter, and then consider whether special educators are prepared to consider the changes that brain research may bring to their field.

Implications for Education A major theme in Chall and Mirsky (1978) was the central role of environmental stimulation and experience, both in normal growth and development of human and animal brains and in overcoming the effects of inherited deficiencies or of injuries. In essence, the neuroscientists writing in the volume were saying to educators that education is indispensable for optimal development of the brain. While some progress was noted concerning pharmacological methods for effecting behavioral and cognitive changes, greater stress was placed upon education. Even though neurological deficiencies and injuries are feared because of their effects on learning and intelligence, evidence was presented to show that training and experience often help to overcome these handicaps. Research with children and with animals was cited to show the value of training; even if immediate success is not evident, it is often helpful in the long run for the learner to continue to practice. For many difficulties related to brain dysfunctions, emphasis was placed on the individual’s practice under

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the supervision of a knowledgeable and sensitive teacher. When drugs were mentioned, they were placed in a broader context of training and environmental stimulation. Another theme was the importance of timing. In general, stimulation and appropriate training help most when given early. But even among adults there can be some recovery of function with proper stimulation and retraining. Epstein (1978; 1979) has suggested the provocative hypothesis that growth of brain and of intelligence proceeds by spurts at well-defined ages, with plateaus between the spurts. According to Epstein, attempts at compensatory education are helpful only if given during the periods of rapid brain growth, not if given during plateaus. Although Epstein has gathered much evidence in support of this hypothesis, it has not yet been tested by other investigators and so must be considered as a topic for further research. The yearbook was written during a crest of interests in brain lateralization, and it included possible educational implications of differences in function between the left and right hemispheres of the brain. It now seems likely that the hemispheric differences were exaggerated and that they will not be a source of valuable recommendations for educational practice. At any rate, some of the discoverers of hemispheric differences are now stressing the necessity of coordinated activity of the two hemispheres for normal behavior (Gazzaniga & LeDoux, 1978). Because the brain is so complex and because knowledge about it is constantly growing and changing, the nonspecialist may be perplexed and not know how to incorporate findings of the neurosciences into educational practice. Chall and Mirsky (1978), therefore, hope to see as early as the 1980s a fruitful collaboration between neuroscientists and educators – a collaboration ultimately as productive as the long-standing one between educators and psychologists. Looking further ahead, they foresee the possibility in the next century of a new speciality of educational neuroscientist or educational neuropsychologist. A practitioner of this new profession would be well versed in the latest pedogogical methods as well as in neuropsychological and neurophysiological methods and techniques. Each child who needed special assistance would be tested by new neurophysiological and neurochemical as well as by behavioral methods in order to assess his/her individual strengths and weaknesses and developmental progress. Such a program would be intended to permit early and continuing identification, assessment, and remediation in individually planned pedogogical efforts. But are special educators aware of these developments and prepared for cooperative efforts? The answer, of course, can neither be absolute nor certain. However, in 1976, when PCMR (1976a, 1976b) was predicting that a substantial decrease in the incidence of mental retardation could be achieved by the end of this century, another study also appeared – “A Forecast of Events Affecting the Education of Exceptional Children: 1976–2000” (Schipper & Kenowitz, 1976). This was a study undertaken by the National Association of State

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Directors of Special Education in order to make present decisions with a view to the future, an effort to avoid “future shock.” A pool of 121 special education administrators from all regions of the United States were surveyed into two successive rounds, using the Delphi method to stimulate brainstorming and planning. More than 800 statements about future events were screened down to 60, and these were rated as to the year in which they were likely to occur and their value to special education – positive, neutral, or negative. The main categories of prediction were legal/statutory, administrative, instructional, and teacher education. It should be noted that not 1 of the 60 predictions dealt with a possible change in the incidence of cases requiring special education. What changes did the experts foresee in 1973–74 when the survey was conducted? Here are some of the most highly rated changes, along with the median year forecast for each: Due process procedures are guaranteed to all exceptional children in the public schools (1980). Seventy percent of all teacher training programs require 6 credits of course work with exceptional children (1980). Twenty-five states have moved teacher-training programs from the college/university campus to the public schools (1985). School calendars adapt the hours in the day and the days in the year to the needs of the handicapped (1990).

And here are some of the negatively valued predictions: The Supreme Court rules that compulsory school attendance is unconstitutional (1990). The pendulum swings back from mainstreaming to segregating handicapped children in the public schools (1990). Teacher unions dominate decisions affecting educational enrollments and in-service training systems for special education programs in 25 states (1990).

Only a few of the forecast trends had to do with biological research on retardation or other handicapping conditions. These were the following: Preservice training of special educators includes an academic and clinical course on basic concepts of medicine and principles of drug-induced behavior modification (1990; somewhat favorably evaluated). Drugs control 40% of the behavioral results of handicapped conditions (1990; neutrally evaluated). Non-habit-forming drugs that accelerate learning are administered daily by school personnel to 40% of the public school population (2000; somewhat negatively evaluated).

Note that these predictions all had to do with drugs, and that whereas threequarters of all the predictions were evaluated favorably, only one of the these

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three was so evaluated. It appears that this pool of special education administrators did not expect much from biological research and their view of this field was rather narrow, being largely confined to drug therapy. As was noted in the preceding section, this emphasis on drug therapy is not characteristic of current research on brain mechanisms of learning and memory. If the prediction of the President’s Committee on Mental Retardation (1976a, 1976b) that a substantial decrease in mental retardation by the end of the century proves correct, and if Chall and Mirsky (1978) are correct in foreseeing collaboration between educators and neuroscientists, then special educators may indeed encounter future shock.

Notes 1. The National Institute of Education sponsored a state-of-the-art symposium on neural mechanisms of learning and memory in 1974, and an updated collection of the papers appeared later (Rosenzweig & Bennett, 1976a). A further sign of developing interest by educators in the neural bases of learning is that The 77th Yearbook of the National Society for the Study of Education was devoted to the topic “Education and the Brain” (Chall & Mirsky, 1978). 2. A review of causes of mental retardation was presented in a publication of the U.S. President’s Committee on Mental Retardation (1976a).

References Adelman, H. S., & Compas, B. E. Stimulant drugs and learning problems. Journal of Special Education, 1977, 11, 377–415. Altschuler, R. A. Changes in hippocampal synaptic density with increased learning experience in the rat. Society for Neuroscience Abstracts, 1976, 2, 438. Banks, M. S., Aslin, R. N., & Letson, R. D. Sensitive period for the development of human binocular vision. Science, 1975, 190, 675–677. Bennett, E. L., Rosenzweig, M. R., Morimoto, H., & Hebert, M. Maze training alters brain weights and cortical RNA/DNA ratios. Behavioral and Neural Biology, 1979, 26, 1–22. Bloch, V. Brain activation and memory consolidation. In M. R. Rosenzweig & E. L. Bennett (Eds.), Neural mechanisms of learning and memory. Cambridge, Mass.: MIT Press, 1976. Chall, J. S., & Mirsky, A. F. (Eds.), Education and the brain, The seventy-seventh year-book of the National Society for the Study of Education, Part II. Chicago: The University of Chicago Press, 1978. Chang, F. F., & Greenough, W. T. Increased dendritic branching in hemispheres opposite eyes exposed to maze training in split-brain rats. Society for Neuroscience Abstracts, 1978, 4, 469. Davenport, J. W. Environment as therapy for brain effects of endocrine dysfunction. In R. N. Walsh & W. T. Greenough (Eds.), Environments as therapy for brain dysfunction. New York: Plenum Press, 1976. Davis, W. J., & Gillette, R. Neural correlate of behavioral plasticity in command neurons of pleurobranchaea. Science, 1978, 199, 801–804. Davis, K. L., Mohs, R. C., Tinklenberg, J. R., Pfefferbau, A., Hollister, L. E., & Kopell, B. S. Physostigmine: Improvement of long-term memory processes in normal humans. Science, 1978, 201, 272–274.

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Deutsch, J. A. The cholinergic synapse and the site of memory. Science, 1971, 174, 788–794. de Weid, D., van Wimersma Greidanus, T. B., Bohus, B., Urban, I., & Gispen, W. H. Vasopressin and memory consolidation. In M. A. Corner & D. F. Swaab (Eds.), Perspectives in brain research. Amsterdam: Elsevier, 1976. Diamond, M. C., Law, F., Rhodes, H., Lindner, B., Rosenzweig, M. R., Krech, D., & Bennett, E. L. Increases in cortical depth and glia numbers in rats subjected to enriched environment. Journal of Comparative Neurology, 1966, 128, 117–125. Diamond, M. C., Lindner, B., Johnson, R., Bennett, E. L., & Rosenzweig, M. R. Differences in occipital cortical synapses from environmentally enriched, impoverished, and standard colony rats. Journal of Neuroscience Research, 1975, 1, 109–119. Dunn, A. J. Neurochemistry of learning and memory: An evaluation of recent data. Annual Review of Psychology, 1980, 31, 343–390. Edwards, H. P., Barry, W. F., & Wyspianski, J. O. Effect of differential rearing on photic evoked potentials and brightness discrimination in the albino rat. Developmental Psychobiology, 1969, 2, 133–138. Epstein, H. T. Growth spurts during brain development: Implications for educational policy and practice. In J. S. Chall A. F. Mirsky (Eds.), Education and the Brain. Chicago: The University of Chicago Press, 1978. Epstein, H. T. Correlated brain and intelligence development in humans. In M. E. Hahn, C. Jensen, & B. C. Dudek (Eds.), Development and evolution of brain size: Behavioral implications. New York: Academic Press, 1979. Flood, J. F., & Jarvik, M. E. Drug influences on learning and memory. In M. R. Rosenzweig & E. L. Bennett (Eds.), Neural mechanisms of learning and memory. Cambridge, Mass.: MIT Press, 1976. Flood, J. F., Jarvik, M. E., Bennett, E. L., Orme, A. E., & Rosenzweig, M. R. Memory: Modification of anisomycin-induced amnesia by stimulants and depressants. Science, 1978, 324–326. Foster, M., & Sherrington, C. S. A textbook of physiology. Part III. The central nervous system. New York: Macmillan, 1897. Gazzaniga, M. S., & Le Doux, J. E. The integrated mind. New York: Plenum Press, 1978. Globus, A., Rosenzweig, M. R., Bennett, E. L., & Diamond, M. C. Effects of differential experience on dendritic spine counts. Journal of Comparative and Physiological Psychology, 1973, 82, 175–181. Goldman, P. S. An alternative to developmental plasticity: Heterology of CNS structures in infants and adults. In D. G. Stein, J. J. Rosen, & N. Butters (Eds.), Plasticity and recovery of function in the central nervous system. New York: Academic Press, 1974. Goldman, P. S. The role of experience in recovery of function following orbital prefrontal lesions in infant monkeys. Neuropsychologia, 1976, 14, 401–412. Goldman, P. S., & Lewis, M. E. Developmental biology of brain damage and experience. In C. W. Cotman (Ed.), Neuronal plasticity. New York: Raven Press, 1978. Greenough, W. T. Enduring brain effects of differential experience and training. In M. R. Rosenzweig and E. L. Bennett (Eds.), Neural mechanisms of learning and memory. Cambridge, Mass.: MIT Press, 1976. Grouse, L. D., Schrier, B. K., Bennett, E. L., Rosenzweig, M. R., & Nelson, P. G. Sequence diversity studies of rat brain RNA: Effects of environmental complexity on rat brain total RNA diversity. Journal of Neurochemistry, 1979, 30, 191–203. Harlow, H. F. The development of learning in the rhesus monkey. American Scientist, 1959, 47, 459– 479. Hebb, D. O. The organization of behavior. New York: Wiley, 1949. Holden, R., & Willerman, L. Neurological abnormality in infancy, preschool intelligence, and social class. In P. Trapp & P. Himmelstein (Eds.), The exceptional child (2nd ed.). New York: Appleton-Century-Crofts, 1973.

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Huttenlocher, P. R. Synaptic and dendritic development and mental defect. In N. A. Buchwald & M. A. B. Brazier (Eds.), Brain mechanisms in mental retardation (UCLA Forum in Medical Sciences, No. 18). New York: Academic Press, 1975. Kandel, E. R. Cellular basis of behavior: An introduction to behavioral neurobiology. San Francisco: Freeman, 1976. Kandel, E. R. Small systems of neurons. Scientific American,1979, 241, 66 –76. Katz, H. B., Rosett, R. E., & Ostwald, R. The compensatory role of food motivation in the maze learning performance of lactationally undernourished rats. Developmental Psychobiology, 1979, 12, 305–315. Lambert, J.-F., & Truong-Ngoc, A. Influence de l’environnement instrumental et social sur la structure d’un échantillon du cycle veille-sommeil chez le rat Wistar mâle: Corrélations avec les modifications de l’excitabilité du système réticulo-cortical. Agressologie, 1976, 17, 19–25. Mailloux, J. G., Edwards, H. P., Barry, W. F., Rowsell, H. C., & Achorn, E. G. Effects of differential rearing on cortical evoked potentials of the albino rat. Journal of Comparative and Physiological Psychology, 1974, 87, 475– 480. McGinty, D. J. Encephalization and the neural control of sleep. In M. B. Sterman, D. J. McGinty, & A. M. Adinolfi (Eds.), Brain development and behavior. New York: Academic Press, 1971. Mpitsos, G. J., Collins, S. D., & McClellan, A. D. Learning: A model system for physiological studies, Science, 1978, 199, 497–506. Mykleburst, H. R., & Boshes, B. Final report, minimal brain damage in children. Washington, D.C.: U.S. Department of Health, Education and Welfare, 1965. Nguyen, M. L., Meyer, K. K., & Winick, M. Early malnutrition and “late” adoption: A study of their effects at the development of Korean orphans adopted into American families. American Journal of Clinical Nutrition, 1977, 30, 1734–1739. Oliveros, J. C., Jandali, M. K., Tinsit-Berthier, M., Remy, R., Benghezal, A., Audibert, A., & Moeglen, J. M. Vasopressin in amnesia. Lancet, 1978, 1, 42. Peters, B. H., & Levin, H. S. Effects of physostigmine and lecithin on memory in Alzheimer disease. Annals of Neurology, 1979, 6, 219–222. Purpura, D. P. Normal and aberrant neuronal development in the cerebral cortex of human fetus and young infant (UCLA Forum in Medical Sciences, No. 18). In N. A. Buchwald & M. A. B. Brazier (Eds.), Brain mechanisms in mental retardation. New York: Academic Press, 1975. Richardson, S. A. The influence of severe malnutrition in infancy on the intelligence of children at school age: An ecological perspective. In R. N. Walsh & W. T. Greenough (Eds.), Environments as therapy for brain dysfunction. New York: Plenum Press, 1976. Rosenzweig, M. R. Responsiveness of brain size to individual experience: Behavioral and evolutionary implications. In M. Hahn, C. Jensen, & B. Dudek (Eds.), Development and evolution of brain size: Behavioral implications. New York: Academic Press, 1979. Rosenzweig, M. R., Bennett, E. L. (Eds.). Neural mechanisms of learning and memory. Cambridge, Mass.: MIT Press, 1976. (a) Rosenzweig, M. R., & Bennett, E. L. Enriched environments: Facts, factors, and fantasies. In L. Petrinovich and J. L. McGaugh (Eds.), Knowing, thinking, and believing. New York: Plenum Press, 1976 (b) Rosenzweig, M. R., & Bennett, E. L. Effects of environmental enrichment or impoverishment on learning and on brain values in rodents. In A. Oliverio (Ed.), Genetics, environment, and intelligence. Amsterdam: Elsevier/North-Holland, 1977. Rosenzweig, M. R., & E. L. Bennett. Experiential influences on brain anatomy and brain chemistry in rodents. In G. Gottlieb (Ed.), Studies on the development of behavior and the nervous system (Vol. 4). Early influences. New York: Academic Press, 1978.

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Rosenzweig, M. R., Bennett, E. L., Hebert, M., & Morimoto, H. Social grouping cannot account for cerebral effects of enriched environments. Brain Research, 1978, 158, 563–576. Rutledge, L. T. Synaptogenesis: Effects of synaptic use. In M. R. Rosenzweig & E. L. Bennett (Eds.), Neural mechanisms of learning and memory. Cambridge, Mass.: MIT Press, 1976. Schipper, W. V., & Kenowitz, L. A. Special education futures—a forecast of events affecting the education of exceptional children: 1976–2000. The Journal of Special Education, 1976, 10, 401–413. Schwartz, S. Effect of neonatal cortical lesions and early environmental factors on adult rat behavior. Journal of Comparagive and Physiological Psychology, 1964, 57, 72–77. Sitaram, N., Weingartner, H., & Gillin, J. C. Human serial learning: Enhancement with arecholine and choline and impairment with scopolamine. Science, 1978, 201, 274–276. Stratton, L. O., & Petrinovich, L. F. Post-trial injection of an anticholinesterase drug and maze learning in two strains of mice. Psychopharmacologia, 1963, 5, 47–54. Tagney, J. Sleep patterns related to rearing rats in enriched and impoverished environments. Brain Research, 1973, 53, 353–361. Tanzi, E. I fatti e le induzioni nell’odierna isologia del sistema nervoso. Revista Sperimentale di Freniatria e di Medicina Legale, 1893, 19, 419– 472. U.S. President’s Committee on Mental Retardation. Mental retardation: The known and the unknown. Washington, D.C., 1976 (DHEW Publication No. (OHD) 76 -21008). (a) U.S. President’s Committee on Mental Retardation. Mental retardation: Century of decision. Washington, D.C. (DHEW Publication No. (OHD) 76-21013). (b) Walsh, R. N., & Cummins, R. A. Mechanisms mediating the production of environmentally induced brain changes. Psychological Bulletin, 1975, 82, 986 –1000. Walters, E. T., Carew, T. J., & Kandel, E. R. Classical conditioning in Aplysia californica. Proceedings National Academy of Sciences U.S.A., 1979, 76, 6675–6679. Wedenberg, E. Auditory training of severely hard-of-hearing preschool children. Acta OtoLaryngologica, Suppl. 110, 1954. Wells, A. M., Geist, C. R., & Zimmermann, R. R. Influence of environmental and nutritional factors on problem solving in the rat. Perceptual and Motor Skills, 1972, 35, 235–244. West, R. W, & Greenough, W. T. Effect of environmental complexity on cortical synapses of rats: Preliminary results. Behavioral Biology, 1972, 7, 279–284. Will, B. E., & Rosenzweig, M. R. Effets de l’environnement sur la récupération fonctionnelle après lésions cérébrales chez des rats adultes. Biology of Behavior, 1976, 1, 5–16. Will, B. E., Rosenzweig, M. R., & Bennett, E. L. Effects of differential environments on recovery from neonatal brain lesions, measured by problem-solving scores. Physiology & Behavior, 1976, 16, 603–611. Will, B. E., Rosenzweig, M. R., Bennett, E. L., Hebert, M., & Morimoto, H. Relatively brief environmental enrichment aids recovery of learning capacity and alters brain measures after postweaning brain lesions in rats. Journal of Comparative and Physiological Psychology, 1977, 91, 33–50. Winick, M., Meyer, K. K., & Harris, R. C. Malnutrition and environmental enrichment by early adoption. Science, 1975, 190, 1173–1175.

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SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY

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SAGE LIBRARY OF EDUCATIONAL THOUGHT AND PRACTICE

SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY VOLUME III

Edited by

Neil J. Salkind

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Introduction and editorial arrangement © Neil J. Salkind 2011 First published 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Every effort has been made to trace and acknowledge all the copyright owners of the material reprinted herein. However, if any copyright owners have not been located and contacted at the time of publication, the publishers will be pleased to make the necessary arrangements at the first opportunity. SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1, Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 33 Pekin Street #02-01 Far East Square Singapore 048763 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN: 978-0-85702-178-6 (set of five volumes) Library of Congress Control Number: 2010923776 Typeset by Mukesh Technologies Pvt. Ltd., Pondicherry, India. Printed on paper from sustainable resources Printed by MPG Books Group, Bodmin Cornwall

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Contents Volume III Section II: Curriculum, Instruction and Learning (Continued) 36. 37. 38. 39. 40. 41.

Human Intelligence: An Introduction to Advances in Theory and Research David F. Lohman Cognitive Demands of New Technologies and the Implications for Learning Theory Richard J. Torraco Cognitive Conceptions of Learning Thomas J. Shuell Meaning in Complex Learning Ronald E. Johnson Phases of Meaningful Learning Thomas J. Shuell Growth, Development, Learning, and Maturation as Factors in Curriculum and Teaching William C. Trow

3 51 79 109 141 161

Section III: Motivation 42. 43. 44. 45. 46. 47. 48. 49. 50.

Maslow, Monkeys and Motivation Theory Dallas Cullen Maslow’s Theory of Motivation: A Critique Andrew Neher Caught on Fire: Motivation and Giftedness Ann Robinson An Empirical Test of Maslow’s Theory of Motivation Eugene W. Mathes and Linda L. Edwards Meaningfulness, Commitment, and Engagement: The Intersection of a Deeper Level of Intrinsic Motivation Neal Chalofsky and Vijay Krishna Motivation and Human Growth: A Developmental Perspective M.S. Srinivasin Evolutionary Perspectives on Human Motivation Jutta Heckhausen The Debate about Rewards and Intrinsic Motivation: Protests and Accusations Do Not Alter the Results Judy Cameron and W. David Pierce A Comprehensive Expectancy Motivation Model: Implications for Adult Education and Training Kenneth W. Howard

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The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education Robert J. Vallerand, Luc G. Pelletier, Marc R. Blais, Nathalie M. Brière, Caroline Senécal and Evelyne F. Vallières Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again Edward L. Deci, Richard Koestner and Richard M. Ryan Beyond the Rhetoric: Understanding Achievement and Motivation in Catholic School Students Janine Bempechat, Beth A. Boulay, Stephanie C. Piergross and Kenzie A. Wenk Dimensions of School Motivation: A Cross-cultural Validation Study Dennis M. McInerney and Kenneth E. Sinclair Achievement Motivation in Children of Three Ethnic Groups in the United States Manuel Ramirez III and Douglass R. Price-Williams Motivation and Learning Environment Differences between Resilient and Nonresilient Latino Middle School Students Hersholt C. Waxman, Shwu-yong L. Huang and Yolanda N. Padrón Attracting and Retaining Teachers: A Question of Motivation Karin Müller, Roberta Alliata and Fabienne Benninghoff

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Section II: Curriculum, Instruction and Learning (Continued )

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36 Human Intelligence: An Introduction to Advances in Theory and Research David F. Lohman

W

hat is intelligence? How does it develop? Does it decline? Has cognitive science really changed our understanding of this construct? Old questions about intelligence have been raised with a renewed vigor, and new questions have been posed. In short, there has been a remarkable resurgence of research on human abilities in the past 15 years, fueled in part by legal challenges to intelligence tests, but in even larger measure by a renewed interest in cognition in psychology. New methods of investigation and theories of cognition have been applied to old tests and theories of individual differences. Although the results have not met the loftier expectations of some advocates, progress has been made. The purpose of this paper is to provide a sampling of this progress, to note some of the problems that have attended it, and to suggest some research strategies for future research on human intelligence. I focus on three research traditions: trait theories of intelligence, informationprocessing theories of intelligence, and general theories of thinking. The discussion of trait theories of intelligence focuses on Cattell’s (1963) theory of fluid and crystallized abilities, particularly the elaborations of this theory proposed by Horn (1985) and by Snow (1981). Their work provides a convenient framework for the discussion of information-processing theories of intelligence. First, I summarize attempts to build process theories of the major factors identified in Horn’s (1985) model, such as the work of Jensen (1982) and Eysenck (1982) on mental speed, of Hunt (1985) and Frederiksen (1982) on verbal-crystallized abilities, of Sternberg (1977) on fluid-reasoning abilities, Source: Review of Educational Research, 59(4) (1989): 333–373.

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and of Pellegrino and Kail (1982) and Lohman (1988) on spatial-visualization ability. This section concludes with a discussion of Sternberg’s (1984, 1985) recent attempts to develop a comprehensive theory of intelligence. I then turn the problem around. Instead of asking how cognitive science might help us understand existing tests or ability constructs, I ask how a theory of intelligence might be derived from the sort of general theories of thinking currently advanced in cognitive psychology and artificial intelligence (AI). Here the discussion emphasizes Anderson’s (1983) ACT∗ theory (the latest version of his Adaptive Control of Thought system) and the “New Connectionism” of Rumelhart, McClelland, and the PDP (Parallel Distributed Processing) Research Group (1986). The paper concludes with some speculations about the meaning of the construct intelligence and some suggestions for research on it. The resurgence of general ability. Several developments converged in the early 1970s to renew interest in the construct intelligence. First, there was the growing realization that the ability profiles provided by multiple-aptitude batteries were not as useful for prediction as many had hoped (McNemar, 1964). Although there were exceptions, the predictive validities of the several scores from multiple-aptitude batteries were repeatedly found to be little better than the corresponding validity of one general factor estimated from the same battery.1 Nor were the specific abilities that Thurstone (1938) and Guilford (1959) had identified of much use in attempts to adapt instructional methods to the ability profile of the learner. Instead, general ability accounted for most of the findings. In their summary of 20 years of research on Aptitude X Treatment interactions, Cronbach and Snow (1977) concluded: It has become fashionable to decry the use of measures of general ability, and sometimes their use has been prohibited in school systems. The attackers usually insist that the tests do not assess ability to learn, and it is often proposed to substitute measures of achievement or “learning styles.”. . . While we see merit in a hierarchical conception of abilities, with abilities differentiated at coarse and fine levels, we have not found Guilford’s subdivision a powerful hypothesis. . . . Instead of finding general abilities irrelevant to school learning, we find nearly ubiquitous evidence that general measures predict amount learned or rate of learning or both. And, whereas we had expected specialized abilities rather than general abilities to account for interactions, the abilities that most frequently enter into interactions are general. Even in those programs of research that started with specialized ability measures and found interactions with treatment, the data seem to warrant attributing most effects to general ability, (pp. 496 – 497)

Thus, on one hand, special abilities failed either to predict educational outcomes better than general ability or to predict which students would profit from specialized educational interventions designed to match their particular patterns of abilities. On the other hand, American theorists gradually adopted a hierarchical model of abilities which, while allowing for both broad and narrow abilities, clearly emphasized the role of general ability.

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The cognitive revolution. The second development was an outgrowth of the cognitive revolution in psychology. From Watson (1925) until Skinner (1953), American psychology was dominated by the belief that mind was not the proper subject matter for psychology. Studies of animal learning or conditioning were the norm. Thinking and reasoning were considered complex behaviors that would be explained sometime in the future after elementary mechanisms of learning were adequately understood. By the mid-1960s, however, this promise was wearing thin. Psychology seemed not to be building toward the explanation of complex phenomena but, if anything, was digging increasingly deeper into reductionism. Some had already called for a rejection of radical behaviorism on theoretical grounds (Chomsky, 1959). But it was the emergence of the computer as a metaphor for mind and as a vehicle for testing theories about thinking that finally dethroned behaviorism. Rather swiftly, the mainstream of psychology moved from conditioning to perception and then to thinking and problem solving. By 1985, in the first paragraph of his introductory text on cognitive psychology, Anderson was proclaiming, “the goal of cognitive psychology is to understand human intelligence and how it works” (p. 1). Thus, in 2 decades, the word intelligence moved from the periphery of American psychology to its center.2 The cognitive revolution had two rather different influences on theories of human intelligence. There were some who saw that the methods and theories of the cognitive psychologists provided a new way to understand what intelligence and other ability tests were really measuring. Carroll (1976), Glaser (1972), Hunt (e.g., Hunt, Frost, & Lunneborg, 1973), Sternberg (1977), and Snow (e.g. Snow, Marshalek, & Lohman, 1976) were leaders in this effort. There were others, however, who were not at all concerned with intelligence as an individual difference construct. These investigators sought to develop theories of human cognition and, at times, to simulate their theories in computer programs that then displayed AI. Both of these efforts will be briefly reviewed in this paper.

The Challenge of Process Although most research on intelligence has focused on the products of intelligence, both theoreticians and clinicians have long called for greater attention to the process of intelligent thinking.3 Nobody has ever made an inventory of tasks [that define the universe of intellectual tasks], determined the correlation of each with intellect, selected an adequate battery of them, and found the proper weights to attach to each . . . If anybody did this wisely, a large fraction of his labor would be precisely to find out what abilities our present instruments did measure, and how these abilities were related to intellect; or to find out what abilities

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constituted intellect, and how these abilities were measured by our present instruments. (E. L. Thorndike, Bregman, Cobb, & Woodyard, 1926, p. 2)

Three decades later in his call for the unification of the two disciplines of scientific psychology – the correlational psychology of mental testing and the experimental psychology of learning – Cronbach (1957) argued Sophistication in data analysis has not been matched by sophistication in theory. The correlational psychologist was led into temptation by his own success, losing himself first in practical prediction, then in a narcissistic program of studying his tests as an end in themselves. A naive operationism enthroned theory of test performance in the place of theory of mental processes. (p. 675)

In this Cronbach echoed Thurstone (1947), who considered a factor-analytic study of abilities only the first step in a research program. Ability factors identified in such studies should be investigated in experiments designed to manipulate and thus identify “the processes which underlie” the factors (p. 55). But such experiments had little appeal in a psychology dominated by behaviorism, and so the research program Thurstone advocated had to await the rediscovery of mental process by the mainstream of American experimental psychology.

Cognitive Science and the Computer Metaphor Recent research on intelligence has been driven by a renewed interest in cognition in psychology and in many other fields. Cognitive science is the term now commonly used to refer to this new blend of computer science, cognitive psychology, linguistics, neuropsychology, philosophy, and instructional psychology. Although roots of the cognitive revolution may be traced to many earlier sources, several observers see 1956 as the pivotal year in the development of cognitive science. In that year, Newell and Simon (see Newell, Shaw, & Simon, 1957) reported their success in devising a computer program that could actually prove theorems in logic. In the same year, Bruner, Goodnow, and Austin published their Study of Thinking, and Miller published a seminal paper on short-term memory in which he argued that the capacity of this memory store seemed to be limited by “the magic number seven” (Newell & Simon, 1972, p. 4). The cognitive revolution gathered momentum in the 1960s and achieved ascendency during the 1970s (see Gardner, 1985). The computer has contributed importantly to this revolution in at least two ways. The most obvious contribution of the computer has been as a metaphor for human cognition.4 This metaphor has taken several forms. At the simplest level, direct analogies have been made between the hardware of

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the computer and the human cognitive system. Computers have devices for encoding information from external sources (card readers, keyboards), temporarily storing it (memory buffers), transforming it (central processors), retaining it on long-term storage devices (tapes, disks), and producing output (printers, video displays). Early models of human information processing relied heavily on this analogy in positing similar structures in the human cognitive system. When used in this way, the computer is but the latest mechanical metaphor for mind in psychology (Marshall, 1977). Although more sophisticated than previous metaphors such as the wax tablet or the hydraulic pump, the computer metaphor is incomplete and even misleading. For example, some researchers have begun to question the extent to which theorizing has been artificially constrained by the serial-processing, digital computer. New research programs based on parallel processing may circumvent some of these problems, particularly for modeling perception and other nonlinguistic processes. But, as will be explained, these theories have their critics too. Some analogies between computers and human cognition go considerably beyond comparisons of the superficial characteristics of system hardware. In particular, it is argued that similar principles govern the functioning of any system that processes information. Fodor (1981) and others who espouse this computational metaphor for thought treat the mind as a device for manipulating symbols. At this level of abstraction, differences in hardware, whether electronic or neurophysiological, are thought to be irrelevant. Whether such an assumption is tenable is a hotly debated issue in cognitive science. However, all would agree that the contribution of the computer has far exceeded its admittedly limited value as a metaphor for the human cognitive system. The greater contribution of the computer has been as a tool for developing and testing theories of cognition or, as Anderson and Bower (1973) put it, for experimenting on the nature of the connection between stimulus and response. In this way, the computer has changed the evidentiary base to include something other than human behavior. Theories of thinking and learning can be formalized as computer programs. Programs gain a measure of plausibility if they solve problems using sequences of steps that are similar to the steps used by successful human problem solvers or if, when failing to solve problems, they make errors that mimic human errors. A constant exchange between those who study human problem solving in the psychological laboratory and those who attempt to develop computer programs that display AI serves to refine and extend both efforts. Some would object that such comparisons between humans and computers diminish human dignity. However, cognitive science makes no pretense that computational theories completely account for human cognition. Computational models of thought are in principle no different from computational models of the weather (Miller, 1981). Yet, as Miller observes, no one fears that a tornado might destroy the computer center when the computer is used to model the behavior of tornadoes. Nor do we dismiss efforts to model the weather

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because such models will never produce rain. Perhaps we expect more from computational models of thought because “the brain is itself a computer in a sense in which the weather is not,” and so a “computer that models an intelligent brain is expected to be a brain” (p. 220).

Contributions of Cognitive Research Cognitive science has contributed to the understanding of human intelligence in three ways. First, methods and theories of cognitive science have been applied to existing tests of intelligence, either through experimental analysis of tasks taken from intelligence and other ability tests, or through careful study of the problem-solving or other information-processing characteristics of individuals identified as more or less able by existing tests. In this way, cognitive psychology offers a new source of evidence on the construct validity of tests and the ability factors they define. Second, tests of intelligence and narrow abilities are often used to predict performance in some non–test situation (e.g., conventional schooling). Careful study of the knowledge and processing demands of these criterial performances has led to the development of new measurement strategies and suggestions for the refinement of existing measures (Frederiksen, 1984; Snow & Lohman, 1989). Third, cognitive science has sought to move beyond existing definitions of intelligence grounded in individual differences to develop general theories of thinking and learning. New measures are then developed to estimate particular processes or knowledge structures hypothesized by these theories. Patterns of individual differences on these new measures are then investigated, usually by determining relationships between new measures and scores on existing tests or experimental tasks. The following section contains a brief review of attempts to understand intelligence through the study of existing tests or ability constructs defined by such tests. Cattell’s theory of fluid and crystallized abilities has had a major impact on these efforts, particularly the theories of Horn (1985), Snow (1981), and Sternberg (1985), and so his theory and recent extensions of it are summarized first. Then, experimental research on four of the major ability constructs identified by Cattell, Horn, and other theorists is summarized. The four constructs are verbal-crystallized (Gc) ability, spatial-visualization (Gv) ability, fluid-reasoning (Gf ) ability, and mental speed (Gs).

Controversies about Intelligence Controversies about the nature of intelligence seem to repeat themselves. Two of the most important controversies relate to the question of whether the general (sometimes called g) factor that is commonly equated with intelligence should be viewed as a psychological entity, or whether it is merely a mathematical abstraction. E. L. Thorndike (see E. L. Thorndike et al., 1926)

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and Thomson (1920) were early advocates of the view that responses to items on intelligence tests represent a particular sample of mental bonds, and thus intelligence was better understood as a mathematical abstraction than as a psychological entity. Humphreys (1985) gives a modern statement of this view. Spearman, on the other hand, interpreted the g factor as the ability to reduce relations and correlates. Sternberg’s (1977) early work on analogical reasoning constitutes a modern version of this view. This controversy has important implications for the potential contributions of cognitive theory to a theory of intelligence. If the ubiquitous general factor is simply a mathematical dimension (Humphreys, 1985), then analyses of tasks used on intelligence tests are unlikely to isolate a particular set of mental processes that are the core of intelligence. In fact, tests that are a good measure of this dimension should be composed of maximally heterogeneous items and thus would be psychologically complex (Humphreys, 1986). However, higher order processes such as coordination of existing routines or assembly of new routines (Snow, 1981) might still emerge across diverse performances (see Butcher, 1968, p. 25).5 The second controversy, often correlated with the first, is whether intelligence is an innate cognitive capacity or, instead, an acquired set of cognitive competencies.6 Hereditarians such as Burt (1958), Terman (1922), Jensen (1980), and Eysenck (1982) argue that good intelligence tests are – or should be – measures of this basic, biologically-based capacity. Others, such as Humphreys (1986) and Cronbach (1972), claim that potential and capacity are pie-in-the-sky concepts with no place in a scientific account of human ability. In fact, both argue that the psychology of individual differences would be well rid of the term intelligence. This controversy is reflected in the search for neurological correlates of intelligence test scores among the hereditarians and perhaps in the search for an explanation of intelligence in terms of structural differences (e.g., capacity of working memory, rate of information transfer in memory) by like-minded cognitive psychologists.7 On the other hand, those who believe that abilities are acquired competencies tend to emphasize the importance of knowledge in thinking (Glaser, 1984), to study the development of abilities rather than attempt to explain individual differences at a particular point in time (Kail & Pellegrino, 1985), and to view intelligence as a product of formal schooling, not simply as a predictor of success in that medium (Snow & Yalow, 1982). The third perennial controversy concerns the question of whether intelligence is unitary, as Spearman emphasized, or has multiple dimensions, as E. L. Thorndike, Thurstone, and Guilford emphasized.

The Theory of Fluid and Crystallized Abilities It is fitting that the most popular current resolution to the debate between Spearman, Thorndike, and Thurstone about the dimensions of intelligence was proposed by an Englishman who received his PhD under Spearman (in 1929),

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completed a postdoctoral fellowship under E. L. Thorndike (in 1937), conducted research with both Burt and Thurstone (Cattell, 1971, p. ix), and eventually took up permanent residence in the United States. In 1941, shortly after accepting a position at Harvard, Cattell proposed a quasi-hierarchical model of human abilities with two general factors at the apex (rather than the one advocated by Spearman). Each was defined by several of the primary factors Thurstone had identified. Cattell called these two factors fluid intelligence (Gf ) and crystallized intelligence (Gc). In the earliest published account of the theory, Cattell (1943) argued that fluid ability was “a purely general ability to discriminate and perceive relations between any fundaments, new or old” (p. 178). Fluid ability was hypothesized to increase until adolescence and then slowly decline. It was thought to represent the “action of the whole cortex” (p. 178). Further, fluid intelligence was thought to be the cause of the general factor found among ability tests administered to children and among the “speeded or adaptation-requiring” (p. 178) tests administered to adults. Crystallized intelligence, on the other hand, was thought to consist of “discriminatory habits long established in a particular field” that were originally acquired through the operation of fluid ability but that no longer required “insightful perception” (p. 178). The empirical facts Cattell hoped to explain by this theory were the relative independence of individual differences in speed and power in adult intellectual performance and their different patterns of growth and decline. The important psychological distinction in the theory was between process (fluid intelligence) and product (crystallized intelligence) (Cattell, 1963). The theory of fluid and crystallized ability attracted little attention, possibly because Cattell soon left Harvard for a research professorship at the University of Illinois. There he turned away from the study of human abilities and returned to his earlier research interest of applying the methods of factor analysis to the study of personality. He later wrote, “I had not learned . . . that more original and vital ideas than mine have collected dust on bookshelves for lack of exegesis by their parent or some scholarly leader” (Cattell, 1971, p. x). Twenty years were to elapse before Cattell was to return to the theory of fluid and crystallized abilities with new data. In the 1963 formulation of the theory, Gf was hypothesized to reflect the physiological integrity of the organism useful for adapting to novel situations that, when invested in particular learning experiences, produced Gc. Thus, Gf was now hypothesized to be physiologically determined, whereas Gc was “a product of environmentally varying, experientially determined investments of Gf.” (Cattell, 1963, p. 4) Although intuitively appealing, the hypothesis that Gf reflects physiological influences and is thus a better measure of the true intelligence of an individual is perhaps the most controversial aspect of the theory. Several prominent theorists accept the fluid-crystallized distinction, and some also subscribe to the investment theory of aptitude. But they do so without assuming that

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fluid ability represents something more innate than crystallized ability. For example, Cattell’s student and collaborator, Horn (1976), interpreted Gf simply as “facility in reasoning, particularly in fìgural or non-word symbolic materials” (p. 445). Cronbach (1977) went even further and argued that “fluid ability is itself an achievement” that reflects the “residue of indirect learning from varied experience” (p. 287). More recently, Horn (1985) echoed the same theme: “There are good reasons to believe that Gf is learned as much as Gc, and that Gc is inherited as much as Gf” (p. 289). Gc, said Horn, reflects individual differences in “acculturation learning” whereas Gf reflects individual differences in “casual learning” and “independent thinking” (Horn, 1985, pp. 289–290). Horn and others point out that, if tests of fluid abilities were somehow better estimates of the physiological integrity of the organism and if achievement tests were more a product of experience, then scores on tests of fluid abilities should show relatively higher heritabilities, which they do not (Horn, 1985; Humphreys, 1981; Scarr & Carter-Saltzman, 1982). These theorists also reject using tests of fluid ability as measures of “capacity” or “potential” against which achievement can be gauged (Cronbach, 1977; Humphreys, 1985; R. L. Thorndike, 1963). On the contrary, some argue that fluid abilities are among the most important products of education and experience (Snow & Yalow, 1982).

Recent Changes in Gf–Gc Theory The most important change in Gf–Gc theory in recent years has been the addition of several other second-order factors to the model. These developments are summarized somewhat differently by Cattell (1971) and by Horn (1985). Horn identified 10 second-order factors: two deep processing factors (Fluid Ability and Crystallized Ability), three perceptual organization factors (Visualization, Clerical Speed, and Auditory Thinking), three associational processing factors (Short-Term Acquisition and Retrieval, Long-Term Storage and Retrieval, and Correct Decision Speed), and two sensory reception factors (Visual Sensory Detection and Auditory Sensory Detection). Figure 1 shows how these factors can be arrayed along a continuum that progresses from surface to deep processing or from infancy to adulthood. The model is frankly speculative. “I know very little about human abilities,” writes Horn (1985). “All I can do is write articles about them, talk about them, and specify models for them. The more I talk and write and model, the more I realize how little I really know about this complex realm of human functioning” (p. 293). Nevertheless, the model summarizes much of what is known about the organization of human abilities, and it is, in the main, consistent with the abilities Carroll (in press) has thus far identified in his massive review and reanalyses of 60 years of factor-analytic studies of human abilities. Recent research on the four most widely studied broad factors in this model is presented in the next section.

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Relation eduction

Intensional Extensional Knowledge

Adulthood

Gf Fluid ability

Deep Processing

Gc Crystallized ability

Perceptual organization Gn Broad visualization

Gs Clerical speed Reaction time

Youth Dealing with visual novelty

Awareness

Childhood

Infancy

Developmental hierarchy

SAR Short-term acquisition retrieval

nSD Visual sensory detectors

Ga Broad auditory thinking

CDS Correct decision speed

Association processing

TSR Long-term storage retrieval

aSD Auditory sensory detectors

Sensory reception

Sensorimotor circular activities

Information-processing hierarchy

Note: From “Remodeling Old Models of Intelligence” by J. L. Horn in B. B. Wolman (Ed.), Handbook of Intelligence (p. 295). New York: John Wiley & Sons. Copyright 1985 by John Wiley & Sons, Inc. Reprinted by permission.

Figure 1: A model of ability organization within developmental and information processing hierarchies

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Unpacking Existing Tests and Constructs Tests of Fluid and Crystallized Abilities Tests of fluid ability require novel problem solving, much like many of the intelligence tests developed during the first half of the century – particularly the so-called nonverbal or performance tests such as matrices or block design. These tests require subjects to reason with moderately novel fìgural or symbolic stimuli. For this reason, complex spatial tests often load strongly on the Gf factor (Lohman, 1979). Span tests and other measures of what Jensen (1969) calls Level I ability also often load significantly on the Gf factor (Horn, 1985). Tests of crystallized ability, on the other hand, require the examinee to display an understanding of concepts and skills taught in some domain, particularly in school. Verbal knowledge and skills are emphasized, although numerical computation and mechanical knowledge tests often load significantly on Gc factors. Recently the Stanford-Binet was revised along the lines of the theory of fluid and crystallized abilities. The particular version of Gf–Gc theory on which the new Stanford-Binet is based combines the hierarchical model of intelligence of Vernon (Vernon, 1950) and the quasi-hierarchical model of intelligence of Cattell (1963). The three-level hierarchy includes a General Reasoning factor, G, at the top. Three broad group factors – Crystallized Abilities, Fluid-Analytic Abilities, and Short-Term Memory – constitute the second level. Three more specific factors make up the third level. G is interpreted “as consisting of the cognitive assembly and control processes that an individual uses to organize adaptive strategies for solving novel problems” (R. L. Thorndike, Hagen, & Sattler, 1986, p. 3). Thus, the authors adopt Snow’s (1981) definition of Gf as their definition of G. This is a reasonable equation since the Gf factor is invariably highly (Cattell, 1971; Lohman, 1979), or even perfectly (Gustafsson, 1984), correlated with G. Crystallized abilities are represented by both verbal and quantitative reasoning tasks. These abilities “are greatly influenced by schooling, but they are also developed by more general experiences outside of school” (p. 4). Fluid-analytic abilities are estimated by fìgural and spatial tasks. Fluid abilities are thought to involve “the flexible reassembly of existing strategies to deal with novel situations.” Further, the authors acknowledge that these abilities are also developed, but they are developed from more general experiences than schooling. Finally, the Short-Term Memory factor is represented by tests requiring memory for beads, sentences, digits, or objects. Thus, the new Stanford-Binet attempts to fit old tasks into a more recent theory of intelligence. But do we really understand these tasks well enough to defend the inference that different combinations of them reflect different abilities? What happens when we try to look at the processes subjects use when solving test items or when acquiring the knowledge they sample? In other words, is it possible to develop process theories of abilities?

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Verbal-Crystallized Ability Specific verbal processes. Verbal abilities hold a prominent place in all theories of intelligence. It is not surprising, then, that some of the first efforts to understand intelligence in terms of cognitive processes focused on verbal abilities. Hunt and his colleagues have reported several studies of the informationprocessing characteristics of subjects who differed in verbal-crystallized abilities. Their work is of particular interest because it deals with an important facet of intelligence and because it shows the strengths and weaknesses of both the newer cognitive-experimental approach and the traditional correlational approach to the study of intelligence. The aim of this line of research is aptly summarized in the question, “What does it mean to be high verbal?” which was the title of a report by Hunt, Lunneborg, and Lewis (1975). The method used in this and several other studies was to select college students with extremely high or low scores on the verbal section of a college entrance examination, to administer to these subjects a battery of presumably well understood experimental tasks, to estimate information-processing scores for each subject on each experimental task, and then to relate these scores to scores on the reference verbal-ability tests using some type of correlational analysis. For example, in one experimental task, subjects were required to compare pairs of letters of the alphabet, and to respond “yes,” if the two letters were physically identical (as in “aa” or “AA”), or “no”, if they were different (as in “aA” or “ab”). In a second task, similar pairs of letters were presented, but this time pairs were to be judged according to their names. Thus, in Task 1, the correct answer to the pair “Aa” would be “no,” whereas in Task 2, the correct answer would be “yes.” An information-processing model for Task 1 (Physical Comparison) would posit processes for encoding the appearance of the two letters, comparing these representations, and then responding. A model for Task 2 (Name Comparison) would include all of the processes required by Task 1 plus an additional process to retrieve the name codes. Thus, the difference between the time to respond to a given pair of letters in Task 2 and the same pair of letters in Task 1 provides an estimate of the time needed to perform this additional process. The resulting score is called the NIPI (Name Identity minus Physical Identity) difference and has been widely studied as a measure of the speed of accessing overlearned name codes. Correlations between the NIPI score and measures of verbal comprehension are typically about r = −.3, suggesting that subjects high in verbal ability access name codes faster than subjects low in verbal ability.8 These and other results are consistent with both a hierarchical model of human abilities and with current theories of the way knowledge is represented in memory. In particular, the information-processing tasks used by Hunt et al. (1975) appear to measure specific verbal abilities found in the lower branches of hierarchical models of abilities. Performance on many of these tasks depends on the subject’s ability (a) to produce a rapid, fluent

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response and /or (b) to remember the order in which information was presented. This latter ability is sometimes represented in models of memory by a special type of memory code called a linear order (Anderson, 1983). Such a code preserves the sequential structure of an event: what came first, then next, then next, and last. Spelling tests require this sort of memory code; one must not only remember the correct letters but also their proper sequence. Similarly, sequencing arbitrary phonemes into words, such as when learning a new language, or sequencing arbitrary words into strings of words, such as when memorizing the names of the letters of the alphabet, days of the week, or lines in a poem, seems to depend in part on the ability to code information in this way. Research relating scores on experimental tasks to scores on verbal ability tests also has revealed important limitations in efforts to generalize from laboratory tasks to test behavior. First, seemingly simple experimental tasks can measure different abilities in different subjects. For example, Hunt and others (see, e.g., Hunt, Lunneborg, & Lewis, 1975) have used a sentence verification task in which subjects are shown a phrase such as “star above plus” and a picture which either conforms with or contradicts the sentence. Subjects must determine whether the picture and sentence agree. However, minor variations in procedure can substantially alter the way subjects solve this task (Glushko & Cooper, 1978). More importantly, in any given procedure, subjects can differ in the way they solve the task: some create a mental picture from the phrase and compare it with the picture, and some convert the picture to a verbal description and compare that description with the phrase (Macleod, Hunt, & Mathews, 1978). A second limitation stems from the low correlations between scores representing particular information processes on experimental tasks and scores on reference tests of verbal abilities. Keating and MacLean (1987) argue that the main contribution of the information-processing approach to the analysis of intelligence is that it permits investigators to identify particular mental processes such as rate of rotation or speed of lexical access. The value of the process approach diminishes quickly when these parameters show low correlations with other measures or with similarly labeled parameters derived from other tasks. Keating and MacLean are particularly critical of studies in which Hunt abandoned process parameters and instead defined latent “process factors” based on correlations among total reaction time (RT ) or errors on experimental tasks. Using composite indices in this way, they claim, comes close to “dismissing the logic of the original cognitive correlates approach” (p. 259). Such composite indices cannot be used to “explain” composite indices computed in the same way on ability tests. Part of the confusion here surely stems from different expectations about what process parameters represent. It is commonly assumed that, by fitting an information-processing model to a task and by decomposing a composite index (total correct or total latency) into component indices, one has also

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decomposed individual differences on the task into cleaner components. This is not the case. Actually, individual differences in component scores (e.g., rate of rotation) salvage individual differences relegated to the error term when performance for each individual is summarized in a single score such as number of problems solved correctly, or mean response latency. Recapturing variance from the error term might be a profitable activity but only when items on the task show poor internal consistency. Even then, it must be recognized that such scores do not represent a decomposition of the individual differences variance reflected in total or average scores. Low correlations between scores thought to represent particular verbal processes and reference verbal-ability tests may also mean that much of the knowledge or some of the cognitive processes that account for general crystallized abilities (Gc) as measured by tests are not required by the experimental tasks. Experimental tasks in which subjects are required to infer the meaning of unfamiliar words from context sometimes show much higher correlations than do simple laboratory tasks with both Gc scores and general reasoning scores (Sternberg & Powell, 1983). This suggests that the low correlations obtained by Hunt et al. (1975) may estimate the contribution of specific verbal processes to Gc. Much of the remaining variability in Gc is better attributed to the ability to apply general reasoning skills and prior knowledge to the task of understanding verbal material and learning from it. Reading comprehension. Nowhere is this interdependence of specific component processes, general reasoning abilities, and prior knowledge better demonstrated than in reading. Reading comprehension is highly correlated with general verbal abilities, particularly in school-age populations. Thus, research on reading comprehension not only illuminates an important aspect of Gc but also shows how diagnostically useful tests can be derived from theory and how studies of individual differences can in turn reveal needed changes in the theory. J. R. Frederiksen’s (1982) work is perhaps the best example of this reciprocity. Frederiksen began by developing a general model of reading from his own research and that of many other investigators. He eventually distinguished three types of information-processing skills used in reading: word-analysis processes (e.g., encoding single- and multiletter units, using phonics skills), discourse analysis processes (e.g., retrieving word meanings, resolving problems of reference), and integrative processes (e.g., combining information from pictures and text). Frederiksen then constructed a test battery to measure some of these skills. Measures were validated by using both experimental and correlational techniques. Later, training tasks were devised to assist poor readers in acquiring deficient skills. Other theories of reading ability have been advanced in recent years. For example, Perfetti (1986) distinguishes three types of component processes in his theory: lexical access, proposition encoding, and text modeling. Lexical access refers to the process by which word meanings are activated in longterm memory. Individual word meanings are then combined and retained in

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working memory in predicate-like structures called propositions. These in turn are combined with the reader’s prior schematic knowledge to form a text model. This model, then, represents the reader’s understanding of the text. Kintsch (1986), in another theory of text comprehension, argues that two types of mental models must be coordinated: a text model, which contains the reader’s representation of the propositions embedded in the text, and a situation model, which might be a mental image of the situation described by the text. For example, in following directions to assemble a toy, the text model might represent the ideas implied by the words, “Attach wheel K to spindle Q using two 5/16 washers and a large hex nut.” The situation mental model might be represented by an image of what one is supposed to do. Pictures, illustrations, good description, metaphor, and analogy facilitate the generation of good situation models. A well structured text that follows a familiar schema and uses familiar words facilitates the construction of a coherent text model. Mental models may be an important link in the individual difference equation as well. A central problem in the definition of verbal abilities has been the overlap between measures of reasoning abilities and measures of verbal comprehension. However, theories of reasoning (Holland, Holyoak, Nisbett, & Thagard, 1987; Johnson-Laird, 1983) also emphasize the construction and the coordination of mental models. Thus, process analyses reveal commonalities between tasks (and the ability constructs they define) not apparent in armchair analyses. A similar argument may account for the high correlation between reasoning and vocabulary scores. The meaning of an unfamiliar word is usually inferred from the contexts in which the word has been embedded. (DaalenKapteijns & Elshout-Mohr, 1981; Marshalek, 1981; Sternberg & Powell, 1983). This process is most successful when the learner generates a good schema (or model or working hypothesis) about the meaning of an unfamiliar word when it is first encountered. This schema can then be confirmed or contradicted by evidence from subsequent contexts. Low-verbal subjects are less likely to use this strategy than are high-verbal subjects. Thus, vocabulary tests that use abstract words (i.e., words whose meanings are difficult to infer from a single context) show higher correlations with reasoning than do vocabulary tests of comparable difficulty composed of infrequent words (Marshalek, 1981).

Spatial-Visualization Ability Spatial tasks have long been used as psychological tests. Before 1915, Porteus had used such “performance” tasks to estimate the intelligence of linguistically different or disabled examinees. Spearman also originally used such “performance” tests as a measure of g, a tradition he attributes to Itard (1801, cited

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in Spearman & Wynn Jones, 1950). Spatial tasks also figured prominently in the Army Beta examinations of World War I. However, beginning with Kelley (1928) and then El Koussy (1935), such tasks were studied in their own right, and several specific spatial abilities were identified (Smith, 1964). Nevertheless, spatial or figural reasoning tasks have continued in their role as measures of general abilities, particularly Gf. As with verbal abilities, cognitive research on spatial abilities may be divided into (a) attempts to develop general theories of spatial thinking that ignore individual differences (e.g., Pinker, 1984; Shepard & Cooper, 1982), and (b) attempts to explain individual differences on existing tests of spatial abilities, either through correlations between scores on spatial tests and performance on laboratory tasks or through the construction of informationprocessing models for particular spatial tests. In contrast to recent research on verbal abilities, however, only a few studies have examined correlations between scores from laboratory tasks and scores from existing tests. Instead, most effort has been directed toward attempts to build information-processing models that describe how subjects solve particular spatial tests (see, e.g., Pellegrino & Kail, 1982). This is because most spatial tests are process-intensive in the same way that most verbal tests are knowledge-intensive. In other words, although some interesting processing occurs when subjects take a vocabulary test (Sternberg & McNamara, 1985), most of the complex processing occurred at the time the words were learned. Conversely, although spatial knowledge has an important impact on spatial problem solving (Lohman, 1988), whether subjects solve such problems depends heavily on the processes they employ during the test. Theories of spatial thinking (e.g., Kosslyn, 1980) distinguish two types of spatial knowledge: knowledge best modeled by quasi-pictorial mental representations (e.g., appearance of a particular object) and knowledge best modeled by abstract, proposition-based memory representations (concepts of symmetry, proportionality, closure, etc.). Each type of representation can be transformed by a different class of mental operators or procedural knowledge. Quasi-pictorial representations can be subjected to various analog transformations such as a rotation or synthesis (Shepard & Cooper, 1982). Propositional representations can be subjected to the same general and specific cognitive operators (e.g., means ends analysis) that can be applied to propositional knowledge derived from other sources (e.g., linguistic inputs). Transformations such as rotation, then, are of interest primarily for the constraints they place on the type of mental representation used. Thus, many spatial-ability tests present items which seem to require for their solution analog transformations such as rotation, reflection, transposition, or synthesis. Research on how subjects solve spatial tests has turned up several surprises. One persistent finding has been that all subjects rarely solve fìgural tasks in the same way. For example, in a series of experiments on visual comparison processes, Cooper (1982) identified two markedly different strategies. Some

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subjects appeared to rely on a serial, analytic process to compare forms whereas others relied on a parallel, holistic process. Complex tasks – such as the paper-folding tasks or form-board tasks commonly seen in mental tests – elicit an even wider range of alternative solution methods. Some subjects solve items on such tests by generating mental images that they then transform holistically. These high-spatial subjects excel in generating, retaining, and transforming mental representations that preserve information about the configuration of a figure. They also use their spatial knowledge to decompose unfamiliar visual shapes into simpler, more familiar shapes. Other subjects rely on general reasoning skills or external aids (such as line drawings) to solve problems. Others use still different processes. But most subjects use more than one type of processing, generally shifting from one strategy to another as problems increase in difficulty (Lohman, 1988). Such within-subject variability in solution strategy challenges simple information-processing models of spatial tests. Strategy shifting may partially explain why complex spatial tests are often good measures of g or Gf. Appropriate flexibility in adapting solution methods to meet personal limitations and changing item demands appears to be a central aspect of any process theory of Gf (Snow & Lohman, 1989).

Fluid-Reasoning Ability There has been considerably more research on reasoning or general fluid ability than on either general crystallized or general visualization abilities. However, attempts to understand how subjects solve Gf tasks such as analogies, classification, and series completion that have ignored differences in processing strategy (by averaging over items) or reduced the need for alternative strategies (by drastically simplifying items) have generally produced experimental tasks that show little relationship with scores on reference Gf tests. Put another way, simple items that are all solved in the same way by all subjects probably require little of what we call intelligence. The effects of simplifying a complex task so that it could be studied experimentally and ignoring within-person strategy shifts were perhaps most evident in Sternberg’s (1977) first investigation of analogical reasoning. Sternberg hypothesized that subjects use several different or “component” processes when solving analogies such as “Up is to down as left is to (a) back (b) right” or A:B::C:D1, D2. According to Sternberg’s theory, subjects (a) first read and understand each term in the analogy (encoding), (b) determine the relationship between the A and B terms (inference), (c) infer the relationship between the A and C terms (mapping), (d) generate an ideal answer by applying the A-B relationship to C (application), and (e) compare their ideal answer with the options provided (comparison). If none of the presented options meet the subjects’ criterion for acceptability, they then recycle through some or all

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of the preceding steps ( justification) and finally choose an option and respond (response). Component processes were assumed to be executed serially. Different models were then formulated by deleting particular processes (e.g., mapping, justification) and by specifying different modes of execution for a given process (e.g., self-terminating or exhaustive). Three important results were obtained. First, models were quite successful in accounting for variabilities in response latencies and, to a lesser extent, in response errors. Second, the data from most subjects were well fitted by a single model, suggesting that most subjects used the same strategy. Third, estimates of speed of executing particular component operations showed small and inconsistent relationships with reference reasoning tests. Unexpectedly, the highest correlations were observed for the preparation-response component. Thus, the componential analysis appeared successful, but those components hypothesized to reflect the essence of reasoning seemed not to measure reasoning at all. Later studies in which better practiced subjects attempted more complex items did show significant correlations between component scores and scores on reasoning tests (Bethell-Fox, Lohman, & Snow, 1984; Sternberg & Gardner, 1983). It appears that problems must be more than trivially difficult before individual differences in reasoning are observed. Further, items must also vary somewhat in the processing demands they place on examinees.9 This means that problems must be moderately novel. Novelty is an ancient theme in the psychology of individual differences. From Stern (1912/1914) to Sternberg (1985), theorists have argued that intelligence is best displayed when tasks are relatively novel. Cognitive psychologists are only beginning to understand how subjects transfer prior learning to analogous situations (Gick & Holyoak, 1983). The problem, of course, is that what is novel for one person may not be novel for another person or even for the same person at a different time. It appears that inferences about how subjects solve items that require higher level processing must be probabilistic, since the novelty of each item varies for each person. Snow (1981) has integrated these and other research results in the following hypothesis on the nature of fluid and crystallized abilities. Gc may represent prior assemblies of performance processes retrieved as a system and applied anew in instructional or other performance situations not unlike those experienced in the past, while Gf may represent new assemblies of performance processes needed for more extreme adaptations to novel situations. The distinction is between long-term assembly for transfer to familiar situation vs. short-term assembly for transfer to unfamiliar situations. Both functions develop through exercise, and perhaps both can be understood as variations on a central production system development. (p. 360)

The point about “exercise” derives from E. L. Thorndike’s (1903) theory of learning whereas the point about “production system” derives from the ACT∗ model of Anderson (1983), which is discussed later.

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Mental Speed The fourth and last broad factor in Horn’s (1985) model that will be examined here is sometimes called General Speed, sometimes Clerical Speed, or sometimes simply, Mental Speed. There is a new interest in this construct, whatever it is called. However, like most other ability constructs, mental speed has a long history in educational and psychological measurement. E. L. Thorndike, Spearman, and Thurstone all addressed the question of whether mental speed should be distinguished from power (or altitude). For example, although mental speed was one of the four dimensions of his model of intelligence, E. L. Thorndike considered speed less important than altitude (see E. L. Thorndike et al., 1926). On the other hand, Spearman (1927), citing studies which showed high correlations between scores on a time limit test and scores on the same test after an extended period of time, concluded (erroneously) that speed and power (or altitude) were interchangeable. Thurstone (1937) proposed a three-dimensional model that related ability, speed, and motivation. Like E. L. Thorndike, he defined ability in terms of power or altitude in his model (although many of the ability factors he identified in his empirical studies were based on simple, highly speeded tests). Individual differences in mental speed have been studied in several paradigms, two of which are summarized here. Research in the first paradigm at first sought to estimate the subjects’ “natural” rate of thinking (Hunsicker, 1925). This search led to the identification of several personality factors such as Carefulness, Persistence, and Impulsivity that described subjects’ typical trade-off between speed and accuracy. It also led to the identification of several cognitive speed factors, such as Perceptual Speed, Clerical Speed, and eventually, to claims of a General Speed factor. Research in the second paradigm, which may be traced back to Galton (1869) has sought to define intelligence as a physiological rather than as a psychological or sociocultural construct. Thus, the aim is to determine the integrity and efficiency of neurological mechanisms thought to underlie intelligent thought and action. Preferred indicators of intelligence in this paradigm are measures of sensory acuity, speed of detecting a stimulus or discriminating between two stimuli, and, in more recent work, patterns in recordings of electrical activity in the brain. Correlations are then computed between these measures and more global indices of intelligence, such as teacher ratings, course grades, or scores on existing intelligence tests. Work in this paradigm had hardly begun when it was abandoned by most psychologists, partly because of studies like that of Wissler (1901), but perhaps in larger measure because of the success of Binet’s test. Wissler, working under the direction of James McKeen Cattell at Columbia (who had in turn worked with Galton for a short time), found that a measure of RT was uncorrelated with grade point average in a sample of university students. The RT paradigm has recently been revived by Jensen, Eysenck, and others.

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Speed factors. Variation in the relative emphasis tests placed on speed or level of performance is an important confound in much of the literature on human abilities. The primary factors identified by Thurstone and his followers, particularly Guilford, were often defined by tests that contained simple, similar, highly speeded items. Complex versions of the same tests administered under conditions which emphasize level or altitude invariably show stronger loadings on the general factor and little evidence of the fractionalization of ability that occurs when simple, speeded tests are administered (Lohman, 1979). This is because individual differences in the speed with which subjects can solve relatively simple problems in a domain show only weak correlation with the complexity of a problem of the same type which subjects can solve when time is not a factor (Horn, 1985; Kyllonen, 1985). The question remains, though, whether some or all of these various speed primaries may define a higher order or General Speed factor. Although several investigators have claimed to have identified a General Speed factor, closer examination shows that such factors are often little more than overblown Clerical Speed or Perceptual Speed factors. General differences in speed of processing may well exist, but they are difficult to identify by factor analyzing speed scores from a battery of tests. The major reasons are that one cannot make unambiguous comparisons of response latencies across individuals unless (a) all subjects correctly solve all items, (b) all subjects adopt the same trade-off between speed and accuracy, and (c) neither of these factors vary systematically across tasks. One way to avoid these problems would be to use a single task that is so simple that everyone can solve it and that is not much influenced by the individual’s decision to emphasize speed or accuracy. Recent studies of reaction time aim to fit both of these criteria. Recent research on reaction time. The primary dependent measure in much cognitive research is response latency, usually on simple tasks. Those who study individual differences raised the question of whether individual differences in latencies on these laboratory tasks would show any relationship with individual differences on other tasks that presumably required the same processes (Underwood, 1975) or with ability variables commonly assessed by mental tests (Hunt et al., 1973; Snow et al., 1976). But the main goal of researchers like Hunt, Snow, and Sternberg was to develop and test information-processing models of theoretically interesting cognitive tasks or of tests commonly used to estimate important ability constructs, not to propose new measures of mental speed. However, this was precisely the goal of another group of researchers. Led by Jensen in the United States and Eysenck in the United Kingdom, these researchers saw possibilities for new measures of intelligence in response latencies on simple tasks and other indices of cognitive efficiency presumably unaffected by intention or experience. Jensen’s work. Jensen sparked new interest in the relationship between RT and G (intelligence) by showing significant correlations between choice (or discrimination) RT and measures of G. Jensen’s work has generated much

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discussion. In part this is because his goal seems to be to isolate a culture-free measure of intelligence. Individual and group differences on such a measure could then not be interpreted “as reflecting only differences in cognitive contents and skills that persons have chanced to learn in school or acquire in a cultured home” (Jensen, 1980, p. 704). The apparatus Jensen has used in his studies contains a center “home button” surrounded by 8 light/button pairs. Different light/button pairs can be covered to manipulate the number of stimulus–response pairs between 1 and 8. The task is to hold a finger on the home button until one of the exposed lights is activated and then turn it off as quickly as possible by moving the finger from the home button to the button directly below the activated light. Two time intervals are recorded: (a) the time between the onset of the stimulus light and the release of the home button (called RT), and (b) the additional time required to move the finger to the button below the activated light and press it (called movement time). In a typical experiment, subjects receive a few practice trials, followed by 15 trials at each of four levels of task complexity: 1, 2, 4, or 8 light/button pairs exposed. Typically, RT increases linearly with the log of the number of buttons exposed. Jensen found that the slope of this function, which is taken as an estimate of the rate at which a person processes a single unit of information, and G correlated negatively, with. r = −.41 being the most often cited correlation. In addition, the correlation between RT and G increases as task complexity is increased from 1 to 8 light/button pairs, suggesting that the greater the information-processing burden, the greater the demand on G. Jensen’s work has been praised by some (e.g., Eysenck, 1982) and criticized by others (e.g., Longstreth, 1984; Carroll, 1987). In particular, Jensen’s claim that performance on the choice RT task is not influenced by practice, motivation, or instructions to alter speed-accuracy trade-off has been questioned (Carroll, 1987; Longstreth, 1984). Longstreth also raises a number of fundamental questions about Jensen’s procedure, such as the routine confounding of practice with task complexity. Carroll questions the replicability and interpretation of Jensen’s results. He suggests that differences between individuals in average RT may better be described as differences in the variability in RT for a given person over trials. This is because RTs have a lower limit, and thus individuals with more variable RTs would tend to have higher mean RTs because they are more likely to deviate upward from the lower limit. This suggests that the observed correlation between RT and G may in part reflect differences in attentional control and not simply differences in the speed of neural conduction or the rate of neural oscillation, as Jensen hypothesizes. Attempts to replicate Jensen’s findings usually find some relationship between RT and G (most often between the variability of RTs for individual subjects and G, with lower G subjects having more variable RTs). But replications consistently fail to find that low G subjects show greater increases in RT

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with increases in the number of exposed light/button pairs than do high G subjects (Barrett, Eysenck, & Lucking, 1986; Carlson, C. M. Jensen, & Widaman, 1983; Jensen, 1987). Although controversy about Jensen’s work continues, there is some consensus on the main findings. First, correlation between G and RT is generally somewhat lower for the simple RT condition (one light/button pair exposed) than for the discriminative RT conditions (two or more light/button pairs exposed). Second, correlations between discrimination RT and G vary widely. However, replicable correlations are generally in the −.2 to −.4 range. Conditions with more light/button pairs (e.g., 8) do not yield dependably higher correlations with G than conditions with fewer light/button pairs (e.g. 2). Indeed, it is a common finding that correlations between RT and G decline as more and more complex information processing is required. More complex tasks allow multiple strategies and are prone to differences in the speed–accuracy trade-off subjects adopt. Third, the variability in RT over trials often correlates as highly with G as does mean or median RT. Thus, attention control (or, conversely, distractibility) may be as important as speed of processing in this task. Fourth, Jensen’s claim that RT increases linearly with the log of the number of exposed light/button pairs has been repeatedly confirmed. However, other investigators have not been able to confirm his claim that individual differences in the slope of this line correlate with G. It is unclear whether this is due to persistent methodological inadequacies in these studies (which usually follow Jensen’s procedures), as Longstreth (1984) notes, or whether this reflects a more fundamental error in Jensen’s theory, as Eysenck (1987b) now claims. Eysenck’s work. Eysenck (1982; 1988) has proposed a theory of intelligence with an even stronger physiological flavor. Following Hebb (1949), Eysenck (1988) distinguished among biological intelligence, psychometric intelligence, and social intelligence. Biological intelligence “refers to the structure of the human brain, its physiology, biochemistry, and genetics which are responsible for the possibility of intelligent action” (p. 3). Eysenck considers biological intelligence to be the purest, most fundamental intelligence because it is “least adulterated by social factors.” He claims it can be measured by the electroencephalogram (EEG), evoked potentials, galvanic skin responses, and perhaps reaction times. Psychometric intelligence is defined as that intelligence which is measured by psychometric tests. In addition to the core of biological intelligence, is determined by cultural factors, education, family upbringing, and socioeconomic status. However, since only a fraction of the variance in psychometric intelligence (i.e., IQ) can be attributed to genetic factors (Eysenck estimates 70%), IQ should not be confused with biological intelligence. Social intelligence reflects the ability to solve problems an individual encounters in life. But since so many noncognitive factors are reflected in such performances, Eysenck (1988) argues that “social intelligence is far too

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inclusive a concept to have any kind of scientific meaning” (p. 45). Thus, for Eysenck, intelligence is a concept that is best studied at the physiological (or even neurological) level, only indirectly represented in intelligence tests, and obscured almost entirely in performances in the real world. This is an extreme view and is not widely shared, at least not by American academics. As with Jensen’s work, much of the controversy surrounding Eysenck’s work has centered not so much on the finding of significant correlations between G and EEGs, cortical evoked potentials, and other physiological indices but on the reported magnitude of the correlations. For example, Eysenck’s colleague, Hendrickson (1982), reported a correlation of r = .83 between a measure of evoked potentials and Wechsler IQ for a sample of 219 15-year old children. In 1984, Eysenck claimed that “several replications . . . have shown the results are essentially reproducible” and that these results were “a most important validation of Galton’s concept” of intelligence (published in Eysenck, 1987a, p. 359). However, by 1988, presumably on the basis of new evidence, Eysenck had changed his mind. “It seems unlikely that the correlation between IQ and a physiological measurement of biological intelligence . . . can exceed the square root of the heritability of IQ,” and thus correlations such as those obtained by Hendrickson (1982) are “inherently improbable and unlikely to be replicated” (Eysenck, 1988, p. 12). Inspection time. A similar history attends the reports on correlations between inspection time and IQ. Inspection time is the minimum duration for which two different stimuli must be presented if they are to be perceived as different. Nettelbeck and Lally (1976) reported a correlation of r = −.92 between the Wechsler Adult Intelligence Scale performance scale and inspection time, but, for a sample of only 10 subjects, 2 of which were retarded. The magnitude of the reported correlations gradually declined as larger and less wide-ranging samples were tested. By 1984, Irwin reported correlations of r = −.32 and r = −.09 for auditory and visual inspection times with a verbal intelligence test and correlations of r = −.23 and r = −.27 for those same inspection times with a nonverbal intelligence test for a sample of 50 12-year-old children. In the meantime, Nettlebeck and Kirby (1983) had gathered new data on a large sample of adults and had reanalyzed data from one of their earlier studies. This time they found no correlation between G and slope in the Jensen task and a weak correlation between inspection time and G (r = −.3) when retarded subjects were excluded. They therefore concluded that their earlier correlations had been inflated by the inclusion of retarded subjects, who were “markedly less efficient” (p. 39) on these tasks. Their conclusions run completely counter to earlier claims: This outcome raised doubt about the validity of combining data from retarded and nonretarded subjects. Our results ran counter to claims that tasks of the kind used [in this study] are largely uninfluenced by cognitive

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variables [such as strategy], so that findings are not necessarily explained satisfactorily in terms of a mental speed factor. These measures of timed performance do not, at this time, provide a basis from which a reliable, culture-fair measure of intelligence might be devised. (p. 39)

Summary. Critics of studies that report correlations between measures such as RT, inspection time, evoked potentials, and G cynically argue that the best predictor of the correlation obtained is the date of the study. The first correlation reported is usually strikingly high, but then the magnitude of the reported correlation declines almost linearly with year of publication, eventually stabilizing on a value in the −.1 to −.4 range. Such correlations are theoretically interesting, but they do not justify attempts to replace existing intelligence tests with RT measures, or interpretations of G as a purely physiological phenomena. One need not descend to the level of neurons to find a plausible account of the role of mental speed in models of intelligence. For example, the rate at which activation spreads through regions of memory, the rate at which an activated memory loses its activation, and the level of activation needed to allow further processing are all important constructs in modern theories of memory (Anderson, 1983). Direct study of these variables would seem more useful than the study of isolated tasks that have not been designed to estimate specific cognitive processes. Even then, variables thought to reflect the physiological action of the cortex are useful only to the extent that they predict individual differences in behavior labeled “intelligent” in the culture. E. L. Thorndike saw this clearly: Psychologists would of course assume that differences in intelligence are due to differences histological or physiological, or both, and would expect these physical bases of intelligence to be measurable . . .. [However], even if one aimed at discovering the physiological basis of intellect and measuring it in physiological units, one would have to begin by measuring the intellectual products produced by it. For our only means of discovering physiological bases is search for the physiological factors which correspond to intellectual production. (E. L. Thorndike et al., 1926, p. 12)

Individual differences in mental speed have an important impact on all of cognition. But neither theory nor empirical evidence justifies attempts to define G in terms of speed, while ignoring the larger contributions of level or altitude in both process and knowledge to this construct we call intelligence.

Attempts to Move Beyond Existing Tests It has long been recognized that theories of human intelligence have been limited by the selection of tasks included in particular intelligence tests or in factor-analytic studies of abilities. Several theorists (e.g., Cattell, 1971;

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Guilford, 1959) have proposed schemes for defining the universe of intelligent behaviors, cognitive functions or tasks. The framework can then be used to select or construct tests of different facets of intelligence. In this section, I briefly survey two rational models of this sort; Guilfor’s (1959, 1985) structure of the intellect (SOI) model and Sternberg’s (1985) triarchic theory of intelligence.

Guilford’s SOI Model As director of the Aviation Psychology Research Unit during World War II, Guilford saw the number of factorially defined abilities grow as tests were developed to measure abilities hypothesized to be important in the training and performance of air crews. After the war, he continued to investigate new abilities in his Aptitudes Research Project at the University of Southern California. By the mid-1950s, approximately 40 ability factors had been identified in one or both of these efforts (Guilford, 1985). In searching for a way to organize these factors and guide the search for new abilities, Guilford hit upon the idea of grouping abilities by a three-way classification: by the kind of mental process required, by the kind of information processed, and by the mental products generated. The combination of five types of mental processes, four types of content, and six types of product defined the 120 abilities in the structure of the intellect model.10 Although the model has generated considerable research, it has declined in influence in recent years. Questions have been raised about the factoranalytic methods used to identify factors (Horn & Knapp, 1973), about the seeming fractionation of ability (McNemar, 1964, called the scheme “scatter-brained”), and about the adequacy of the SOI model itself. Some of these challenges have been countered. Elshout, van Hemert, and van Hemert (1975) showed that Guilford’s Procrustean factor-analytic methods were not as bad as Horn and Knapp (1973) had claimed. Following Humphreys’ (1962) suggestion, Guilford (1985) countered criticisms of fractionation by agreeing that higher order abilities may be defined by averaging over cells within the SOI model. In addition, he countered objections that the model did not include auditory abilities by adding another level to the content facet for auditory abilities – raising the total number of cells in the model from 120 to 150. Nevertheless, levels of facets have no convincing foundation other than rational appeal, and the entire product dimension remains poorly validated (Cronbach & Snow, 1977). Excepting the addition of 30 new auditory abilities, over 20 years of research has produced no substantive changes in the model. Perhaps this is because research sought to demonstrate the validity of the model rather than to identify and correct its weaknesses.

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Triarchic Theory Overview of the theory. Sternberg’s (1985) theory of intelligence contains three subtheories: a contextual subtheory, an experiential subtheory, and a componential subtheory. The contextual subtheory attempts to specify those behaviors that would be considered intelligent in a particular culture. Sternberg argues that, in any culture, contextually intelligent behavior involves purposeful adaptation to the present environment, selection of an optimal environment, or shaping of the present environment to fit better one’s skills, interests, and values. The nature of the adaptation, selection, or shaping can vary importantly across cultures. For example, navigational skills, hunting skills, and academic skills are highly valued as markers of intelligence in different cultures. However, even if a particular task is thought to require intelligence, contextually appropriate behavior is not equally “intelligent” at all points along the continuum of experience with that class of tasks. According to the experiential subtheory, intelligence is best demonstrated when the task or situation is relatively novel or when learners are practicing their responses to the task so that they can respond automatically and effortlessly. Although many have suggested that tasks must be moderately novel to measure intelligence, Sternberg’s theory is unique in its claim that the ability to automatize processing is also a good indicator of intelligence. To date, no convincing evidence has been advanced to support this hypothesis. In the componential subtheory, Sternberg attempts to specify the cognitive structures and processes that underlie all intelligent behavior. Contextually appropriate behavior at relevant points in the experiential continuum is said to be intelligent to the extent to which it involves certain types of processes. Three types of processes are hypothesized: metacomponents, which control processing and enable one to monitor and evaluate it; performance components, which execute plans assembled by the metacomponents; and knowledge acquisition components, which selectively encode and combine new information and selectively compare new information to old information. Thus, Sternberg’s contextual subtheory describes what types of tasks, situations, and behaviors might be considered intelligent. It is relativistic with respect to individuals and to the sociocultural settings in which they live. In the United States, the prevailing contextual theory of intelligence involves problem-solving, or fluid abilities; knowledge-based, or crystallized abilities; and social and practical abilities. The experiential subtheory claims that intelligence is relative to each individual’s experience with the task or situation. Only the componential subtheory claims to describe the mechanisms of thought that would be used in any intelligent act. Evaluation of the Triarchic Theory. Some argue that intelligence as measured in the tradition of Binet and Wechsler is best construed as scholastic aptitude. This tendency to narrow the scope of intelligence tests has been

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countered repeatedly by those who would extend measurement to domains such as social intelligence (E. L. Thorndike, 1920), creativity (Guilford, 1959), or musical ability (Gardner, 1983) that are sampled inadequately or not at all by existing tests. Those who would extend the purview of existing tests tend to view intelligence as an adjective rather than a noun and argue that tests of intelligence should sample all domains of activity that are valued as intelligent in the culture. Sometimes these unmeasured abilities are essential features of the theorist’s implicit theory of intelligence or that of a larger social group. Those who view intelligence as a noun usually equate intelligence with individual differences in a particular type of cognition, such as “eduction of relations and correlates” (Spearman, 1927) or “judgment” (Binet & Simon, 1905). However, others view the noun as a shorthand expression for all individual differences in cognition and argue that a good test of intelligence presupposes a good theory of cognition (Hunt, 1986) or at least a good sample of “the repertoire of intellectual skills and knowledge available to the person at a particular point in time” (Humphreys, 1986, p. 98). Sternberg’s triarchic theory attempts to satisfy both of these demands. His contextual theory recognizes the cultural relativity implied when intelligence is treated as an adjective, and his componential theory “[covers] most if not all of the territory of cognitive psychology” (Carroll, 1986, p. 325). Reactions to Sternberg’s theory have been mixed. Some argue that his triarchic theory is not a theory at all but a “conceptualization” of intelligence (Humphreys, 1984). Sternberg’s theory for testing implies that one should model individual performance on cognitive tasks that represent fluid and crystallized abilities, so that component scores and solution strategy may be estimated for the individual; recognize that comparisons of individuals and especially of groups may be misleading, because tasks are differentially novel or practiced for different individuals and groups; and broaden the sample of tasks included on intelligence tests to better represent skills in adapting to the environment, shaping the environment, or selecting new environments. Here, Sternberg (1985) sees a special need for tests that measure “real-world” or practical intelligence. In several studies, questionnaires designed to assess repondents’ tacit knowledge about managing self, others, and career have shown moderate correlations with various objective criteria of success in the domain (Wagner & Sternberg, 1986). Cronbach (1986) agrees that this is a worthwhile goal for measurement, but he is unimpressed with the verbal tests of practical intelligence Sternberg has thus far developed. He claims that Sternberg’s tests are “quizzes on gamesmanship” (p. 24). Sternberg counters that scores on his questionnaires are generally uncorrelated with measures of verbal intelligence. Perhaps Ford’s (1986) research on the measurement of social intelligence can provide some useful cues for the measurement of practical intelligence. He argues that better measures can be obtained when social intelligence is

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defined in terms of outcomes (i.e., social competencies) rather than in terms of social cognition (e.g., understanding verbal or pictorial displays of social events). However, practical and social intelligence differ in several respects, and each has its roots in a different tradition. Whereas research on social intelligence stemmed from the observation that academic intelligence was no guarantee of social competence, research on practical intelligence began with the observation that academic intelligence was also no guarantee of “common sense.” Thus, studies of social intelligence are rooted in the research on social judgments, whereas studies of practical intelligence developed from research on “tacit” knowledge – that is, knowledge that is not explicitly taught or discussed but that may facilitate performance or even be necessary for success in some domain. Whether or not Sternberg succeeds in his efforts to develop new measures of practical intelligence or better measures of other aspects of intelligence, he has clearly succeeded in unifying diverse – even antagonistic – traditions in research on intelligence. With his prolific research, writing, and editing activities, Robert Sternberg has probably done more than any other contemporary psychologist to bring back into attention fundamental questions about intelligence – what it is, how it can best be observed and measured, and how it relates to other domains of behavior. (Carroll, 1986, p. 325)

Integrative Theories in Cognitive Science All of the research efforts described to this point have involved the study of individual differences, either in existing tests of intelligence or achievement, or in tasks taken from the laboratories of experimental cognitive psychologists. However, there is an obvious circularity in attempts to understand the nature of intelligence by studying existing tests of intelligence or by identifying the information-processing characteristics of people who have been labeled high or low ability because of their scores on existing tests. Attempts to specify the cognitive character of the target behaviors or achievements such tests aim to predict expand the circle significantly but do not remove the circularity. What is needed is a general theory of human cognition. Measurements of individual differences could then be derived from this theory rather than in a theoretical vacuum. There have been several attempts to put theory before assessment, particularly in the measurement of reading disabilities (Frederiksen, 1982) and (less successfully) in the measurement of spatial abilities (Poltrock & Brown, 1984). But the term intelligence connotes a much broader effort. A central question in cognitive science is whether human cognition is best modeled as a unitary system or as a collection of independent systems or

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modules. This debate parallels the Spearman-Thorndike/Thurstone controversy over g versus multiple factors in differential psychology (see R. M. Thorndike & Lohman, 1989). Much early theorizing presumed a unitary system, as Newell and Simon (1972) advocated in their General Problem Solver. This program aimed to solve a broad array of reasoning problems using general heuristics. By the late 1970s, however, the pendulum was beginning to swing the other way. Led by Chomsky (1980) and Fodor (1981), a modular view of cognition gained popularity. Modularists argue that the mind is best construed as a collection of independent information-processing systems, including systems for language, visual processing, music, and other specialized mental contents. Chomsky even describes such faculties as “mental organs,” analogous to physical organs such as the heart. Modularists point to findings from neuropsychology on apparent localization of different mental functions in different regions of the brain and to factor-analytic and other studies of individual differences which show that musical, spatial, numerical, and other abilities can be distinguished (Gardner, 1983). Most modularists deny the need for a central or executive processor. Some recognize these higher thought processes but argue that cognitive science cannot explain them (Fodor, 1981). [Modularists recognize higher thought processes, but they deny the need for a central or executive processor and argue that cognitive science also cannot explain them (Fodor, 1981)].

Anderson’s ACT∗ Theory Several research efforts, most notably that of Anderson and his colleagues, have opposed this side of modularity. In a series of monographs (Anderson & Bower, 1973; Anderson, 1976, 1983), Anderson has developed and refined his Adaptive Control of Thought (ACT) system, culminating in the latest version, ACT∗. The system is too complex to describe more than its general features here. (The reader is referred to Chap. 1 of Anderson, 1983.) First, Anderson (1983) claims that all “higher cognitive processes, such as memory, language, problem solving, imagery, deduction, and induction, are different manifestations of the same underlying system” (p. 1). Nevertheless, ACT∗ posits special-purpose “peripheral systems” that convert information presented to the senses into distinctive perception-based memory representations or codes, such as images (that preserve information about configuration) and temporal strings (that preserve information about temporal order). Other perception-based memory codes (e.g., olfactory, kinesthetic) seem likely, but they have not been much studied. The peripheral systems that create and process these perception-based codes function like the modules Fodor posits. Higher cognitive processes, however, are thought to depend more heavily on a different type of memory representation that preserves the meaning of

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an event. Indeed, Anderson (1983, 1985) argues that this type of abstract code dominates long-term memory, even for memories that might appear to be more perception based. For example, much of what we remember about a visual scene depends on our interpretation and understanding of the visual display. On this view, meaning-based representations (such as the idea of roundness) are derived from particular perception-based memories (such as memories for many particular round objects). This multicode theory of memory has several interesting analogs in research on individual differences. For example, specific learning disabilities may be caused by a dysfunction in one or more peripheral systems that encode information from the environment into memory or decode the products of thinking into particular responses. Conversely, the dominance of the meaning-based code in human cognition corresponds to the dominance of the general factor in individual differences on complex tasks that seemingly emphasize different mental contents or processes. Indeed, general ability – as typically estimated – may reflect the ability to create, transform, and retain meaning-based mental representations (Snow & Lohman, 1989). A second feature of Anderson’s ACT∗ theory that can inform theorizing about intelligence is the distinction between declarative and procedural knowledge. These two types of knowledge are posited in many, although certainly not all, AI theories. Declarative knowledge is knowing that something is the case. Procedural knowledge is knowing how to do something.11 Declarative knowledge is represented by a network in which nodes are like idea units, and procedural knowledge is represented by conditional imperatives of the form, “If a certain condition holds, then perform a certain action.” Thus, procedural knowledge is dynamic; declarative is static. Procedural knowledge can be executed automatically, even unconsciously; declarative knowledge is often accessed slowly and consciously. Each is also acquired with different proficiency and by different methods. On one hand, new declarative knowledge can be acquired relatively quickly (often in a single trial), often by elaborating relationships with previously acquired knowledge. On the other hand, proceduralization generally requires more extensive practice. The declarative-procedural distinction has several implications for a theory of intelligence. First, cognitive skills are modeled as forms of procedural knowledge in ACT∗. Therefore, those parts of the theory which describe how declarative knowledge is converted to procedural knowledge also describe an important aspect of ability development. Second, the theory predicts the gradual differentiation of abilities some have hypothesized (Garrett, 1946; Anastasi, 1970), and it can explain how the same task (e.g., division) can require general problem-solving skills for the inexperienced examinee and specific problem-solving skills for the more experienced examinee. Third, attempts to measure declarative and procedural knowledge suggest new

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ways to separate students’ factual knowledge in a domain from their ability to solve unfamiliar problems in the domain. This is an old (Lindquist, 1948) but seldom attained goal in educational measurement. Attempts to assess declarative knowledge usually involve the construction of a map of the examinee’s factual knowledge base. Attempts to assess procedural knowledge emphasize speed of solving problems, methods of classifying them, or errors made in such processing. Kyllonen and Christal (1989a) have shown that Anderson’s theory can be used as a general framework for the assessment of individual differences. They argue that individual differences on a wide variety of cognitive tasks arise from differences in four primary sources: cognitive processing speed, working memory capacity, breadth and pattern of declarative knowledge, and breadth and pattern of procedural knowledge. Working memory occupies a central position in ACT∗ and in applications of the four-sources framework to problems of skill acquisition (Woltz, 1988) and reasoning abilities (Kyllonen & Christal, 1989b). For example, in one series of studies, Kyllonen and Christal (1989b) found strikingly high correlations between theory-based measures of working memory capacity and traditional measures of reasoning ability (or Gf ). While acknowledging that such correlations are open to multiple interpretations, they argue that individual differences in working memory capacity cause individual differences in reasoning. One interesting implication of this view is that attempts to localize reasoning ability in a particular component process (e.g., inference) are bound to fail since working memory capacity affects success across all component stages of reasoning tasks. These are but a few of the sorts of connections that can be made between a general theory of cognition and concepts familiar in measurement, particularly educational measurement. Some of these hypotheses may prove useful; others will surely be discarded. Nevertheless, it would appear that any good theory of intelligence must distinguish between higher level cognitive representations and the processes that operate on them and lower level representations and the processes that mediate between the world and the individual. Such a differentiation may take the form of a hierarchical system: a base of built-in, primitive mechanisms that operate in parallel with processes not accessible to introspection and a second level of processing that is serial, often open to introspection, and can be modified with some flexibility (Gardner, 1985). A good theory of intelligence must also acknowledge the crucial role of knowledge in all of cognition (Glaser, 1984). A major implication of Anderson’s theory of research on skill acquisition and of research on expertise is that aspects of thinking that were once considered elementary, wired-in processes are now understood to be knowledge that has been automatized (“compiled” or “proceduralized”) through practice. Thus, understanding abilities means understanding individual differences in learning and development.

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The “New Connectionism” Critics of the computer metaphor for human thought have long pointed to the discrepancy between the serial, digital “Von Neumann” computer and the parallel, analog nature of much human thought. Cognitive psychologists countered that it was often impossible to distinguish between a serial model, in which one stage of processing follows on the heels of another, and a parallel model for the same task, in which all processes start at the same time, run in parallel, but finish at different times. Further, it was argued that parallel processing could be simulated – albeit clumsily – on a serial computer. These arguments began to lose their appeal as parallel processing computers were constructed and as deliberate efforts were made to make computational models of thought conform better to biological theories of brain function. This new breed of neurally inspired models of cognition is best exemplified in the work of Rumelhart, McClelland, and the PDP Research Group (1986) and their Parallel Distributed Processing (PDP) approach. Instead of a series of operations on symbols, a PDP model contains thousands of connections among hundreds of cognitive units. Excitations or inhibitions are signaled from one unit to another until the network momentarily achieves a stable state. “Thinking” or “action” occurs as strengths of the connections among units are momentarily altered. Memory is thus modeled as the set of relationships among aspects of events encoded in groups. The pattern of connections and their strengths allow particular “memories” to be recreated when the network is activated. The PDP approach signals a significant shift from purely serial models of thinking to parallel models. Some have already suggested that a comprehensive account of thinking will require both types of processing – for example, a richly interconnected hierarchy with parallel-processing modules at the base that are dedicated to particular sensory inputs or response systems and a serial, limited capacity system at the apex to model higher order thinking (Gardner, 1985). Such a system mirrors the sort of hierarchial model of human intelligence advocated in various guises by Spearman, Burt, Vernon, and Cattell. The PDP approach also reflects a shift from theories rich in process but short on knowledge to theories that are rich in knowledge but short on process. There has been a gradual realization in all of cognitive science of the importance of an extensive, accessible, and well organized knowledge base for intelligent performance. In AI, early efforts to avoid knowledge in the interest of simplification only served to make the task of modeling human reasoning “harder than it needed to be” (Dehn & Schank, 1982, p. 373). Similarly, there has been a gradual shift in cognitive psychology from the sort of knowledge-free information-processing models that can be neatly summarized in a flow chart to the study of the role of prior knowledge represented as scripts (Schank & Abelson, 1977), schema (Rumelhart & Ortony, 1977),

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mental models (Johnson-Laird, 1983), and belief systems (Carey, 1986). The importance of knowledge has even emerged in process-intensive tasks, such as those used to estimate spatial abilities. Further, many functions formerly represented as wired-in processes in information-processing models are now seen as acquired proficiencies (i.e., procedural knowledge). Indeed, the goal of measuring knowledge- or experience-free cognitive processes may be a measurement pipe dream, as E. L. Thorndike et al. (1926) suggested. In a way, this newfound role of knowledge in cognitive science parallels the gradual realization by differential psychologists that intelligence is not an innate characteristic of the person but an acquired set of competencies (Anastasi, 1986; Cronbach, 1984).

Future Directions Including Affect Kant popularized Aristotle’s threefold categorization of mental faculties: cognitive, affective, and conative (knowing, feeling, and willing). By this account, a complete theory of mind must explain not only the cognitive dimension but also the emotional and intentional dimensions as well. Attempts to simply the task of understanding intelligence by ignoring emotion and intention may prove as ineffective as early attempts to ignore knowledge in AI. Indeed, theorists are once again beginning to argue that affect must be included in accounts of learning and cognition (Snow & Farr, 1987). Thus, one direction research on intelligence seems to be taking is to expand its horizons to include affective dimensions long recognized as central to intelligence (e.g. Wechsler, 1939) but rarely combined with the systematic study of the cognitive dimensions (see Royce, 1979, however, for one effort). A theory of intelligence thereby becomes more than an account of human cognition. It becomes an account of affect and perhaps even volition as well. Even when intelligence is treated as a noun, its purview knows no bounds.

From Crystallized to Fluid A second trend in research on intelligence is moving in the opposite direction. Binet’s test was originally designed to predict performance in school. Whatever larger purposes he might have hoped the test might serve, or that others have actually used tests for, it is clear that intelligence tests have always been most heavily used as measures of scholastic aptitude. Researchers have begun to uncover the reasons why such tests predict success in conventional forms of schooling as they have begun to understand the nature of the knowledge and thinking skills that are required by school-learning tasks that are also estimated by intelligence tests. Items on intelligence tests often appear

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to differ markedly from the sort of school-learning tasks they predict. For example, matrix completion problems and/or paper folding problems do not appear to have much in common with understanding a story or solving an algebra word problem. Yet intensive analyses reveal a commonality in the processes students use to solve both test problems and school-learning tasks (Snow & Lohman, 1984). Analyses of existing intelligence tests and of the school-learning tasks such tests were originally designed to predict will continue to be important activities in measurement and instructional psychology. However, the study of school-learning tasks is now viewed by some as the research activity most likely to produce useful results (Cronbach, 1984, p. 300). In fact, there has been a subtle shift in recent years from the study of intelligence to the study of achievement, particularly the acquisition, organization, and use of knowledge in particular domains such as science, mathematics, and literature (Glaser, Lesgold, & Lajoie, 1987). Thus, somewhat paradoxically, new developments in the measurement of intelligence – particularly the sort of intelligence required by and developed through formal schooling – may well come about more through the careful study of achievement than through continued scrutiny of tasks modeled after existing intelligence tests. And there are reasons to be optimistic that such research may produce intelligence tests that are useful for instruction in more ways than are existing tests. This possibility can be better understood if intelligence and achievement are viewed as points on a continuum of transfer or novelty rather than as qualitatively distinct constructs. Figure 2 shows one such continuum. The horizontal line symbolizes the amount of transfer required by the test or the average novelty of the problems for the typical examinee. At the far left, problems on the test duplicate those taught. As one moves to the right, problems become increasingly novel and require increasing transfer. For example, if students have learned to add numbers in columns, then one could present these same addition facts in column format to require minimum transfer. Presenting the same facts horizontally would require a bit of transfer; embedding the problems in a sentence would require more transfer; and embedding them in a matrix problem in which the rule is “add row 1 to row 2” requires even more transfer. Perhaps creating the matrix items in the first place requires the most transfer. As this example demonstrates, the continuum of novelty in Figure 2 is not limited to general ability but can apply to narrower ability constructs as well. It also illustrates the principle that the same task can elicit different processes from different people, depending on their prior experience. Mastery Tests

Familiar

Final Exams

General Ach.

Fluid Ability

Insight

Novel

Figure 2: Hypothetical continuum of transfer for general achievement and ability tests

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Important educational objectives may be identified all along this line (Elshout, 1987). Students must learn specific skills, but they must also learn to transfer their learnings to unfamiliar situations and to be creative. Unfortunately, measurement problems increase as one moves from left to right on this scale. Tests that sample no more than those facts and skills explicitly taught are relatively easy to defend, especially when only limited inferences are made from test scores. Tests that require transfer are more difficult to defend because problem novelty varies from individual to individual and because such tests are usually constructed in ways that encourage grander inferences. Some argue that defensible tests of insight (on the far right) are nonexistent. Much of the research on intelligence and intelligence tests conducted by Sternberg, Snow, Hunt, Pellegrino and others during the 1970s could be seen as an effort to start in the middle of Figure 2 and move to the left. Both Snow (1978) and Glaser (1976) argued that the ultimate goal of their research on intelligence was to discover how the thinking skills required by such tests are also required for learning in schools. Although much has been learned from these efforts, dependable methods for encouraging the development of fluid abilities have not been discovered, even though many recommendations have been made (e.g., Wagner & Sternberg, 1984). In part, this may be an inevitable consequence of studying tests that were designed to work rather than to reflect a particular theory of cognition. A more fruitful avenue, for education at least, might be to begin somewhere near the left of Figure 2 and work toward the right. Perhaps then educators might finally learn what to teach the so-called “overachiever,” who scores higher on tests of crystallized abilities than on tests of fluid abilities. The recent work of Brown and Ferrera (1985) in estimating a student’s “zone of proximal development” exemplifies one effort toward this goal.

Process Sensitive Tasks A third trend in research on intelligence is a renewed emphasis on the contextual foundation of the concept “intelligence” in the culture and lifehistory of the individual. In part, this represents a rediscovery of the fact that, as E. L. Thorndike et al. (1926) put it, “measurements of intelligence rest on judgements of value” (p. 12). But it also represents a breaking down of artificial barriers within psychology, such as between learning and the context in which learning occurs (Brown, Collins, & Duguid, 1989; Greeno, 1989) or between learning and development (Chi, 1978; Glaser, 1984). Renewed linkages between the psychologies of learning and development are particularly noteworthy. Understanding how abilities develop is central to the task of understanding what abilities are. It is no accident that qualitative advances in our understanding of the mental processes which produce

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intelligent performances have come from those who studied the development of intelligence rather than those who focused exclusively or primarily on the organization of individual differences at a particular point in time. Much of this can be explained by a closer examination of the type of task typically studied by the developmentalist. All scientific measurements of intelligence that we have at present are measures of some product produced by the person or animal in question, or of the way in which some product is produced [italics added]. A is rated as more intelligent than B because he produces a better product, essay written, answer found, choice made, completion supplied or the like, or produces an equally good product in a better way, more quickly or by inference rather than by rote memory, or by more ingenious use of the material at hand [italics added]. (E. L. Thorndike et al., 1926, p. 11–12)

Thorndike et al. (1926) here describe two types of tasks: tasks which permit inferences about the nature of intelligence from the type of response made (often a qualitative judgment) and tasks which permit inferences about the rank order of individuals in ability by counting up the number of responses scored “correct” (usually a quantitative judgment). Psychometrics has understandably followed the quantitative route. Items are scaled for difficulty and examinees are ranked by how far up the ladder they can climb. Developmentalists from Piaget to Siegler have followed the qualitative path. The same problem is presented to all children and their developmental level is inferred from the sophistication of the response given. Indeed, early efforts to develop tests which provided a qualitative assessment of intelligence, such as the tests of Healy and Fernald (1911) or even the Binet scale of 1905, “did not emphasize the objective score which the child made so much as his general behavior and the way in which he went about the tasks which were set him” (Freeman, 1926, p. 108). However, judgments about process were clearly less dependable than judgments about whether the subject gave a keyed response, and so qualitative assessments of process were quickly displaced by quantitative assessments of product. Furthermore, tests which provided a score that could be immediately ranked better fit the requirements of a burgeoning test industry that was more interested in identifying who was intelligent than in understanding what intelligence was. By the 1970s, however, cognitive psychologists had developed new methods for testing inferences about process – methods that were more sophisticated and objective than clinical judgments. Many tried to apply these new methods for detecting process to experimental tasks modeled after existing intelligence tests. Of all the “strange ironies” which have attended the history of mental testing (Cronbach, 1975), none is stranger than the attempt to apply powerful methods for detecting individual differences in processing strategy to a class of test-like tasks carefully pruned of such differences. It is a tribute to the power of the methods and the ingenuity of the researchers

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who employed them that anything interesting was found at all. Perhaps process analyses would be more successful in revealing interesting individual differences in process if they were to be applied to tasks deliberately designed to elicit such differences than to tasks modeled after existing mental tests.

Summary and Evaluation Summaries broader than the scope of this paper are available (see Snow & Lohman, 1989; Sternberg, 1985), but several themes emerge in all of them. First, much of the optimism about the potential impact of cognitive psychology on the study of human intelligence (e.g., Hunt et al., 1973; Sternberg, 1977) has been tempered by experience. Hunt now sees some fundamental incompatibilities between the correlational and experimental camps in psychology. He notes: Cronbach [1957] thought that general theories of psychological process ought not to ignore individual differences, and vice versa. He was right, and in a general sense the union of the camps is well underway. In my opinion . . . the way to achieve the scientific union is to concentrate on understanding how individual differences variables, such as age, sex, genetic constitution, and education, influence the processes of cognition. It does not seem particularly fruitful to try to derive the dimensions of . . . [a trait model] of abilities from an underlying process theory. (Hunt, 1987, p. 36)

Like Hunt, Sternberg has also modified his views, although he sees more compatibility than Hunt. In 1977, Sternberg described a method for testing information-processing models of tasks that he called componential analysis. He then compared his method of componential analysis with factor-analytic methods for understanding abilities and found the latter seriously wanting. More recently, he has claimed that “cognitive approaches to intelligence are basically compatible with psychometric and other approaches” (1985, p. 108), each better suited to addressing different questions about the same phenomenon. Sternberg (1985) argues that his triarchic theory recognizes the contributions not only of the correlational and the information-processing approaches to the study of intelligence but also of theorists such as Berry (1972) and E. L. Thorndike et al. (1926) who point out that the list of behaviors and accomplishments valued as “intelligent” varies over cultures and contexts. The conclusion that trait and process approaches are in some ways fundamentally incompatible may seem overly pessimistic. Nevertheless, it at least acknowledges that the two approaches make completely different demands on the basic person by item data matrix. Each partitions the data matrix in completely different ways. The trait theorist focuses on variation

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in row means whereas the experimentalist focuses on variation in column means. The trait theorist is concerned with covariances computed over persons whereas the experimentalist should be more concerned with covariances computed over items. It is possible – even likely – to propose a processing model that does an excellent job of accounting for variability in item difficulties or latencies, either for all subjects or separately for each subject, and yet have no explanation for individual differences on the task. On the other hand, the trait theorist constructs measures of broad abilities by making items (or subtests) as heterogeneous as possible (Spearman, 1927; Humphreys, 1985), thereby making a process analysis of the test either impossible or so general that it is uninformative.12 Thus, the two approaches are in some ways complementary but in other ways incompatible (Ippel & Lohman, 1990). There has been a similar tempering of enthusiasm about the prospects for an easy victory over the problem of human intelligence in other quarters of cognitive science – particularly AI. Increasingly, those who have attempted to develop artificially intelligent systems have come to question their efforts and the constraints that the digital computer has placed on their work. In a summary of this recent history of AI, Dehn and Schank (1982) note, “Arrogance about the potential superiority of machine-specific intelligence slowly gave way to a growing respect for human intelligence and its operation. Characteristics of human intelligence . . . that had at first seemed to be weaknesses began to be recognized as strengths” (p. 354). For example, humans tend not to consider all aspects of a problem or to generate and evaluate all possible answers to a problem before deciding upon a course of action. Computers are easily programmed with algorithms that painstakingly consider all factors in a problem before choosing the best answer. However, the computer begins to drown in computation as problems increase in complexity, such as when the input is a visual scene or when the number of alternatives that could be generated is unlimited, as in a chess game. Further, this problem will not be solved by building computers with greater computational speed and power. Therefore, AI has shifted from programs that solve problems by brute force to programs modeled after the “satisficing” sort of rules of thumb humans use – balancing effort and time against expected payoff – in complex situations. The recent shift to parallel-processing computers and to models of cognition that conform to current theories of brain function takes an even larger step away from the conventional digital computer and the constraints it imposes on efforts to model human cognition. However, some predict that even these efforts are doomed to fail, either because human cognition is not rule bound (Dreyfus & Dreyfus, 1986) or because higher level cognitive processes such as judgment and reasoning can be influenced by one’s beliefs, values, and intentions (Pylyshyn, 1984; Fodor, 1981). In short, there has been a growing respect for human intelligence, and a realization that it will not yield to ready explanation by the methods of

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cognitive science any more than it yielded to ready explanation by the method of factor analysis. Yet factor analysis contributed – and continues to contribute (Carroll, in press; Gustafsson, 1984) – to our understanding of human intelligence. Cognitive science will also continue to contribute to our understanding in spite of the dire warnings of the pessimists and in spite of difficulties already encountered. But it will do so with a little less arrogance and, hopefully, with a little greater appreciation for the contributions of Binet, E. L. Thorndike, and others who have traveled this path before.

Notes 1. Special abilities often improve the prediction when samples are large or restricted on general ability (R. L. Thorndike, 1986). Note, too, that the issue is not general versus special abilities but whether to give each ability factor a unique weight or to give all the same weight in forming a single composite to be correlated with a criterion. It has long been known that a weighted average differs little from a simple average (Burt, 1907, cited in Butcher, 1968, p. 68). Instability of regression weights for correlated predictors demands it. Pooling correlations from different studies (e.g., Hunter, 1986) further exaggerates the role of general abilities (Linn, 1986). Finally, multiple aptitude batteries can still provide important information for guidance (Tyler, 1986). 2. Like many cognitive psychologists, Anderson (1985) usually uses the word intelligence as a synonym for cognition, not the individual difference construct associated with intelligence tests. The implications of this view for an individual difference interpretation are outlined in the third section of this paper. 3. For example, Freeman (1926) notes the need “to identify the mental processes which are measured by [existing ability tests]” (p. 127). He also provides a remarkably balanced summary of early research on intelligence. 4. Norman (1986) claims that the architecture of the digital computer was heavily influenced by the designers’ tacit theories of human cognition. Nevertheless, many who came later turned the metaphor around and looked for parallels between physical structures in the computer and psychological structures. 5. There are several intermediate cases as well. For example, Cronbach (1977) argues that “intelligence” is an abstraction much like “efficiency”. On this view, one cannot locate production efficiency in a particular department of a factory: rather, it is a term that describes the overall functioning of the system relative to comparable factories. Another possibility is that intelligence is something like Spearman’s (1927) mental energy or Jensen’s (1982) neural efficiency. Once again, one could not isolate “intelligence” in particular processes, but one might equate it with some general characteristics of cognition, such as attentional resources or speed of processing. 6. Fancher (1985) offers a fascinating historical perspective on the controversy. Using biographical sources, he traces the conflict from the disparate life experiences of John Stuart Mill and Frances Galton, through the lives of the major players in this controversy, to the recent debates between Kamin and Eysenck. 7. Humphreys (1986) aptly describes those who openly espouse environmental explanations for intelligence but who then assume some biological capacity not measured by existing intelligence tests that would be assessed by a properly constructed test as “closet hereditarians”. The description seems also to apply to some cognitive scientists. 8. Carroll (1980) suggests that the correlation with verbal ability may be more parsimoniously attributed to a general or perceptual speed dimension. In a hierarchical model, however, factors such as perceptual (or clerical) speed, memory span, and fluency are

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located below verbal comprehension and thus represent specific verbal abilities. Carroll’s critique is troublesome only if one views verbal comprehension as the sole verbal ability (see Snow & Lohman, 1989). Low correlations with external criteria for all component scores except the intercept parameter is a statistical necessity unless task scores have poor internal consistency. This point is discussed below and in greater detail in Ippel and Lohman (in preparation). Thus, low correlations between components and other variables do not invalidate the models, although they do challenge the goal of estimating component scores for individuals. For the there-is-nothing-new-under-the-sun folks, E. L. Thorndike et al. (1926) proposed that the various “products” of the human intellect be more systematically sampled from tests that differed in content (“including situations containing other human beings,” p. 20) that required different “internal . . . processes” or “operations performed with the words, numbers, pictures, and other content” (p. 21). Although procedural knowledge is said to be developed out of declarative knowledge, Anderson uses the term “procedural knowledge” more restrictively than some theorists. Knowledge of how to do something that is not yet compiled (or automatized) would be called declarative knowledge. Clearly, one can have declarative knowledge of a procedure or can have proceduralized that knowledge and not have a declarative representation of it, or one could have both. As previously suggested, more informative process analyses demand tasks that allow ready inference of how subjects solved a problem, or what knowledge they brought to bear on it by the type of response they gave, not by the presence or absence of a correct response. In other words, the fundamental problem should be one of response categorization, not response scoring. Analyses of individual differences in response latencies introduce even more problems, such as what to do with error-response latencies or how to equate subjects on speed-accuracy trade-off. These problems are routinely ignored or incorrectly dismissed (for further discussion, see Lohman, 1989).

References Anastasi, A. (1970). On the formation of psychological traits. American Psychologist, 25, 899–910. Anastasi, A. (1986). Intelligence as a quality of behavior. In R. J. Sternberg & D. K. Detterman (Eds.), What is intelligence? Contemporary viewpoints on its nature and definition (pp. 19–21). Norwood, NJ: Ablex. Anderson, J. R. (1976). Language, memory, and thought. Hillsdale, NJ: Erlbaum. Anderson, J. R. (1983). The architecture of cognition. Cambridge, M A: Harvard University Press. Anderson, J. R. (1985). Cognitive psychology and its implications (2nd ed.). New York: W. H. Freeman. Anderson, J. R., & Bower, G. H. (1973). Human associative memory. Washington, DC: Winston. Barrett, P., Eysenck, H. J., & Lucking, S. (1986). Reaction time and intelligence: A replicated study. Intelligence, 10, 9 – 40. Berry, J. W. (1972). Radical cultural relativism and the concept of intelligence. In L. J. Cronbach & P. Drenth (Eds.), Mental tests and cultural adaptation (pp. 77–89). The Hague: Mouton. Bethell-Fox, C. E., Lohman, D. F., & Snow, R. E. (1984). Adaptive reasoning: Componential and eye movement analysis of geometric analogy performance. Intelligence, 8, 205–238. Binet, A., & Simon, T. (1905). New methods for the diagnosis of the intellectual level of subnormals. L’ Annee Psychologique, 11, 245–336.

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37 Cognitive Demands of New Technologies and the Implications for Learning Theory Richard J. Torraco

A

t a basic level, learning to perform work-related activities requires the engagement of one’s cognitive processes with the task to be accomplished. Few studies trace prescriptions for learning all the way back through human cognitive processes to the specific characteristics of the tasks that determine what should be learned in the first place. This study identifies the cognitive demands of new technologies by first examining the specific requirements of tasks involving the use of new technologies. These tasks are shown to place unique cognitive demands on those who use new technologies. Then, the question is raised, How well do current theories of learning address these cognitive demands? Four theories – those of Scribner, Schon, Wenger, and Hutchins – are analyzed for their power to explain human cognition and learning as they relate to the use of new technologies. Finally, the article offers new directions for future research on learning theory, studies of workplace learning, and theory building. Although the primary focus of this article is the need for better theory to explain learning at work, I start by addressing the nature of the task itself. This honors Gagne’s (1962) seminal admonition that scholars should first examine the task to be learned in order to specifically address what learning should accomplish and Hackman’s (1969) treatise on the centrality of the task to research on work behavior. The concept of task remains a prominent construct in models of work design (Campion & Medsker, 1992; Hackman & Oldham, 1980; Smith, Source: Human Resource Development Review, 1(4) (2002): 439–466.

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Henning, & Smith, 1994), work motivation (Locke & Latham, 1990), work complexity (Campbell, 1988; Khurana, 1999; Weick, 1990), and human cognition (Engestrom & Middleton, 1996; Hutchins, 1995; Simon, 1981).

Characteristics of Tasks Involving New Technologies Any meaningful discussion of task characteristics must be framed in the context of a work or organizational environment. For the purpose of this article, the work environment is composed of the immediate physical environment of the worker and the organizational demands placed on the worker. The immediate physical environment is constituted by the tools, equipment, electronic devices, and other material resources needed by the worker to accomplish the task. Organizational demands relate to work requirements placed on the individual that go beyond one’s immediate responsibilities such as project deadlines, the need for administrative approvals, expected rates of transaction, and other requirements imposed on the individual by the organization that indicate how one’s job activities fit with process requirements. Technology is also a key concept in this discussion. Like other tools, technology is a means through which work is accomplished. Berniker’s (1987) definition of technology is adopted for this discussion. Berniker embedded the concept of technology within the larger structure of a technical system, which is a specific combination of machines, equipment, and methods used to produce some valued outcome. . . . Every technical system embodies a technology. It derives from a large body of knowledge which produces the basis for design decision. Technology refers to a body of knowledge about the means by which we work on the world, our arts and our methods. (p. 10)

Four specific characteristics of tasks involving the use of new technologies are described next. These concepts – contingent versus deterministic tasks, distancing technologies, stochastic events, and systemic interdependence – compose a proposed model of task characteristics associated with new technologies. These concepts were identified as elements of the model through a comprehensive review of the literature that sought task characteristics distinctively associated with the use of new technologies. These four characteristics are central to understanding the unique cognitive demands faced by users of new technologies and are not meant to be an exhaustive review of constructs related to the use of new technologies. Mindful of the need for balance between a model’s comprehensiveness and parsimony, I developed two concepts to explain new phenomena (contingent versus deterministic tasks and distancing technologies), whereas two others are adapted from existing theories (stochastic events and systemic interdependence). Thus, all concepts in the model are either developed and justified by the author or grounded in existing literature.

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• Contingent versus deterministic tasks: When applied to what were once routine, predictable tasks (deterministic tasks), new technologies have substantially increased the contingency of these tasks by increasing their complexity and speed (Pentland, 1997). • Distancing technologies such as digital displays, controls, and sensor technologies, remove the operator from the operating location and eliminate the physical cues and sentient information from which knowledge can be derived (Woods, O’Brien, & Hanes, 1987; Zuboff, 1988). • Stochastic events are randomly occurring and unpredictable events that are properties of new technologies (Weick, 1990) and flexible manufacturing systems (Norros, 1996). • Systemic interdependence is the system of relationships needed to ensure that one’s work is coordinated with that of others within the work system (Adler, 1986). These four task characteristics of new technologies place unique cognitive demands on those who use them. The next section discusses the cognitive demands associated with each task characteristic. The task characteristics and the cognitive demands associated with them are listed in Table 1. Cognitive demands of contingent tasks. New technologies have fundamentally changed the character of technical work by removing it further than ever from its historical and deterministic origins. The work of technicians and craftsmen has always been contingent on contextual factors because task execution is frequently altered by temporal, material, social, economic, and other factors that reflect the changing properties of the task environment. However, new

Table 1: The cognitive demands of task characteristics Task characteristic

Cognitive demand

Contingent versus deterministic tasks Unanticipated problems Expanded menu of solutions

• • •

Distancing technologies Physical separation Psychological separation

•

• Stochastic events Disruptions to work process Premature task termination

Systemic interdependence Partial versus complete knowledge Transforming inputs to outputs

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•

Mental reconstruction of problem and causes Capability for systematic search and pragmatic solutions Ability to go beyond scripted procedures Capabilities for inference, imagination, and mental modeling to understand what is going on elsewhere Reconciliation of mental representation of work process with actual work process

•

Movement from emotional arousal to constructive thought and action Memory (information storage and retrieval) to reconsider means-ends relationships and desired end states Improvisation – the abilities of the bricoleur

• •

Interpersonal skills Transactive memory

•

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technologies have substantially increased task contingency by reducing their transparency, increasing their speed, and expanding the menu of options available for task accomplishment. Work processes that were formerly transparent (i.e., separate, observable, and easily deconstructed into their systemic components) have been combined through process engineering (Davenport, 1993) and have disappeared into computer-controlled machines and communication technology (Weick, 1990), thereby reducing their transparency and complicating efforts to discern process interactions. Technicians and customers can no longer see the flow of information and materials and must infer from outputs what occurred earlier in the work process. Replacing the industrial-era belief in “one best method” for each operation (Woodward, 1994), the reduced transparency of technology now masks an Internet-like network of possible paths for processing materials and information. These factors contribute directly to the contingency of tasks associated with the use of new technologies. New technology also feeds social pressures for rapid transactions, especially in customer service situations. Predetermined procedures are frequently abandoned to expedite customer requests. Expanded options for task accomplishment within and among distinct technologies further contributes to the contingent nature of work strategies. Although the Internet offers alternative paths for processing information (e-mail, Web sites), it can be circumvented altogether through the use of the phone, facsimile, satellite links, paper, or personal contact for communication. As witnessed in equipment repair (Orr, 1996), software support (Pentland, 1997), science laboratories (Barley & Bechky, 1994), insurance claim processing (Wenger, 1998), automobile assembly (Graham, 1993), and military operations (Hutchins, 1995; Weick & Roberts, 1993), new technologies have made technical tasks more contingent than ever. Because technical work is filled with novel or poorly defined problems that cannot be fully anticipated in advance (Barley & Orr, 1997), technicians are often confronted by technology breakdowns of ambiguous origins that cannot be resolved with schematics and procedural knowledge alone. Because their problem-solving algorithms are inadequate for the variety and unpredictability of these problems, technicians must rely instead on pragmatic rules of thumb and other shortcuts afforded by the task environment. Thus, successful performance requires employees to go beyond scripted procedures to resolve problems in innovative ways. This does not mean that job aids, operating procedures, and training in the use of such resources are of no value. Effective work systems should provide such technical assistance in ways that are easily referenced to minimize mental and computational loads, so workers are free to do higher level evaluation and problem solving (Norman, 1988). However, to understand and respond to ambiguous situations, workers must make use of improvised materials, local conditions, and social circumstances, thus deploying contingent work strategies that reflect the changing properties of the task environment.

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The cognitive demands faced by those who deal with these problems include a considerable amount of systematic mental search to identify pragmatic solutions. Workers frequently must reconstruct the situation that led to the problem, identify the causes, develop a solution strategy, and ensure that the proposed solution is satisfactory and feasible. Assistance in this regard from the information storage and retrieval capabilities of technology may not always help workers deal with these cognitive demands. Technologybased troubleshooting aids that provide workers with problem-solving heuristics, algorithms, and databases are just as likely to hinder as help the worker’s problem-solving efforts (Norman, 1993). Moreover, as Griffith and Northcraft (1996) demonstrated, the implementation of technologies may be better served when less, rather than more, information about the technology is provided to users. Regardless of whether the technology itself is seen as valuable or detrimental, the contingency of tasks associated with new technologies places important cognitive demands on those who must use them. This characteristic of new technologies (contingent vs. deterministic tasks) and the cognitive demands associated with it are listed in Table 1. Cognitive demands of distancing technologies. Distancing technologies are present in work environments ranging from industrial factories (Zuboff, 1988) to high technology settings (Pentland, 1997). Digital displays, controls, and sensor technologies at the operator’s workstation are symbolic representations that distance workers from the physical and sensory referents present at the actual sites of operation. In the pulp and paper mills studied by Zuboff (1988), instrumentation formerly was located on or close to the operating equipment, allowing the operator to combine data from an instrument reading with data from his or her own senses. Distancing technologies removed the operator from the operating location and eliminated the physical cues and sentient information in which knowledge was based. In addition to physical distance from customers, the software support technicians studied by Pentland (1997) were expected to solve software problems over the telephone despite customers’ diverse software and hardware configurations. Problem solving was difficult due to the ambiguity of the problems as described by customers, who were unable to identify specific conditions that were relevant to troubleshooting the problems. Technicians had difficulty visualizing the situations that gave rise to the problems. The problems created by physical distance are magnified by the computer controls of most distancing technologies that display information on separate screens of a computer monitor. To recognize irregular patterns among the data or to initiate novel search sequences, the technician must remember what earlier screens have shown and hope that the readings have not changed while subsequent screens are accessed. However, human factors research has shown that it is easier to recognize patterns when data are presented simultaneously rather than serially. Technicians in production control rooms who use technology based on this research can easily create novel search sequences

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when they are able to sweep visually across an array of indicators that present data at the lowest level of detail (Woods, O’Brien, & Hanes, 1987). Similar principles of task design have been applied to high-technology work settings. For example, Gill (1996) showed that changing the task characteristics of expert information systems to enhance the user’s sense of control over task activities and their own performance increased the intrinsic motivational character of these tasks, which, in turn, enhanced the workers’ motivation to increase their use of these expert systems. Separation from the operating environment requires workers to interpret symbolic, electronically presented data. The ability to make sense of what is going on at remote operating sites is vital to competent performance in these work environments. Software support technicians and production control room operators have to imagine the conditions in the operating environment that cannot be displayed by their information systems. Before attempting to solve problems, they must first mentally visualize the conditions that give rise to the problems. Thus, the physical and psychological separation created by distancing technologies increases cognitive demands for inference, imagination, and mental modeling to understand what is happening elsewhere. Human-computer-robot manufacturing systems, known as automatic manufacturing technology, provide an example of the performance problems associated with distancing technologies. Early automatic manufacturing technology systems provided the operator with a televised view of the robot’s actions at the point of manufacturing located away from the operator’s control panel. But because the televised view distorted the spatial properties of visual feedback to the operator, control panels were relocated closer to the point of operation to allow direct viewing of the automatic manufacturing technology robot by the operator, thus reducing the cognitive demands on the operator (Smith, Henning, & Smith, 1994). The physical and psychological distance from one’s work caused by these technologies can be reduced by designing work environments according to principles of human factors and ergonomics (Salvendy, 1987) and by giving more attention to the importance of inference and mental modeling in employee training and development. A discussion of how well these employee needs are addressed in current theories of learning is presented later in the article. This characteristic of new technologies (distancing technologies) and the cognitive demands associated with it are listed in Table 1. Cognitive demands of stochastic events. Complexity is added to the task when it is interrupted by stochastic events. Stochastic events are randomly occurring and unpredictable events that are properties of new technologies (Weick, 1990) and flexible manufacturing systems (Norros, 1996). When new technologies are implemented in industrial work processes, they frequently produce system disturbances to which operators must respond, even though they have not yet developed expertise in the use of these technologies (Norros, 1996). Pre–industrial era technologies were predictable and easily understood

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because key operating mechanisms followed clear cause-effect relationships. However, today’s technologies are more complex and present problems due to their instability, reduced transparency, and tendency to break down. New technologies have always been accompanied by problems. Early mass production lines were plagued by incessant breakdowns. Although stochastic events are not new, Weick (1990) noted that new technologies are unique in that the uncertainties are permanent rather than transient. Many softwaredependent systems are intentionally pushed through product development and quickly delivered to market. Product testing is short-circuited because implementation is often the means by which the technology itself is designed. Such development-delivery tradeoffs result in “buggy” software, incomplete information networks, and password-activated technologies that will not start. Even common technologies are not free of breakdowns (e.g., disconnection from the Internet, power failures, being cut off during telephone calls). Dealing with the disruptions from unfinished technologies and prototypes increases the cognitive demands placed on technicians who must use them. When a sudden, unpredictable event disrupts a task, it triggers emotional arousal (Weick, 1990). Once emotion is stimulated, it increases as long as the interruption remains unexplained, especially when work stoppage is costly or risky. Stochastic events require rapid movement from emotion to action, that is, from arousal, to the search for explanations, to actions that produce information about possible causes. This occurs as the worker tries to subdue emotional interference with thought and action. Sudden work stoppage also forces the reconsideration of means-ends relationships and of desired end states. Are alternative paths available for project completion that circumvent the disabled technology? Can the project be completed elsewhere, by someone else, or at a later time? How much can the desired end state of the transaction be modified? The cognitive demands on memory (information storage and retrieval) and search for additional information from such disruptions can be considerable. A sudden system failure challenges workers to make do with the tools and materials at hand. As they improvise to complete their tasks, they invoke the skills of contemporary bricoleurs – resourceful craftsmen who make use of whatever materials are available to complete the project (Levi-Strauss, 1966). This characteristic of new technologies (stochastic events) and the cognitive demands associated with it are listed in Table 1. Cognitive demands of systemic interdependence. The interdependencies needed to ensure that one’s work is coordinated with the work of others have been termed “systemic interdependence” by Adler (1986). Systemic interdependence requires ongoing and flexible integration of hitherto distinct functions of operations, systems, design, and training. The reciprocal nature of this interdependence in operations is exemplified in the reliance on common databases. Users thereby become dependent on other users’ data input accuracy. (p. 19)

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Systemic interdependence requires interpersonal skills and the ability to work effectively with others on the same project despite different social and technical backgrounds. Such interdependence is strengthened through the use of transactive memory systems (Wegner, Erber, & Raymond, 1991). Transactive memory is based on the premise that we need not know a particular subject ourselves if we know where to find information about it. Transactive memory systems are integrated and differentiated structures in which related information is held by different group members working on a common project. It is the sharing of relevant data that yields the higher order insights and generalizations that are valued in these work environments. Workers who contribute to transactive memory systems participate in the sharing and integration of technical knowledge and, in turn, further develop their networks of social and technical interdependencies. This characteristic of new technologies (systemic interdependence) and the cognitive demands associated with it are listed in Table 1. These four characteristics – contingent versus deterministic tasks, distancing technologies, stochastic events, and systemic interdependence – are fundamental elements of new technologies that place unique cognitive demands on those who use them. How well do current theories of learning address these cognitive demands? The next section discusses the extent to which four selected theories explain human cognition and learning as they relate to working with new technologies.

Theories of Learning and Cognitive Demands The theories of Scribner (Tobach, Falmagne, Parlee, Martin, & Kapelman, 1997), Schon (1983, 1987), Wenger (1998), and Hutchins (1995) are analyzed for their power to explain human cognition as it relates to the use of new technologies. These four learning theories provide comprehensive and meaningful explanations of how learning occurs in the type of work settings discussed here. The criteria used for selecting these theories for this discussion are that each theory (a) describes specific cognitive processes, (b) addresses learning as both an enabler and product of work practices, (c) explains how learning occurs in authentic work settings, (d) is comprehensive in its treatment of the behavioral and environmental influences on learning, and (e) offers propositions that can be generalized to other settings. Because these four theories explain the phenomena discussed in this article better than most other theories, these five selection criteria are discussed in more detail in the final section of the article as desirable characteristics of sound theory. Scribner’s model of practical thinking at work. Scribner used activity theory as developed by Leont’ev (1981) to bridge the conceptual relationship between knowing and doing in her cognitive studies of work. Activity theory explains purposeful behavior by focusing on the structure of the activity

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itself. For Leont’ ev, the activity is the appropriate unit of analysis for human behavior. An activity can be analyzed at three levels. First, at the highest level of organization is the motivation of the activity, which provides coherence to the other levels. At the next level are goal-directed actions, carried out in the service of the activity. At the third level are operations, or the specific conditions under which actions are carried out. For example, if our action is traveling from one place to another in the service of some activity (e.g., pursuing leisure and recreation), whether we walk, drive, or use some other means of transportation is an operation that depends on distance and other specific conditions related to the action. Because dynamic relationships exist among the three elements of the theory, the theory presents different levels of analysis for studying work activity. Activities, actions, and operations may change positions in the hierarchy relative to one another according to changing situations, new knowledge, and the intentions of the human agent. Because motivated activities, actions, and operations are defined according to their functions rather than properties inherent in the elements themselves, an activity can lose its motivating force and become an action in the service of another activity (e.g., losing interest in the intrinsic value of one’s job and performing it primarily for income). Hence, questions about performance or the structure of work in different environments can be asked at the level of the activity, the action, and the operation. Because an activity is a dynamic system, methods of studying the activity can change as the activity changes and as new questions about it emerge. Scribner’s model of practical thinking is strongly influenced by the notion of activities as mediators of knowing and doing. The collection of Scribner’s cognitive studies of work concludes with a paper that presents her model of practical thinking (Tobach et al., 1997). The model is organized around four principles synthesized from Scribner’s studies of dairy workers (Scribner, 1984), industrial machinists (Martin & Scribner, 1991), bartenders (Scribner & Beach, 1993), indigenous literacy in West Africa (Scribner & Cole, 1981), and practical and theoretical arithmetic (Scribner & Fahrmeier, 1983). Scribner’s research sought support for the premise that cognitive skills take shape in the course of participation in socially organized practices. The results of her work are embodied in the four principles of her model: (a) economy of effort functioned as a criterion distinguishing skilled from amateur performance – the “least-effort strategy” was consistently followed by skilled performers whether mental or physical effort was minimized and regardless of resource constraints in the work environment; (b) problem-solving strategies were dependent on specific knowledge about materials and conditions in the immediate task environment; (c) diversity and flexibility of solution modes distinguished expert problem solvers from beginners; and (d) more experienced workers replaced all-purpose algorithms with a menu of solution modes fitted to properties of specific problems in changing environments. Scribner (cited in

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Tobach et al., 1997) summarized the four principles in this way: “Thinking in the dairy was goal-directed and regulated by a principle of economy which, operating under changing conditions and on the basis of knowledge and information in the environment, generated flexible solution procedures adapted to particular occasions of use” (p. 380). Scribner’s work and the cognitive demands of new technologies. Scribner’s work demonstrated that workers seek pragmatic solutions through economy of effort regardless of the contingent or deterministic structure of the task. Her study of working intelligence (Scribner, 1984) fully accounts for task unpredictability and the need to go beyond scripted procedures to accommodate the changing demands of the task environment. For example, because each dairy order was different, delivery drivers modified their problem framing and arithmetic solutions to conform to the benefits of either their calculators or paper-and-pencil computations. Ways of solving problems followed means of arriving at solutions. Systemic interdependence requires knowledge of how one’s work fits in with the work of others and the ability to work with others on interdependent tasks. Scribner’s theory emphasizes workers’ ability to capitalize on available resources to find successful work strategies, including the efficiencies and reduced effort of relying on one’s coworkers to accomplish related tasks. However, a dominant theme in Scribner’s work, the importance of contextual factors in cognitive studies, is reflected in her theory as a multiplicity of influences, both social and material, on the cognitive strategies people adopt to accomplish their work. Interpersonal relations and interdependencies among workers is one of several key factors identified by Scribner that shape one’s repertoire of work behaviors. Scribner’s model emphasizes that successful work strategies are goal directed and vary adaptively with the changing properties of the problems and resources encountered by workers in the task environment. The model explains how workers might respond to stochastic events by relying on flexible solution strategies and improvising with available tools and materials. Task disruptions might trigger the reassessment of means-ends relationships, and solutions would reflect Scribner’s concept of mental and physical effort saving. Contextual factors would strongly influence how workers in a production environment learn and adapt their skills on the job. The power and endurance of Scribner’s model are evident. Even though it was developed 20 years ago, before technologies considered new today were developed, her theory explains how workers adapt to task contingency and respond to stochastic events. Although today’s workers might use Scribner’s least-effort strategies and context-specific solutions to achieve competence in today’s high-technology work environments, it is not known how well Scribner’s model of practical thinking addresses the cognitive demands of distancing technologies or explains the roles of inference and mental modeling to enable more effective

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use of these technologies. A summary of how Scribner’s theory addresses the cognitive demands of new technologies is given in Table 2. Schon’s theory of reflection in action. Schon (1983) argued for a new epistemology of practice that takes as its point of departure the competence and artistry already embedded in skillful practice – especially, the reflection in action through which professionals think about what they are doing while they are doing it. Reflection in action is a theory of learning that explains how reflective practitioners use knowledge and problem solving in their work. Reflection in action is an iterative process that moves through the stages of (a) assessment of the situation, (b) testing of one’s preliminary sense of the problem through experiments, (c) examination of results, and (d) reassessment leading to another cycle of problem reformulation. Learning occurs through an iterative process of purposeful actions, discovered consequences, implications, reassessments, and further actions. Using reflection in action, we conduct experiments to examine the validity of our judgments and, in the process, expose ourselves to new possibilities for learning. According to Schon (1983), “the situation talks back, the practitioner listens, and as he appreciates what he hears, he reframes the situation once again” (p. 131). This theory of learning prompted Schon to raise a critical question: What kind of professional education would be appropriate to an epistemology of practice based on reflection in action? His subsequent work (Schon, 1987) answered this question by proposing that university-based professional schools should learn from such deviant traditions of education for practice as studios of art and design, conservatories of music and dance, athletic coaching, and apprenticeship in the crafts, all of which emphasize coaching and learning by doing. Professional education, Schon (1987) argued, should be redesigned to combine the teaching of applied science with coaching in the use of reflection in action strategies. He proposed a generalized educational setting, the reflective practicum, as a model for professional development in which learning occurs by doing, with the help of coaching, especially through a dialogue of reciprocal reflection in action between coach and student. The reflective practicum is a methodology for implementing reflection in action in the sense that it brings together the necessary material and contextual resources, along with the coach’s personal and technical support for critical reflection. It provides an environment in which students can learn by doing, not simply through trial and error, but through critical refection as students are coached in refection in action strategies. Reflection in action begins with a situation that yields spontaneous routinized responses. As long as the situation appears normal, our responses are tacit and spontaneously delivered without conscious deliberation. Yet routine responses sometimes produce a surprise – an unexpected outcome, pleasant or unpleasant, that does not fit our present knowledge schema. This unexpected consequence triggers reflection. We think about the consequence and

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Scribner

• Problem-solving strategies are adaptive and dependent on specific knowledge of materials and changing conditions in the task environment. • Workers seek pragmatic solutions that reflect economy of mental and physical effort.

• Although Scribner’s principle of context-dependent problem solving has been broadly applied to work settings, it is not known how well the theory addresses the cognitive demands of distancing technologies.

Task characteristic

• Contingent versus deterministic tasks

• Distancing technologies • Reflection in action is a means for making sense of new situations through an iterative process of purposeful action, discovered consequences implications, reconstruction of our understanding, and further actions.

• Reflection in action enables workers to spontaneously construct solutions to problems that cannot be fully anticipated in advance.

Schon

• Communities of practice provide a social context for learning about distancing technologies and enable the sharing of representations of these technologies among members across locations.

• Communities of practice allow workers to reach pragmatic solutions through mutual engagement. • Communities of practice legitimize peripheral learning and foster adaptation and sense making in changing work environments.

Wenger

Theory of learning

Table 2: Summary of learning theories and the cognitive demands of new technologies

• Hutchins’s model explains how workers’ mental representations of their work allow navigation at night when navigators are “distanced” from the sentient cues that relate the ship’s position to its environment.

• In Hutchins’s model of cultural cognition, practice, learning, and work environment are all simultaneously transformed. • Workers use tools to transform the (navigation) task by mapping it into a domain, using representations and heuristics, where the answer or the path to the solution is apparent.

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• Scribner’s model explains how workers improvise with available tools and materials and use flexible solution strategies to respond to task disruptions and stochastic events.

• Scribner’s model identifies mutual dependencies among colleagues as means for adapting to the changing demands of the task environment.

• Stochastic events

• Systemic interdependence • Relationships among coaches and students based on reciprocal reflection in action are central to professional development in Schon’s model.

• Workers respond to stochastic events through reflection in action. Reflection in action probes the unexpected disruption and allows for tentative understanding, testing, and reframing of the event to reach a resolution.

• Communities of practice build support and interdependencies that foster the sharing of members’ insights and generalizations. • Membership in a community of practice provides access to the knowledge of individuals and of the community of practice.

• Communities of practice strive to make sense of stochastic events as members exchange individual perspectives on their meaning. • The sharing of interpretations of stochastic events is likely to include explanations for successfully resolving these disruptions.

• Hutchins’s conception of distributed cognition reflects the overlapping knowledge among navigation team members and emphasizes networks of interdependence and shared expertise.

• Hutchins describes how workers in crisis overcome emotional arousal and construct solutions from procedural knowledge, environmental shortcuts, and bricolage. In these situations, practice, learning, and work environment are all simultaneously transformed.

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ponder why it occurred and, at the same time, we ask, “How have I been thinking about this?” Our thoughts turn back on the surprising phenomenon and, at the same time, back on themselves. Thus, reflection in action is a critical function through which we consciously or unconsciously question the assumptions of our present knowledge. Schon contrasts reflection in action with the technical rationality of prevailing curricula for professional education. Technical rationality is based on an objectivist view of practice that posits that reality can be known objectively – the reality to be known is distinct from the practitioner’s knowing. According to this view, professional knowledge is founded on facts and data; formal inquiry serves to measure, predict, and control the phenomenon of interest. On the other hand, reflection in action rests on a constructivist view of the reality that professionals face in practice. Reality and its meaning are negotiable, and what is known is influenced by the process of coming to know it. The dynamics of reflection in action cut across the positivist dichotomies of research-practice, means-ends, and knowing-doing. For the reflective professional, practice is researchlike, means and ends are interdependent and may be transformed depending on how the problem is framed, and practice involves personal interaction with the situation in which knowing and doing are inseparable. Schon’s work and the cognitive demands of new technologies. Schon’s theory accounts for contingent tasks by acknowledging that professionals are frequently confronted by novel situations and must construct their interpretations and responses accordingly. Schon recognized that procedural knowledge and problem-solving algorithms have limited applications in practice, where most problems are contextual and difficult to predict. The capability for reflection in action addresses these cognitive demands by allowing workers to bypass scripted procedures to arrive at solutions for problems that cannot be fully anticipated in advance. Reflection in action also explains the cognitive processes needed to respond effectively to stochastic events. A sudden disruption arouses emotion and triggers reassessment of means-ends relationships. Schon’s discussion of mental experimentation explains how workers might respond to a sudden systems failure by probing the unexpected disruption, forming a tentative understanding of the event, testing their understanding, and reframing the problem to arrive at a solution. Thus, workers respond to emergent situations by constructing new knowledge through reflection in action. Reflective practitioners are continuous learners, and those involved in professional practice are regularly confronted by new situations that may be uncertain, ill defined, and incoherent. Problem novelty and ambiguity are among the challenges facing those using distancing technologies that separate people from the physical cues and information present at the operating location. Those who have embraced reflection in action for solving

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problems and making sense of new situations come to rely on their cognitive strategies for constructing understandings of the new problems confronted in practice. Although referring to the problem solving used to resolve architectural problems of an ambiguous nature, the following statement by Schon (1987) applies to other reflective practitioners, including those who use new technologies: “Their designing is a web of projected moves and discovered consequences and implications, sometimes leading to reconstruction of the initial incoherence – a reflective conversation with the materials of a situation” (p. 42). Because relationships among coaches and students based on reciprocal reflection in action are central to professional development in Schon’s model, the theory reflects the systemic interdependence involved in work situations that include the use of new technologies. One learns and refines reflection in action strategies through ongoing exchanges of reciprocal reflection in action with others. As colleagues in a network of practitioners, those working with new technologies share in shaping each other’s problem-solving strategies during reflective practice. A summary of how Schon’s theory addresses the cognitive demands of new technologies is given in Table 2. Communities of practice. Communities of practice are informal associations of workers who share common work problems and seek the benefits of learning from one another. In such communities, learning occurs primarily through participation in social practice (Wenger, 1998). Underlying communities of practice as an observable phenomenon is Wenger’s theory of social learning. The theory embodied in communities of practice builds on previous work in social learning theory and situated cognition. Social learning theory explains learning as a product of the reciprocal interactions among behavior, cognition, and environmental factors. Learning can occur directly, especially when one’s learning self-efficacy is high, or vicariously through behavior modeling by others (Bandura, 1977). Situated cognition originates with engagement in the activity itself, not with a preconceived model of how learning should occur. Situated cognition follows an “activity-perception-representation” model, in which the cognitive dynamics of learning appear less open to the predetermined knowledge schemas that are dominant in formal instruction (Brown, Collins, & Duguid, 1989). When people lack experience with a situation or are introduced to a new concept, presenting a relevant model may catalyze the formation of mental representations of what is learned. Along with new perceptions and relevant past experiences, the model becomes part of the present context for learning, in which the learner’s activities and perceptions precede mental representation. Four constructs compose the framework for Wenger’s theory of learning: practice, the shared historical and social resources, frameworks, and perspectives that can sustain mutual engagement in action; community, the social configurations in which our enterprises are defined as worth pursuing and our participation is recognizable as competence; identity, how learning

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changes who we are and creates personal histories of becoming in the context of our communities; and meaning, the ability to experience our life and world as meaningful. Wenger’s assumptions about learning and the nature of knowledge include the premise that meaning – our ability to experience the world and our engagement with it as meaningful – is ultimately what learning is to produce. Another assumption that grounds communities of practice is that engagement in social practice is the fundamental process by which we learn and so become who we are. Thus, communities of practice provide a broad conceptual framework for thinking about learning as a process of social participation. The concept of practice is carefully defined by Wenger as experiences that include both the explicit and the tacit. Practice involves the language, tools, documents, images, symbols, well-defined roles, specified criteria, codified procedures, regulations, and contracts that various practices make explicit for a variety of purposes. But practice also includes the implicit relations, tacit conventions, subtle cues, untold rules of thumb, and so on. Most of these are never articulated, yet they are unmistakable signs of membership in communities of practice and are crucial to the success of their organizations. Learning in practice addresses the need for members to acquire skills and information, but learning goes beyond gaining competence. Members use competence to form an identify of participation. “Practice connotes doing, but not just doing in and of itself. It is doing in a historical and social context that gives structure and meaning to what we do. In this sense, practice is always social practice” (Wenger, 1998, p. 47). The central issue in learning is becoming a member of a community of practice, not simply learning about practice. A community of practice is a learning community to the extent that it is able to continuously reconfigure the identities of its members and of itself. This flexibility of organization allows it to negotiate and renegotiate the nature of its practice. Identity in a community is fostered by allowing members to participate peripherally, yet legitimately, in practice. Legitimacy and peripheral participation in practice are often mutually exclusive. Newcomers seeking to participate in the work of a community of practice are granted peripherality (e.g., as students) but denied legitimacy. Conversely, newcomers may be granted legitimacy but are denied the opportunity for development through peripheral participation. The periphery of practice not only is an important site for learning but can be a valuable source of innovation. Sustaining the peripherality of members’ perspectives is sought increasingly as a way to generate fresh insights for practice and new directions for the future. Wenger’s work and the cognitive demands of new technologies. Participation in communities of practice allows each member to draw on collective knowledge to construct responses to unanticipated or poorly structured problems, thus enabling members to respond effectively to contingent tasks associated with new technologies. Wenger (1998) illustrated his theory with ethnographic

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accounts of insurance claims processors who had to respond to customers’ questions about claims coverage given only standardized forms and procedures and without full knowledge of how contested claims were ultimately resolved. The tasks they faced were made more contingent by customers’ concerns about copayments and company concerns about overpayments, especially in cases of multiple coverages. Workers tried to make sense of these ambiguous situations primarily through social configurations – the networks that claims processors spontaneously formed with each other, not by following claims processing procedures. Communities of practice allowed workers to go beyond standardized procedures and reach pragmatic solutions through mutual engagement. Stochastic events interrupt work, trigger affective responses, and challenge workers to make sense of unexplained disruptions. Members of a community of practice have the advantage of drawing on collective experiences and emotional support from other members to arrive at explanations and responses for stochastic events. A defining feature of communities of practice is the ability to generate fresh perspectives on practice from members who each develop unique identities within their community of practice. Because unexplained events elicit perspectives from members, some who are central to practice, and others, as newer members, who are more peripheral to practice, responses to a stochastic event are diverse and more likely to include a strategy for explaining and resolving the disruption. Communities of practice are manifestations of Wenger’s theory of social learning, and the relationships and expertise acquired by employees at work are explained in terms of social learning dynamics. This process of social learning helps meet the cognitive demands of distancing technologies, which require users to possess the capabilities for inference, imagination, and mental modeling to understand what is going on elsewhere. Communities of practice have emerged in high technology environments where distancing technologies are present (Marshall & Shipman, 1995; Orr, 1996). Communities of practice enable members to make sense of distancing technologies by supporting a communal memory that allows individuals to understand these technologies without needing to know everything about them and by sharing representations of these technologies among members across locations. Communities of practice provide a social context for learning about new technologies that gives structure and meaning to this process for members. The creation of identity is at the core of how communities of practice enable members to meet the cognitive demands of new technologies. Wenger (1998) maintained that who we are and what we can do are transformed through the process of becoming members of communities of practice. Identity and membership permit further engagement in social practice and access to collective knowledge, thus providing the basis for establishing the systemic interdependencies needed by users of new technologies. Although a member may lack specific knowledge about a problem, communities of practice

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provide collective knowledge that enables a response to the unpredictability and ambiguity of new technology (Orr, 1996). Legitimate peripheral participation (Lave & Wenger, 1991) and identity (Wenger, 1998) allow members of communities of practice to share their insights and generalizations and foster the development of systemic interdependence. Thus, communities of practice can build support and personal interdependencies that help to meet the cognitive demands of new technologies (Weick & Roberts, 1993). A summary of how Wenger’s theory addresses the cognitive demands of new technologies is given in Table 2. Cultural cognition. Hutchins conceptualized cognition as a complex phenomenon in which practice, learning, and the work environment are all simultaneously transformed. Hutchins (1995) stated, “The very same processes that constitute the conduct of activity and that produce changes in the individual practitioners of navigation also produce changes in the social, material, and conceptual aspects of the setting” (p. 374). These changes occur at different rates and degrees of intensity and reflect histories of different lengths, but they all intersect during any moment in human practice. In the course of task performance, learning occurs and subsequent actions are carried out that create elements of representational structure (e.g., written notes or an improvised tool) that survive beyond the end of the task. The artifacts of learning become elements of the environment, just as the environment influences the nature of learning. It is because these processes interact simultaneously that Hutchins considered cognition at work a fundamentally cultural process. Hutchins (1995) argued that as sociocultural systems, work environments have cognitive properties that are distinct from the cognition of those who perform the work. He confronted contemporary thinking in cognitive science by challenging the adequacy of symbolic processing alone to explain how we use cognitive abilities to solve environmental problems. In this regard, Hutchins stated, Notice that when the symbols are in the environment of the human and the human is manipulating the symbols, the cognitive properties of the human are not the same as the properties of the system that is made up of the human in interaction with these symbols. The properties of the human in interaction with the system produce some kind of computation. But that does not mean that the computation is happening inside the person’s head. (p. 361)

This premise that knowledge can only be created through human interaction with a sociocultural system that includes environmental artifacts is the foundation for Hutchins’s theory of cognition. Hutchins’s work and the cognitive demands of new technologies. Hutchins (1995) opened Cognition in the Wild by describing a stochastic event – the USS Palau loses all power and risks running aground in a narrow channel

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while entering San Diego harbor. Only through expert navigational skills and some luck is the crew able to recover the vessel and safely come to anchor. To meet the cognitive demands of these situations, workers must quickly overcome emotional arousal and construct solutions from procedural knowledge, environmental shortcuts, and bricolage (Levi-Strauss, 1966). Hutchins showed that this process is strongly shaped by the tools and techniques of practice, themselves historically developed. Learning is made easier in work settings where tools are used in public and the details of technology are observable, as they are in the practice of navigation. Hutchins described how the difficulty of piloting large ships is made easier by implementing the fix cycle – a series of procedures in which representations of the position of the ship in its environment are propagated across a series of representational media from initial telescope sightings to the actions taken to correct the ship’s course. These tools transform the complex task of navigation by mapping it into a domain, using the navigation chart and other artifacts, where the answer or the path to the solution is apparent. The fix cycle and other strategies allow navigation at night when navigators are distanced from the sentient cues that relate the ship’s position to its environment. They must rely on radar and limited environmental prompts to inform actions to maintain the ship’s course. Navigators’ mental representations of the ship’s position in oceanic darkness strongly influence the nature of the activities navigators use to monitor the ship’s course during their watch. The likelihood of encountering unanticipated contingent tasks increases when navigating through infrequently traveled waters and especially when piloting ships in the restricted waters of harbors and coastlines. Contingent tasks require the generation of novel responses (e.g., altering course and speed in response to approaching pleasure craft or changing weather) that may not be part of established procedures, because this type of navigation requires both adherence to restricted waters protocol (i.e., more frequent implementation of the fix cycle) and a collective awareness among navigation team members of the possibility of encountering an unscripted situation. Hutchins’s model of cultural cognition explains how navigators learn and adapt to rapidly changing navigation conditions through a process in which practice, learning, and the work environment are all simultaneously transformed. Systemic interdependence is accounted for in Hutchins’s model by overlapping distributions of knowledge among members of the navigation team and by the structure of shipboard authority and decision making. Hutchins clearly described the areas of overlapping knowledge among navigation team members, showed how the career trajectories of navigators are advanced through mastering ever-increasing areas of knowledge, and emphasized a decision-making process in which key personnel and environmental cues interact simultaneously, especially during crisis. Hutchins’s notion of distributed cognition reflects the network of interdependencies and the sharing of expertise associated with systemic interdependence.

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The fix cycle also illustrates a central premise of Hutchins’s theory of cultural cognition – technology is best used to address the cognitive demands of complex tasks by using it to simplify the task, not to amplify cognitive ability. Illustrating the same point, Norman (1997) gave the example of using a computer for writing. Instead of designing computers and software programs to help the author create ideas with dialog boxes, menu choices, and other symbolic clutter, the computer should be used as a word processor to simplify the output process. Rather than attempting to use technology to extend one’s cognitive abilities, technology should transform what are normally difficult cognitive tasks into easy ones. A summary of how Hutchins’s theory addresses the cognitive demands of new technologies is given in Table 2.

Implications for Further Research This section summarizes key ideas from the preceding discussion and offers directions for further research on learning theory, future studies of workplace learning, and theory building. Implications for research on learning theory. Several implications for further research emerge from this examination of learning theory and the cognitive demands of new technologies. First, the four theories examined in the article address some of the cognitive demands of new technologies discussed more completely than others. All four theories explain cognitive mechanisms related to how workers deal with the cognitive demands of contingent versus deterministic tasks, stochastic events, and systemic interdependence, albeit from different theoretical perspectives (see Table 2). However, the task characteristic of distancing technologies is only partially addressed by these theories. Hutchins’s theory offers the most complete treatment of this task characteristic with its explicit description of the cognitive strategies and navigation techniques used by navigators to pilot ships in unknown waters at night. In addition, the theories of Wenger and Schon offer plausible explanations of how one might adapt to the cognitive demands of distancing technologies. However, the relevance of Scribner’s theory to this task characteristic is speculative. Distancing technologies separate the worker physically and psychologically from elements of the task environment and require capabilities for inference, imagination, and mental modeling to understand what is going on elsewhere. Technologies with distancing properties such as those enabled by the Internet and satellite technology are among the most recent, complex, and rapidly developing of technologies affecting the workplace. Because technologies with distancing properties are complex and have very short design-implementation cycles (i.e., they quickly become obsolete and replaced by newer technologies), there is little time to assimilate considerations

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from new users and applications before the next generation of technologies is introduced. Thus, the turnover, complexity, and rapid development cycles associated with these technologies account, in part, for why they are incompletely addressed by theories of learning. Future research is needed to further examine the requirements these technologies place on users. Moreover, we need to know more about the human and environmental factors that support effective learning in this context. How do users develop the capabilities for inference, imagination, and mental modeling associated with the effective use of these technologies? What resources and environmental conditions are most conducive to developing expertise in the use of distancing technologies? Research to address these and other questions is needed to formulate new or revised theoretical explanations of effective learning in the use of these technologies. Future research is also needed to examine questions specific to other learning theories examined in this article. Schon’s theory of reflection in action reconceptualized teaching and learning in the professions. Although it explains how professionals engage in reflective practice, its applicability to nonprofessionals (technicians, supervisors, and skilled personnel who may not be considered professionals) is less apparent. Are skilled nonprofessionals included among those for whom learning through reflection in action is intended to apply? Although there is ample evidence that the capability for reflective practice is not limited to professionals, the scope and application of this theory to various populations of employees and occupations remains a question open to further study. Those who have closely studied communities of practice have raised some concern about their nebulous nature. Wenger and Snyder (2000) stated that “the organic, spontaneous and informal nature of communities of practice makes them resistant to supervision and interference” (p. 141). This makes communities of practice difficult to identify, assess, and cultivate. Thus, their existence in organizational contexts presents a paradox. Communities of practice create a type of value increasingly sought by organizations, yet the active development of such communities by organizations destroys them. How can communities of practice be fostered if their organic, spontaneous, and informal nature makes them resistant to supervision and interference? Further research might also attempt to extend Scribner’s model of practical thinking. Although Scribner’s least-effort strategies and context-specific solutions provide valuable insights into the cognitive strategies used by those in the work settings she studied, how well does Scribner’s model explain the contingent, stochastic, interdependent nature of today’s work? Further research to address these questions promises to yield valuable revisions and extensions of these theoretical explanations of how learning occurs in contemporary work environments. What can we learn from these theories? The learning theories examined in this article are but four among many theories that have been developed to explain learning in a variety of contexts including experiential learning,

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learning in formal educational settings, workplace learning and on-the-job training, informal and incidental learning, role- and occupation-specific learning, and other types of learning. The ubiquity of learning and the broad range of contexts in which it occurs constrain the ability of learning theories to explain more than a particular domain within this diverse phenomenon. Even so, a class of learning theories is available to those seeking theoretical explanations of learning in work settings, and from among these, specific theories are available that adequately address the types of work settings and technical tasks discussed in this article. A central contention of this article is that relatively few learning theories that have been applied by scholars to work settings fully capture the behavioral and environmental dynamics of this distinctive phenomenon. Because the four theories selected for discussion here explain this phenomenon better than other theories, their attributes merit further examination. What is noteworthy here is not which of these four theories (Scribner’s, Schon’s, Wenger’s, or Hutchins’s) is the best or right theory for explaining this type of learning but the acknowledgment that this class of theories has theoretical properties that enable them to provide effective explanations of work-related learning and that distinguish them from other learning theories. Some features of sound theories. The four theories discussed in this article provide meaningful explanations of how learning occurs in the type of work settings discussed here. Why is this? Considering the need to reflect the workplace, what are the features of a good theory of learning? Five attributes embodied in the four theories covered in this article are summarized next. 1. Each theory describes specific cognitive processes. All the theories make explicit the cognitive processes for learning and describe the dynamics of learning in particular environments. Schon described the dynamics of learning as iterative cycles of reflection in action. Hutchins explained learning as a sociocultural process that occurs simultaneously with the activities of practice and changes in the environment. Scribner described specific solution strategies and how they were derived through learning by experienced workers. Wenger described how learning in practice is generated by the dynamic tension between experience and competence. All four theories describe specific cognitive processes and clearly explain how the dynamics of learning relate to other aspects of their theories. 2. Each theory addresses learning as an enabler and product of work practices. Rather than treating either learning or work practice as dominant, each theory reflects their reciprocal relationship by grounding learning in the conduct of practice. Work practice is one of four central concepts in Wenger’s theory of social learning; Schon proposed the reflective practicum as the setting to operationalize his theory of learning; Scribner showed how cognitive skills were dependent on the materials and conditions of practice; and

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Hutchins proposed practice as the intersection of work activity, learning, and the environment, where all are simultaneously transformed. 3. Each theory explains how learning occurs in authentic work settings. Each theorist relied on ethnography or intimate knowledge of practice to describe the work settings and define the tasks in which learning was studied. None of the studies from which these theories were derived were purely theoretical or carried out in laboratories or other experimental settings. 4. Each theory is comprehensive in its treatment of the multiple ways in which knowledge can be generated. Rather than conceptualizing working knowledge as arising from cognitive or environmental sources alone, each theory accounts for multiple way s in which knowledge about work is generated and used. The theories explain how working knowledge can emerge from personal reflection and experience; from practicespecific tools, techniques, and conditions; through relationships with others; and through associations with other elements of the system and the environment. 5. Each theory offers propositions that can be generalized to other settings. Although each theory was based in studies of specific work environments and occupations, all theories offer principles of learning that have been applied elsewhere. Scribner’s “least-effort strategy” has been demonstrated in nonindustrial settings (Scribner & Cole, 1981; Scribner & Fahrmeier, 1983). Schon’s reflection in action has been applied to the preparation of architects, urban planners, artists, musicians, and athletes (Schon, 1987). Wenger’s theory has been used to explain communities of practice among photocopier repair technicians (Orr, 1996), refrigeration technicians (Henning, 1998), and insurance claims processors (Wenger, 1998). Hutchins’s original work on navigation technology has been applied to airline pilots (Hutchins & Klausen, 1996) and to the design of the human-computer interface (Hutchins, Hollan, & Norman, 1986). Each theory offers new knowledge about learning that can be applied and extended through further research. These five features of the learning theories discussed in this article provide a basis for developing better theories of learning. In addition to the criteria for evaluating theory offered by Bacharach (1989), Patterson (1983), and Whetten (1989) that can be applied to all theories, the features listed above are specifically applicable to theories intended to model the dynamics of learning and working. An additional distinction that cuts across these five features is also present in the four theories discussed here – the theories of Scribner, Schon, Wenger, and Hutchins conceptualize learning and working as phenomena that occur simultaneously. Learning and working are inseparable. Judging from the volume and variety of such studies, research on workplace learning is appealing to

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many researchers from a variety of disciplines. Many of these studies focus primarily or exclusively on the phenomenon of learning and give secondary or cursory consideration to work activity. How appropriate is such an approach for the study of learning and working? The four theorists discussed in this article provide a clear, coherent response to this question – studies of learning and working should treat these as phenomena that occur simultaneously. The four theories examined here ground learning in the conduct of work practice and emphasize their reciprocal relationship. This important premise is the basis for the following model for studies of learning and working. The fabric of work activity is woven with fibers of work and fibers of learning (see Figure 1). Although learning and working are inseparable during most work activity, there are periods during which one or the other is the dominant or exclusive activity, such as during routine tasks that are performed unconsciously or during periods when learning is uninterrupted by task demands. These separations of learning from working are shown in Figure 1 as discontinuities in the fabric of work activity. However, much work activity can be characterized generally as a phenomenon in which learning and working are inseparable. This feature of work activity is evident in Scribner’s model of practical thinking, Schon’s reflection in action, Wenger’s concept of practice, and Hutchins’s cultural cognition. Future studies of learning and working need to treat these as phenomena that occur simultaneously. This requires giving greater attention to the basic questions such studies seek to answer. Research questions such as, How does learning occur in a particular setting? generally examine only the learning fibers of the fabric of work activity. Although such studies may include references to the conditions or context of the work itself, they do so in a way that marginalizes these factors and provides a central focus on learning. Unlike questions that probe learning only, broader questions that examine the entire fabric of work activity might ask, What is happening as someone works through a task or project? Such a question is more likely to reveal how the fibers of learning and working are woven together to constitute the type of work activity described in this article. Because the theories Working

Learning

Work Activity

Figure 1: The fabric of work activity

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of Scribner, Schon, Wenger, and Hutchins conceptualize learning and working as phenomena that occur simultaneously, this represents an additional feature that distinguishes these from other learning theories and can be added to the five distinctions previously discussed. Those interested in developing broader, more integrative theories of how learning and working occur in contemporary work environments might arrive at better theories of learning and working using this perspective. Implications for theory-building research. Precise and logical conceptual development is the theorist’s central task when working back and forth from general domains to specific concepts and from existing knowledge to new theory. Just as the empirical researcher provides a detailed account of all data sources, instrumentation, and methods of data collection and analysis, the theorist allows other scholars to replicate the theorizing process by explicitly tracing all paths from existing knowledge to new theory. In short, clearly stated relationships among carefully selected concepts produces better theory. Theorizing that is replicable and provocative is more likely to advance our knowledge by stimulating further inquiry that leads to new knowledge. But how does a theory become provocative? Provocativeness (or fruitfulness) is the capacity of a theory to change research and/or practice in the field. A theorist who wishes to accurately model a cross-disciplinary phenomenon such as the cognitive demands of new technologies must confront the limits of her or his own discipline in relation to the cross-disciplinary system being modeled by the theory. Theorists who are interested in cross-disciplinary phenomena, but who venture too far away from domains they understand in attempting to explain these phenomena, risk developing theory that is poorly informed in unfamiliar domains. On the other hand, theorists who embrace the multiple content domains needed to model cross-disciplinary phenomena are likely to produce provocative theory. So the theorist (or cross-disciplinary theory-building team) starts with the accumulated knowledge from fields related to the phenomenon of interest. In the case of the model proposed in this article, relevant knowledge was needed from human factors/ergonomics, industrial engineering, information technology, psychology, cognitive science, and education. The model presented here was constructed to be a carefully selected combination of ideas synthesized from knowledge in all of these areas. Mindful of the need for balance between a theory’s comprehensiveness and parsimony, specific concepts – some created to explain new phenomena and others adapted from existing theories, were integrated into the full model. Because the new model contains thinking from several disciplines, it is more likely to stimulate new ideas within the theorist’s own discipline. New theories are provocative, in part, because they stretch our thinking across existing paradigms and beyond the boundaries of our discipline.

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References Adler, P. S. (1986). New technologies, new skills. California Management Review, 29(1), 9–28. Bacharach, S. B. (1989). Organizational theories: Some criteria for evaluation. Academy of Management Review, 14(4), 496–515. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice Hall. Barley, S. R., & Bechky, B. A. (1994). In the backrooms of science: The work of technicians in science labs. Work and Occupations, 21(1), 85–126. Barley, S. R., & Orr, J. E. (1997). Between craft and science: Technical work in U.S. settings. Ithaca, NY: ILR Press. Berniker, E. (1987, November). Understanding technical systems. Paper presented at the Symposium on Management Training Programs: Implications of New Technologies, Geneva, Switzerland. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. Campbell, D. J. (1988). Task complexity: A review and analysis. Academy of Management Review, 13(1), 40–52. Campion, M. A., & Medsker, G. J. (1992). Job design. In G. Salvendy (Ed.), Handbook of human factors. New York: John Wiley. Davenport, T. H. (1993). Process innovation: Reengineering work through information technology. Boston: Harvard Business School Press. Engestrom, Y., & Middleton, D. (1996). Cognition and communication at work. New York: Cambridge University Press. Gagne, R. M. (1962). Military training and principles of learning. American Psychologist, 17, 83–91. Gill, T. G. (1996). Expert systems usage: Task change and intrinsic motivation. MIS Quarterly, 20, 301–329. Griffith, T. L., & Northcraft, G. B. (1996). Cognitive elements in the implementation of new technology: Can less information provide more benefits? MIS Quarterly, 20, 99–110. Graham, L. (1993). Inside a Japanese transplant: A critical perspective. Work and Occupations, 20(2), 147–173. Hackman, J. R. (1969). Toward understanding the role of tasks in behavioral research. Acta Psychologica, 31, 97–128. Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley. Henning, P. H. (1998). Ways of learning: An ethnographic study of the work and situated learning of a group of refrigeration service technicians. Journal of Contemporary Ethnography, 27(1), 85–136. Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press. Hutchins, E., Hollan, J., & Norman, D. A. (1986). Direct manipulation interfaces. In D. A. Norman & S. Draper (Eds.), User centered system design: New perspectives in human-computer interaction. Hillsdale, NJ: Lawrence Erlbaum. Hutchins, E., & Klausen, T. (1996). Distributed cognition in an airline cockpit. In Y. Engestrom & D. Middleton (Eds.), Cognition and communication at work. New York: Cambridge University Press. Khurana, A. (1999). Managing complex production processes. Sloan Management Review, 40(2), 85–97. Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. New York: Cambridge University Press. Leont’ev, A. N. (1981). Problems of the development of mind. Moscow: Progress. Levi-Strauss, C. (1966). The savage mind. Chicago: University of Chicago Press.

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Locke, E. A., & Latham, G. P. (1990). A theory of goal setting and task performance. Englewood Cliffs, NJ: Prentice Hall. Marshall, C. C., & Shipman, F. M. (1995). Making large-scale information resources serve communities of practice. Journal of Management Information Systems, 11(4), 65–87. Martin, L. M. W., & Scribner, S. (1991). Laboratory for cognitive studies of work: A case study of the intellectual implications of new technology. Teachers College Record, 92(4), 582–602. Norman, D. A. (1988). Knowledge in the head and in the world. In The psychology of everyday things. New York: Basic Books. Norman, D. A. (1993). Things that make us smart: Defending human attributes in the age of the machine. Reading, MA: Addison-Wesley. Norman, D. A. (1997). Melding mind and machine. Technology Review, 100, 29–31. Norros, L. (1996). System disturbances as springboard for development of operators’ expertise. In Y. Engestrom & D. Middleton (Eds.), Cognition and communication at work. New York: Cambridge University Press. Orr, J. E. (1996). Talking about machines: An ethnography of a modern job. Ithaca, NY: ILR Press. Patterson, C.H.(1983). Theories of counseling and psychotherapy. Philadelphia: Harper and Row. Pentland, B. T. (1997). Bleeding edge epistemology: Practical problem solving in software support hot lines. In S. R. Barley & J. E. Orr (Eds.), Between craft and science: Technical work in U.S. settings. Ithaca, NY: ILR Press. Salvendy, G. (1987). Handbook of human factors. New York: John Wiley. Schon, D. A. (1983). The reflective practitioner: How professionals think in action. New York: Basic Books. Schon, D. A. (1987). Educating the reflective practitioner. San Francisco: Jossey-Bass. Scribner, S. (1984). Studying working intelligence. In B. Rogoff & J. Lave (Eds.), Everyday cognition: Its development in social context. Cambridge, MA: Harvard University Press. Scribner, S., & Beach, K. D. (1993). An activity theory approach to memory. Applied Cognitive Science, 7, 185–190. Scribner, S., & Cole, M. (1981). The psychology of literacy. Cambridge, MA: Harvard University Press. Scribner, S., & Fahrmeier, E. (1983). Practical and theoretical arithmetic (Working Paper No. 3). New York: Industrial Literacy Project, City University of New York. Simon, H. A. (1981). The sciences of the artificial (2nd ed.). Cambridge, MA: MIT Press. Smith, T. J., Henning, R. A., & Smith, K. U. (1994). Sources of performance variability. In G. Salvendy & W. Karwowski (Eds.), Design of work and development of personnel in advanced manufacturing. New York: Wiley-Interscience. Tobach, E., Falmagne, R. J., Parlee, M. B., Martin, L. M. W., & Kapelman, A. S. (1997). Mind and social practice: Selected writings of Sylvia Scribner. New York: Cambridge University Press. Wegner, D. M., Erber, R., & Raymond, P. (1991). Transactive memory in close relationships. Journal of Personality and Social Psychology, 61(6), 923–929. Weick, K. A. (1990). Technology as equivoque: Sensemaking in new technologies. In P. S. Goodman & L. S. Sproull and Associates (Eds.), Technology and organizations. San Francisco: Jossey-Bass. Weick, K. A., & Roberts, K. H. (1993). Collective mind in organizations: Heedful interrelating on flight decks. Administrative Science Quarterly, 38, 357–381. Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. New York: Cambridge University Press. Wenger, E., & Snyder, W. M. (2000). Communities of practice: The organizational frontier. Harvard Business Review, 78(1), 139–145.

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Whetten, D. A. (1989). What constitutes a theoretical contribution? Academy of Management Review, 14(4), 490–495. Woods, D. P., O’Brien, J. F., & Hanes, L. F. (1987). Human factors challenges in process control: The case of nuclear power plants. In G. Salvendy (Ed.), Handbook of human factors. New York: John Wiley. Woodward, J. (1994). Industrial organization: Theory and practice (4th ed.). London: Oxford University Press. Zuboff, S. (1988). In the age of the smart machine: The future of work and power. New York: Basic Books.

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38 Cognitive Conceptions of Learning Thomas J. Shuell

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sychologists and educators have long been interested in understanding how people learn, for the concept of learning is central to many different human endeavors. Teaching, child rearing, counseling, and a wide variety of training situations, to name just a few areas, are all concerned in one way or another with individuals learning new knowledge and/or behavior. There is, of course, a long history of empirical research on learning dating back to the classic research of Ebbinghaus (1913) first published in 1885. During the first half of the present century, research on learning flourished (nearly all of it within the behavioral tradition of psychology), and learning theory exerted a strong influence on research and practice in many different spheres of psychology and education. This influence and interest in learning remained strong well into the 1960s. During the late 1960s and early 1970s, however, the zeitgeist of psychology began to change from a behavioristic to a cognitive orientation. Concern for the mind and the way it functions returned to scientific psychology. This cognitive orientation was clearly evident in research on topics such as meaningful verbal learning (Ausubel, 1962, 1963), discovery learning (e.g., Bruner, 1957, 1961), imagery (Paivio, 1969, 1971), “mathemagenic” behaviors (behaviors that give birth to learning) (Rothkopf, 1965, 1970), generative learning (Wittrock, 1974, 1978), and mnemonics (e.g., Bower, 1970). Nevertheless, during the period from about 1960 to 1980, research on learning per se – that is, a concern for those factors that produce changes in an individual’s behavior and/or knowledge – diminished drastically. For a variety of reasons (some of which will be discussed below), cognitive psychologists’ interest in learning gave way to other concerns. Cognitive psychologists occasionally Source: Review of Educational Research, 56(4) (1986): 411– 436.

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acknowledged the importance of learning, but little effort was devoted to furthering our understanding of how learning occurs. In appraising this situation, Voss (1978) concluded that “although the concept of learning may be found in cognitive psychology, it also must be conceded that the cognitive view of learning is vague, is abstract, and, most important, is lacking a substantive data base” (p. 13). Similar conclusions were voiced by other cognitive psychologists (e.g., J. R. Anderson, 1982; Greeno, 1980a; Langley & Simon, 1981). Since about 1975, however, cognitive psychologists have shown a growing interest in learning, and a new era of research on learning may be at hand. Much, but certainly not all, of this more recent research represents an information-processing orientation and involves sophisticated computer models of learning. As one might expect, these cognitive conceptions of learning (both the earlier and the more recent ones) differ from traditional, behavioristic conceptions of learning in ways that enrich our understanding of how humans acquire new knowledge and new ways of doing things. The purpose of this article is to examine current conceptions of learning, primarily from the vantage point of modern-day cognitive psychology. To provide an appropriate perspective, however, similarities and differences between traditional and cognitive conceptions of learning will be discussed. After first highlighting some characteristics of traditional conceptions of learning, ways in which cognitive psychology has influenced research on learning will be considered. Next, several cognitive theories of learning will be described. Finally, implications for future research on learning and for educational practices will be outlined.

Traditional Conceptions of Learning During the 100 years since Ebbinghaus’ pioneering research, nearly all research on learning has been conducted within a behavioral framework. Although the Gestalt psychologists of the 1910s to 1930s (perhaps the chief forerunners of modern-day cognitive psychology) occasionally discussed learning, they were more interested in perception than in learning, and they usually interpreted learning in terms of perceptual principles of organization. For a variety of reasons (see, e.g., Stevenson, 1983), traditional research on learning focused primarily on animal learning rather than human learning (although this research has not been totally void of cognitive influence – see, e.g., Kimble, 1984). As a result, most research on learning has involved relatively simple forms of learning. Even in the case of human learning, most traditional studies of learning have employed simple tasks that involve memorization more than comprehension. But before continuing, perhaps it would be useful to consider what we normally mean by the term learning.

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The Concept of Learning The concern for learning, of course, focuses on the way in which people acquire new knowledge and skills and the way in which existing knowledge and skills are modified. Nearly all conceptions of learning have involved – either explicitly or implicitly – three criteria for defining learning (see, e.g., Shuell & Lee, 1976): (a) a change in an individual’s behavior or ability to do something, (b) a stipulation that this change must result from some sort of practice or experience, and (c) a stipulation that the change is an enduring one. The primary purpose of the latter two qualifications is to exclude certain types of behavioral changes that do not seem to represent what we mean by learning (maturation, temporary changes due to drugs, etc.). Although there appears to be general agreement among behavioral and cognitive conceptions of learning with regard to the defining characteristics of the underlying phenomenon, there are also a number of important differences between the two orientations. The only formal definition of learning from a cognitive perspective that I have been able to find (Langley & Simon, 1981) fits the above criteria almost perfectly: “Learning is any process that modifies a system so as to improve, more or less irreversibly, its subsequent performance of the same task or of tasks drawn from the same population” (p. 367). The main difference appears to be the emphasis on the performance of a system rather than on the behavior of an individual. Cognitive conceptions of learning, however, focus on the acquisition of knowledge and knowledge structures rather than on behavior per se, on “ . . . discrete change between states of knowledge rather than [on] change in probability of response” (Greeno, 1980a, p. 716). The significance of this difference is not as minor as it might appear, for if it is knowledge that one learns, “ . . . then behavior must be the result of learning, rather than that which itself is learned” (Stevenson, 1983, p. 214). There also tends to be general (although not complete) agreement among behavioral and cognitive conceptions of learning that both environmental factors and factors internal to the learner contribute to learning in an interactive manner (e.g., Brown, Bransford, Ferrara, & Campione, 1983). As one might expect, however, the different positions disagree on which side of this learner-environment equation is most important. For example, behavioral approaches focus on changing the environment in order to influence learning (e.g., by providing reinforcement when the appropriate response is made), whereas cognitive approaches focus more on changing the learner (e.g., by encouraging the person to use appropriate learning strategies). There are also considerable differences with regard to both what is learned (e.g., behavior vs. structured knowledge) and the factors that influence the learning process (e.g., reinforcement vs. strategies for obtaining feedback).

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The Transition Begins Although the seeds of modern-day cognitive psychology were present during the 1930s (e.g., Bartlett, 1932; Tolman, 1932), they did not grow to fruition, especially with regard to learning, for many years. During the 1960s, research on learning, especially verbal learning (the main body of research on human learning during this period), began to undergo a change that reflected views more consistent with cognitive interpretations of behavior. Investigators began to question, for example, whether simple conceptions of learning could adequately handle the more complex forms of learning encountered in real-life situations such as the classroom. The debate about whether classical conditioning and operant conditioning represent one or two different types of learning (see Kimble, 1961) was extended by Gagné’s (1962, 1965) postulation of eight types of learning, including complex forms of learning such as concept learning and problem solving. People started to realize that even simple learning materials (e.g., nonsense syllables, isolated words) have meaning and that this meaningfulness can influence the learning process (e.g., Underwood & Schulz, 1960). The realization that learners were not passive during learning (e.g., Bruner, 1957; Miller, Galanter, & Pribram, 1960) began to spread. For example, subjects often selected a stimulus (the “functional stimulus”) that differed from the one intended by the experimenter (the “nominal stimulus”) (Underwood, 1963), and when allowed (e.g., the free-recall paradigm), they organized the material being learned in meaningful ways, even in the absence of obvious bases of organization (Shuell, 1969; Tulving, 1968). Thus, a transition had begun from a strictly behavioristic orientation to one that involved more cognitive activities. But somewhere in the transition the concern for learning got set aside. There are many reasons for the demise of interest in learning. Among the more obvious reasons are the following: 1. The appearance of experimental data that were difficult to reconcile with existing theories of learning (see Stevenson, 1983; White, 1970). Included among the many examples of this problem are age changes in the solution of reversal-nonreversal shift problems (Kendler & Kendler, 1962), the presence of organizational patterns in free recall (Shuell, 1969), and the transfer data that led to the notion of the functional stimulus (Underwood, 1963). 2. The feeling that one must understand the nature of the performance system before one can investigate learning (Newell & Simon, 1972). It is difficult to study transitions between knowledge states without first knowing something about the knowledge states between which the transition is being made, a problem directly analogous to the classical requirements for operational definitions and criterion specification.

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3. The realization that the laws of learning depend on the context in which it occurs and the prior knowledge of the learner (for a good discussion of this point, see Siegler, 1983). 4. The ability of fresh ideas to capture the interest of investigators becoming bored with decades of traditional thinking about learning – that is, the zeitgeist of cognitive psychology. In addition, the cognitive psychologists of the 1960s and 1970s became interested in identifying and describing the various stages and processes involved in human information processing. This focus led naturally to a concern for the nature of the memory system rather than learning – that is, how knowledge is represented in memory rather than how changes in knowledge take place. Different research questions were being asked; different paradigms were being employed; different assumptions were being made; and different theories were being developed.

The Influence of Cognitive Psychology Cognitive psychology is concerned with various mental activities (such as perception, thinking, knowledge representation, and memory) related to human information processing and problem solving, and it presently represents the mainstream of thinking in both psychology and education. The emphasis is no longer strictly on behavior, but on the mental processes and knowledge structures that can be inferred from behavioral indices and that are responsible for various types of human behavior. Thus, with regard to learning, the search by learning psychologists of the 1950s and 1960s for atheoretical, functional relationships (Underwood, 1964) has shifted to a concern for the thought processes and mental activities that mediate the relationship between stimulus and response (see, e.g., Wittrock, 1986). Nevertheless, cognitive psychology has influenced learning theory and research in several significant ways, including (a) the view of learning as an active, constructive process; (b) the presence of higher-level processes in learning; (c) the cumulative nature of learning and the corresponding role played by prior knowledge; (d) concern for the way knowledge is represented and organized in memory; and (e) concern for analyzing learning tasks and performance in terms of the cognitive processes that are involved.

Learning as an Active Process Cognitive approaches to learning stress that learning is an active, constructive, and goal-oriented process that is dependent upon the mental activities of the learner. This view, of course, contrasts with the behavioral orientation that

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focuses on behavioral changes requiring a predominantly passive response from the learner to various environmental factors. Although operant conditioning requires the learner to make an overt response (so that it can be reinforced), the active nature of learning suggested by cognitive psychologists is very different. The cognitive orientation, for example, focuses on the mental activities of the learner that lead up to a response, and it explicitly acknowledges the following: (a) the role of metacognitive processes such as planning and setting goals and subgoals (e.g., Brown et al., 1983; Flavell, 1981); (b) the active selection of stimuli (e.g., the distinction between functional and nominal stimuli; Underwood, 1963); (c) the attempt by learners to organize the material they are learning, even when no obvious bases of organization are present in the materials being learned (e.g., Shuell, 1969; Tulving, 1968); (d) the generation or construction of appropriate responses (e.g., Wittrock, 1974); and the use of various learning strategies (e.g., Weinstein & Mayer, 1986). The suggestion that memory (e.g., Bartlett, 1932; Cofer, 1973; Jenkins, 1974) and learning (e.g., Wittrock, 1974) both require the learner to actively construct new knowledge and strategies is appealing to many cognitive psychologists, but these views are plagued with a theoretical paradox (see, e.g., Bereiter, 1985). The problem arises when a learner acquires a new cognitive structure that is more advanced or complex than the structures that are presently possessed. The paradox involves the need to explain how the learner can acquire the new cognitive structure without already having an existing cognitive structure more advanced or complex than the one being acquired – a situation that is easier to explain in terms of innate mental structures than in terms of learning. Bereiter (1985) suggests 10 “resources” that permit one to avoid this “learning paradox,” but few studies currently support their validity.

Higher-Level Processes in Learning Most cognitive conceptions of learning acknowledge the hierarchical nature of the psychological processes responsible for learning. Miller, Galanter, and Pribram’s (I960) book, Plans and the Structure of Behavior, proved very influential in popularizing the notion that behavior is hierarchically organized. Since the late 1970s, the higher-level (superordinate, executive) processes of learners have typically been referred to as metacognition (see, e.g., Brown, 1978; Brown et al., 1983; Flavell, 1979). Although such analyses raise the homunculus or “inner man” problem, such concerns need not be fatal. (For a discussion of this problem, see Brown et al., 1983.) Generally, two types of metacognitive activities are involved in learning. The first involves regulation and orchestration of the various activities that must be carried out in order for learning to be successful (planning, predicting what information is likely to be encountered, guessing, monitoring the learning process, etc.) (e.g., Brown, 1978). Since learning is goal oriented,

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the learner must somehow organize his or her resources and activities in order to achieve the goal. The second is concerned with what one does and does not know about the material being learned and the processes involved in learning it. Flavell and Wellman (1977) suggest four general classes of metacognitive knowledge: (a) tasks – knowledge about the way in which the nature of the task influences performance on the task; (b) self – knowledge about one’s own skills, strengths, and weaknesses; (c) strategies – knowledge regarding the differential value of alternative strategies for enhancing performance; and (d) interactions – knowledge of ways in which the preceding types of knowledge interact with one another to influence the outcome of some cognitive performance. An example of the hierarchical nature of learning is Sternberg’s (1984a, 1984b) componential theory of knowledge acquisition. Sternberg suggests that performance is regulated by nine metacomponents (executive processes) such as “recognition of just what the problem is that needs to be solved” (Sternberg, 1984b, p. 165). These metacomponents operate on lower-level performance components (processes used in the execution of a task, such as encoding and comparison) and three knowledge-acquisition components: 1. Selective encoding (sifting out relevant information from irrelevant information, in the stimulus environment, in order to select information for further processing). 2. Selective combination (combining selected information in such a way as to render it interpretable; that is, integrating it in some meaningful way). 3. Selective comparison (rendering newly encoded or combined information meaningful by perceiving its relations to old information previously stored). (Sternberg, 1984b, p. 168) These knowledge-acquisition components operate on a variety of cues present in the material being learned, although cue utilization is affected by moderating variables such as number of occurrences, variability of contexts, location of cues, importance of the to-be-learned information, and density of the information to be learned (Sternberg, 1984a).

The Role of Prior Knowledge Learning is cumulative in nature; nothing has meaning or is learned in isolation. Cognitive conceptions of learning place considerable importance on the role played by prior knowledge in the acquisition of new knowledge. Whereas traditional research on verbal learning was Concerned with transfer and the effect of proactive inhibition on retention, concern for what the learner had already acquired focused on associations between individual stimuli and responses rather than on the acquisition of meaning from organized bodies of knowledge.

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In the early 1970s, several studies (Bransford & Johnson, 1972; Dooling & Lachman, 1971) demonstrated that what the learner already knows and the extent to which this knowledge is activated at the time of learning has important implications for what will be acquired and for whether or not the material being studied will make any sense to the learner. Realizations such as these led to the development of schema theory (e.g., R. C. Anderson, 1984), which stresses that the organized, structured, and abstract bodies of information (known as schemata) that a learner brings to bear in learning new material determine how the task is interpreted and what the learner will understand and acquire from studying the task. The traditional concept of transfer was concerned with the way prior learning influences later learning, and this influence was explained in terms of the similarity between stimuli and responses in the two situations. The newer cognitive concern for the role of prior knowledge in learning, however, recognizes that for meaningful forms of learning this process is more complex than the one suggested by earlier approaches to transfer. For example, Bransford and Franks (1976) suggest that the role of prior knowledge is to establish “boundary constraints” for identifying both the “sameness” and the “uniqueness” of novel information: “From the present perspective, growth and learning do not simply involve an expansion of some body of interconnected facts, concepts, etc. Learning involves a change in the form of one’s knowledge so that it can set the stage for new discoveries” (p. 112). Likewise, within the context of cognitive development, Siegler (1983) and Siegler and Klahr (1982), among others, have emphasized the importance of prior knowledge (especially the rules used to perform various tasks) in determining when children are ready to learn new material. Another change that has occurred recently is an emphasis on domainspecific knowledge and learning skills (e.g., Glaser, 1984). Although this change in thinking cannot be attributed directly to the rise of cognitive psychology, it has had a substantial influence on cognitive conceptions of learning. Traditional research on learning sought general laws applicable to all individuals and all subject-matter areas. However, recent research on individuals with differing levels of expertise in a particular subject, such as physics (e.g., Chi, Glaser, & Rees, 1982), has shown convincingly that experts and novices solve problems in fundamentally different ways. Although controversy remains over the relative importance of domain-specific knowledge and general, domain-independent learning strategies (e.g., Block, 1985; Glaser, 1984; Sternberg, 1985), it is generally recognized (e.g., Glaser, 1985; Keil, 1984) that both are important in most learning situations. Consequently, there is an important relationship between the emphasis on domain-specific knowledge and the concern for prior knowledge that is evident in research on cognitive learning.

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The Question of What is Learned One major difference between behavioral and cognitive conceptions of learning concerns the nature of what an individual learns. Behavioral approaches typically suggest either that the learner acquires associations or “bonds” between a stimulus and a response (e.g., Thorndike, 1913) or that the issue of what an individual might acquire internally (i.e., “theories” of learning) is totally irrelevant for understanding the factors responsible for learning (i.e., changes in behavior) (Skinner, 1950). Cognitive psychologists, on the other hand, are primarily concerned with meaning rather than with behavior per se – that is, a concern for the manner in which an individual extracts meaning from some experience. The emphasis is on understanding, not merely on learning how to perform a task, and on the acquisition of knowledge rather than on the acquisition of behavior. If knowledge is what an individual learns, then behavior is the result of learning rather than what an individual acquires (Stevenson, 1983). Generally, this knowledge is best represented by complex knowledge structures rather than by simple associations.1 These knowledge structures are usually conceptualized as networks of information specifying the relationship among various facts and actions (e.g., J.R. Anderson, 1980; Norman et al., 1975). There are, however, other ways of conceptualizing what an individual acquires in cognitive learning. For example, both Scandura (1970, 1977) and Siegler (1983) have suggested that rules are useful units for characterizing what people learn. Actually, it seems likely that humans have several different ways and /or modes for representing knowledge. For example, a distinction is frequently made in cognitive psychology between propositional (or declarative) and procedural knowledge, and it appears likely that there are several additional forms of knowledge representation (e.g., Gagné & White, 1978; Shuell, 1985) and several different memory systems (Tulving, 1985).

Cognitive Process Analysis One important consequence of the cognitive influence on learning has been an interest in analyzing performance and cognitive abilities in terms of the cognitive processes involved in performing a cognitive task, including performance on tests of mental ability such as intelligence (e.g., Carroll, 1976; Snow & Lohman, 1984; Sternberg, 1979), inductive reasoning (e.g., Pellegrino & Glaser, 1982), and deductive reasoning (e.g., Johnson-Laird, 1985). For example, Sternberg (1977) proposed that analogical reasoning – which some (e.g., Rumelhart & Norman, 1981) have suggested is the basis of cognitive learning – involves six cognitive processes: (a) encoding the various terms that

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make up the analogy, (b) inferring the relationship between the first two terms of the analogy, (c) mapping or discovering a higher-order rule that relates the first and third terms of the analogy, (d) applying the results of the inferring and mapping components to the third term in order to generate an appropriate fourth term, (e) an optional justification process in which one of the answers provided is selected as being the closest to the “ideal” answer produced by the application process, and (f ) a response process whereby the solution is translated into a response. This type of cognitive process analysis also has been applied to various types of instructional tasks such as the learning of geometry (e.g., Greeno, 1978, 1980b), physics (e.g., Champagne, K lopfer, & Gunstone, 1982; Heller & Reif, 1984), reading (e.g., Omanson, Beck, Voss, & McKeown, 1984), and addition and subtraction (e.g., Carpenter, Moser, & Romberg, 1982). Such analyses can help us to better understand both the cognitive processes involved in learning and the instructional techniques most likely to facilitate that learning.

Cognitive Theories of Learning Most cognitive conceptions of learning reflect an overriding concern for the more complex forms of learning, that is, the types of learning frequently characterized as “meaningful” or where one “learns for understanding.” For the most part, cognitive psychologists have been interested in the latter approach. As Norman (1978) put it: I do not care about simple learning. . . . that only takes 30 minutes. I want to understand real learning, the kind we all do during the course of our lives. . . . I want to understand the learning of complex topics. . . . [those] with such a rich set of conceptual structures that it requires learning periods measured in weeks or even years. (p. 39)

One problem with meaningful learning is that it is difficult to define. Although an operational definition is not readily available, it is possible to provide examples of the differences that concern many investigators. It makes little sense to most people, for example, to say that one “understands” his or her phone number; we “can learn, know, or remember a phone number, but not understand one” (Markman, 1981, p. 63). Only information that is structured or organized can be thought of as being meaningful and can serve as an object of understanding (Bransford & McCarrell, 1974; Moravcsik, 1979). Although some investigators would apparently limit cognitive learning to the acquisition of information that is structured or organized, higher-order thought processes are involved in many forms of simpler learning as well (e.g., when elaboration occurs or when mnemonics are used – see Pressley & Levin, 1983a, 1983b). It seems reasonable to suggest that all of these different types of situations fall within the domain of cognitive learning.

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Various attempts have been made over the years to articulate the role of learning from a cognitive or human information-processing perspective (for a detailed discussion of these approaches, see Bower and Hilgard, 1981). The following discussion will focus on those theories that have most influenced current thinking and research on cognitive learning.

Early Conceptions During the late 1950s and early 1960s, several writers began to formulate cognitive theories of learning. For example, Bruner (1957, 1961) talked about learning in terms of “discovery” and “going beyond the information given.” According to Bruner (1957), learning occurs when an “organism . . . code[s] something in a generic manner so as to maximize the transferability of the learning to new situations” (p. 51). He goes on to identify four general sets of conditions under which such learning will occur: (a) the “set to learn” or “attitude toward learning”, (b) an appropriate need state (in which an “optimal” level of motivation is discussed), (c) prior mastery of the original learning (and its importance for generic coding), and (d) diversity of training. The first systematic model of cognitive learning, however, was Ausubel’s (1962, 1963) subsumption theory of meaningful verbal learning. Ausubel makes clear that the theory is concerned only with “meaningful” (as opposed to “rote”), “reception” (as opposed to “discovery”) learning. According to this theory, new, potentially logical information is subsumed (incorporated) into the learner’s existing cognitive structure. The availability of an existing cognitive structure – hierarchically organized with progressive differentiation within a given field of knowledge from more inclusive concepts to less inclusive subconcepts – is seen as the major factor affecting meaningful learning, and the use of “advance organizers” (models or other types of representation that provide a structured overview of the material to be learned) can help ensure that such availability exists. Another major factor is the extent to which the new material is discriminable from the existing cognitive structure that subsumes it. This discriminability can be facilitated by repetition and/or by explicitly pointing out the similarities and differences between the new materials and their presumed subsumers in cognitive structure. Finally, the retention of meaningful material was thought to be influenced by repetition, the length of time that relevant subsuming concepts had been part of the learner’s cognitive structure, the use of appropriate exemplars, and multicontextual exposure. Another early theory of cognitive learning was Wittrock’s (1974) model of generative learning. According to this model (Wittrock, 1974, 1978), people learn meaningful material by generating or constructing relationships among new information and knowledge already stored in long-term memory. These verbal and imaginal elaborations occur as the learner seeks to discover the

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underlying rule or relationship “by drawing inferences [about the rule], applying it, testing it, and relating it to other rules and to experience” (Wittrock, 1978, p. 26). It was recognized that individuals might proceed differently and that different instructional adjuncts could elicit the appropriate cognitive processes. It appears that the primary mechanisms of learning, according to the generative model, consist of the learner making inferences about potential relationships and then actively seeking feedback on the adequacy of these relationships. Bransford and Franks (1976) suggested that understanding or comprehension involves the acquisition of novel information that is difficult, if not impossible, for the traditional, “memory metaphor” model of learning to explain. They suggest that learning that involves understanding (i.e., comprehension) occurs via a process of decontextualization. That is, knowledge is initially acquired in a specific context; in order for understanding to occur, this knowledge must become more abstract so that it can be related to a variety of different situations. A mechanism for this decontextualization process is not suggested, but Bransford and Franks suggest that concepts and knowledge become abstract by virtue of being used to clarify a number of situations, and thus stress the importance of the learner encountering relevant examples. Most of the more recent work on cognitive learning has occurred within the area of artificial intelligence (AI), where the goal has been to develop computer programs that can learn. A concern for simulating learning was present in some of the early work on AI – for example, the EPAM model (Feigenbaum, 1959; Simon & Fiegenbaum, 1964). Since about 1975, this interest has intensified, especially with regard to J. R. Anderson’s (1982, 1983) ACT theory. The programs of interest here are those intended to serve as models or theories of human cognitive learning. In general, attempts to define cognitive learning have emphasized a system of processes, relationships among concepts and/or facts, and the restructuring of schemata. The similarities and differences between behavioral and cognitive conceptions of learning can be illustrated by considering several prominent theories of cognitive learning and the mechanisms considered to be responsible in the psychological changes we refer to as learning.

Rumelhart and Norman The first comprehensive theory of cognitive learning was Rumelhart and Norman’s (1978) attempt to account for the process of learning within a schema-based theory of long-term memory, although they emphasized that “learning is not a unitary process: No single mental activity corresponding to learning exists. . . . and no single theoretical description will account for the multitude of ways by which learning might occur”

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(p. 50).2 Rumelhart and Norman suggest three qualitatively different kinds of learning: (a) accretion, or the encoding of new information in terms of existing schemata; (b) restructuring or schema creation, or the process whereby new schemata are created; and (c) tuning or schema evolution, or the slow modification and refinement of a schema as a result of using it in different situations. Most models of memory involve learning by accretion. New information is interpreted in terms of preexisting schemata, and this process occurs most readily when the material being learned is consistent with schemata already available in memory. The new information is added to knowledge already in memory without any changes being made in the way that knowledge is organized. Accretion involves the acquisition of factual information that some people might refer to as memorization. Resnick (1984) refers to this type of learning as schema instantiation and suggests that it is similar to the Piagetian concept of assimilation. Norman (1978) suggests that “ . . . accretion learning requires study, probably with the use of mnemonic aids (and deep levels of processing). It can be tested by conventional recall and recognition techniques” (p. 40). Interference from related topics tends to be high and transfer to related topics tends to be low. Tuning and restructuring are similar to the Piagetian concept of accommodation (Resnick, 1984). Restructuring may occur without any formal addition of new knowledge – that is, the learner may already have all of the necessary information and the only thing that occurs is a reorganization of existing knowledge. Rumelhart and Norman (1978) suggest two basic ways for restructuring to occur: (a) schema induction, which is a form of learning by contiguity in which certain spatial or temporal co-occurrence of schemata results in the formation of a new schema, and (b) patterned generation, in which a new schema is patterned (copied with modifications) on an old schema. Restructuring occurs as a result of encountering examples, analogies, and metaphors, as well as through tutorial interactions such as Socratic dialogue. Tests of restructuring should include conceptual tests and questions that require inference or problem solving (Norman, 1978). Generally, learning that involves the creation of new schemata occurs as the result of analogical processes – that is, we learn new schemata by relating new information to old schemata in analogical ways (Rumelhart & Norman, 1981). Tuning involves the slow and gradual refinement of existing schemata, a process that lasts a lifetime. Norman (1978) suggests that tuning is “ . . . best accomplished by practice at the task or in using the concepts of the topic matter. Tests of tuning should be measures of speed and smoothness, [including] performance under stress or pressure” (p. 42). With tuning there is low interference from related topics, and transfer to related topics is high with regard to general knowledge and very low with regard to specific (tuned) knowledge.

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John Anderson’s ACT Most cognitive psychologists distinguish between declarative and procedural knowledge. Declarative knowledge is our knowledge about things and is usually thought to be represented in memory as an interrelated network of facts (e.g., 2 + 3 = 5, 5 × 4 = 20) that exist as propositions. Procedural knowledge is our knowledge of how to perform various skills (e.g., produce the correct sum when given an addition problem, solve a word problem). John Anderson (1982, 1983) has developed a computer program (i.e., a theory) called ACT (or ACT∗, as the current version is called) that is capable of learning procedural knowledge such as solving geometry proofs and other types of problems. In contrast to Rumelhart and Norman’s (1978) belief that there are many forms of learning, ACT is based on the presumption that a single set of learning processes is “ . . . involved in the full range of skill acquisition, from language acquisition to problem solving to schema abstraction” (Anderson, 1983, p. 255). Since ACT is the most explicit and comprehensive of current cognitive theories of learning, it will be described in some detail. The distinction between declarative and procedural knowledge is a fundamental part of the ACT theory. Declarative knowledge is represented in ACT as a network of propositions (i.e., statements of relationships among concepts, events, etc.), and procedural knowledge is represented as a system of productions (i.e., statements of the circumstances under which a certain action should be carried out and the details of what should be done when that action is appropriate). The theory is concerned with the acquisition of both declarative and procedural knowledge, as well as the transition between the two, although the emphasis is more on the latter than the former. According to ACT, knowledge in a new domain always begins as declarative knowledge; procedural knowledge is learned by making inferences from facts available in the declarative knowledge system. Anderson (1982, 1983) suggests that three stages are involved in learning procedural knowledge: the declarative stage, the knowledge compilation stage, and the procedural stage. These stages are similar to the three phases of skill learning suggested by Fitts (1964). The ACT theory is basically organized for problem solving in the belief that problem solving is the basic mode of cognition (Anderson, 1982; Newell, 1980). Consequently, the ACT system is organized in a hierarchical, goal-structured manner, with both performance and the various learning mechanisms operating under the control of some goal or subgoal. When new information is encountered, it is coded probabilistically into a network of existing propositions as declarative knowledge. The activation of various propositions in this network is determined by the strength of nodes – that is, points in the knowledge structure representing specific

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concepts, relationships among concepts (propositions), or images – which varies directly with practice and inversely with the passage of time. This declarative knowledge has little, if any, direct control on behavior. Rather, the impact of declarative knowledge on behavior, according to Anderson (1982), is filtered through an interpretive system that is well oiled in achieving the goals of the system. . . . New information should enter in declarative form because one can encode information declaratively without committing control to it and because one can be circumspect about the behavioral implications of declarative knowledge. (pp. 380–381)

During the declarative stage, general problem-solving procedures are used to interpret new information in a way that directs the learner’s behavior toward dealing with the task at hand. At some point this declarative knowledge is compiled into higher-order procedures (productions) that apply the knowledge and increase efficiency in dealing with the learning task (e.g., problem). Finally, ACT uses an adaptive production system that engages in the type of learning referred to in the preceding section (on Rumelhart and Norman) as tuning, a process that refines the procedure. Three learning mechanisms are used as the basis of this tuning: (a) generalization, a process by which production rules become broader in their range of applicability; (b) discrimination, a process by which production rules become narrower in their range of applicability; and (c) strengthening, a process by which better rules are strengthened and poorer rules are weakened. An example of how the ACT theory would explain the way a child learns to do addition problems would begin with statements (perhaps spoken by the teacher or read in a textbook) of certain facts such as: “In addition problems, one first adds the numbers in the rightmost column;” “Next, you add the numbers in the second column”3 and so forth. With some practice (and perhaps examples by the teacher), these statements of fact are transformed into the ability to actually do what these statements say need to be done. (While many educators are aware that knowing about something does not necessarily mean that the student has acquired the procedures for translating that knowledge into practice, this distinction is made explicitly by cognitive psychology.) The ability to carry out the actions specified might be represented as productions (P) such as the following:4 P1. P2.

IF the goal is to do an addition problem, THEN add the numbers in the rightmost column. IF the goal is to do an addition problem and the rightmost column has already been added, THEN add the numbers in the second column.

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With additional experience, these (along with other productions) might be compiled into the following, higher-order productions taken from J. Anderson (1982, p. 371): P3. P4.

IF the goal is to do an addition problem, THEN the subgoal is to iterate through the columns of the problem. IF the goal is to iterate through the columns of an addition problem and the rightmost column has not been processed, THEN the subgoal is to iterate through the rows of the rightmost column and set the running total to zero.

These and other productions would then be compiled into yet other, more general productions that would enable the student to solve addition problems smoothly and efficiently. As other tasks are encountered, however, generalization may occur with the result that various production rules will become broader in their range of applicability. Generalization in ACT is similar to the traditional concept of generalization, except that in ACT generalization involves the learner (i.e., the program) searching for appropriate similarities among production rules and then creating a new production rule that combines those features that the two rules have in common. The search for rules is, of course, the feature that distinguishes this cognitive version of generalization from more traditional behavioristic ones. For example, in learning to solve addition problems, the student acquired production P3 above. Later, in learning to solve subtraction problems, the following production may be acquired: P5.

IF

the goal is to do a subtraction problem,

THEN the subgoal is to iterate through the columns of the problem.

The similarity between productions P3 and P5 are noticed, and the following generalization is formed: P6.

IF the goal is to do an LV problem, THEN the subgoal is to iterate through the columns of the problem.

LV is a “local variable” defined by the specific instances in which the production might apply. The new, more general production would not replace the two original ones; they would continue to apply in special circumstances. Transfer is facilitated, according to ACT, if the same components are taught in two different procedures so that the commonality is more likely to be noticed and generalization can occur. Thus, the transfer involved in learning to drive a new car will be greater if the individual has previously driven several different cars rather than only a single car, a position that is consistent with the results of a number of transfer studies (see e.g., Shuell & Lee, 1976, pp. 71–72) and more recent work on cognitive learning (e.g., Sternberg, 1984a). There are, of course, many situations in which the range of applicability of a production needs to be limited – that is, discrimination needs to

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occur if the learner is to produce appropriate behavior. For example, in learning to solve addition problems, the student may have acquired the following production: P7.

IF

the goal is to iterate through the rows of a column and the top row has not been processed, THEN the subgoal is to add the digit of the top row into the running total.

Through the use of this production in a variety of different types of addition problems, generalization may have occurred. In fact, when first encountering subtraction problems, the learner may attempt to employ this production, which has worked in the past. Obviously, if the student is to be successful in solving subtraction problems, he or she must learn to discriminate between production P7 and a similar production in which the action specified involves subtracting (rather than adding) the digit from the running total. Discrimination in ACT depends on the learner experiencing both correct and incorrect application of the production, a requirement that is consistent with the well documented need for the learner to encounter both positive and negative exemplars in concept learning (see, e.g., Tennyson & Park, 1980). Two types of discrimination are involved in ACT: action discrimination involves learning a new action and can occur only when feedback is obtained about what action is correct in the situation being considered. Condition discrimination involves restricting the conditions under which the old action was carried out, although the new, more restrictive productions coexist with the original production rather than replacing it. Generalization and discrimination are viewed as the inductive components of the learning system embodied in ACT. Due to the nature of induction, generalization and discrimination will err and produce incorrect and/ or inappropriate productions – for example, overgeneralizations and useless discriminations. A mechanism that strengthens successful productions will help to ensure that appropriate behavior will occur. While the strengthening mechanism in ACT is fairly complex, it functions basically by modifying the probability attached to a given production, depending on the positive and negative feedback it receives.

Implications for Future Research A new wave of research on learning is beginning within the various cognitive sciences. Although much of this research holds promise for new and more powerful theories of human learning, considerable work remains to be done before a truly viable and comprehensive theory (or theories) of learning (i.e., capable of accounting for both simpler and more complex forms of learning) is available. As might be expected, given its relative newness,

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much of this research has focused on theoretical discussions of its nature and empirical demonstrations that certain types of processes and factors have been overlooked in traditional research on human learning – e.g., learners construct appropriate responses rather than merely react to environmental stimuli (see Wittrock, 1974) and encoding plays a crucial role in learning; see Siegler, 1983 and Sternberg, 1984a. A number of problems should be addressed by future research, and some of the challenges will be discussed in terms of: (a) variables that affect the learning process; (b) the relationship between knowledge and learning, including the role of prior knowledge and domain-specific versus domain-independent (general) aspects of learning; and (c) phases of learning.

Variables Affecting Learning Little is known about the specific variables (e.g., environmental events) that influence the learning process. Future research should develop more precise, operational definitions of variables that can influence cognitive learning (e.g., those that a teacher or counselor might use in trying to facilitate the learning of a student or client) so that they can be systematically investigated. Current theories of cognitive learning have identified various functions that must be performed if learning is to occur. For example, Sternberg (1984a) suggests that many, if not all, cognitive theories of learning incorporate three functions that must be performed if learning is to occur: (a) the collection of new information (encoding), (b) the combination of disparate pieces of new information, and (c) the relating of new to old information. It seems to me that several other functions, such as evaluation, are also involved. Little research has been done on variables that affect these factors within a complex learning situation (e.g., the specific variables that determine what does and what does not get encoded), although Sternberg (1984a) identifies and discusses five variables that appear to affect the learning of verbal concepts: (a) the number of occurrences of the new item of knowledge, (b) the variability of contexts in which multiple occurrences of the new item of knowledge occur, (c) location of cues relative to the to-be-learned item of knowledge, (d) importance of the to-be-learned item of knowledge, and (e) density of items of knowledge to be learned. It is interesting to note how similar most of these variables are to those variables responsible for more traditional types of learning (e.g., practice, contiguity, and reinforcement). (For a discussion of how these variables provide the basic conditions for learning to occur, see Shuell and Lee, 1976.) In fact, it should be evident from the preceding sections that many of the current theories use traditional concepts from the psychology of learning to explain cognitive learning – for example, generalization. It has been suggested that new schemata are learned by establishing analogies between old and new schemata (Rumelhart & Norman, 1981). If such is the case, it seems likely that

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the process is one of generalization and transfer. But generalizations based on analogies may be rather different from traditional conceptions of generalization, since analogies involve structured relationships whereas traditional conceptions typically involve unidimensional stimuli and responses. Tversky (1977) has proposed a contrast model of similarity in which perceived similarity is determined by the individual matching features (both common and distinctive) between two objects or families of objects (e.g., an analogy). The extent to which psychological processes related to generalization are the same in the two situations (traditional vs. cognitive learning) is an empirical question that remains to be investigated. For instance, if there are generalization gradients for analogies similar to those that exist for simpler forms of learning (and this assumption is not unreasonable), then what are the relevant dimensions in analogies along which generalization can occur and the structures to which they apply? A variety of other variables such as elaboration and advance organizers that have been investigated within the traditional framework of research on learning clearly involve mental activities and make assumptions similar to those discussed above for cognitive learning. In addition, Norman (1978) has suggested various ways in which interference and transfer (two very traditional factors in research on human learning) might be involved in various types of cognitive learning. Although a detailed discussion of the possible integration of these variables and phenomena with the types of concerns associated with cognitive learning is beyond the scope of the present article, a simple example may prove useful. Contiguity (the proximity of two events) is well established as one of the fundamental variables affecting traditional types of learning (e.g., Shuell & Lee, 1976). In these simpler forms of learning, contiguity is nearly always defined in terms of time intervals (e.g., the time between the conditioned stimulus and unconditioned stimulus in classical conditioning, the time between response and reinforcement in operant conditioning), but other forms of contiguity (e.g., spatial, semantic) appear just as reasonable. Thus, in learning more complex material, contiguity between disparate pieces of information may determine the likelihood that the individual will induce a schema. In Sternberg’s (1984a) list of five variables affecting the acquisition of concepts from text, one (location of cues) is a clear example of contiguity, and another (density of items) could involve contiguity, (e.g., the point at which cognitive overload occurs). In some cases, the learner may actively try to establish contiguity through the use of various learning strategies. Although contiguity may seem like an esoteric variable to some educators, it is a variable over which teachers and instructional designers (e.g., textbook authors) have considerable control; if more were known about the way contiguity affects meaningful learning, perhaps they could use it more effectively. In any case, a combination of concerns from traditional learning psychology and modern-day cognitive psychology should serve as a focal point of future research on cognitive learning.

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Knowledge and Learning Traditional conceptions and theories of learning are, for the most part, content free – that is, learning occurs in basically the same way, or follows the same principles, in all situations. Gradually, however, it has become increasingly clear that the amount of knowledge that one possesses has a substantial impact on the learning process (e.g., Chi, Glaser, & Rees, 1982). For example, adults normally are able to remember more (e.g., have considerably longer digit spans) than children, yet Chi (1978) found that 10-year-old chess experts remembered more about the placement of chess pieces on a board than adults who were only novice chess players (the traditional finding of adult superiority was obtained when the same subjects were asked to remember digits). In addition, individuals who know a great deal about something (experts) encode new material related to that knowledge in a different way than individuals who know little about the topic (novices) (see, e.g., Chi et al., 1982; Siegler, 1983). While these expert/novice differences demonstrate that cognitive learning involves qualitative and not merely quantitative changes, we need to know more than the nature of the differences; we need to know how the transition between novice and expert takes place, especially if education is to facilitate the process. There is also evidence that learning is much more domain specific than earlier learning theorists believed (for a good discussion of this point, see Glaser, 1984). For example, Chase and Ericsson (1981) report on an average college student (SF) who over a period of 25 months of practice steadily increased his average digit span from seven to over 80 digits. This feat was accomplished by encoding the digits into running times (SF was an avid and proficient long-distance runner) and developing an elaborate, hierarchical retrieval structure. But the skills SF learned in acquiring the largest memory span ever reported in the literature are domain specific; SF’s memory span is normal (about seven symbols) when recalling other types of stimuli such as random consonants. Apparently SF lacked the knowledge base relevant to consonants, for example, that would be necessary to demonstrate his proficiency with other types of stimuli. Yet it seems unlikely that all learning is domain specific. If it were, then it would be difficult to explain how individuals deal with novel situations or learn material that is totally new to them. Obviously, learning involves both domain-specific and domain-independent processes. One challenge for future research to address is how these two aspects of learning interrelate with one another and with the skill and/or knowledge that is being acquired. Another issue that is likely to be addressed by future research is the relationship between various types of knowledge. As already noted, cognitive psychologists frequently distinguish between declarative and procedural knowledge, and other types of knowledge have also been suggested (e.g., Gagné & White, 1978). But is one type more basic than other types? For

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example, Rumelhart and Norman (1981) propose that “ . . . all knowledge is properly considered as knowledge how but . . . the system can sometimes interrogate this knowledge how to produce knowledge that ” (p. 343). This emphasis on “learning by doing” (Anzai & Simon, 1979) – “ . . . expertise comes about through the use of knowledge and not by analysis of knowledge” (Neves & Anderson, 1981, p. 83) – is reminiscent of themes heard in education, although the emphasis is somewhat different. In any case, these issues have important implications for educational practices, since different types of knowledge have different instructional requirements.

Phases of Learning The notion that learning progresses in what might be thought of as phases or stages is not a new idea. Some 30 years ago, Fleishman and Hempel (1954, 1955) provided evidence that psychomotor learning proceeds in this manner with performance in the various stages drawing upon different abilities. Other researchers provided evidence for stages in both pairedassociate (e.g., McGuire, 1961; Underwood, Runquist, & Schulz, 1959) and free-recall learning (Labouvie, Frohring, Baltes, & Goulet, 1973). In addition Brainerd (1985) and Brainerd, Howe, and Desrochers (1982) developed a sophisticated mathematical model of learning. Nearly all of this research, however, deals with relatively simple forms of learning. Very little empirical evidence is available on the phases that learners might go through in learning more complex, meaningful material. In recent years, several cognitive theorists have suggested that stages are involved in cognitive learning. For example, Bransford and Franks (1976) have argued that learning that involves understanding moves from concrete to abstract representations, and J. R. Anderson’s (1982) ACT theory postulates that learning proceeds from declarative knowledge to procedural knowledge. Other types of stages or phases of learning are possible, and it is reasonable to expect that different variables may be involved during the various phases. For example, in school we typically expect students to acquire complex bodies of knowledge with some degree of understanding. When the individual begins this undertaking, he or she normally begins by acquiring a number of relatively disparate pieces of information (e.g., the “basic facts” stressed in most classrooms). During this early phase of learning, pictorial and verbal mnemonics (or various other learning strategies) may facilitate learning by providing the conceptual glue necessary to hold these disparate pieces in memory, and variables such as repetition may play a relatively important role. As learning progresses, however, and the individual begins to fit some of the pieces together, mnemonics may play a less important (or different) role and other types of factors (organizational strategies?)

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may play an increasingly important role. Still later, as performance becomes well established, mnemonics may have little or no effect on learning since the underlying knowledge structure now holds the information together in some meaningful, integrated whole – to use an extremely elementary example, C - A - T has become CAT. Thus, given variables may facilitate acquisition during one phase of learning and have little, if any, effect during other phases. Although retention is not really a phase of learning in the sense being discussed here, perhaps the relationship is similar enough to provide a useful analogy. Elaboration clearly has a facilitative effect on learning, but it has been found not to affect retention independent of learning (Olton, 1969); likewise, immediate feedback normally facilitates learning, but delayed feedback appears to facilitate retention (Surber & Anderson, 1975).5 The more important forms of human learning that interest most cognitive scientists and educators involve what is fundamentally a long-term process involving weeks, months, and even years. The phases that we go through as we engage in long-term learning are unknown at present, but they undoubtedly exist and deserve our attention.

Implications for Education Changes in the way we think about learning and what we know about the way learning occurs have important implications for those situations in which we want to facilitate changes in what people know and/or do. In education, for example, corresponding changes are occurring in the way we think about teaching. Since learning is an active process, the teacher’s task necessarily involves more than the mere dissemination of information. Rather, if students are to learn desired outcomes in a reasonably effective manner, then the teacher’s fundamental task is to get students to engage in learning activities that are likely to result in their achieving these outcomes, taking into account factors such as prior knowledge, the context in which the material is presented, and the realization that students’ interpretation and understanding of new information depend on the availability of appropriate schemata. Without taking away from the important role played by the teacher, it is helpful to remember that what the student does is actually more important in determining what is learned than what the teacher does. Although many educators have long advocated that teachers actively engage their students in the learning process, there has not been a great deal of scientific knowledge to support these contentions. “Open education” and “discovery learning” are just two examples of educational practices that failed to produce encouraging results due, at least in part, to the lack of a viable theory of cognitive learning. Many other educators, of course, have advocated “back to the basics” and other approaches stressing more behavioral

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forms of learning variables. With the advent of cognitive theories of learning and knowledge of how specific learning processes in the student are engaged by specific instructional variables, we may have the beginning of a viable body of scientific knowledge on how best to capitalize on the active nature of learning. Some of the cognitive research on learning discussed earlier may form the basis for this endeavor, although the nature of an instructional situation does have some unique characteristics. Theories of learning from instruction are somewhat different from regular theories of learning (Shuell, 1980), and important research on cognitive theories of learning from instruction (e.g., Leinhardt & Putnam, 1986; Snow & Lohman, 1984) is beginning to appear in the literature. With regard to prior knowledge, we know that students often begin learning with substantial misconceptions about the material they are studying (e.g., Champagne, Klopfer, & Gunstone, 1982) and that remnants of these misconceptions even persist in students who receive high grades in the course (Champagne, Klopfer, & Anderson, 1980; Gunstoke & White, 1981). Students also make systematic errors (such as always subtracting the smallest digit from the largest digit, regardless of which one is on top) sometimes referred to as “buggy algorithms” (Brown & VanLehn, 1982; Resnick, 1982). There errors are not careless mistakes or even the result of faulty reasoning; rather, they represent what students reasonably consider to be appropriate ways of dealing with the problem on which they are working, given their current knowledge structure (i.e., prior knowledge). Analysis of these errors can provide the teacher (or textbook writer, etc.) with useful insights into the type of instruction that has the best chance of being successful; at the very least, it highlights the crucial role played by prior knowledge in any real-life learning situation. What these concerns mean is that the teacher’s role is different from the one frequently envisioned in traditional conceptions of teaching. What have changed are the focus and the realization that good teachers are not merely people who can articulate a large number of relevant facts and ideas (although a sound understanding of the subject matter they are teaching is certainly essential); effective teachers must know how to get students actively engaged in learning activities that are appropriate for the desired outcome(s). This task involves the appropriate selection of content, an awareness of the cognitive processes that must be used by the learner in order to learn the content, and understanding of how prior knowledge and existing knowledge structures determine what and if the student learns from the material presented (and hopefully being studied). Consequently, we need to know more about the way in which specific content and instructional procedures engage and/or elicit the psychological processes and knowledge structures appropriate for the desired learning outcome(s) to be achieved – fortunately, some advances are beginning to be made in this direction (e.g., Winne & Marx, 1983).

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Summary The cognitive sciences have begun to give serious consideration to research on human learning, and several different theories of cognitive learning have been suggested. Although the orientation of those interested in cognitive learning differs considerably from the more traditional, behavioral orientation toward learning, there are also similarities and common concerns between the two approaches. Learning is now viewed as being active, constructive, cumulative, and goal oriented. Yet, concerns for cognitive learning do not necessarily invalidate traditional concerns of learning psychology, and for investigators who look at learning in simpler terms, many of the traditional concerns of learning research remain viable. Individual learners go about learning in different ways (Bruner, 1985), and there are different types of learning outcomes (e.g., Gagné, 1965, 1984; Rumelhart & Norman, 1978). Thus, the more traditional principles of learning may be appropriate for certain types of learning while new principles need to be forged for other types of learning, especially those more complex forms of learning in which the desired outcome involves the understanding of relationships among many separate pieces of information. The possibility of identifying and integrating these multiple aspects of learning presents an important challenge to future research on learning and its application to a variety of applied problems, including classroom learning and instruction.

Notes 1. Most discussions of knowledge structures by cognitive psychologists go beyond the associative networks and habit-family hierarchies sometimes discussed by associationists. 2. The suggestion that there is more than one type of learning is not unique to current concerns for cognitive learning. For example, Kimble (1961) discussed differences between classical and operant conditioning, and Gagné (1965) postulated eight different types of learning ranging from classical conditioning to problem solving. 3. The process of carrying will be ignored for the sake of simplicity. 4. While individual productions may appear to some readers as being very similar to stimulus-response associations in which a particular response is under the control of a discriminating stimulus, production systems are different from S-R associations in the way individual productions are interrelated in an organized system under the control of various goals and subgoals. Note also how control of the system can be shifted from a goal to a subgoal, as in production P3 below. 5. For a more complete discussion of this point, see Shuell and Lee (1976).

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Sternberg, R. J. (1979). The nature of mental abilities. American Psychologist, 34, 214 –230. Sternberg, R. J. (1984a). A theory of knowledge acquisition in the development of verbal concepts. Developmental Review, 4, 113–138. Sternberg, R. J. (1984b). Mechanisms of cognitive development: A componential approach. In R. J. Sternberg (Ed.), Mechanisms of cognitive development (pp. 163–186). New York: W. H. Freeman. Sternberg, R. J. (1985). All’s well that ends well, but it’s a sad tale that begins at the end: A reply to Glaser. American Psychologist, 40, 571–573. Stevenson, H. (1983). How children learn – The quest for a theory. In P. H. Mussen (Ed.), Handbook of child psychology: Vol. I. History, theory, and methods (4th ed., pp. 213–236). New York: Wiley. Surber, J. R., & Anderson, R. C. (1975). Delay-retention effect in natural classroom settings. Journal of Educational Psychology, 67, 170–173. Tennyson, R. D., & Park, O. C. (1980). The teaching of concepts: A review of instructional design research literature. Review of Educational Research, 50, 55–70. Thorndike, E. L. (1913). Educational psychology: Vol. 2. The psychology of learning. New York: Teachers College. Tolman, E. C. (1932). Purposive behavior in animals and men. New York: Appleton-CenturyCrofts. Tulving, E. (1968). Theoretical issues in free recall. In T. R. Dixon & D. L. Horton (Eds.), Verbal behavior and general behavior theory (pp. 2–36). Englewood Cliffs, NJ: Prentice-Hall. Tulving, E. (1985). How many memory systems are there? American Psychologist, 40, 385–398. Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327–352. Underwood, B. J. (1963). Stimulus selection in verbal learning. In C. N. Cofer & B. S. Musgrave (Eds.), Verbal behavior and learning: Problems and processes (pp. 33– 48). New York: McGraw-Hill. Underwood, B. J. (1964). Laboratory studies of verbal learning. In E. R. Hilgard (Ed.), Theories of learning and instruction (The sixty-third yearbook of the National Society for the Study of Education, Part I, pp. 133–152). Chicago: University of Chicago Press. Underwood, B. J., Runquist, W. N., & Schulz, R. W. (1959). Response learning in pairedassociate lists as a function of intralist similarity. Journal of Experimental Psychology, 58, 70–78. Underwood, B. J., & Schulz, R. W. (1960). Meaningfulness and verbal learning. Philadelphia: Lippincott. Voss, J. F. (1978). Cognition and instruction: Toward a cognitive theory of learning. In A. M. Lesgold, J. W. Pellegrino, S. D. Fokkema, & R. Glaser (Eds.), Cognitive psychology and instruction (pp. 13–26). New York: Plenum Press. Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 315–327). New York: Macmillan. White, S. H. (1970). The learning theory tradition and child psychology. In P. H. Mussen (Ed.), Carmichael’s manual of child psychology (3rd ed., Vol. 1, pp. 657–701). New York: John Wiley. Winne, P. H., & Marx, R. W. (1983). Matching students’ cognitive processes and teacher skills to enhance learning from teaching. (Instructional Psychology Research Group, Final Report). Burnaby, B. C., Canada: Simon Fraser University. Wittrock, M. C. (1974). Learning as a generative process. Educational Psychologist, 11, 87–95. Wittrock, M. C. (1978). The cognitive movement in instruction. Educational Psychologist, 13, 15–29. Wittrock, M. C. (1986). Student’s thought processes. In M. C. Wittrock (Ed.), Handbook of research on teaching (3rd ed., pp. 297–314). New York: Macmillan.

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39 Meaning in Complex Learning Ronald E. Johnson

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hat role does meaningfulness play in the learning of complex verbal materials? If meaningfulness does facilitate learning, how is the facilitation accomplished? What are the means by which learning may be made meaningful? Answers to these questions depend upon a satisfactory definition of meaningfulness and valid measures of the construct. Unfortunately, a review of the literature shows that meaningfulness has been neglected both theoretically and empirically. Perhaps the most important reason for neglect has been the intuitive certainty that meaningfulness does influence learning. In addition, investigators have had so much faith in intuition that meaningfulness levels typically have been decreed simply by personal judgment (e.g., English, Welborn, & Killian, 1934). When empirical assessments have been attempted, researchers have been stymied by excessive reliance upon the classical methods of assessing meaningfulness. Neglect also stems from the lack of agreement in defining meaningfulness (Alston, 1964; Creelman, 1966; Fries, 1954). Given the differences in theoretical viewpoints, it is not surprising to find Bousfield (1961) viewing meaning as “an unnecessary concept for verbal learning” (p. 81), while Osgood (1961) views meaning as “the single most important variable in human learning, verbal or otherwise” (p. 91). A thesis of this critical review is that meaningfulness is potentially the most powerful variable for explaining the learning of complex verbal discourse. In the review, the possibility is examined that meaningfulness may be pivotal in explaining the effects of other variables. Next, it is argued that the classical methods of measuring meaningfulness are generally

Source: Review of Educational Research, 45(3) (1975): 425–459.

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inappropriate for calibrating the meaningfulness of verbal discourse. Attention is drawn to variables that influence meaningfulness, and suggestions are made regarding requisite conditions for adequately measuring meaningfulness. Finally, the need for additional research is emphasized by sampling problem areas in which productive research could be conducted.

Meaning – A Theory of Associational Reference Through experience, a person acquires knowledges about an object or idea. Such knowledges include associations about attributes, properties, functions, interrelationships, contextual correlates, and affect. The synthesis of these knowledges constitutes meaning. Although meaning may consist of a vast number of associations, the constituent knowledges are not just a random concatenation. Certain attributes, such as form, are more likely to be salient and to be entered into associational structure. Similarly, organization is derived from learners’ tendencies to bias input data through reliance on preferred cognitive transformations such as grouping and contrast (Campbell, 1958; Deese, 1965). The associational acquisition may or may not include the name of the object or idea. Furthermore, the referential knowledges are not necessarily in conscious awareness, although the existence of such knowledges may be verified by various semantic analyses and experimental techniques. As compared with previous theories in philosophy or linguistics, the present view of meaning is an extreme version of a referential or ideational theory (Alston, 1964).1 Stated as an ideational theory, meaning is asserted to be nothing more nor less than a person’s referential knowledge about a word, an object, or idea. Rephrased as a theory of reference, the referential object of a word or phrase is asserted to be a conceptual class designated by referential associations. Rather than restricting the term referent to an objective tangible object, the referent of a word is presumed to be a psychological entity. Each word in the subjective lexicon thus represents an entity of particular associations. Such referential associations are not assumed to be derived from a hypothetical construct called meaning; instead, the referential associations are the meaning. By thinking about a concept in relation to other concepts, however, the learner can expand or change existing meanings by acquiring new referential associations. The adoption of an ideational-referential stance does provide heuristic advantages in examining the relationship of meaning and learning. Although objections have been raised to similar theories of meaning (Alston, 1964, pp. 10–31; Church, 1961, pp. 124 –132; Fodor, Bever, & Garrett, 1974, pp. 141–170; Lyons, 1968, pp. 400– 442; Miller, 1965, pp. 16–18), the objections are not disabling. Space does not permit analysis of all criticism, but the reassertion of an ideational-referential theory argues the necessity for reexamining the most common objections (see Table 1).

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Example “George Washington” vs. “first President of the United States.” Abstract words such as rule or duty. Also, combinations of words/concepts that have never been experienced directly.

Utterance “I” has similar meaning for each person, but referent of the phrase depends upon the speaker. “The centaur is a strange creature.” “It was nothing.” “The cat began to bark.” “The apple was calendar.”

Criticism

1) Two phrases may have identical referents and yet have different meanings (Miller, 1965).

2) “Sentences are meaningful, but their meaning cannot be given by their referent, for they may have none.” (Miller, 1965, p. 16).

3) Phrases may have identical meaning and yet have different referents (Alston, 1964; Black, 1968; Pollio, 1974).

4) Sentences can express hypothetical events, assertions of nonexistence, untrue events, and nonsensical events (Black, 1968; Church, 1961, p. 124).

(Continued )

No claim is made that semantic propositions mirror reality or truth, or that referential associations are veridically congruent with reality, but verbal descriptions of hypothetical events do arouse associations or meaning.

Objection unnecessarily assumes a single objective referent which is invariant for all users, and further assumes that meaning is invariant in all lexical contexts.

The referent need not be a concrete tangible object – abstract words also arouse associations. For each sentence, referential associations are aroused by the individual words, the various combinations of words, earlier sentences, and the psychological situation in which the sentence is uttered.

Since each of the two phrases arouses different associations, the phrases do not have the same referents or meaning even though ostensibly referring to the same individual.

Present theory

Table 1: Examination of criticisms of referential or ideational theories of meaning in relation to present view

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Example A particular name such as “Ralph Jones.”

Words such as in, on, with, despite.

In arouses association of out. Similarly, big produces little or small, man results in associate of woman, and buy arouses sell.

Criticism

5) Since proper names refer only to a particular person, object, or situation, this restriction allegedly eliminates the possibility of meaning (Fodor et al., 1974, p. 145; Terwilliger, 1968, pp. 149–150).

6) Function words such as articles, conjunctions, and prepositions have no external or denotative reference (Fodor et al., 1974; Glanzer, 1962; Pollio, 1974), and some words in a sentence do not arouse a distinguishable idea (Alston, 1964, pp. 12, 24).

7) Referential associations to a word may indicate the arousal of a meaning opposite to the usual meaning.

Table 1: (Continued )

Componential analyses of semantic features show that antonymous associations are closely related semantically to the stimulus word (Clark, 1970; Lyons, 1968). The single reversed semantic feature indicates as much focus on the critical defining dimension as occurs in the production of a synonymous associate.

Function words do have denotative meaning (Carroll, 1964a) and do arouse associations (Kanungo, 1968; Palermo & Jenkins, 1964). Each word in discourse is a unique functional component of the total meaning which is engendered; any change in wording, including substitution of synonyms, usually alters meaning (Alston, 1964, pp. 44–49; Lyons, 1968; Quine, 1960).

Referential associations (i.e., meaning) are established to the entity designated by the proper noun. Such associations may include memorial representations in the form of referential imagery (Paivio, 1971, pp. 50–77), either as a particular image, as one of a number of particular images, or as a schematized generic image.

Present theory

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Meaningfulness Assuming meaning to be a synthesis of referential associations, meaningfulness may be defined literally as the extensiveness of the network of referential associations. However, the type, relevance, and organization of associations may be more important than quantity in determining the “fullness” of meaning. In any event, a differentiation between meaning and meaningfulness has important educational implications in that the “fullness” of meaning appears critical in learning. Learning may be said to be meaningful to the extent that the new learning task can be related to the existing cognitive structure of the learner, i.e., to the residual of his earlier learnings. The presence of meaning, however, does not guarantee meaningfulness. A person’s associations to a conceptual entity may include all of the associations that normatively define the concept. Yet, the sparseness of associations, or the quality of the associations, may make it difficult for the learner to establish useful associational linkages. Whether a concept is meaningful thus depends upon the associational background of the learner and also the semantic structure of the concept within the linguistic community.

Indirect Experimental Manipulations of Meaningfulness If meaningfulness is a powerful variable in learning, its influence ought to be evident even in experimental comparisons designed to test the influence of other variables. To illustrate, the active learner learns better than the passive learner, and this superiority may result because the active learner is more successful in relating the new material to existing ideas. Bobrow and Bower (1969) compared the remembering of learners who generated their own linking sentences for a noun pair as opposed to merely reading an equivalent linking sentence. When learners generated their own associative links, recall was superior, and Bobrow and Bower concluded that recall was facilitated when learners comprehended the meaning of the sentence. In their words, “the mere act of searching for something in memory at the time of input of a noun pair is not the beneficial factor. Rather it appears that the memory search has to be relevant to constructing a relational bridge between the two nouns” (p. 457). Similar conclusions were reached by R. C. Anderson and his associates (Anderson, Goldberg, & Hidde, 1971; Anderson & Kulhavy, 1972). In the 1971 experiment, learners who were required to fill in blanks at the end of sentences learned more than those who read whole sentences. As interpreted by Anderson et al., the completing of the sentence forced the learner to comprehend the other words in the sentence. Thus, the advantage was thought to result from “the process of giving meaningful representation to the words” (Anderson et al., 1971, p. 398).

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In another experiment, Watts and Anderson (1971) inserted different types of questions into textual prose. A criterion test required learners to select correct examples of the learned concepts. Some learners received textual questions requiring them to identify the example used in the passage. Other learners received inserted questions requiring them to identify a new example. On the final criterion test, the group that applied their knowledge to new examples showed the best overall performance. As interpreted by Watts and Anderson (1971), the application questions induced the learners to process the text more thoroughly. In the present context, the “more thorough processing” suggests that the questions induced the learners to relate the new learning to old learnings, i.e., to learn meaningfully. Associations that are aroused during learning usually are semantically related to the new content (Clark & Card, 1961; Fillenbaum, 1971; Sachs, 1967), but the insertion of questions into text also can influence meaningfulness by directing the learner to relevant associations or concepts (e.g., Frase, 1970; Rothkopf & Bisbicos, 1967). Questions appearing prior to the relevant content influenced the learning of that content, but relatively little incidental information was remembered. When the inserted questions occurred after the relevant segment of text, learners recalled incidental information as well as relevant information. Questions also can influence the depth of processing (Rickards & Di Vesta, 1974). Interspersed post-questions directing attention to the learning of specific facts resulted in high levels of factual recall, but poor recall of superordinate generalizations. Querying the learner’s knowledge of superordinate generalizations, however, resulted in high recall both of the superordinate statements and also the subordinate facts. When the questions requested the retrieval of subordinate facts to substantiate the superordinate statement, as opposed to simply requesting the recall of the superordinate idea, the patternings of later recall suggested a greater cognitive integration of the superordinate and subordinate facts. Consistent with the results of Rickards and Di Vesta (1974), the effects of questioning procedures may be viewed as a direct derivative of the extent to which the questions induce the retrieval and processing of a particular set of referential associations. Orientation also may be provided by organizational subheadings. The title of a passage ordinarily orients the learner to the central theme, and the learner’s apprehension of the theme presumably would increase the meaningfulness of relevant segments. In a test of this hypothesis, learners who were permitted to see a thematic title showed significantly greater recall of the passage (Dooling & Lachman, 1971). A second experiment indicated that superiorities in recognitive performance were limited to content words that were semantically related to the theme. Evidence that the locus of the effect is in the learning phase is shown by the finding that receiving the title after the passage has been learned does not influence recall (Bransford & Johnson, 1972; Dooling & Mullet, 1973).

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Meaningfulness also may be increased by inducing appropriate learner strategies. Bower and Clark (1969) presented 12 successive serial lists consisting of 10 concrete nouns. One group of learners was told to construct a meaningful story woven around the words to be remembered. A yoked control group received the usual serial learning instructions. Immediate recall performances were virtually perfect, but on a delayed recall test, the average median recall of the narrative group was 93%, whereas the control group recalled only 13%. The difference in performance presumably resulted from the learners’ differential success in relating the learning material to some central theme or organizational framework. In short, the narrative strategy allowed the learners to learn meaningfully. Consistent with this interpretation, Thieman’s (1973) extension of Bower and Clark’s (1969) experiment led to the conclusion that the differences in remembering corresponded directly with the degree of meaningful processing induced by the learning task. Finally, the ubiquitous influence of meaningfulness also appears critical in the distinction between short-term and long-term memory. Since 1965 the verbal learning literature has been dominated by multistore theories of memory in which processing flows from a limited capacity short-term store to a larger capacity long-term store (Atkinson & Shiffrin, 1968; Waugh & Norman, 1965). Encoding in the short-term store is assumed to be mainly acoustical, and forgetting is assumed to be rapid unless the learner engages in repetitive rehearsal. Long-term memories, however, are relatively permanent, and the encodings are presumed to be semantic. Entry into the longterm store is assumed to be directly related to the amount of time spent in short-term storage. At both the empirical and theoretical levels, the distinctions between short-term memory and long-term memory are fading rapidly (Craik, 1973; Craik & Lockhart, 1972; Wickelgren, 1973). As a theoretical replacement for multistore models, Craik (1973) suggests that differences in capacity, encoding, and rates of forgetting depend upon the learner’s “depth of processing.” A rehearsal process that simply holds or maintains the trace in short-term memory, for example, does not lead to long-term retention, whereas rehearsal that involves associative encoding does (Craik & Watkins, 1973; Gardiner, 1974; Jacoby, 1973; Woodward, Bjork & Jongeward, 1973). Thus, linguistic units that are encoded meaningfully are more likely to be remembered. The behavioral evidence that purportedly differentiates two types of memories actually may reflect differences in the extent to which linguistic units are encoded meaningfully. To summarize, some variables appear to exert their influence by increasing or decreasing meaningfulness. Note, however, that the experimenters did not intentionally manipulate meaningfulness. Let us now examine direct attempts to measure meaning, and inquire into the validity of such techniques for complex learning materials.

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Classical Methods of Measuring Meaning Associational Frequency The meaningfulness of verbal units in isolation has been assessed by the frequency of individuals reporting an association (Archer, 1960; Glaze, 1928; Noble, 1961), the mean production of associates (Noble, 1952), and categorical ratings of associational frequency (Noble, 1961). Such techniques have successfully measured variations in meaningfulness among nonsense syllables. Among words, however, differentiations in levels of meaningfulness are virtually nonexistent, and such measurements are not obviously applicable when the words occur in a prose context.

Word Frequency and Readability Indices A classical measure of meaningfulness is the frequency of occurrence of words (Thorndike & Lorge, 1944). Over the entire frequency range, frequently occurring words are more likely to be meaningful. The degree of relationship, however, is not strong. If one considers recall to be a validating measure of meaningfulness, for example, the relationship between word frequency and recall is slight or nonexistent (Hall, 1971; Saltz, 1971; Underwood & Schulz, 1960). Word frequency also has been an important component in readability formulas, along with variables such as word length, number of syllables, sentence length, number of personal words, and measures of grammatical complexity (e.g., Flesch, 1948). However, in eight validation studies comparing readability indices with independent measures of comprehension or retention, only three studies reported validity coefficients higher than .50, whereas four studies had coefficients below .50 (Klare, 1963, pp. 148–156). Aside from disappointing validity, readability indices can be computed only for large sections of prose, and the method cannot accurately gauge the meaningfulness of individual linguistic subunits.

Word Associations The classical technique of free association also has been used to uncover the structure of meaning. In an examination of associative overlap, Deese (1959) demonstrated that the frequency with which words in a list elicited each other as associates was highly correlated (r = .88) with free recall. Such associative networks, derived from the cognitive operations of contrast and grouping, were presumed to be the essence of meaning (Deese, 1965). Deese’s theorizing was based upon free-association responses given to single-word stimuli, but he concludes that the meaning of sentences also is derived from the

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associational structures of the individual words. The major difference, he assumes, is that associations to words in a sentence are influenced by syntactic and referential constraints. As evidence, Deese (1965, pp. 168–170) cites Clark’s (1966) study in which participants produced sentence-associations having the same grammatical format as the stimulus sentence. To the sentence The lazy student failed the exam, sentence associations included The smart girl passed the test and The industrious pupil passed the course. Individuality in producing sentence-associations was quite evident, but the sentence-associations did bear semantic similarities to the original sentences. Word replacements were similar in kind and in frequency to the distribution of associations given to the same stimulus words in the classical free association task (Deese, 1965, p. 169). Are the meanings of sentences derived from the dominant associations aroused by the individual words? To the stimulus words of working, good, from, comes, and health, the corresponding primary associates are hard, bad, to, goes, and sickness (Palermo & Jenkins, 1964). Yet, if the words are arranged in the sentence Good health comes from working, an individual does not give as a sentence association the words Bad sickness goes to hard. Clearly, when words are placed into sentences, associations are aroused that are not predictable from the individual words (Barclay, Bransford, Franks, McCarrell, & Nitsch, 1974). What, then, were the forces that resulted in sentential associations that were both semantically related and in the form of a sensible sentence? Perhaps the simplest explanation is that subjects were required to provide associational sentences in the same grammatical form as the original sentence. If grammatical congruence had not been required, the number of primary associates probably would have been substantially smaller. Equally important, Clark’s subjects apparently were producing associational sentences in response to the overall meaning (Gestalten) of the stimulus sentence. Understanding and responding to a sentence requires not only knowledge of the individual words, but also an understanding of the relationships among the words (Anisfeld, 1970; Fillenbaum, 1974a; Olson, 1970). Individuals do retrieve and synthesize referential associations that link the individual words of a sentence.

Associative Communality Howe (1972) has argued that the meaningfulness of sentences may be determined by the communality of word associations. According to Howe, meaningful words are more likely to produce the same associational response in each person. The occurrence of a variety of associates presumably reflects the greater possibilities of associative interference. This view is very similar to Martin’s (1968) theory that units of low meaning are likely to be variably

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encoded; units of high meaning are likely to be encoded in the same way on each occasion. Ironically, a greater number of associates, when evoked from individuals, is one index of higher meaningfulness (m via Noble’s (1952) production method), whereas the occurrence of a greater number of different associates in a group of individuals, as measured only for the first occurring associate, is said to signal the existence of lower meaningfulness. As evidence that associative communality measures meaningfulness, Howe (1972) cites Clark’s (1966) data relating the learning of individual sentences to the normative popularity of associations given in response to the stimulus-sentences. Recall of sentence parts was found to be best when the corresponding sentence associations showed less diversity. Is associative communality a valid measure of meaningfulness? As would be expected, communality does predict recall. Furthermore, communality conceivably might signal the degree of accord with associational backgrounds. Common associates, however, also could hinder or be irrelevant to the required learning. Like Deese (1965), Howe’s (1972) premise is that the meaningfulness of a sentence is derived from the word-associations given to the individual words. The theory thereby is subject to the same limitations noted for Deese’s theory and Clark’s (1966) data.

Cloze Procedure In the cloze procedure, readers’ success in guessing deleted words is said to measure readability or comprehensibility. Might cloze also be used to assess meaningfulness? A passage containing frequently used words, rather than uncommon words, ordinarily would be more meaningful. Meaningfulness also might be higher when verbal passages contained higher proportions of concrete referents and lower proportions of abstract referents. In agreement, Coleman (1971) found substantial correlations between cloze scores and the densities of either concrete or abstract nouns. Similarly, a passage containing some redundancies ordinarily would be easier to comprehend than prose without redundancy. The extremely high correlations with measures of redundancy (Coleman, 1971; MacGinitie, 1971; Taylor, 1954), however, suggest that missing words often could be inserted without understanding the passage. Learning performances on high redundancy passages also may be misleadingly high. As evidenced in programmed instruction, learning may be hindered when the learner is provided with too many cues (Anderson, 1970). Research on reading also shows that excessive cues or redundancy may make the passage become less meaningful to the learner by reducing his attention and making him less active in his learning efforts (Samuels, 1970). In sum, the cloze procedure does tap certain aspects of meaningfulness, although the extent to which these dimensional taps are contaminated remains

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to be seen (Bormuth, 1965; Weaver & Kingston, 1963). Second, the technique offers simplicity of measurement for comparisons of two or more passages. Disadvantages include the excessively heavy reliance on redundancy. Another disadvantage is that the method offers only gross comparisons among passages, and does not offer calibrations of smaller linguistic units such as the sentence or phrase.

Semantic Differential A valid measure of semantic similarity conceivably could be an appropriate measure of the meaningfulness of complex verbal units. Osgood, Suci, and Tannenbaum (1957) have proposed that the connotative meaning of a word can be represented in a three-dimensional semantic space on the factorial dimensions of evaluation, activity, and potency. Words close to each other in semantic space are said to be similar in meaning. An acknowledged limitation of Osgood’s semantic differential, as a general measure of meaning, is that it mainly measures connotative meaning. The technique has not been successfully applied to the measurement of denotative meaning. The words boat, income, bright, and eat occupy virtually identical locations in semantic space, but it is obvious that the differences in meaning are great. Whether the semantic differential could be revised to include dimensions measuring denotation is not known, but the number of potentially independent dimensions appears insurmountably large. In addition, another limitation of the semantic differential is that its usefulness is restricted largely to isolated words rather than words in context.

Toward Adequate Measures of Meaningfulness As noted, inadequacies are evident in existing methods of measuring meaningfulness in verbal discourse. Even when the classical methods reveal differences among words (Paivio, Yuille, & Madigan, 1968), the relationship to meaningfulness is not always obvious. To illustrate, in the Paivio et al. study, more associations supposedly signaled greater meaningfulness, but the following alignment of production values does not arouse faith in the scaling procedure: FATIGUE, 3.88; FACT, 4.29; SALUTATION, 5.24; PASSION, 5.68; SCIENCE, 6.56; PELT, 6.76; SOVEREIGN, 7.12; WHALE, 7.24; FIRE, 7.36; SEASON, 7.88; PRAIRIE, 8.16. The classical methods of measuring meaningfulness (Archer, 1960; Noble, 1961) also have located almost all words at the extreme high end of the scale, and thus have been insensitive to actual differences in meaningfulness. One possible solution might be to use scaling procedures that force raters to make differentiations. Using an eliminative method analogous to

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the forced-choice technique, for example, Johnson’s (1973) raters judged meaningfulness by eliminating prose units of lower meaningfulness until only a specified proportion of the content remained. Similar procedures could be fruitful in calibrating the meaningfulness of a sample of isolated words. Another source of difficulty is that experimenters have focused on prose segments that were inappropriate in size. Some procedures are applicable only to gross comparisons between lengthy passages. Other methods have failed, in part, because the unit of analysis was the single word. If the previous assessment was correct, the associational linkages among words make it highly unlikely that the meaning of a sentence can be derived solely from an analysis of words studied in isolation. A successful measure of meaningfulness, then, must be responsive to semantic relationships among words. Compare, for example, “the nail file was used to remove the small screw,” with “the nail screw was used to remove the small file.,, The words in the two phrases are identical, as are the grammatical structures, but the exchange of file and screw has rendered the second phrase into a less meaningful assertion. In an analogous experiment, Rosenberg (1968) found that sentences containing words having strong associative relations with each other, such as The old king ruled wisely, were remembered better than poorly integrated sentences such as The poor king dined gravely. Even with strongly integrated sentences, however, the arousal of particular referents depends upon the associational context provided by other words in the sentence (Barclay et al., 1974). For example, when nouns from different taxonomic categories were presented in a list, recall patterns showed evidence of categorical clustering (Bousfield, 1953). When such nouns were integral parts of sentences, however, Cofer (1968) found that clustering and recall were disrupted by sentential context. Similarly, the importance of context is evident from Harris and Brewer’s (1973) experiments on the accuracy of recalling tenses of verbs. When sentences contained a temporal adverb such as “yesterday,” as opposed to a nontemporal adverb such as “accidentally,” memory for verb tense was more accurate. According to Harris and Brewer, the temporal context imparted greater meaning to verb tense, and hence resulted in better remembering. Similar outcomes were evident in the recall of other sentence elements in which full meaning depended upon a sentential context designating a particular time, place, or speaker (Brewer & Harris, 1974). Grammatical structure also determines the particular associational linkages induced by words in a sentence. The grammatical usage of a word, e.g., train as either a noun or a verb, may determine meaning, and differences in meaningfulness are evident even when different grammatical usages lead to essentially the same semantic interpretations, e.g., fill as a verb or as a noun (Brown, 1958b, pp. 247–253; Carroll, 1970). Even when the object and subject of a sentence remain the same, as in active and passive transformations, different

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understandings are engendered (Anisfeld & Klenbort, 1973; Herriot, 1970; Offir, 1973). As a consequence, slight variations in syntax can lead to sizable differences in recall (Bock & Brewer, 1974). The recall of two temporally ordered events, for example, is best when the presentation order in the sentence corresponds to the actual ordering of events (Blount & Johnson, 1973; Clark & Clark, 1968). Thus, as evidence has increasingly shown (e.g., Fodor et al., 1974; Weisberg, 1971), sentence structure serves as one basis for sentence interpretation. Here again we have evidence for contextual influence in determining meaningfulness. In sum, meaningfulness judgments are not likely to be valid unless the linguistic units are in their appropriate verbal contexts (Jenkins, 1974). Classical measures of the meaningfulness of individual words hinted at the possibility that such measures, in conjunction with a set of some unknown combinatorial rules, might predict the learning of any possible combination of words and phrases. That aim has not been achieved, and the contextual determinancy of meaningfulness makes it unlikely that adequate combinatorial rules will be devised. Given the infinite number of possible word combinations, and the realization that contextual specificity does not allow prediction to other combinations of words, is it worthwhile to measure the meaningfulness of prose subunits? Although the generality of measurement is disappointing, the potential usefulness of such endeavors appears undeniable. One possible use might be pilot assessments of textual subunits prior to their use. Important segments rated low in meaningfulness could be revised to increase the probability of learning. At first glance, the task appears forbidding, but reliable ratings can be achieved with small numbers of raters (Johnson, 1973). Furthermore, when categorical ratings are made of phrase units in prose, the task requires relatively little time beyond that of normal reading, and the relative rankings are very similar to those produced by the eliminative method. Equally important, measures of the meaningfulness of prose could be useful either as independent or dependent variables in research on learning. Experimental variations in semantic or syntactic variables, for example, presumably would influence the meaningfulness of linguistic subunits, and the ease with which the subunits were learned. Through such studies, generalizable relationships could be established.

A Sampler of Needed Research Assessing Meaning Empirical studies are needed to determine whether meaningfulness ratings are more valid when raters make global judgments in contrast to ratings made of dimensional indicants such as amount or quality of aroused imagery.

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If raters are instructed to attend simultaneously to the various components of meaningfulness, the task may be too complex. Alternately, if raters are told simply to furnish an overall global rating, they may develop differing reliances on the various component dimensions. An empirical solution may be to collect global ratings and also ratings of the component dimensions, and then determine the degree of overlap and the extent to which the various ratings predict criterion performances. The development of alternative methods of measuring meaning and meaningfulness also warrants research priority. Methods used in measuring comprehension (Carroll, 1972), for example, perhaps could be adapted for global measurements of meaningfulness. In developing analytical measures of particular aspects of meaning, researchers perhaps could improvise on techniques used with individual words. Fillenbaum and Rapoport (1971), for example, show much ingenuity in analyzing similarity judgments by techniques such as nonmetric multidimensional scaling, graph theoretic analysis, and hierarchical clustering analysis. Fillenbaum (1974a) probed other aspects of meaning through analyses of attempted paraphrases (also see Fillenbaum, 1974b; Gleitman & Gleitman, 1970), as well as raters’ judgments of sentence equivalence, informativeness, and semantic plausibility. Using a method of componential paraphrasing, followed by empirical analyses of subjects’ sortings of words according to “similarity of meaning,” Miller’s (1969, 1972) research provides additional directionality for developing alternative methods. To appreciate the facilitation that could result from new methods of assessing meaning, consider the catalytic influence of Bransford and Franks’ (1971) research in stimulating additional research (Barclay, 1973; Barclay & Reid, 1974; Bransford, Barclay, & Franks, 1972; Cofer, 1973; Franks & Bransford, 1972, 1974; Johnson, Bransford, & Solomon, 1973; Paris & Carter, 1973; Peterson & Mclntyre, 1973; Potts, 1972; Singer & Rosenberg, 1973). Although Bransfors methodological procedures and conclusions have been criticized (Katz, 1973; Katz & Gruenewald, 1974; Reitman & Bower, 1973), the powerful impact of Bransford and his associates appears derived from the use of an explicit method for showing that learners have considerable recognitive difficulty in distinguishing between the semantic content of the message and their own implicitly generated semantic content. In agreement with Bartlett’s (1932) reconstructive theory of remembering, the semantic content of a message appears to become fused with existing referential knowledges. The adoption of a referential theory of meaning argues the importance of developing methods that assess the referential associations of discourse units. As noted earlier, word associations to individual stimulus words do not necessarily predict the associations that will be given when the stimulus words are embedded in discourse. Yet, free associations to prose units undoubtedly tap certain aspects of the structure of meaning. Typically, the bulk of the associations can be categorized as being either opposites;

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synonyms; superordinates; subordinates; logical coordinates, e.g., apple-pear; or functional, e.g., needle-thread (Karwoski & Berthold, 1945; Moran, Mefferd, & Kimble, 1964). Is there a code that allows translation of such word associations into structural or theoretical representations of meaning? In an important paper, Clark (1970) postulates the existence of associational rules that govern the production of word associations. Theorizing from the linguistic viewpoints of Katz and Fodor (1963) and of Chomsky (1965), Clark assumes a componential approach in which a word is comprehended as a set of syntactic and semantic features. The features of man, for example, might be characterized as: +Noun, +Det –, +Count, +Animate, +Human, +Adult, and +Male. In the free-association task, an “associating rule” is applied to the list of features, such as “change the sign of the last feature” (e.g., +Male to –Male), and an association is then produced which is congruent with the altered feature list, e.g., woman. “Changing the sign of the last feature” is also labeled the “minimal contrast rule” because the antonymous associations produced by the rule have the maximum number of features in common with the stimulus. Within the context of a referential theory of meaning, the “last” feature which is reversed appears centrally related to the semantic dimension on which the word is primarily defined. The word short, for example, leads to the association of long because length is the primary defining attribute of short. In experience, the psychological attribution of short is contrasted with the anchoring alternative of long. The “marking rule” describes another associational transformation common in word associations (Clark, 1970). With antonymic words, one member of the pair is marked or positive with regard to the presence of a feature, whereas the opposite member is neutral or unmarked (Greenberg, 1966). For example, dog is unmarked with regard to the classification of sex, but bitch is marked. In association tasks, marked stimulus words show a greater tendency to produce their unmarked counterparts as associational responses (Clark, 1970). The marked stimulus of better, for example, produces the unmarked response of good more often than good produces better. In the recall of sentences, unmarked words are remembered better than marked, and qualitative changes in memory also tend to proceed from the marked to the unmarked form rather than in the reverse direction (Benjafield & Giesbrecht, 1973; Carpenter, 1974; Clark & Card, 1969). Significantly, however, memorial change toward the unmarked form tends to occur only when such change allows the preservation of the original meaning of the sentence (Brewer & Lichtenstein, 1974). Although linguistic criteria are used to differentiate marked from unmarked words (Greenberg, 1966), Deese (1973) notes that unmarked members of a pair occur more frequently in written language and that children learn to use unmarked words prior to marked words. Equally important, marked words tend to be rated negatively on evaluative scales of affect (Deese, 1973). Given such differential experiences, it is not surprising that unmarked words have priority in associational structures. For the marking

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rule, as well as other associational rules, regularities in associative responses may prove useful in delineating the structure of meaning. Similar associational transformations presumably mediate the referents aroused to linguistic units in prose. With linguistic units larger than one word, however, verbal associations elicited in free association probably represent associational structure only indirectly. In analyses of verbs of motion, for example, Miller (1972, pp. 345, 369) suggests that some concepts have associational representations which are blends of existing concepts or words. Such associational blends, coupled with the person’s ability to derive a fused representation of separate semantic knowledges, may play a determining role in the excellence of college students in judging the adequacy of their own paraphrases (Fillenbaum, 1974b). In any event, with passages of prose containing many ideational units, the associational blends sometimes become even more composite, until it is perhaps appropriate to speak of schemas (Bartlett, 1932), surrogate structures (Pompi & Lachman, 1967), themes (Dooling & Lachman, 1971), or conceptual macro-structures (Bower, 1974). If schemas do mediate the recall of prose, research ought to be able to discover the basis of such mediation. The foggy notion of schema, unfortunately, has not been operationalized empirically (Oldfield, 1972), but ingenious experimenters should be capable of closing this empirical gap. Perhaps schemas can be inferred from various regularities in recall. Recall patternings in prose, for example, appear analogous to the clusterings observed in categorized lists (Bousfield, 1953), and such clusterings appear to represent the influence of organizational processes during learning (Mandler, 1967). If schemas do determine clustering in the recall of prose, it should be possible to infer the schemas from clustering. Similarly, patternings of errors also might signal the existence of schemas in remembering (Bartlett, 1932). Learners under the influence of preexisting schematic knowledge, for example, made recognitive errors which were thematically congruent with their schema (Sulin & Dooling, 1974). Schemas also might be inferred from the effectiveness of linguistic segments in inducing the recall of other linguistic segments. The use of cuing to assess memory structures in prose learning has its counterpart in the cuing techniques used to induce the remembering of list members (e.g., Mandler, 1967; Tulving & Pearlstone, 1966; Slamecka, 1968). Just as with previous research, it may be assumed that the cuing taps existing superordinate categories that have not been remembered. With the development of such methods for measuring schemas, insight may be gained into the manner in which referential meanings are translated into recall.

Empirical Issues The adoption of a referential theory of meaning bespeaks the importance of relating learning to the organizational structure of existing referential knowledges. The organization and availability of referential associations, for

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example, can be influenced by the input order of a sequence of sentences (Anderson, J., & Hastie, 1974). Furthermore, even subtle differences in the focus of a sentence can arouse different associational referents. The sentence “It was Mr. Smith who ordered the coffee,” for example, presupposes that coffee was ordered by someone, whereas “It was the coffee that Mr. Smith ordered” presupposes that Mr. Smith ordered something. When recognitive paraphrases of such sentences violated presuppositional knowledges, changes in wording were more easily detected than when the alternative phrasings did not violate presuppositions (Offir, 1973). Referential emphasis within a paragraph appears to be another factor influencing the associative representation of embedded sentences (Perfetti & Goldman, 1974). For a sentence such as The serfs rebelled against the baron, the extent to which the paragraph focused on either the subject or object of the sentence was related to the effectiveness of that subject or object as a retrieval cue in remembering the remaining portion of the sentence. Additional evidence for the importance of referential availability may be found in the results of Haviland and Clark (1974) and Moesner and Bregman (1972). In the latter study, when learners attempted to acquire an artificial phrase-structure language without the aid of semantic referents, there was practically no learning of the syntactic rules even after hundreds of trials. With the referential availability of geometrical forms that portrayed the syntactic relations, the learners readily learned the grammatical rules. An important area for empirical investigation is the determination of variables influencing the availability of referential knowledges. One determinant may be the structural organization of the learning material (Anderson, J., & Hastie, 1974). Material that possesses a logical or hierarchical structure may facilitate the arousal of subsuming associations (Ausubel, 1963; de Villiers, 1974). Meaningfulness also may be fostered by the adoption of a set to learn meaningfully rather than by rote (Ausubel, 1963). In turn, the major consequence of adopting a meaningful set may be the arousal of referential associations relating to the material to be learned. Similarly, gaining access to appropriate referential associations can be accomplished by redirection of the learner’s set (e.g., Luchins, 1942). The quality and organization of aroused referents also appears important in learning. Sentences containing pronouns as the subject are remembered better than sentences with nouns as the subject, even though the pronouns themselves are not remembered better (Martin & Walter, 1969). In contrast, the denotative specificity of nouns in prose, as gauged by superordinatesubordinate status, is positively related to remembering, even when the nouns are equivalent in concreteness-abstractness (August, Proctor, Hynes, & Johnson, Note 1). Similarly, memory is better for sentences having specific verbs than for sentences having general verbs (Thios, 1975). Increases in denotative specificity via the restriction of a noun modifier, however, do not influence remembering (August et al., Note 1). Are such differences in

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remembering due to differences in the parceling of referential associations? The partitioning of referents for superordinates and subordinates, and for verbs, occurs along the referential boundaries of existing concepts, whereas the denotative restriction enjoined by an adjectival modifier is an arbitrary parceling of a noun’s referential class. As an alternate hypothesis, the effects of adjectival modification might be related to the extent to which the parceling induces the retrieval of concrete associates to the noun (Anderson, 1974). What are the dimensional or functional attributes of referents that influence learning? Are referents more easily aroused to specific categories such as diamond than to general categories such as gem? Or, as suggested by Brown (1958a) and by Loftus and Bolton (1974), perhaps the retrievability of referents is partially determined by usage habits. Do subordinate nouns evoke referential associations that are qualitatively different from the referents of superordinate nouns? For example, are referents to subordinate nouns more likely to be concrete? Are the meanings of general words stored in the format of a specific exemplar (Anderson & McGaw, 1973)? If so, why are errors in recall more likely to be memorial changes from specificity to generality than from generality to specificity (August et al., Note 1)? Is there a quantitative difference in the number of referents evoked by superordinates and subordinates (Smith, Shoben, & Rips, 1974)? Does the storage node for a word contain only referential distinguishers, and not the referential attributes common to the superordinate of the word (Collins & Quillian, 1969)? The answers to questions like these will add to our knowledge regarding the processes by which meaningfulness influences learning and retention. Studies of the component dimensions of meaningfulness could test Paivio’s (1971) conclusion that meaningfulness is important only when a particular sequential ordering is required in recall. Under other conditions, says Paivio, imagery is a more important predictor than meaningfulness. As evidence, Paivio cites a widely quoted study by Paivio, Smythe, and Yuille (1968) in which imagery influenced learning even when differences in meaningfulness were equated. When meaningfulness was varied and imagery was constant, meaningfulness exerted no additional effect on learning. An examination of the words used in the Paivio et al. study, however, suggests the possibility of bias in the selectional procedure. As assessed by the production method, the mean number of associates to the high-m list averaged only two more than the low-m list. The high-m list was designed to be high in meaningfulness and low in imagery, and included words such as abode, molecule, theologian, and whalebone. Suppose, instead, that the high-m list was composed of words such as answer, cost, idea, law, and duty, and the low-m list was composed of words such as labyrinth, rosin, and edifice. In such a comparison, the outcome might be different. Paivio’s (1971) dual coding hypothesis has received considerable experimental support, but Goldfarb, Wirtz, and Anisfeld’s (1973) evidence suggests that all verbal material is coded for referential meaning, and that

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differences in recognitive memory for abstract and concrete phrases are due to differences in denotative distinctiveness rather than imagibility. Since denotative distinctiveness was operationally defined by judgments of personal relevancy, the generality of the Goldfarb et al. conclusion is uncertain. Paivio and Olver (1964), however, found that stimulus imagery did not influence the learning of paired associates when specificity was held constant, whereas stimulus specificity was significantly correlated with recall (r = .41) even when imagery was held constant. Imagery and denotative specificity also show some independence in factor analyses (Paivio, 1968; Spreen & Schulz, 1966). Coupled with the conflicting research evidence on the longevity of learning mediated by imagery (Begg & Robertson, 1973; Postman & Burns, 1973), research clearly is still needed on the relationships among imagery, meaningfulness, denotative specificity, and learning. Research also is needed to determine the influence of multiple referents on learning. Words such as tripod have quite limited sets of referential associations. Others, e.g., triangle, have extensive associations. For some concepts with multiple referents, such as scare, the referential associations are all related conceptually, whereas the multiple referents of words such as light are related to different denotative meanings. Based upon studies of verbal learning and retention, it might be predicted that ambiguous words and phrases would be more susceptible to negative transfer and interference. Complicating the prediction, however, is the fact that frequently occurring nouns, as measured in the Thorndike-Lorge (1944) count, are more likely to have a greater number of meanings (Saltz & Modigliani, 1967). Even with Thorndike-Lorge frequencies controlled, however, Saltz and Modigliani found superior learning of paired associates when the response terms were nouns having a greater number of meanings. Contrary to expectations, Saltz and Modigliani (1967) found that the number of meanings was virtually unrelated to Noble’s (1952) production measure of meaningfulness. If associative production is unrelated to the number of meanings, what associations are being tapped? Saltz (1971) suggests that the associations given to a stimulus word tend to exhaust a single meaning, and that words with high-m values differ from low-m words in the richness of their connotative meanings. Other explanations are possible, and it is clear that analytical investigations are needed to determine the relationships between the production of associations, the number and types of meanings associated with a linguistic unit, and learning. Empirical studies also could delineate the conditions under which separate knowledges become fused. Unification tends to be enhanced by a correct temporal sequencing of events (Clark & Clark, 1968), perceived cause and effect relationships (Fillen-baum, 1971), pronominalization (Lesgold, 1972), and the use of the definite article (de Villiers, 1974). When a series of sentences were perceived as a unified story, rather than an unrelated set, de Villiers’ (1974) learners recalled more sentences, recalled the sentences more often in

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their story order, and more often showed gist recall. Furthermore, ratings of thematic centrality were directly related to sentential recall, whereas ratings of imagery were unrelated to remembering. In contrast, when not viewed as a story, centrality ratings were unrelated to recall, and imagery ratings were directly related. The associative relatedness of the input units thus appears critical in determining semantic fusion. As further evidence, when semantically related sentences are presented, learners cannot later discriminate the input sentences from distractor sentences containing semantically compatible content (Bransford & Franks, 1971; Franks & Bransford, 1974; Peterson & Mclntyre, 1973). If presented with a lengthy series of semantically unrelated sentences, however, learners are quite accurate in discriminating old from new sentences (Shepard, 1967). A related empirical problem is that of understanding memory for gist. Semantic changes in recognitive foils are detected much more readily than syntactical changes (Sachs, 1967; Begg & Wickelgren, 1974), and the verbatim recall of prose is a rarity (Bartlett, 1932; Johnson, 1974). The learner’s remembering of gist displays itself through the recall of verbal equivalences, the selective remembering of important content (Johnson, 1970), and the occurrence of meaning-preserving errors (Fillenbaum, 1966). Since judgments about the equivalence of meaning involve some subjectivity, experimenters have tended to study verbatim memory and to avoid studies of gist. As demonstrated by Fillenbaum (1966), however, gist can be studied objectively, and there is critical need for describing and understanding the transformational changes that occur from the original input of sentences to the display of gist. Probes also are needed to ascertain the relationships between learning and the organizational complexity of the referential associations. Perhaps the major characteristic differentiating abstract from concrete units is the complexity of the referent package. Verbal units that are concrete, such as chair, have referential attributes that are organized conjunctively. To be a chair, an object must have a base, a seat, and a back. The referential dimensions of a concrete unit ordinarily can be specifically denoted, and a potential instance or example of the category can be identified by noting the co-occurrences of the criterial attributes. Such co-occurrences, e.g., size and weight, are so regular that children often have difficulty in disentangling the attributes on occasions in which the attributes are not correlative (Ervin & Foster, 1960; Piaget, 1947/1960). The defining attributes of abstract categories are less obvious or distinct and are more highly interrelated with other concepts (Goldfarb et al., 1973). As shown by Carroll’s (1964b) analyses of immigrant and tort, abstract concepts are more likely to have complex referential systems requiring knowledges of relationships and disjunctive combinations. From a research viewpoint, the descriptive classification of concepts needs to progress beyond Bruner, Goodnow, and Austin’s (1956) categories of conjunctive, disjunctive, and relational.

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Empirical information also is needed on the referential attributes that are salient to the learner. Deese (1973), for example, notes the pervasiveness of spatial information in our cognitive and affective categories. Apparently, then, spatiality is salient to the learner, and spatial information has a high probability of becoming incorporated into referential structures. In identifying other salient attributes, Rips, Shoben, and Smith (1973) had raters judge how typical each word (e.g., chicken) was of its superordinate category (e.g., birds). A multidimensional scaling procedure was then applied to the ratings to suggest salient dimensions (e.g., size and predaciousness). Using another method of identifying salient attributes, Bruner, Olver, and Greenfield (1966) had children judge how objects were alike and different. Developmental changes were found in the various modes by which equivalence judgments were made. Similarly, a mapping of salient semantic attributes might be obtained through the use of a set of structured questions to judges. Scattered throughout the developmental literature, there are studies reporting developmental changes in children’s understanding of different tasks and instructions (e.g., Luria, 1961; Piaget, 1947/1960; Piaget & Inhelder, 1968/1973). Data also exist on the ages at which particular words are acquired, developmental changes in word associations (Entwisle, 1966), and systematic progressions in the usage of different syntactical structures (Fodor, et al., 1974; McNeill, 1970; Menyuk, 1969). Surprisingly little research, however, has focused on semantic development (Anglin, 1970; McNeill, 1970; Palermo & Molfese, 1972). The discovery of developmental regularities in the acquisition of meanings could provide insights into both the cognitive functioning of children (Barclay & Reid, 1974) and also the structural representation of meaning in adults. For many of the research questions raised in the present review, counterpart questions exist regarding developmental regularities in semantic development. Although theoretical statements on the developmental acquisitions of meanings are virtually nonexistent, recent speculations by E. V. Clark (1973) and by Nelson (1974) appear to have ended the drought. Recent theorizing on the acquisition of semantic meanings in adults, to be discussed in the next section, also may provide impetus for comparable theorizing on developmental regularities in the acquisition and use of referential knowledges.

Associative Network Models The conceptual emphasis of the present review has much in congruence with the recent spate of associative network models of semantic memory (Anderson & Bower, 1973; Collins & Quillian, 1972; Kintsch, 1972; Quillian, 1968; and Rumelhart, Lindsay, & Norman, 1972). Quillian’s (1968) view, for example, is that the full meaning of a concept consists of all the memory nodes that can be reached from the concept via an exhaustive tracing process. In these network

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models, the basic unit of analysis typically is the “proposition,” consisting of a “relation” (usually verbs, adjectives, conjunctions) and one or more “arguments” (usually a noun or other proposition). Within a proposition, the semantic destiny of a lexical item is partially determined by case-grammar rules regarding acceptable parsings. If the word has been encountered previously, the existing storage node is used. Otherwise, a new node is formed automatically. Meanings of lexical items thus become defined through cumulative entries of propositional statements containing the unit. After entry into memory, words also can gain new meanings through the operation of various inferential and transformational rules. Although the network models are couched in the familiar jargon of associationism, such speculations represent new vistas for psychologists and educators. In the empirical testing of the network models, one important consequence may be a shift in the dominant learning paradigm from serial lists and paired associates to studies of prose. With respect to theories of meaning, the network theories may become battlefields that will provide insights into the role of meaningfulness in learning. Since Anderson and Bower’s (1973) associative model (HAM) is the most explicit formulation and has received considerable support, it is appropriate to sample some of their assumptions that deserve empirical testing. Most basic, perhaps, is Anderson and Bower’s (1973) assertion that propositional organization is required for the formation of associations. Although Rohwer (1966) has demonstrated that propositional formats often aid learning, words ostensibly can be associated without benefit of verb or other propositional connectives. Furthermore, even if propositional structure is required, the structural components of such propositions are not obvious. Anderson and Bower postulate the existence of a context subtree containing location and temporal information, and also a fact subtree representing a topic, predications about the topic, and adverbial or adjectival modifiers of the predications. The resultant associative structures may be represented graphically by sentence diagrams similar to the tree-like parsings rendered by grammarians and generations of schoolboys. Figure 1, for example, shows Anderson and Bower’s (1973, p. 160) associative representation of “During the night in the park the hippie touched the debutante.” Note that other associative paths might be assumed, but Anderson and Bower (1973, p. 167) allow representation only of ideational combinations

L Park

C

F

T

S

P

Night Hippie

R

Touch

O Debutante

Figure 1: Anderson & Bower’s (1973) associative representation of “During the night in the park the hippie touched the debutante”

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that represent direct predications. Might not a direct object (e.g., debutante), however, be directly associated with a location (e.g., park)? In what sense, psychologically, is location information (e.g., park) more closely associated with the subject (e.g., hippie) than with the object (e.g., debutante)? Is the verb concept (e.g., touch) semantically closer to the object (e.g., debutante) than to the subject (e.g., hippie)? Might the associational structure of a proposition be related to the syntactical structure in which the proposition is phrased? Anderson and Bower (pp. 295–314) examined the validity of their structural hierarchy by providing cues for stimulating the recall of previously unrecalled components. Their predictions of directional differences in recall received only marginal support from the data. Overall, then, the validity of HAM’s associational structure is not obvious, and reaction-time probes and other techniques might provide evidence of different associational structures. Anderson and Bower (1973, p. 284) assume that all associative links within a proposition are actually transformed into long-term memory associations. On the initial input, for example, the sentence in Figure 1 becomes parsed into 13 associational linkages that are equivalent in strength. With such an assumption, unfortunately, the problem of meaningfulness is skirted, and prior associational memories are assigned no role in determining whether an association will be formed. Differential recall of propositional components does occur (Anderson & Bower, 1973, pp. 295–329), and this fact suggests differential encodings during input. A successful model of associative memory needs to account for this fact, as well as evidence showing that the encoding and recall of linguistic units are related to dimensional characteristics such as abstractness-concreteness, semantic importance, and interest (Gomulicki, 1956; Johnson, 1974). Another challenge to researchers and theorists is to develop a memory system sensitive to the fact that associative representations depend on learners’ encoding strategies. The accuracy of remembering semantic content depends, in part, upon the learner’s set for accurate remembering (Brockway, Chmielewski, & Cofer, 1974). Similarly, with certain types of encoding strategies, the learner shows a long-term remembering of the grammatical voice of input sentences (Anderson & Bower, 1973, pp. 224–228). An adequate theory of semantic memory also needs to account for persistent encoding biases such as preferences for recoding linguistic units into abbreviated forms, and for the remembering of gist rather than surface detail. Additional structural components also may be required to adequately represent the full array of semantic information. Function words, for example, are given only token representation in HAM (pp. 139, 206), but our earlier analysis argued that each word carries elements of meaning. Furthermore, if a separate component is needed for storing temporal information, why is there not comparable representation for spatial information? As another example, informational predications regarding the meaning of verbs occur only infrequently in everyday discourse, and the operation of HAM appears

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to offer limited opportunity for establishing equivalences in the meanings of verbs (Anderson & Bower, 1973, pp. 193–196). Yet, contrary to what might be predicted from HAM, subsets of verbs, such as verbs of motion, appear to be organized in meaning by rather complex structures of shared semantic components (Miller, 1972). Finally, HAM appears deficient in representing semantic information regarding the structural importance of the various propositions within a message. Informational input in HAM is not categorized according to importance or saliency, and all propositional inputs have equal representation. According to Anderson and Bower (1973, pp. 383–386), the saliency of propositions is determined by input frequency and recency, but there is evidence that structural importance is related to remembering even when input frequency is equivalent for all propositions (Johnson, 1970). On this issue, and on other issues, theorists and researchers have ample opportunity for challenging Anderson and Bower’s conception of associative meaning.

Concluding Comment The present designation of meaningfulness as an area needing research is predicated on the assumption that meaningfulness is a critical variable in learning. Empirical support for this assumption is evident in a study relating the recall of textual prose to meaningfulness (Johnson, 1973). Textual subunits rated in the highest level of meaningfulness were recalled approximately three to eighteen times better than subunits ranked in the lowest level of meaningfulness. Further evidence of the importance of meaningfulness may be found in paraphrasing studies (Fillenbaum, 1974a, 1974b) in which subjects were explicitly instructed “not to improve the sentences or make them more sensible, but to paraphrase them, rewording each in a way that captures its meaning as accurately as possible.” However, when the semantic content violated the paraphrasers’ existing knowledge, they nevertheless paraphrased the content more meaningfully even though they were aware of differences between the original content and their own paraphrases. In the words of Fillenbaum (1974b), “even in the peculiar circumstances of the psychological laboratory Ss seem to be acting on the basic assumption that what is described in discourse will be sensible, that what is described will conform to the customary order of events and will satisfy normal qualitative and causal relations between events or actions” (p. 577). As Bartlett (1932) said in his classical work on remembering, the person’s learning may be characterized as “an effort after meaning.” To quote Bartlett (1932), “there is a constant effort to get the maximum possible of meaning into the material presented” (p. 84). How may this be accomplished? Quoting Bartlett again, “such effort is simply the attempt to connect something that is given with something other than itself” (p. 227). Thus, when learning occurs, the learner inevitably attaches the new experience to the residual of previous experiences. In turn, the residual of past experiences, organized into schemas,

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determines the quality of remembering. Bartlett’s research (1932), as well as others (e.g., Campbell, 1958; Johnson, 1962; Paul, 1959), have provided convincing evidence that qualitative distortions in remembering are related to the individual’s cognitive structure. Furthermore, whether a particular verbal unit is remembered or not remembered is also determined by the organized residual of the learner’s past experiences (Gomulicki, 1956; Johnson, 1974; Zangwill, 1972). It has been more than 40 years since the publication of Bartlett’s (1932) work. Since that time, there have been methodological advances which allow for a renewed attack on the problem of meaning. The time does seem ripe for theoretical and empirical reexaminations of the role of meaning in complex learning.

Note 1. Dixon’s (1965, pp. 23–104) historical account of western thoughts about language, from Plato to Chomsky, documents the everpresent attempts to grapple with the concept of meaning. A review of earlier experimental attempts to assess meaning may be found in Creelman (1966). Modern philosophers and linguists continue to write copiously and polemically on the topic of meaning, and reference to this literature may be initiated through Alston (1964), Lehrer and Lehrer (1970), and Lyons (1968).

Reference Note 1. August, G. J., Proctor, D. L., Hynes, K. P., & Johnson, R. E. Recall of prose as a function of denotative specificity. Paper presented at the meeting of the American Psychological Association, New Orleans, September 1974.

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40 Phases of Meaningful Learning Thomas J. Shuell

L

earning is a much more complex and drawn out process than generally acknowledged. The type of complex, meaningful learning that occurs in school and throughout the life span occurs over a period of weeks, months, and years, and there is good reason to believe that the nature of the learning process changes as the task of mastering a complex body of knowledge unfolds. For example, there is good evidence that experts and novices in a field respond to tasks in fundamentally different ways (e.g., Chi, Glaser, & Rees, 1982). As one progresses from the initial encounter with a complex body of knowledge to the point where the expert is able to demonstrate understanding of that knowledge in ways that are more-or-less automatic, a task that once constituted a problem for the new learner (and elicited various problem-solving strategies) becomes little more than a simple recall task for the more experienced and sophisticated learner. This article will explore the notion that distinct stages or phases can be identified along the journey from knowing virtually nothing about a complex body of knowledge to the demonstration of a highly proficient mastery of that knowledge. After a discussion of several issues related to a phase theory of learning, research relevant to phases in simpler types of learning will be presented, followed by a similar discussion of phases in more meaningful learning. Finally, implications of these reviews will be discussed with regard to both theories of learning and educational practices. The idea of stages is certainly not new to psychology. There are the developmental stages of Piaget and Bruner and a long-standing concern for stages in problem solving (Andre, 1986; Mayer, 1983). Over the years, a variety of stage

Source: Review of Educational Research, 60(4) (1990): 531–547.

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theories have been suggested for various types of learning (e.g., Anderson, 1982; Fleishman & Hempel, 1954, 1955; McGuire, 1961; Underwood, Runquist, & Schulz, 1959), and Brainerd (1985; Brainerd, Howe, & Desrochers, 1982) has developed a sophisticated mathematical two-stage model of learning. A concern for stages is clearly evident, at least implicitly, in the current literature on cognitive learning – for example, the growing body of literature on expert-novice differences (e.g., Chi, Glaser, & Rees, 1982). A number of factors have contributed to this concern for stages, or phases, in meaningful, cognitive learning. Current theories of learning, for instance, emphasize that learning is an active, constructive, cumulative, and goal-oriented process that involves problem solving (e.g., Shuell, 1986a, 1990). This view of learning as a complex, drawn-out process (e.g., Norman, 1978) that depends on factors from many sources suggests that learning may change as it progresses. This possibility, coupled with evidence that performance is strongly influenced by one’s prior knowledge (e.g., Bransford & Johnson, 1972; Chiesi, Spilich, & Voss, 1979), makes a concern for phases in meaningful learning an appropriate and timely pursuit. Although many cognitive theorists seem to accept the notion of phases in meaningful learning, there have been few systematic attempts to explore the issue in depth. Most of the empirical evidence on stages of learning deals with simpler forms of learning. Although the evidence of phases in long-term meaningful learning is not as convincing at present as one would like, there is good reason to postulate their presence. The following review combines evidence from empirical studies and theoretical discussions of both simple and meaningful forms of learning in order to evaluate this possibility.

An Overview of the Problem Imagine yourself about to embark on a long journey, a journey that involves learning a complex body of knowledge with which you currently are unfamiliar. At first, the new terrain appears strange, although certain similarities with familiar territory can be identified. During the first leg of this journey you find yourself primarily memorizing isolated facts (i.e., landmarks), for you do not yet possess a schema for interpreting and integrating the various pieces of information that are encountered. Initially, for example, you may find that mnemonics are helpful in remembering these more-or-less isolated facts. As learning progresses, however, and you begin to group and organize the facts and integrate them into higher order structures, you may find that mnemonics play a less beneficial role. In their place, various types of organizational aids (e.g., developing hierarchies and matrices) that were of little help initially may begin to play a more important role. But the nature of the learning process is not the only thing that changes as learning progresses; the learning process also becomes more diverse. Initially,

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the learner must rely on experiences associated with a particular course or on a few books selected for self-study. As the learner becomes more familiar with the territory through which he or she is traveling, the learner is likely to encounter a variety of relevant books, to attend lectures, to discuss issues with other students (at the same and/or more advanced levels), to use his or her knowledge to interpret various situations (e.g., a play, a movie, the failure of something to work in the way it is supposed to, the behavior of other people), and so forth. In short, meaningful learning in any field is a much more complex process than often realized; different types of learning are involved, and – as this article will address – various phases or stages occur during which the nature of the learning process changes in systematic ways.

What Is a Phase of Learning? Because special connotations are usually associated with the use of the term stage within psychology, it seems advisable to address at the onset the way in which phase will be used in this article. Generally speaking, the term stage is used to refer to distinct time periods. Each period is characterized by psychological functioning that is qualitatively different from that which occurs during other periods. The most notable examples, of course, are developmental stages such as those of Jean Piaget. Developmental stages of this type are structural in nature and apply across all domains. For example, most developmental stage theories consider it impossible for a child to be in the formal-operations stage in mathematics and in the concrete-operations stage in social studies, and thus developmental stages are considered to be independent of specific content domains. There is a growing body of literature, however, that challenges the validity of developmental stages conceived in this way (e.g., Keil, 1986). There is increasing evidence that the qualitative changes that occur with age are the result of knowledge-based competencies within a particular content domain, although the possibility of general competencies arising from similarities among the various domains is not ruled out. An example of such knowledgebased competencies is demonstrated in a study by Chi (1978). There is a great deal of evidence, reviewed by Chi, that memory span (the number of items that can be recalled after a single presentation) increases in a linear manner with age, with adults remembering up to twice as much as children five- to seven-years old. Chi’s study involved children in the third through eighth grade (mean age was 10.5 years) who were experts at playing chess and adults who were only novice chess players. The typical finding of adult superiority in memory span was obtained when the subjects were asked to remember digits (6.1 vs. 7.8). However, when the subjects were asked to remember the placement of pieces on a chess board, the performance of the 10-year-old experts far surpassed that of the adults (9.3 vs. 5.9).

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This finding is often cited as evidence that knowledge differences may explain many of the developmental differences (and perhaps developmental stages as well) typically found in the literature. Karmiloff-Smith (1984, 1986) makes a useful distinction among stage, phase, and level Stage is used to refer to periods of time that differ qualitatively from both the preceding and succeeding stages. These stages, by definition, cover performance in a variety of content domains, and, once a person achieves a particular stage, he or she cannot return to a preceding one. Although a phase may include behavior across several domains, phases are recurrent in that individuals can pass through the various phases in each of many different content domains. Phases are based on: . . . the hypothesis that children (and adults, for that matter) attack any new problem by going through the same three phases, both within the various parts of particular domains and across different domains. The phase concept is focused on underlying similarity of process [italics added], whereas the stage concept usually refers to similarity of structure. (Karmiloff-Smith, 1984, p. 41)

Level refers to qualitative changes within a particular domain (e.g., the proper use of modifiers such as adjectives and adverbs) and accounts for specific changes within that domain. “Like stages, and unlike phases, levels are not recurrent. Once a child is at, say, level 3 in a specific domain, she does not return to level 1” (Karmiloff-Smith, 1984, pp. 41– 42). Within this type of conceptual framework, it makes most sense to think in terms of learning phases rather than learning stages, and, consequently, that term is used in this article.

Do Phases Add Anything Worthwhile to Our Understanding of Human Learning? If learning is a continuous process – and most of us would agree that it is – then one might reasonably ask what the notion of phases adds to our understanding of complex, meaningful learning. Both theoretical and practical implications exist if in fact the nature of the learning process changes in fundamental ways as learning progresses. Theoretically, it means that learning is a much more complex process than we had imagined. Not only must the type of learning be considered when conducting research but factors related to the length of time that the learning has been taking place must be considered, and prior knowledge will need to be considered in a much more explicit manner than is typically the case. In addition, concern for boundary conditions of various learning principles will need to include factors related to the phase of learning in which the learner is working. On the more practical side, there are also implications for teaching. Just as one should not teach in the same way when different types of outcomes

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are sought (e.g., the acquisition of concepts vs. the acquisition of facts), one should teach differently when different phases of learning are involved. The teaching methods employed, as well as the content, should be appropriate for the phase of learning in which the students are engaged. For example, one would teach differently if a new topical area is just being introduced than if the students had already gained some proficiency in the domain. Thus, introductory courses should be taught differently from more advanced courses – at least in part – but, in more instances than not, introductory and advance courses in a particular content area are taught in basically the same way.

Procedures for Identifying Phases In order to study phases of learning, objective and methodologically valid techniques must be used to distinguish among the various phases. Merely postulating their existence, defining them in terms of how much time has passed (how much practice/experience has occurred), and/or giving them plausible names are clearly insufficient methods of establishing their existence. The qualitative differences that presumably exist among the phases must be specified and verified in some objective way. One, and perhaps the best, way to differentiate among phases is to identify variables that function differently and /or have different effects during the various phases. For example, the distinction between short-term and long-term memory has been validated in this manner, for the effects of acoustic similarity are much greater in short-term than in long-term memory, whereas the effects of semantic similarity are much greater in long-term than in short-term memory (e.g., Baddeley, 1966; Conrad, 1964; Kintsch & Buschke, 1969). Expanding on this approach, a matrix can be established to portray the way in which relevant factors/variables operate in the various phases. Such a matrix, from Dreyfus and Dreyfus (1986), is presented in Table 1. In developing a matrix of this type, the defining factors/variables should be specified in an objective manner, and there needs to be clear evidence that these variables/processes actually change in the systematic manner specified. Various techniques have been used to identify the presence of stages or subproblems in problem solving. For example, Restle and Davis (1962) calculated the number of stages involved in solving a problem by dividing the square of the average time (across subjects) to solve a problem by the square of the standard deviation. Hayes (1965, 1966) and Thomas (1974) identified subproblems by comparing response times at each step of a well-defined problem. They assumed that response time is faster as a subject completes a subproblem and slower as he or she begins working on a new subproblem (presumably because time is required to think about a solution to the next subproblem).

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Table 1: Stages of skill acquisition Skill level

Components

Perspective

Decision

Commitment

Novice

Context-free

None

Analytical

Detached

Advanced beginner

Context-free and situational

None

Analytical

Detached

Competent

Context-free and situational

Chosen

Analytical

Detached understanding and deciding. Involved in outcome

Proficient

Context-free and situational

Experienced

Analytical

Involved understanding. Detached deciding

Expert

Context-free and situational

Experienced

Intuitive

Involved

Note: From Mind Over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (p. 50) by Hubert L. Dreyfus and Stuart E. Dreyfus, 1986, New York: The Free Press. Copyright 1986 by Hubert L. Dreyfus and Stuart E. Dreyfus. Reprinted by permission of The Free Press, a Division of Macmillan, Inc.

All three techniques are based on questionable assumptions and serve merely to identify the number of stages involved. They provide little, if any, information on the nature of each stage or the variables that affect learning during that stage. In addition, they do not lend themselves to the more complex and meaningful forms of learning addressed in this article.

Phase Theories in Simpler Forms of Learning Various forms of a phase theory of learning have existed since the earliest days of research on learning. For example, Bryan and Harter’s (1897, 1899) well-known studies of telegraph operators learning Morse code provide evidence for a phase theory of learning. The beginner first learns the alphabet of dots and dashes and sends and receives words on a letter-by-letter basis. With practice, the operator begins to combine these individual letters into higher order units that correspond to words, and, with continued practice, he or she combines words into units comprised of several words (i.e., phrases and short sentences). During the 1950s and 1960s, phase theories of paired-associated learning and skill learning were proposed. Later, John Anderson (1982) suggested a phase theory of procedural learning. These various theories of learning phases in complex forms of human learning will be discussed in the remainder of this section. Phase theories concerned with more meaningful forms of learning will be discussed in the subsequent section as well as expert-novice differences in processing meaningful material and the corresponding concern for the nature and development of intellectual competence that implies, at least implicitly, a phase theory of learning.

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Paired-Associated Learning During the early 1960s, several theorists proposed multiprocess theories of paired = associate (PA) learning. In their influential book, Meaningfulness and Verbal Learning, Underwood and Schulz (1960) suggested a two-stage analysis of PA learning consisting of a response learning, or response recall, stage (in which the subject learns the various responses that are used on the list) and an associative, or hook up, stage (in which the connection between each response and its corresponding stimulus is acquired). A number of other investigators, however, suggested two-process theories consisting of a stimulus differentiation phase and an associative phase in situations in which the responses were all well learned (see Battig, 1968, for a comprehensive discussion of multiprocess theories in PA learning). McGuire’s 1961 report of his 1954 dissertation suggested the first three-stage theory consisting of (a) stimulus encoding, or stimulus predifferentiation (in which the subject learns to discriminate among the various stimuli on the list); (b) mediation (in which a link is found for associating each stimulus with the appropriate response); and (c) response learning (in which the subject must learn the various response items that are being used). For example, in learning the list DOG-TREE, CAT-TABLE, and MAN-CHAIR, the learner must first learn to differentiate among dog, cat, and man (the task would be more difficult, of course, if the stimuli were XJC, YJB, and XKB). Note that it is not necessary to learn the responses, because they are always presented. Then he or she must find a way of linking each member of the pair (the cat is on the table, etc.). In addition, the subject must learn that tree, table, and chair (but not house) are the various responses that are appropriate. Evidence to support these stage theories of paired-associate learning are based on findings that response meaningfulness affects paired-associate learning to a greater extent than stimulus meaningfulness and that intralist response similarity (i.e., all of the responses in the list are more-or-less synonymous in meaning vs. being unrelated) has a facilitative effect on response learning but a detrimental effect on overall learning (that presumably includes both the response-learning and associative phases; Underwood, Runquist, & Schulz, 1959). In addition, McGuire (1961) presented support for his three-stage theory in a detailed analysis of correct responses and intrusion errors in the learning of pairs in which the stimuli were solid black circles of varying diameters and the responses were numbers. In a related study involving free-recall learning, Labouvie, Frohring, Baltes, and Goulet (1973) compared the correlation patterns between free-recall performance of pictorial stimuli (with recall commencing either immediately after presentation or after a 30-second delay) and a battery of eight intelligence and memory-ability tests. Although the same acquisition curves were obtained under immediate and delayed recall, there were systematic differences in the patterns of correlations obtained for the two conditions. Intelligence variables

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correlated to a fairly high extent (.53 to .77) with recall during the later stages of acquisition under conditions of delayed recall, but the correlations were considerably less (.29 to .45) during the early stages of delayed recall and during all stages of immediate recall. Memory variables, on the other hand, were significantly correlated (.55 to .56) with performance during early stages of acquisition under conditions of immediate recall but not under the other conditions. Thus, it appears that the task demands involved in learning may change systemically as learning progresses.

Skill Learning More research has been done on phases in skill learning than in any other type of research on learning, beginning with Bryan and Harter’s (1897, 1899) classic studies on learning Morse code. During the mid-1950s, a series of factor analytic studies by Fleishman and Hempel (1954, 1955) revealed systematic changes in the particular combination of psychomotor abilities (e.g., reaction time, manual dexterity, rate of movement, spatial relations) most important for performance as learning progressed. For example, nonmotor abilities such as verbal ability and spatial relations play an important role early in learning, but their importance decreases progressively with practice. Motor abilities (e.g., reaction time, rate of movement), on the other hand, play an increasingly important role as learning progresses, as does a factor specific to the task itself. Later, Fitts (1962; 1964) suggested that skill learning consists of three phases: (a) cognitive, (b) associative, and (c) autonomous (although the labels applied to the three phases differ somewhat in his various writings). The initial phase can be relatively short, depending on the complexity of the task and consisting of “the time required to understand instructions, to complete a few preliminary trials, and to establish the proper cognitive set for the task” (Fitts, 1964, p. 262). The intermediate phase involves mediation and learning to associate various responses to specific cues as well as cognitive set learning. During the late phase, highly skilled performance continues to improve indefinitely. It should be noted that Fitts (1964) emphasizes that skill learning is a continuous process, without distinct stages as such. Instead, we should think of gradual shifts in the factor structure of skills, or in the nature of the processes (strategies and tactics; executive routines and subroutines) employed, as learning progresses. The evolving process is revealed by the organization of behavior into larger and larger units . . . and toward hierarchical organization. (pp. 261–262)

Dreyfus and Dreyfus (1986) have also suggested a phase theory of skill learning. Based on their observations of skill acquisition in airplane pilots, chess players, automobile drivers, and adults learning a second language, they

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suggest that five stages are involved in learning a complex skill: (a) novice, ( b) advanced beginner, (c) competent, (d) proficient, and (e) expert. The phases are defined largely by the manner in which four factors (components, perspective, decision, and commitment) operate in the respective phases (see Table 1). Each stage is described in considerable detail, but the data on which their theory is based are never presented. Consequently, it is difficult to determine the validity of the various stages or whether five stages (e.g., rather than three) are really needed to explain the data. Benner (1984), starting with the five stages as a given, attempted to validate the Dreyfus model in an interview study of expert and novice nurses. The nurses were asked for their perceptions of what was important in a series of case studies and how they would seek a solution to the problem involved in the case. Unfortunately, the study does not provide a clear test of the theory.

Procedural Learning In some ways, Anderson’s (1982, 1987) research could be included in the section on meaningful learning, for it deals with intellectual skills, such as solving mathematics problems, and Anderson argues that the model applies to all complex learning. It is represented in this section, however, because it has been presented consistently as a model of skill learning. Anderson proposes three phases of procedural learning very similar to Fitts’ (1964) three phases of skill learning; Anderson refers to them as the (a) declarative, ( b) knowledgecompilation, and (c) procedural phases. Anderson (1982, 1987) makes the common distinction between declarative, or propositional knowledge (knowledge about something), and procedural knowledge (knowledge of how to do something), and he argues that we begin learning a new domain by encoding a set of facts in largely unanalyzed form that we subsequently can interpret without allowing it to control our behavior. That is, we can withhold judgment about the behavioral implications of such declarative knowledge until we see examples of and reflect upon ways in which it can be used. With additional experience, we begin to combine some of this declarative knowledge into procedures that allow us to apply it on a limited basis that still does not demand explicit control of our behavior. Ultimately, procedures evolve that do control our behavior without a great deal of conscious thought and effort – that is, the behavior comes automatic.

Phase Theories in Meaningful Learning Phase theories have also been discussed with regard to more meaningful forms of learning, although usually on a more implicit and less well-developed basis than the phase theories discussed in the preceding section. Although there

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is considerable agreement among various investigators on the viability of phases in long-term meaningful learning, the empirical evidence to support their presence is not overwhelming at present. Thus, the following review of the literature is based more on theoretical arguments than on empirical evidence, although the latter will be discussed whenever possible. Perhaps the earliest discussion of stages in meaningful learning was Wallas’ (1926) suggestion that problem solving involves four stages: preparation, incubation, illumination, and verification. Unfortunately, Wallas’ stages, as well as similar ones suggested by other investigators, are based more on introspection than on sound scientific investigations of any kind. A good critique of this literature is contained in Mayer (1983). Within the context of a schema-based theory of long-term memory, Rumelhart and Norman (1978) have suggested three qualitatively different types of learning: (a) accretion, or the encoding of new information in terms of existing schemata; ( b) tuning, or schema evolution, the slow modification and refinement of a schema as a result of using it in different situations; and (c) restructuring, or schema creation, the process by which new schemata are created. Rumelhart and Norman imply that these three kinds of learning occur sequentially, but, whereas there is consistency in listing accretion as the first phase, they interchange the order of tuning and restructuring in their discussion. Spiro, Coulson, Feltovich, and Anderson’s (1988) cognitive flexibility theory focuses on advanced knowledge acquisition. This phase of learning occurs between one’s initial attempts to study a subject area and the high levels of expertise that come with massive amounts of experience. According to Spiro et al.: This often neglected intermediate stage is important because the aims and means of advanced knowledge acquisition are different from those of introductory learning. In introductory learning the goal is often mere exposure to content and the establishment of a general orientation to a field; objectives of assessment are likewise confined to the simple effects of exposure (e.g., recognition and recall). At some point in learning about a knowledge domain the goal must change; at some point students must ‘get it right.’ This is the stage of advanced knowledge acquisition. (p. 1)

Although the phase aspect of the Spiro et al. theory is based more on their experience than on sound empirical evidence that the various phases actually exist, it does provide an example of current thinking among cognitive psychologists on the topic. Probably the best developed and most empirically based phase theory of meaningful learning is Karmiloff-Smith’s (1984, 1986) theory of cognitive development, discussed in the following section. The subsequent section will explore the implications of research on expert-novice differences (and the corresponding concern for the development of competence) for a phase theory of learning.

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Developmental Learning Based on evidence from several studies, Karmiloff-Smith (1984, 1986) has developed a knowledge-based theory of cognitive development. She believes that the theory is relevant to individuals of all ages who are learning a new content area. Phases and levels are distinguished from stages (as described earlier in this article), and she postulates the involvement of three phases/levels referred to as: (a) procedural, (b) metaprocedural, and (c) conceptual.1 During the procedural phase/level, the individual’s responses are generated primarily by data-driven processes generated by the individual’s adapting to external stimuli. The person’s behavior is controlled predominantly by the environment. During this initial phase/level, one observes behavioral change with no attempt to develop an overall organization capable of linking the isolated behavioral units into a consistent whole. During the second (or metaprocedural) phase, the individual beings to work in a “top-down” manner on the mental representations formed during the first phase – that is, the person begins to reflect or think about these representations as entities in their own right. During this phase, external stimuli become secondary to an internal representation that the person imposes on the environment. The person’s external behavior may actually deteriorate somewhat from what was observed during the preceding phase, for external stimuli are ignored as he or she experiments with the internal representation. The third (or conceptual) phase is governed by a subtle control mechanism that modulates the interaction between the data-driven processes characteristic of the first phase and the top-down processes characteristic of the second phase. The person is now in control of both environmental stimuli and the internal representations that guide his or her behavior. During this phase, the individual is able to consider environmental feedback without jeopardizing the structure of the internal representations.

Expert-Novice Differences Research on expert-novice differences grew out of a concern for the nature of intellectual competence and the way it develops. Because experts and novices are presumed to differ primarily, if not exclusively, in terms of the experience they have had in a particular subject-matter domain, we are dealing once again with a knowledge-based approach to learning. Although there is general agreement that a continuum exists as the individual moves from novice to expert in a particular field, most of the research to date has been concerned with describing differences in the way the two groups solve problems. It should be noted that in this research novice typically refers to someone who has had limited experience with the field or material being investigated, not someone with no experience. For example, in research on physics problem solving, a novice

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might have had one undergraduate course in physics, whereas an expert might be a professor of physics or someone with comparable experience. Such minimal experience for the novice is necessary in order to have a reasonable basis for comparison, for data on how novices solve a problem could not be obtained if the subjects could not solve the problem at all. A number of qualitative differences between experts and novices have been identified (for a brief review, see Glaser & Chi, 1988; Shuell, 1986b). For example, in solving physics problems, experts tend to perform a qualitative analysis of the problem prior to deciding which equations to use, whereas novices tend to focus on equations from the onset and engage in a direct syntactic translation (e.g., identifying variables and then plugging them into an equation) rather than generating a physical representation of the problem situation. Likewise, novices tend to focus on literal objects and/or key terms explicitly mentioned in the problem, whereas experts tend to identify features that reflect the states and conditions of the physical situation described in the problem (Chi et al., 1982). Thus, novices might respond to (identify or classify) a problem in terms of “friction” or “gravity,” whereas experts might refer to it in terms of “given initial conditions” or “no external force” (p. 64). Few attempts have been made to identify stages or phases that might exist between the two states, although Voss, Greene, Post, and Penner (1983) discuss differences among undergraduates (novices), graduate students, and experts. Chi (1978) distinguishes among novice, advanced novice, and expert. Champagne, Klopfer, and Gunstone (1982) differentiate between uninstructed, or preinstructional students (i.e., those who have no experience studying the topic); novice (those with minimal experience in the field – i.e., the typical novice in expert-novice studies); and experts in their discussion of research relevant to the teaching of physics. Not only do Champagne et al. (1982) provide a detailed description of differences between the schemata of students in these three phases of learning based on their analysis of various empirical studies, they also discuss ways in which these differences are related to teaching students in each phase. Uninstructed students, for example, use principles that are little more than generalized rules derived from their everyday experiences. Consequently, these principles tend to be imprecise due to the students’ vague understanding of concepts, errors of magnitude, and inappropriate formulations of general rules. For novices, however, principles involve relationships between physical variables in the form of equations or rules. Although the major laws of physics are expressed in equation form, there is no indication that these equations serve an organizing function (e.g., as schemata). For experts, principles represent major laws of physics in a highly abstract form that expresses relationships with great generality. Each principle includes the conditions under which the principle applies and has an associated schema that serves to organize the relevant material.

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Conclusions Meaningful cognitive learning is an active, constructive, and cumulative process that occurs gradually over a period of time (Shuell, 1986a). It is a goal oriented process best characterized in terms of problem solving (Anderson, 1987; Bereiter, 1989; Shuell, 1990). Learning is not merely an additive process – qualitative, as well as quantitative, changes occur, and qualitative differences are evident in both the substance of what is being learned and in the learning processes most appropriate for acquiring additional knowledge. The preceding review of the literature reveals reasonable agreement among investigators that a learner passes through a series of phases as his or her knowledge about something evolves. During these phases, the learning process and the variables influencing it change in systematic ways. The exact number of phases that might be involved is not clear (although most theories have postulated three), and the characteristics of each phase have not been worked out in any detail. Needless to say, the number of phases and the defining characteristics of each one must be established on the basis of sound methodology, as discussed earlier in this article. Merely postulating their existence is not enough. Nevertheless, it may prove useful to attempt an initial description of what the various phases might be like as well as some speculative comments about the transition from one phase to another.

Initial Phase During the initial phase of learning, the individual encounters a large array of facts and pieces of information that are more-or-less isolated conceptually. Merely because someone familiar with the topic (teacher, expert, etc.) may see an organizing structure with many interrelationships among the various facts does not mean that the novice learner can make sense out of them. Initially, there appears to be little more than a wasteland with few landmarks to guide the traveler on his or her journey toward understanding and mastery. Under the circumstances, the learner does the only thing that is reasonable: memorizes facts and uses preexisting schemata to interpret the isolated pieces of data. Some of this new information is added to existing knowledge structures – for example, Rumelhart and Norman’s (1978) notion of accretion – and these preexisting knowledge structures are used for interpreting the new information and giving it meaning. If no meaning can be found, the information remains as isolated facts. Because the learner has little specific knowledge of the domain, the initial processing is global in nature (Sternberg, 1984). The learner must rely on general, domain-independent problem solving strategies and knowledge

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from other domains to interpret the new information, to make comparisons and contrasts, and to find analogies that appear relevant to the learner (Anderson, 1987; Brown, Bransford, Ferrara, & Campione, 1983). The information acquired during this initial phase is concrete rather than abstract and bound to the specific context in which it occurs (Bransford & Franks, 1976). Thus, the encounter with a new domain of knowledge involves the rote learning of more-or-less isolated facts (we memorize new terms or what appear to be key facts – if we are learning a structured body of knowledge such as history, literature, or psychology – or we identify and try to remember key landmarks if we are learning to navigate around a large city.2 Gradually, the learner begins to form an overview of what the new domain is all about. In pursuing this task, our prior knowledge provides some help (or in certain cases hindrance) by suggesting initial possibilities and by establishing boundary constraints that assist in identifying both the sameness and the uniqueness of the new information (Bransford & Franks, 1976). Analogies from other domains may be used to represent the new domain, although these initial analogies must be modified as learning progresses (Anderson, 1987). The sophisticated learner may make assumptions, based on previous learning experiences, such as (a) “the knowledge [I am learning] has a structure that is more complex than [presently evident],” (b) “[I am] going to have trouble judging the importance of information and [it is] better to err on the side of overestimating importance,” and (c) “familiar words may have special meanings in the [new] domain” (Bereiter, 1989, p. 4). The fog that has shrouded the terrain is beginning to lift, but it is still difficult to see things clearly. During the initial phase, relatively simple forms of learning (e.g., operant conditioning, verbal learning) account for a large part of the learning that occurs. Classical conditioning may also be relevant with regard to establishing an emotional/affective predisposition to learning within that domain. Early stages of concept learning (e.g., grouping) may also occur, but the learner has acquired insufficient information for more complex forms of propositional and procedural learning to occur – such as Rumelhart and Norman’s (1978) tuning and restructuring. Thus, one might reasonably expect mnemonic strategies (a form of elaborative encoding) to have a greater affect on learning than chunking (a form of reductive encoding).3

Intermediate Phase Gradually, the learner begins to see similarities and relationships among these conceptually isolated pieces of information. The fog continues to lift but still has not burnt off completely. As these relationships become better developed, they are formed into higher order structures and networks. New schemata that provide the learner with more conceptual power are formed, but these

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new structures and schemata do not yet allow the learner to function on a fully autonomous, or automatic, basis. More meaningful forms of propositional and procedural learning predominate – what Spiro et al. (1988) refer to as advanced knowledge acquisition – and the student must now “ ‘get it right’ . . . attain a deeper understanding of content material, reason with it, and apply it flexibly in diverse contexts” (Spiro et al., 1988, p. 1). We extend our knowledge by applying it to new situations and by learning by doing – that is, the information acquired during the initial phase is now applied to the solution of various problems that the learner encounters, including understanding and explaining various situations such as might be involved in answering an essay question. An important advantage of this phase is that we can try out new knowledge in various ways and receive feedback on its appropriateness without its having autonomous control over our behavior (Anderson, 1982). Thus, there is the opportunity for reflection. As our knowledge becomes more abstract and more capable of being generalized to a variety of situations, it becomes less dependent on the specific context in which it was originally acquired (Karmiloff-Smith, 1984, 1986). During this phase, there may be a temporary deterioration in performance as all of these competing factors are sorted out (Karmiloff-Smith, 1984, 1986; Lesgold et al., 1988). Does learning automatically progress to this intermediate phase? Not necessarily. To insure that the transition occurs from the initial to the intermediate phase, certain things need to occur. Unfortunately, these things often are missing from an educational system that emphasizes the accumulation of more and more factual information – that is, an additive model of learning. In order for information to become more abstract, or decontextualized, Bransford and Franks (1976) suggest that concepts and knowledge should be used to clarify different situations, and they stress the importance of encountering relevant examples, a recommendation that is similar to Spiro et al.’s (1988) emphasis on learning by cases. The teacher and/or the learner can additionally employ various organizational strategies such as outlining and cognitive mapping (that can help the learner to identify and develop higher order relationships in the information being learned) and use the information to solve problems of various types (learning by doing). Variables such as mnemonics, for example, that had substantial affects on learning during the initial phase may have little, if any, affect on learning during the intermediate phase.

Terminal Phase During the last phase of learning, the knowledge structures and schemata formed during the intermediate phase become better integrated and function more autonomously. In most situations, performance will be automatic,

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unconscious, and effortless, because relevant knowledge structures now control behavior in a more direct manner (Anderson, 1982). The individual relies heavily, if not exclusively, on domain-specific strategies for solving problems, answering questions, and so forth. The emphasis in this phase is on performance rather than learning, because any change in performance is most likely the result of different task requirements rather than changes in one’s cognitive structure or potential for performing in a particular manner. In fact, performance (e.g., solving a mathematics problem) that may have involved learning during an earlier phase may involve little, if any, learning during the terminal phase. The ability to perform a task (including answering certain questions about a complex body of knowledge) that is accomplished in a straightforward, automatic manner (i.e., one merely utilizes preexisting procedures) involves neither learning nor problem solving. The learning that does occur during this phase most likely consists of either: (a) the addition of new facts to preexisting schemata (i.e., accretion), or (b) increasingly higher levels of interrelationships (e.g., where the schemata consist of other schemata rather than facts). In one sense, learning in a particular domain never ends, but a point is reached when the expert (not necessarily defined in the traditional sense) functions autonomously on automatic pilot, giving little thought and /or exerting little mental effort to the control of what he or she is doing.

Transition Between Phases The most problematic part of any phase theory concerns the transition between phases. What, for example, is the nature of the change that occurs as one moves from one phase to the next? And what factors lead to the changes that are purported to occur? To many people, phases suggest the presence of separate and distinct entities with clear-cut boundaries between adjoining stages. But it seems unlikely that such is the case. It probably is best to think of learning as a continuous process; the boundaries between phases are most likely fuzzy, and the transitions between phases gradual rather than dichotomous (see Fitts’ [1964] quote cited earlier). The truth of the matter is that we currently have a very poor understanding of how these transitions occur and what factors precipitate them. But this type of problem is not unique to psychology; the physical sciences have similar problems – for example, understanding the changes and precipitating factors when a steady stream of water turns into a series of discrete drops. In both instances, the separate states (phases) can be documented, but the transition escapes current understanding. It may be the case, at least with phases of learning, that during the transition characteristics of both phases are operating in an overlapping manner. Thus, the learner might continue to rely on mnemonics even though their usefulness has diminished and the need for organization has become paramount. Such duplication could even

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serve a functional purpose in that new behavior is often unstable and the involvement of more than one factor could minimize the potentially negative effect of phenomena such as regression and forgetting. Finally, it is reasonable in an educational context to raise the issue as to whether the transitions between phases can be stimulated or encouraged. Given our lack of understanding of how these transitions operate, little can be said that might be helpful in this regard. Nevertheless, it does not seem unreasonable (neither is it anything new) to speculate that transitions can be facilitated by encouraging learners to utilize strategies consistent with the phase they are about to enter. However, the good teacher, as well as the good learner, will be aware that premature involvement of facts may be counterproductive. Let them be available in working memory, but let them enter into the learning process in their own due time. One final possibility is worth considering – namely, that phases and the transitions between them may be by-products of the learning process rather than an integral part. If such is the case, attempts to facilitate transitions per se will accomplish little.

Other Considerations There appears to be sufficient rationale to support the notion that learning a complex body of knowledge – whether it be the type we learn in school, the compilation of life experiences, or the mastering of the skills inherent in a craft, trade, or profession – involves a series of phases during which the learning process is fundamentally different. It is usually assumed that these phases are organized in a linear manner, but it is possible that they may be organized in a hierarchical, spiral, and /or concentric manner as well (Wade, 1989). Earlier phases may be subsumed into subsequent phases (in much the same way as the developmental stages of Piaget), or new phases may exist side-by-side (as in the developmental stages of Jerome Bruner).4 The present analysis and review has focused on cognitive aspects of learning, but learning, especially the type of long-term learning being discussed, involves emotional, affective, and social aspects as well. The extent to which these various aspects of learning interrelate in a manner conducive or detrimental to learning may also vary as a function of the phase of learning. For example, an individual may begin studying a domain of knowledge with considerable enthusiasm and interest only to discover later that the domain was not what he or she originally expected, and the converse can exist as well (e.g., begin with low expectations and a dislike that becomes more positive as learning progresses). In closing, it must be cautioned that, although a phase analysis of learning is appealing in many ways, much more evidence is needed if the existence of phases is to be established in a scientifically valid manner. Some of the methodological concerns have been discussed in this article, but delineation of the

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phases (with regard to both the number of phases that might be involved and the characteristics of each phase) must await future research. In the meantime, the realization that phases most likely exist in the learning of complex and potentially meaningful knowledge provides useful insights into the learning process (including a basis for explaining why certain variables affect learning in some situations but not in others). In teaching such knowledge, it also suggests that we should pay attention to the way the teaching / learning process changes as learning progresses.

Notes 1. Phases and levels are similar in that both involve equivalency of process across either various domains or within a specific domain, respectively. They differ in that an individual progresses through the same three phases whenever he or she acquires a new body of knowledge (e.g., physics vs. literature), but, once he or she moves from Level 1 to Level 2 in a particular domain (e.g., the American novel), returning to Level 1 in that domain is not possible. 2. Too frequently, rote learning and meaningful learning are pitted against one another in a good /bad or either /or manner. In reality, both play an important role in learning from instruction, for at times it is intelligent to memorize something by rote, especially in the present context where rote learning is a means to an end rather than necessarily being an end in itself. 3. See Norman (1978) for a detailed discussion of learning strategies, general characteristics, modes of testing, and transfer relevant to accretion, restructuring, and tuning. 4. The distinctions that Karmiloff-Smith (1984, 1986) makes among stages, phases, and levels are also relevant here.

References Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406. Anderson, J. R. (1987). Skill acquisition: Compilation of weak-method problem solutions. Psychological Review, 94, 192–210. Andre, T. (1986). Problem solving and education. In G. D. Phye & T. Andre (Ed.), Cognitive classroom learning: Understanding, thinking, and problem solving (pp. 169–204). Orlando, FL: Academic Press. Baddeley, A. D. (1966). The influence of acoustic and semantic similarity on long-term memory for word-sequences. Quarterly Journal of Experimental Psychology, 18, 302–309. Battig, W. F. (1968). Paired-associate learning. In T. R. Dixon & D. L. Horton (Eds.), Verbal behavior and general behavior theory (pp. 149–171). Englewood Cliffs, NJ: Prentice-Hall. Benner, P . (1984). From novice to expert: Excellence and power in clinical nursing practice. New York: Addison-Wesley. Bereiter, C. (1989, March). The role of an educational learning theory: Explaining difficult learning. In W. J. McKeachie (Chair), Toward a unified approach to learning as a multisource phenomenon. Symposium conducted at the meeting of the American Educational Research Association, San Francisco. Brainerd, C. J. (1985). Model-based approaches to storage and retrieval development. In C. J. Brainerd & M. Pressley (Eds.), Basic processes in memory development: Progress in cognitive development research (pp. 143–207). New York: Springer-Verlag.

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Brainerd, C. J., Howe, M. L., & Desrochers, A. (1982). The general theory of two-stage learning: A mathematical review with illustrations from memory development. Psychological Bulletin, 91, 634 –665. Bransford, J. D., & Franks, J. J. (1976). Toward a frame wok for understanding learning. In G. H. Bower (Ed.), Psychology of learning and motivation ( Vol. 10, pp. 93–127). New York: Academic Press. Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites for understanding: Some investigations of comprehension and recall. Journal of Verbal Learning and Verbal Behavior, 11, 717–726. Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering, and understanding. In P . H. Mussen (Ed.), Handbook of child psychology: Vol. III. Cognitive development (J. H. Flavell & E. M. Markman, Vol. Eds.) (4th ed., pp. 77–166). New York: John Wiley & Sons. Bryan, W. L., & Harter, N. (1897). Studies in the physiology and psychology of the telegraphic language. Psychological Review, 4, 27–53. Bryan, W. L., & Harter, N. (1899). Studies on the telegraphic language: The acquisition of a hierarchy of habits. Psychological Review, 6, 345–375. Champagne, A. B., Klopfer, L. E., & Gunstone, R. F. (1982). Cognitive research and the design of science instruction. Educational Psychologist, 17, 31–53. Chi, M. T. H. (1978). Knowledge structures and memory development. In R. S. Siegler (Ed.), Children’s thinking: What develops? (pp. 73–96). Hillsdale, NJ: Lawrence Erlbaum Associates. Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. Sternberg (Ed.), Advances in the psychology of human intelligence (Vol. 1, pp. 7–75). Hillsdale, NJ: Lawrence Erlbaum Associates. Chiesi, H. L., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain-related information in relation to high and low domain knowledge. Journal of Verbal Learning and Verbal Behavior, 18, 251–273. Conrad, R. (1964). Acoustic confusions in immediate memory. British Journal of Psychology, 55, 75–83. Dreyfus, H. L., & Dreyfus, S. E. (1986). Mind over machine: The power of human intuition and expertise in the era of the computer. New York: Free Press. Fitts, P . M. (1962). Factors in complex skill training. In R. Glaser (Ed.), Training research and education (pp. 177–197). Pittsburgh: University of Pittsburgh Press. Fitts, P . M. (1964). Perceptual-motor skill learning. In A. W. Melton (Ed.), Categories of human learning (pp. 243–285). New York: Academic Press. Fleishman, E. A., & Hempel, W. E., Jr. (1954). Changes in factor structure of a complex psychomotor test as a function of practice. Psychometrika, 19, 239–252. Fleishman, E. A., & Hempel, W. E., Jr. (1955). The relation between abilities and improvement with practice in a visual discrimination reaction task. Journal of Experimental Psychology, 49, 301–310. Glaser, R., & Chi, M. T. H. (1988). Overview. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.). The nature of expertise (pp. xv–xxviii). Hillsdale, NJ: Lawrence Erlbaum Associates. Hayes, J. R. (1965). Problem topology and the solution process. Journal of Verbal Learning and Verbal Behavior, 4, 371–379. Hayes, J. R. (1966). Memory, goals, and problem solving. In B. Klenmuntz (Ed.), Problem solving: Research, method, and theory. New York: John Wiley & Sons. Karmiloff-Smith, A. (1984). Children’s problem solving. In M. E. Lamb, A. L. Brown, & B. Rogoff (Eds.), Advances in developmental psychology ( Vol. 3, pp. 39–90). Hillsdale, NJ: Lawrence Erlbaum Associates. Karmiloff-Smith, A. (1986). Stage /structure versus phase/process in modelling linguistic and cognitive development. In I. Levin (Ed.), Stage and structure: Reopening the debate (pp. 164 –190). Norwood, NJ: Ablex.

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Keil, F. C. (1986). On the structure-dependent nature of stages of cognitive development. In I. Levin (Ed.), Stage and structure: Reopening the debate (pp. 144 –163). Norwood, NJ: Ablex. Kintsch, W., & Buschke, H. (1969). Homophones and synonyms in short-term memory. Journal of Experimental Psychology, 80, 403–407. Labouvie, G. V., Frohring, W. R., Baltes, P. B., & Goulet, L. R. (1973). Changing relationship between recall performance and abilities as a function of stage of learning and timing of recall. Journal of Educational Psychology, 64, 191–198. Lesgold, A., Rubinson, H., Feltovich, P ., Glaser, R., Klopfer, D., & Wang, Y. (1988). Expertise in a complex skill: Diagnosing x-ray pictures. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise (pp. 311–342). Hillsdale, NJ: Lawrence Erlbaum Associates. Mayer, R. E. (1983). Thinking, problem solving, cognition. New York: W. H. Freeman. McGuire, W. J. (1961). A multiprocess model for paired-associate learning. Journal of Experimental Psychology, 62, 335–347. Norman, D. A. (1978). Notes toward a theory of complex learning. In A. M. Lesgold, J. W. Pellegrino, S. D. Fokkema, & R. Glaser (Eds.), Cognitive psychology and instruction (pp. 39– 48). New York: Plenum Press. Restle, T., & Davis, J. H. (1962). Success and speed of problem solving by individuals and groups. Psychological Review, 69, 520–536. Rumelhart, D. E., & Norman, D. A. (1978). Accretion, tuning, and restructuring: Three modes of learning. In J. W. Cotton & R. L. Klatzky (Eds.), Semantic factors in cognition (pp. 37–53). Hillsdale, NJ: Lawrence Erlbaum Associates. Shuell, T. J. (1986a). Cognitive conceptions of learning. Review of Educational Research, 56, 411–436. Shuell, T. J. (1986b). Individual differences: Changing concepts in research and practice. American Journal of Education, 94, 356–377. Shuell, T. J. (1990). Teaching and learning as problem solving. Theory into Practice, 29, 102–108. Spiro, R. J., Coulson, R. L., Feltovich, P . J., & Anderson, D. K. (1988). Cognitive flexibility theory: Advanced knowledge acquisition in ill-structured domains (Tech. Rep. No. 5). Springfield, IL: Southern Illinois University School of Medicine, Conceptual Knowledge Research Project. Sternberg, R. J. (1984). Mechanisms of cognitive development: A componential approach. In R. J. Sternberg (Ed.), Mechanisms of cognitive development (pp. 163–186). New York: W. H. Freeman. Thomas, J. C. (1974). An analysis of behavior in the hobbits-orcs problem. Cognitive Psychology, 6, 257–269. Underwood, B. J., Runquist, W. N., & Schulz, R. W. (1959). Response learning in pairedassociate lists as a function of intralist similarity. Journal of Experimental Psychology, 58, 70–78. Underwood, B. J., & Schulz, R. W. (1960). Meaningfulness and verbal learning. Philadelphia: Lippincott. Voss, J. F., Greene, T. R., Post, T. A., & Penner, B. C. (1983). Problem-solving skill in the social sciences. In G. H. Bower (Ed.), The psychology of learning and motivation: Advances in research and theory ( Vol. 17, pp. 165–213). New York: Academic Press. Wade, L. (1989). General and domain-specific elements related to stages of learning. Unpublished manuscript, State University of New York at Buffalo. Wallas, G. (1926). The art of thought. New York: Harcourt Brace Jovanovich.

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41 Growth, Development, Learning, and Maturation as Factors in Curriculum and Teaching William C. Trow

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n bringing together the diverse aspects of educational psychology indicated by the title of this chapter, and in focusing them on instructional practice, it would seem appropriate first to look for the hypotheses that lie behind both research and practice. The ideas embodied in these hypotheses can then be noted in the implications of the research that has been done. The treatment of this chapter will therefore concentrate on learning and the curriculum since growth and development are reviewed in a separate issue of this journal.

Hypotheses for Research and for School Practice Affecting Teaching and the Curriculum The basic idea that underlies all the others in modern education is to be found in John Dewey’s recurrent emphasis on the experience of the child as of central concern. Regard for his all-round development has shifted the emphasis from forcing him to acquire knowledge, to making provision for an inner living motivation (22). We cannot know what to teach, how to teach, or when to teach until we know whom we are teaching, and from what homes, groups, and cultures our students come, and until we can identify

Source: Review of Educational Research, XXI(3) (1951): 186–195.

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and project their current needs against the future needs of a dynamic society (55). If these things are to be done, man in general, and the pupil in particular, must be viewed not as a free soul, a capricious creature, but as a dynamic, lawful system interacting with the energy systems of the environment. Anderson (3) contrasted the effects on one’s view of pupil capacity, motivation, practice, and transfer when man is viewed (a) as a “free soul” and (b) as a “dynamic lawful system.” American psychology supports the latter under which there have been identified two basic systems of behavior and of learning, the association system and the field system. If man is viewed as a dynamic lawful system, it means, first, that the individuals in a learning group must be perceived as a group of dynamically interactive personalities (27). Included in this perception will be what Snygg and Combs (69) called the “phenomenological” approach, which seeks to explain behavior on the basis of the behaver’s perception of himself and of the world, and deal with it accordingly. It means, second, that the objectives of education will include the development of attitudes toward other individuals and groups (50), but they must be “right” attitudes. Research is therefore needed on value concepts and their role and influence in social behavior (87). Modern education is actually reoriented toward human values (14), yet probably few teachers realize they are applying axiology when they try to answer a child’s, “Why do we have to?” If axiology is viewed as the science of preferential behavior (53), the way is open for the coalescence of value concepts with those derived from the study of personality and referred to as needs. The third implication of the view of man as a dynamic lawful system, then, is that the educator, in working out a curriculum to promote pupil learning, will be a student of the dynamics of normal personality. As expounded by Dollard and Miller (29) and others, knowledge of personality is derived from psychoanalysis, social psychology, and anthropology, from which areas light is being thrown on the nature of social motivation and drive, of fear and conflict, and on the cultural conditions of learning. Harsh and Shrickel (41) traced successive developmental stages which produce changes in motivation, ability, and learned adjustment patterns and which must be more adequately reflected in the school program. For example, Miller (56) found that rats learned to get relief from an electric shock by striking each other, and when alone they struck a celluloid doll. The rats, like the child in play who beats the “mamma doll,” revealed the Freudian mechanism of displayed aggression, as would the child restricted by too narrow curriculum demands. Kluckhohn and Murray (51) in one of the chapters in Personality: In Nature, Society and Culture, to which a number of authors have contributed, outlined their dynamic, organismic concept of personality. They see it as a continuity of functional forces and forms manifested thru sequences of organized integrative processes. Its functions, among others, are self-expression and the reduction of

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tensions and conflicts thru social conformity, identification, and the creation of a design for living that permits periodic and harmonious appeasement of most of the needs, and gradual progression toward distant goals. If education is to profit from the empirical approach here indicated, as Bode (15) pointed out, it will have to accept Dewey’s view (28) and abandon its hope for truth as conformity to an alleged immutable cosmic order. Money (57) suggested a kind of compromise in view of the fact that the difference between private delusions, widely accepted beliefs, and scientific facts, is one of actual or possible validation. Since some beliefs, referred to as absolutes, have eased psychosomatic existence, may they not be considered as axioms that might be changed in content by common agreement; thus satisfying the philosophical need for stability and also the scientific need for change? In any case, scientific method has a long way to go in the social realm before it will be able to keep the thinking even of trained minds out of the mental channels of the child, the primitive, and the psychotic. These channels of thinking were mapped in detail by Werner (85) as follows, each with its scientific opposite: the syncretic to the discrete, the diffuse to the articulated; the indefinite to the definite, the rigid to the flexible, and the labile to the stable.

Learning and the Curriculum Since the relation of mental hygiene and guidance to the curriculum and teaching is not included in this issue of the Review, suffice it to say with Wright (88) that two kinds of childhood needs should be differentiated: the normative as they are appreciated by the adult, and the psychological as felt by children. But since the child responds not to generalities but to specifics, it is important, if his basic needs are to provide effective motivation, that curriculum activities be cognitively well-structured so that the activities lead clearly to the goals sought. Blair (11) indicated the nature of the psychologically effective curriculum as one which makes provision for varying maturity and experience levels, gears learning activities to pupil needs and goals, provides units of experience that have structure and meaning for the pupil, and selects and appraises projected pupil activities in terms of their transfer value to life situations. Brownell (19), however, pointed to the discrepancy between learning experiments and improvement in teaching, and called for long-term studies of the learning process in the classroom. He contended (18) that less emphasis in initial learning should be placed on speed and accuracy in the product, and more on improvement of the process and the establishment of principles transferable to new procedures. Detailed applications of educational psychology to the processes of learning and instruction are to be found in the Forty-Ninth Yearbook of the

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National Society for the Study of Education (58). In this volume, a number of authors who themselves have long been conducting research in this field interpreted their findings and those of their colleagues. Following chapters on the nature of learning and of motivation, a second section describes the ways children learn motor types of activities, concepts and generalizations, interests and attitudes, personal and social adjustments, esthetic responses, and the technics of problem solving. Implications of learning principles are then pointed out for the different school levels and for teaching procedures. English (31) presented a brief discussion of the nature of learning and a history of learning theory, while the most intensive and extensive treatment was given by Hilgard (45), who critically reviewed the current theories. Tolman (78) sought to explain the divergencies in theory by asserting that there is more than one kind of learning and listed six kinds including field expectancies, field cognition modes, drive discriminations and motor patterns. A detailed report of the psychological studies from which the theories are derived is to be found in the Annual Review of Psychology (70), the first issue of which appeared last year. In it there is a critical review of learning theory, with a 101-item bibliography by Melton, an analysis of problem-solving processes by Johnson, a report of growth and age changes especially in motor functions and mental abilities by Jones and Bayley, and a review of studies of assorted items from the field of educational psychology by Cronbach. Piaget (62) continuing his long series of studies on the genetics of mental functions traced four stages in the development of what he called the moral judgment of the child by analyzing children’s acceptance of the rules in the game of marbles. Gebhard (37) found that the attractiveness of an activity is determined not only by past experience of success but by the expectation of future success. Grace (39) concluded that verbal approval was more effective with the well-adjusted and emotionally stable. Postman (63), drawing from a 332-item bibliography, summarized the history and present status of the law of effect. In this connection, mention should be made of Thorndike’s Selected Writings from a Connectionist’s Psychology (76), in which studies published from 1913 to 1947 were chosen and arranged by the author himself including seven on learning. Postman (65) is the senior author of one study in which it was found that forgetting in the form of retroactive inhibition is smaller when a change of set (e.g., direction of association or type of logical relationship) is used. In another study (64) he concluded that the subject’s readiness for a particular type of test is a factor influencing set. A 136-item bibliography accompanies an article (36) on the measurementof transfer of training which concluded that various methods of measurement suggest that different functions are being measured. Not only is it time that some of these functions are isolated, but the conditions under which what is learned in school will be used when needed require still further study.

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School experimentation is bedeviled by the almost insuperable difficulty of the control of innumerable variables. Some, however, have faced up to the task. Anderson (2) found little difference in the result when pupils were taught arithmetic by a “drill method” (connectionist) and a “meaning method” (gestalt), altho the latter proved superior, for pupils scoring high on ability but low on initial achievement, in improving transfer to different kinds of materials. A study of pupil interests (47) revealed that variations are determined largely by opportunities and incentives of the environment, which means that the learning factor is important; and since elementary pupils revealed more interest in school than high-school pupils, it was concluded that the schools can use their influence to create more satisfactory learning situations. Studies of learning when learner differences are extreme have important implications for the wide range of abilities in the schools. Cruickshank (25) found that mentally retarded boys were less competent than normal boys of the same mental age in solving arithmetic problems. Strauss and Lehtinen (72) summarized 20 years of outstanding research on brain-injured children, reviewing especially-devised test situations, the characteristic behavior found, and applications to learning situations. Gesell (38) added a collection of papers reporting significant aspects of his work at the Yale Clinic previous to his retirement. And Terman and Oden (74) published the fourth volume of the Genetic Studies of Genius – The Gifted Child Grows Up, a 25-year follow-up of the original superior group, with educational and other implications, among which is the conclusion that in spite of the environmentalist’s efforts, the hereditary hypothesis seems to stand up.

General Sources Based on Research Studies with Implications for the Curriculum and Teaching Brickman (17) reviewed 16 texts and reference works in the field of educational psychology which were published during the years 1945–1948, and since then other texts have appeared including those by Beaumont and Macomber (8), Simpson (67), and Trow (79). Specific relationships of educational measurements and of a knowledge of individual differences to the curriculum were pointed out by Freeman (35) and Cook (24) respectively. Williams (86), after a survey of 223 titles, analyzed the approaches directed toward reducing and controlling intergroup tensions to which curriculum activities can make an important contribution. Growth factors influencing the curriculum have received detailed standard treatment by a number of authors including Breckenridge and Vincent (16) and Hurlock (48, 49). Rasey (66) brought out a unique volume which develops the implications of the whole-child concept and includes reports from the autobiographies of 1600 students with critic-teacher comments.

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Olson (60) is the author of a significant report of research which has been continuing over a period of 20 years. The results of longitudinal studies are presented with important implications for elementary-school activities. Remedial reading, self-selection, and promotional policies, among other problems, receive attention. Beck in Human Growth (9) described the physiological changes accompanying adolescence as was done in the film by the same name. Havighurst (42) defined an interesting concept, the “developmental task,” as one which, if successfully accomplished, leads to happiness and success with later tasks, but if not results in unhappiness, disapproval by society, and difficulty with later tasks. At the elementary-school level, Averill (5) prepared a textbook for the whole period, and Forest (33) for students of early childhood education. Lee and Lee (52) emphasized the “integrative approach” in handling the elementary-school subjects, and Hildreth (44) developed the principles of organized and unified learning in harmony with behavioral development, interpreting them in terms of realistic life experiences. At the high-school level two rather unique documents have appeared. One of them (73) presents in pamphlet form discussions of a hypothetical workshop group relating learning to diverse viewpoints and to curriculum experiences. The other (43), the “Prairie City” studies of the development of personality, is full of implications for programs of character education. Mention should also be made of a selected list of 72 references on gifted children (82), and of the Yearbook of the National Society for the Study of Education on The Education of Exceptional Children (59).

Curriculum and Co-curriculum – What Shall Students Learn? While curriculum revisions are predominantly in the field of social learning, among the others that might be selected for special mention is Cureton’s (26) work on physical fitness appraisal and guidance, which, along with other influences is likely to have the effect of improving the physical education program. Luchins and Luchins (54) carried forward the structural approach to the comprehension of spatial relationships, supplementing Wertheimer’s chapter on the area of a parallelogram which appeared in his Productive Thinking. Geometry teachers will find here suggestions for improving pupil comprehension. Meaning in arithmetic was studied by Van Engen (83) who favored what he called the concept of operational arithmetic, in which meanings are derived from acts or operations, in contrast with the social-meaning theory, the structural-meaning theory, and the nihilistic theory of numbers as meaningless symbols, which latter is perhaps far too prevalent.

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A number of papers at the college level deal with the place of educational psychology in the training of teachers. Anderson (1, 4) defined the field of educational psychology, enumerating the contributions it can make to the education of teachers and indicated suggestions for content. Trow (80) carried the analysis further enunciating the objective as effective pupil participation, this to be attained in proportion to the extent to which the teacher can learn to structure the school environment, organize activities (curriculum), recognize proper objectives in terms of needs and values, and teach appropriate behavior (knowledge, skills, and attitudes). Details were elaborated by others: Blair analyzed the content of various texts (12) and pointed out what teachers should know about adolescence (13). Bruce indicated relationships with general psychology (21) and the importance of a knowledge of child development (20). Freeman (34) and Cook (23) emphasized the importance of a study of individual differences and of educational measurement (24) for the curriculum and for the education of teachers. The direct approach has been made to the high-school pupil in order to facilitate his social learning thru the medium of the textbook. Thorpe (77) discussed understanding ourselves and others, maintaining personal and social integrity, character and religion, and personality and the welfare of society. Smart and Smart (68) were primarily concerned with the feelings and attitudes of children and their parents, and Duvall (30) prepared a text for family living which includes chapters on personality development, family interrelationships, boy-girl relationships, and preparation for marriage. Weiner (84) reported on the prolonged preacademic curriculum of the Wayne County Training School and furnished a guide for the objective observation of preacademic achievements, a program designed for high-grade feeble-minded children, but which should be taken to heart by elementary teachers in regular schools. The important implications of play therapy for general educational activities were elaborated by Axline (6) and helpful hints for an elementary-school council were provided by O’ Toole (62). The lush growth of group dynamics at Bethel and elsewhere forces the question of its place in the regular school situation where the teacher perforce has the roles both of leader and of resource person, as well as others. After doing background reading on group dynamics (10) and the psychodrama technic (40) developed by Moreno, the reader may wish to consider some of the implications of considering the class as a group (75, 81). From the social-work angle, group work has been going on for some time, and Strang (71) showed its relevance to schools and institutions of higher learning. Such efforts would seem appropriate since Bath (7) in a follow-up study found little to distinguish the winners from the non-winners of a junior high-school efficiency certificate in a good citizenship program of 20 years ago. Perhaps the most promising program for the future was initiated by the Horace Mann-Lincoln Institute of School experimentation. If effective improvements are to be made in the curriculum they will be made

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not so much as a consequence of what is done to teachers as of what they themselves do. This involves (46) research on the educational program thru cooperative teacher research and planning. More schoolwork than in the past will probably be carried on thru problem-centered group activities (32). This term designates an educational process by which teachers and students work cooperatively to solve problems related to the experiences, interests, and concerns of young people, in the process of which attitudes are structured and self-evaluation is encouraged.

Bibliography 1. Anderson, George Lester. “Educational Psychology and Teacher Education.” Journal of Educational Psychology 40: 275–84; May 1949. 2. Anderson, George Lester. “Quantitative Thinking as Developed Under Connectionist and Field Theories of Learning.” In Swenson, Esther; Anderson, G. Lester; and Stacey, Chalmers L. Learning Theory in School Situations. Minneapolis: University of Minnesota Press, 1949. 103 p. 3. Anderson, George Lester. “Theories of Behavior and Some Curriculum Issues.” Journal of Educational Psychology 39: 133–40; March 1948. 4. Anderson, George Lester. “What the Psychology of Learning Has To Contribute to the Education of the Teacher.” Journal of Educational Psychology 41: 362–65; October 1950. 5. Averill, Lawrence A. The Psychology of the Elementary School Child. New York: Longmans, Green and Co., 1949. 459 p. 6. Axline, Virginia Mae. Play Therapy: The Inner Dynamics of Childhood. Boston: Houghton Mifflin Co., 1947, 379 p. 7. Bath, John A. “A Study of Selected Participants and Non-Participants in a Program Directed Toward the Development of Initiative and Good Citizenship.” Journal of Experimental Education 16: 161–75; March 1948. 8. Beaumont, Henry, and Macomber, Freeman G. Psychological Factors in Education. New York: McGraw-Hill Book Co., 1949. 318 p. 9. Beck, Lester F. Human Growth. New York: Harcourt, Brace and Co., 1949. 124 p. 10. Benne, Kenneth D.; Bradford, Leland P.; and Lippitt, Ronald. Group Dynamics and Social Action. New York: Anti-Defamation League of B’nai B’rith, 1950. 62 p. 11. Blair, Glenn M. “HOW Learning Theory Is Related to Curriculum Organization.” Journal of Educational Psychology 39: 161–66, March 1948. 12. Blair, Glenn M. “The Content of Educational Psychology.” Journal of Educational Psychology 40: 267–74; May 1949. 13. Blair, Glenn M. “What Teachers Should Know About the Psychology of Adolescence.” Journal of Educational Psychology 41: 356–61; October 1950. 14. Bode, Boyd H., and others. Modern Education and Human Values. Pittsburgh: University of Pittsburgh Press, 1947. 165 p. 15. Bode, Boyd H. “John Dewey’s Philosophy of Education.” New Republic 121: 10–39, October 1949. 16. Breckenridge, Marian E., and Vincent, Elizabeth Lee. Child Development. Revised edition. Philadelphia: Saunders Co., 1943. 1949. 622 p. 17. Brickman, William W. “Educational Psychology: A Review.” School and Society 68: 218–23; September 1948. 18. Brownell, William A. “Criteria of Learning in Educational Research.” Journal of Educational Psychology 39: 170–82; March 1948.

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19. Brownell, William A. “Learning Theory and Educational Practice.” Journal of Educational Research 41: 481–97; March 1948. 20. Bruce, William F. “HOW Can the Psychology of Development in Infancy and Childhood Help Teachers?” Journal of Educational Psychology 41: 348–55; October 1950. 21. Bruce, William F. “The Relations of Educational Psychology with General Psychology.” Journal of Educational Psychology 40: 261–66; May 1949. 22. Caswell, Hollis L. “Influence of John Dewey on the Curriculum of American Schools.” Teachers College Record 51: 144–46; December 1949. 23. Cook, Walter W. “Individual Differences and Curriculum Practice.” Journal of Educational Psychology 39: 141–418; March 1948. 24. Cook, Walter W. “What Educational Measurement in the Education of Teachers?” Journal of Educational Psychology 41: 339–47; October 1950. 25. Cruickshank, William M. “Arithmetic Ability of Mentally Retarded Children.” Journal of Educational Research 42: 161–70, 279–88; November–December 1948. 26. Cureton, Thomas K. Physical Fitness Appraisal and Guidance. St. Louis: C. V. Mosby Co., 1947. 566 p. 27. Dennis, Wayne, editor. Current Trends in Social Psychology. Pittsburgh: University of Pittsburgh Press, l948. 299 p. 28. Dewey, John. Reconstruction in Philosophy. Enlarged edition. Boston: Houghton Mifflin Co., 1948. 224 p. 29. Dollard, John, and Miller, Neal E. Personality and Psychotherapy. New York: McGrawHill Book Co., 1950. 488 p. 30. Duvall, Evelyn Millis. Family Living. New York: Macmillan Co., 1950. 410 p. 31. English, Horace B. Learning as Psychotechnology. Columbus: Ohio State University, 1949. 81 p. 32. Evans, Hubert M., editor. “The Problem-Centered Group and Personal-Social Problems of Young People.” Teachers College Record 51: 438–59; April 1950. 33. Forest, Ilse. Early Years at School. New York: McGraw-Hill Book Co., 1949. 381 p. 34. Freeman, Frank S. “The Study of Individual Differences in the Education of Teachers.” Journal of Educational Psychology 41: 366–72; October 1950. 35. Freeman, Frank S. “How the Curriculum Is Evaluated and Modified Through Educational Measurement.” Journal of Educational Psychology 39: 167–69; March 1948. 36. Gagne, Robert M.; Foster, Harriet; and Crowley, Miriam E. “The Measurement of Transfer of Training.” Psychological Bulletin 45: 97–130; March 1948. 37. Gebhard, Mildred E. “The Effect of Success and Failure upon the Attractiveness of Activities as a Function of Experience, Expectation, and Need.” Journal of Experimental Psychology 38: 371–88; August 1948. 38. Gesell, Arnold. Studies in Child Development. New York: Harper and Brothers, 1948. 224 p. 39. Grace, Gloria Lauer. “The Relation of Personality Characteristics and Responses to Verbal Approval in a Learning Task.” Genetic Psychology Monographs 37: 73–99; 1948. 40. Grambs, Jean E. “Dynamics of Psychodrama in the Teaching Situation.” Sociatry 1: 383–99; March 1948. 41. Harsh, Charles M., and Schrickel, H. G. Personality, Development and Assessment. New York: The Ronald Press Co., 1950. 518 p. 42. Havighurst, Robert J. Developmental Tasks and Education. Chicago: University of Chicago Press, 1948. 86 p. 43. Havighurst, Robert J., and Taba, Hilda. Adolescent Character and Personality. New York: John Wiley and Sons, 1949. 315 p. 44. Hildreth, Gertrude. Child Growth Through Education. New York: The Ronald Press Co., 1948. 437 p. 45. Hilgard, Ernest R. Theories of Learning. New York: Appleton-Century-Crofts, 1948. 409 p.

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46. Horace Mann-Lincoln Institute of School Experimentation. “The Social-Cultural Context of the School Program.” Teachers College Record 49: 325–29; February 1948. 47. Horace Mann-Lincoln Institute of School Experimentation. “Child Development and the Curriculum.” Teachers College Record 49: 314 –24; February 1948. 48. Hurlock, Elizabeth B. Adolescent Development. New York: McGraw-Hill Book Co., 1949. 566 p. 49. Hurlock, Elizabeth B. Child Development. Second edition. New York: McGraw-Hill Book Co., 1950. 669 p. 50. John Dewey Society. Intercultural Attitudes in the Making. Ninth Yearbook. New York: Harper and Brothers, 1947. 246 p. 51. Kluckhohn, Clyde, and Murray, Henry A., editors. Personality: In Nature, Society, and Culture. New York: Alfred A. Knopf, 1948. 561 p. 52. Lee, Jonathan Murray, and Lee, Doris May. The Child and His Curriculum. Second edition. New York: Appleton-Century-Crofts, 1950. 710 p. 53. Lepley, Ray, editor. Value: A Cooperative Inquiry. New York: Columbia University Press, 1949. 487 p. 54. Luchins, Abraham S., and Luchins, Edith H. “A Structural Approach to the Teaching of the Concept of Area in Intuitive Geometry.” Journal of Educational Research 40: 528–33; March 1947. 55. MacLean, Malcolm S. “Adolescent Needs and Building the Curriculum.” Trends in Student Personnel Work. (Edited by E. G. Williamson.) Minneapolis: University of Minnesota Press, 1949. p. 27–39. 56. Miller, Neal E. “Theory and Experiment Relating Psychoanalytic Displacement to Stimulus-Response Generalization.” Journal of Abnormal and Social Psychology 43: 155–78; April 1948. 57. Money, John. “Delusion, Belief, and Fact.” Psychiatry 11: 33–38, February 1948. 58. National Society for the Study of Education. Learning and Instruction. Forty-Ninth Yearbook, Part I. Chicago: University of Chicago Press, 1950. 352 p. 59. National Society for the Study of Education. The Education of Exceptional Children. Forty-Ninth Yearbook. Part II. Chicago: University of Chicago Press, 1950. 350 p. 60. Olson, Willard C. Child Development. Boston: D. C. Heath and Co., 1949. 417 p. 61. O’Toole, John F., Jr. “A Study of the Elementary School Student Council.” Elementary School Journal 50: 259–67; January, 1950. 62. Piaget, Jean. The Moral Judgment of the Child. Glencoe, Ill.: Free Press, 1948. 418 p. 63. Postman, Leo J. “The History and Present Status of the Law of Effect.” Psychological Bulletin 44: 489–563; November 1947. 64. Postman, Leo J., and Jenkins, William O. “An Experimental Analysis of Set in Rote Learning.” Journal of Experimental Psychology 38: 683–89; December 1948. 65. Postman, Leo J., and Postman, Dorothy L. “Change in Set as a Determinant of Retroactive Inhibition.” American Journal of Psychology 61: 236–42; April 1948. 66. Rasey, Marie I. Toward Maturity. New York: Hinds, Hayden, and Eldredge, 1947. 242 p. 67. Simpson, Robert G. Fundamentals of Educational Psychology. New York: J. B. Lippincott Co., 1949. 380 p. 68. Smart, Mollie S., and Smart, Russel C. Living and Learning with Children. Boston: Houghton Mifflin Co., 1949. 271 p. 69. Snygg, Donald, and Combs, Arthur W. Individual Behavior, A New Frame of Reference. New York: Harper and Brothers, 1948. 386 p. 70. Stone, Calvin P., editor. Annual Review of Psychology. Stanford, Calif.: Annual Reviews, 1950. 330 p. 71. Strang, Ruth. “Group Work in Schools and Institutions of Higher Learning.” A Decade of Group Work. (Edited by Charles E. Hendry.) New York: Association Press, 1948. p. 95–104.

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72. Strauss, Alfred A., and Lehtinen, Laura E. Psychopathology and Education of the Brain-Injured Child. New York: Grune and Stratton, 1947. 220 p. 73. Sugarman, Myrtle F., editor. Effective Learning for Use in Junior High School. Denver Public Schools, 1949. 72 p. 74. Terman, Lewis M., and Oden, Melita H. The Gifted Child Grows Up. Stanford, Calif.: Stanford University Press, 1947. 448 p. 75. Thelen, Herbert A. “Human Dynamics in the Classroom.” Journal of Social Issues 6: 30–55; 1950. 76. Thorndike, Edward L. Selected Writings from a Connectionist’s Psychology. New York: Appleton-Century-Crofts, 1949. 370 p. 77. Thorpe, Louis P. Personality and Youth. Dubuque, Iowa: William C. Brown Co., 1949. 378 p. 78. Tolman, Edward C. “There Is More than One Kind of Learning.” Psychological Review 56: 144 –55; May 1949. 79. Trow, William Clark. Educational Psychology. Revised edition. Boston: Houghton Mifflin Co., 1950. 761 p. 80. Trow, William Clark. “Educational Psychology Charts a Course.” Journal of Educational Psychology 40: 285–94; May 1949. 81. Trow, William Clark, and others. “Psychology of Group Behavior.” Journal of Educational Psychology 41: 322–38; October 1950. 82. U.S. Office of Education. “Selected References on Gifted Children.” Understanding the Child 17: 56–64; April 1948. 83. Van Engen, Henry. “An Analysis of Meaning in Arithmetic.” Elementary School Journal 49: 321–29, 395– 400; February-March 1949. 84. Weiner, Bluma Beryl. “The Use of Systematic Classroom Observation To Aid in Curriculum Planning and Guidance for Young Mentally Retarded Boys.” American Journal of Mental Deficiency 52: 331–36, April 1948. 85. Werner, Heinz. Comparative Psychology of Mental Development. Revised edition. Chicago: Follett Publishing Co., 1948. 564 p. 86. Williams, Robin M., Jr. The Reduction of Intergroup Tensions: A Survey of Research on Problems of Ethnic, Racial, and Religious Group Relations. Social Science Research Council Bulletin No. 57, 1947. 153 p. 87. Woodruff, Asahel D. “Motivation Theory and Educational Practice.” Journal of Educational Psychology 40: 33–40; January 1949. 88. Wright, Herbert F. “How the Psychology of Motivation Is Related to Curriculum Development.” Journal of Educational Psychology 39: 149–56; March 1948.

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Section III: Motivation

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42 Maslow, Monkeys and Motivation Theory Dallas Cullen

T

he influence of Abraham Maslow’s (1943) hierarchy of needs is ubiquitous in management education and theory. Despite the common belief that Maslow’s theory is outdated and ignored (see, for example, Greiner, 1992: 61), current textbooks present the theory in approving terms. It is described as the ‘most widely recognized theory of motivation’ (Hellriegel et al., 1995: 174), the ‘most well-known need theory’ (Moorhead and Griffin, 1995: 83) and a ‘classic paper’ (Luthans, 1995:150). At the level of theory, Maslow’s hierarchy is so pervasive that it has almost become invisible, in that its basic framework and concepts are accepted without question. Managerial practices that permit or encourage employee autonomy and personal growth are justified on the grounds that such practices will enable employees to satisfy the esteem and self-actualization needs in Maslow’s hierarchy. Some of the recent literature on employee empowerment, for example, suggests that only those employees who value higher order needs such as personal growth will respond positively to being given greater autonomy in their work (Lawler, 1992: 83). This argument both follows from and incorporates the concept of ‘growth need strength’ in Hackman and Oldham’s (1980) theory of job design. In turn, the theory of job design draws on expectancy theory, which itself incorporates the needs in Maslow’s hierarchy (Porter and Lawler, 1968: 131). These links are not in any way hidden, but the question arises of why there is this acceptance of Maslow’s theory as the starting point, given that this acceptance coexists with the recognition that there is, at best, limited empirical evidence for the hierarchy (see, for example, Mitchell and Moudgill, 1976; Wahba Source: Organization, 4(3) (1997): 355–373.

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and Bridwell, 1976). Indeed, Maslow himself wrote in his journal in 1962 that ‘My motivation theory was published 20 years ago, and in all that time nobody repeated it, or tested it, or really analyzed it or criticized it. They just used it, swallowed it whole with only the most minor modifications’ (Lowry, 1982: 63). In terms of research, the usual explanation is that the theory has not been disconfirmed; rather, research has not yet supported it, because of methodological problems in interpreting, operationalizing and measuring its concepts (Wahba and Bridwell, 1976: 235). The textbook explanations for including the needs hierarchy are that it ‘implicitly states the goals people value’ (Hellriegel et al., 1995: 176), makes managers ‘aware of the diverse needs of employees at work’ (Luthans, 1995: 152) and ‘makes a certain amount of intuitive sense’ (Moorhead and Griffin, 1995: 85). Because the hierarchy seems to describe what the average employee seeks, it gives management a simple and quick means of understanding differences or changes in employee motivation (Huczynski, 1993: 24). These explanations, however, beg the question. Why are there problems in interpreting the hierarchy’s central concepts? Why does it make intuitive sense? One answer to these questions lies in recognizing that Maslow’s humanistic psychology contains both a ‘democratic’ premise, which emphasizes authenticity, self-fulfillment and respect for the choices, preferences and values of each individual, and an ‘aristocratic’ premise, which emphasizes vocational competence, self-criticism and deference to the choices, preferences and values of the self-actualizing elite (Aron, 1977). The presence of these contradictory premises might then lead to difficulties in measuring or implementing the hierarchy’s concepts. But why does Maslow’s theory contain these contradictory premises? One explanation has focussed on the hierarchy’s links to liberal democratic theory. For example, Buss (1979) argues that Maslow’s humanistic psychology was a liberal reaction to the conservatism embodied in both the positivistic methodology of behaviorist psychology and the pessimistic determinism of Freudian psychoanalysis. The democratic premise of individual autonomy and self-fulfillment in Maslow’s hierarchy is the psychological counterpart of liberal democratic theory’s stress on individual rights. However, in modern liberal democratic societies control belongs to a small and powerful elite rather than the masses, and the aristocratic premise reflects this reality. Consequently, Buss believes, the contradictions in Maslow’s theory are real rather than conceptual, since the theory is grounded in the historical and social realities of the growth of democratic elitism. Shaw and Colimore (1988) take this analysis one step further. Given the close link between politics and economics, they argue, Maslow’s theory is best understood as an affirmation of capitalist ideology. Since growth comes through vocational achievement, Maslow’s theory glorifies individual initiative for personal gain. At the same time, its hierarchical structure justifies the class system found in capitalistic societies, treating this system as both inescapable and beneficial.

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While Buss believed that the needs hierarchy was a reflection of Maslow’s own liberal values, Shaw and Colimore (1988: 69) were ‘stunned’ by the ‘non-liberating quality’ of Maslow’s contributions to the management literature. However, they, like Buss, conclude that Maslow’s contradictions are unintentional and unconscious reflections of the society of which he was a part. This rather benign assessment suggests that Maslow was unaware of the implications of his theory, an assessment that I believe reflects an incomplete understanding of the framework in which he developed the hierarchy. That framework was his own earlier empirical research on the importance of dominance in explaining non-human primate (that is, monkey and ape) and human behavior. Maslow’s dominance studies were a significant contribution to the primatology research of the 1930s and references to them occur throughout a variety of more recent literature in that field (see, for example, de Waal, 1996; Fedigan, 1992; Haraway, 1989; Rowell, 1974). Whereas writers on primatology who mention Maslow’s dominance research often note that he is most known for the needs hierarchy (Haraway, 1989: 409, who describes this fact as ‘ironic’; de Waal, 1996: 99), other writers’ assessments of Maslow’s work slight or ignore this connection. Neither Aron’s nor Buss’s discussions refer to the dominance research, while Shaw and Colimore (1988: 63), in noting Maslow’s ‘lifelong fascination with individual superiority and social dominance’, cite but do not discuss it. My focus in this paper is an analysis of the ways in which the dominance research was the foundation of self-actualization theory and the implications of the link between the dominance studies and the needs hierarchy. Such an analysis, I believe, suggests why the hierarchy is intuitively appealing and, consequently, why it remains so influential. As I will demonstrate, Maslow explained dominance in terms of the characteristics of the individuals involved, not in terms of the attributes of either the interaction between them or the setting in which this interaction occurred. Instead, group organization or behavior was due to individual psychology. A given individual’s ability to be dominant over others was due to that individual’s acknowledged natural superiority, and differences in human or monkey groups and cultures occurred because of differences in the exercise of dominance by the individuals in those groups and cultures. As I will also demonstrate, the incorporation of these ideas into the needs hierarchy means that Maslow’s theory justifies managerial power, and enables managers to adopt motivational practices that appear to be responsive to employee needs while at the same time absolving them of accountability for the ineffectiveness of those practices. To set the context for this analysis, I begin with a description of Maslow’s career up to the point at which the needs hierarchy was published. I then discuss Maslow’s primate research and his study of dominance in humans and demonstrate how the findings from these studies form the basis for the needs hierarchy. I next move to the implications of the dominance

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research for motivation theory, and finish with a discussion of the implications of a reexamination of Maslow’s monkey research in light of current primatological research.

Maslow’s Early Career Maslow was the first doctoral student of Harry Harlow (Suomi and LeRoy, 1982: 341), who later gained fame for his work on monkeys raised by surrogate mothers (see, for example, Harlow, 1974). In ‘an effort to see who was more correct, Freud or Adler, sex or dominance’ (Lowry, 1982: 55), Maslow focussed his doctoral research on the relationship between sexual behavior and dominance behavior in monkeys. He completed his doctorate in 1934 and became a research assistant to Edward Thorndike in the Institute for Educational Research at Columbia University the following year. Thorndike had his assistants complete a variety of intellectual and scholastic aptitude tests; Maslow’s tested IQ was an ‘astounding’ 195 (Hoffman, 1988: 74). For Maslow, this was evidence of his ‘factual superiority’, which meant that ultimately he was correct in his observations, intuitions and conclusions (see, for example, Lowry, 1982: 122–3). For his part, Thorndike was so impressed that he permitted Maslow to study whatever he wanted, which allowed Maslow to extend his studies of dominance and sexuality to humans (Wilson, 1972: 141). Maslow collected most of these data during 1936 (Hoffman, 1988: 75, 80), the same year that his studies of dominance in monkeys were published. Between this time and the publication of the needs hierarchy in 1943, his intellectual interests and activities also included other disciplines. In 1937, he began teaching at Brooklyn College, where he co-authored a textbook on abnormal psychology (Maslow and Mittelmann, 1941) and, relying primarily on his intuition, provided informal therapy to students (Hoffman, 1988: 142). He wrote a book chapter on the influence of culture on personality (Maslow, 1937c), and, in the summer of 1938, following anthropologist Ruth Benedict’s suggestion, undertook fieldwork among the Blackfoot in southern Alberta (Hoffman, 1988: 114). During this general time period, he also studied with the German psychoanalysts and Gestalt psychologists who had left Nazi Germany to work at the New School of Social Research (Hoffman, 1988: 87), including Kurt Goldstein, who had initially coined the term ‘self-actualization’ (Maslow, 1943: 382). He also continued to publish other papers based on his monkey studies (Maslow, 1937a; 1940a). While the studies of human dominance and sexuality were critical to the development of the needs hierarchy (Cullen, 1994; Lowry, 1973), for Maslow, ‘My primate research is the foundation upon which everything rests’ (Hoffman, 1988: 49). His confidence in the insights he gained from ‘my monkeys’ was in part due to his belief that this research involved a form of

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‘loving perception’; it was ‘more “true”, more “accurate”, in a certain sense, more objectively true’ because he was both ‘fond of’ and ‘fascinated’ by the monkeys (Maslow, 1971: 17, emphasis in original). We turn now to what he learned from his monkeys.

Dominance in Monkeys While the study of animal behavior is of value and interest in and of itself, its greater appeal for many people is its potential for teaching us about human behavior. This is particularly true of the study of non-human primates, especially the great apes (orangutans, chimpanzees and gorillas) and the Old World (Asian and African) monkeys such as macaques and baboons. One reason for this importance is that studies of non-human primates can give us insights into the behavior of our own earliest ancestors. How has human behavior evolved? What behaviors have led to, or ensured, our survival as a species? A second, but clearly related, reason is that non-human primate behavior can give us insights into behaviors that present-day humans share regardless of culture. In other words, monkeys and apes can tell us something about ‘true’ human nature, or, as Haraway (1986: 77) has observed, ‘what is “beneath”, “at the heart of”, or “outside”, our own behavior. Maslow himself clearly believed that this was the case. As noted earlier, his dissertation research was an attempt to compare the relative importance of sex and dominance in explaining behavior. He reported, however, that his initial attempt to study this relationship was a ‘failure’, both because of the ‘complexity of the problem’ and because his ‘own personality and social norms acted like a sieve or a filter’ (Maslow, 1937a: 488). In order to achieve ‘impartiality and objectivity’ he turned to animal studies, which allowed him to develop ‘a specific objective criterion or scale by which to judge human behavior’ and to see relationships among dominance, sexuality and social behavior that were ‘less confused by repression, inhibition, social norms and social values’ (Maslow, 1937a: 489). Comparative research of this type, Maslow argued, was a means of developing insights into ‘general humanness’ (1937a: 487). Given this potential significance, it is not surprising that primatology is a field of conflicting and contested data and interpretations, a field which is ‘politics by other means’ (Haraway, 1986). Conscious and unconscious social and political considerations influence what is observed about primate behavior; in turn, what is observed about primate behavior has social and political meaning. Consequently, each era of primatological research reflects the wider concerns of its time period (Haraway, 1989). Primatology in the 1930s, a time of political, social and economic turmoil, concentrated on the general themes of aggression and its control, cooperation and competition, and the means by which social order was maintained. Primatology researchers identified the

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dominance hierarchy, which is, quite literally, the ‘pecking order’ among the members of a group, as that means of social control. To the researchers of the era, the dominance hierarchy was the ‘foundation of cooperation’, ensuring that the social order did not collapse into destructive competition (Haraway, 1978a: 33). Moreover, dominance was inextricably linked with sexuality: the potentially destructive competition amongst males was for access to sexually receptive females, who accepted a subordinate status in order to gain access to desired items (such as food) which the males’ greater size enabled them to control. This difference in size and subsequent subordination of females, it was argued, influenced or led to the creation of a patriarchal family unit (see, for example, Yerkes, 1939: 131). In other words, dominance was necessary and male dominance was natural. For those working within this paradigm, the issue was not the presence or absence of dominance, but rather its manifestations and underlying sources. In 1929, Yerkes and Yerkes emphasized that ‘Dominance and subordination are evident in every group of primates . . . Dominance may be by either sex, but dominance there must be’ (p. 250). Ten years later, in a review of the ‘social psychology’ of vertebrates, Crawford (1939: 418) observed that ‘exploration of the significance of the concept of dominance has hardly begun’, since knowledge of the connection between dominance relations and other social relations, as well as the factors that determined which animal was dominant, were still not fully clear. However, some answers were emerging. In his discussion of dominance in primates, Crawford focussed on two main studies: Maslow’s four papers and Zuckerman’s (1932) study of dominance in baboons. Zuckerman had argued that, because female primates (unlike other animals) are continually sexually receptive, and hence continually sexually active, male and female primates live in permanent groupings. The fighting and aggression inherent in this continual association is regulated through a dominance hierarchy in the form of a harem, in which a male ‘overlord’ controls access to as many females as he can, thus ensuring his own reproductive success. Zuckerman’s theory of females’ continual sexual receptivity is now considered to be an oversimplification (Fedigan, 1992: 158), but, at the time, it was enormously influential, helping to establish dominance as a fact rather than as a concept (Haraway, 1978b: 47). Maslow’s own papers refer to Zuckerman’s ‘excellent study’ which gives ‘a clear indication of the importance of the dominance principle in primate sociology’ (1936a: 261). At the same time, however, Maslow contended Zuckerman had ‘missed the full significance’ and ‘grossly underestimated the importance’ of dominance as a cause of social behavior (1936a: 262, 275). The constant sexual activity in monkeys, Maslow believed, occurred because of both the hormonal cycle and a dominance drive (1936b: 330), a conclusion that was based on his own observational and experimental studies.

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During most of 1932 and the first few months of 1933, Maslow had made observations of small groups of monkeys housed at the Vilas Park Zoo in Madison, Wisconsin (Maslow, 1936a). An ‘experienced observer’ like Maslow was easily able to determine which monkey was dominant in a group: it ‘struts’ rather than ‘slinks’, has a ‘cocky, aggressive and confident air’, and ‘stares fixedly and ferociously’ at the other monkeys (Maslow, 1936a: 266). Maslow later labelled this stare ‘the ‘Gaze’: it was a ‘look of command’ that is ‘level, unwavering, unyielding, even unself-conscious & spontaneous’, with which ‘the overlord just looked, & the other dropped his [sic] eyes as if he’d been mastered & admitted it’ (Lowry, 1982: 89, 30). From his observations, Maslow concluded (as had Zuckerman) that each group had a dominance hierarchy with one monkey who was the overlord. Dominance was related to size and age, but was not related to gender. Females could be dominant over both males and other females, and the behavior of dominant females was the same as that of dominant males. However, dominant females seemed to lose their dominance when they came into heat. These observations were followed in 1933–4 by the study of experimental pairings of macaque (rhesus) monkeys at the Primate Laboratory at the University of Wisconsin (Maslow, 1936b, 1936c; Maslow and Flanzbaum, 1936). In the pairings, monkeys which had been caged separately were brought together in another chamber for a varying number of brief experimental periods, during which all their behavior was recorded. Based on his analysis of these behaviors, Maslow (1936c: 183) decided that the best indicators of dominance were mounting (taking the male role in sexual behavior) and bullying, while the best indicators of subordinance were cringing and flight. It was on this basis that he argued that there was a continuum of sexual behavior, with one end being sexual behavior motivated by ‘sexual drive’ and the other end being sexual behavior motivated by ‘dominance drive’, with the latter type being used as a ‘power weapon’ (Maslow, 1936b: 319, 336). Maslow also concluded that all monkeys have this dominance drive; a subordinate monkey is one ‘whose dominance has been overshadowed by greater dominance’ (1936a: 264). What, then, causes a given monkey’s ability to overshadow another? According to Maslow, dominance is ‘determined by or actually is a composite of social attitudes, attitudes of aggressiveness, confidence or cockiness that are at times challenged, and which must then, of course, be backed up by physical prowess’ (Maslow and Flanzbaum, 1936: 305). In the experimental pairings, dominance was usually established quite rapidly since ‘one animal seemed, in most cases, to assume at once that he [sic] was dominant, and that the other animal seemed, just as naturally, to admit that he was subordinate’ (Maslow and Flanzbaum, 1936: 303– 4). The superiority of one was accepted by both.

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The Nature of Dominance The picture of dominance that emerges in Maslow’s monkey studies is an unpleasant one. In Zuckerman’s baboons and Maslow’s macaques, dominance was ‘rough, brutal and aggressive; it is of the nature of a powerful, persistent, selfish urge’ that resulted in bullying and fighting (Maslow, 1940a: 316). Furthermore, dominance status (that is, a given monkey’s position in the dominance hierarchy) was ‘jealously guarded and affirmed’ (Maslow, 1940a: 319). However, not all non-human primates expressed dominance in such a brutal manner. Among chimpanzees, who were considered to be the most sociable of the great apes (Yerkes and Yerkes, 1929: 557), dominance was ‘mostly of a friendly kind’ (Maslow, 1940a: 314). One dominant male exercised his dominance in a teasing and playful, rather than a vicious, way (Maslow, 1935: 57). Another dominant chimpanzee tolerated, or was even apparently amused by, displays of anger or aggression by a subordinate (Maslow, 1940a: 315–16). Maslow was the first researcher to call attention to these genuine differences in the exercise of dominance (de Waal, 1996: 126); what is important to the development of the needs hierarchy is the conclusion he drew about them. He made what he called the ‘far-reaching and important’ suggestion that differences in group behavior were based on differences in individual personality (Maslow, 1940a: 322). The manner in which dominant chimpanzees behaved, as compared to the manner in which dominant baboons and macaques behaved, led to differences in chimpanzee society as compared to baboon and macaque society. As a result, Maslow hypothesized that cultural (or sub-cultural) differences among humans might be based on differences in the manner in which dominant individuals in those cultures behaved. What, then, would lead a dominant human to behave like a benevolent chimpanzee rather than a despotic baboon? The answer, according to Maslow, was the individual’s sense of emotional security. We turn now to the reasoning that led him to this conclusion. Maslow’s discussions of his human data reveal the same intermingling of dominance, sexuality and superiority as do his discussions of his monkey data. However, observing and measuring dominance in humans was more complicated than observing and measuring dominance in monkeys. For example, whereas a woman might dominate her husband, treating him with condescension, pity or aloofness, she might also be dominated by other people, an outcome which would not be apparent if one observed her only with her husband (Maslow, 1937b: 405–7). Consequently, Maslow focussed on people’s feeling of dominance, that is, the attitude which was analogous to the feeling of confidence he had observed in his dominant monkeys. He initially labelled this human attitude ‘dominance-feeling’ (Maslow, 1937b), but over time renamed it ‘ego-level’ (Maslow, 1939) and finally ‘self-esteem’ (Maslow, 1940b, 1942b), although he used the terms interchangeably.

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This renaming was intended to avoid the power-seeking connotation of ‘dominance-feeling’ (Maslow, 1942b: 269). Whatever its name, the attitude was difficult to define, so he provided a list of ‘near-synonyms’ that dominant people used to describe their own feelings about themselves, including selfconfidence, self-respect, forcefulness of personality, feelings that others do and ought to admire and respect one, and a consciousness of superiority in a general sense (Maslow, 1937b: 407). This feeling of superiority that highdominance people experienced was a ‘calm, objective recognition of facts that exist’; when factual inferiority was recognized, however, it did not lead to feelings of inferiority (Maslow, 1937b: 420). The data on the relationship between self-esteem and sexual behavior was collected in intensive, unstructured interviews totalling, on average, about 15 hours with each subject (Maslow, 1940b: 257). Initially, Maslow interviewed both men and women, but he found that ‘the men were far more evasive and tended to lie, exaggerate, or distort their sexual experiences’, whereas women, once they had agreed to participate, were more open (Hoffman, 1988: 77). In addition, Maslow found that interviewing women ‘was more fun – illuminating for me, the nature of women, who were certainly, to a shy boy, still mysterious’ (Wilson, 1972: 157), or, as his biographer describes it, the 28-year-old Maslow ‘got a thrill of excitement interviewing the women’ (Hoffman, 1988: 77). Consequently, the study was limited to women. In all, Maslow interviewed about 140 women, practically all of whom were middle-class college women between the ages of 20 and 28 (Maslow, 1942b: 270). Initially, he recruited subjects through word of mouth, but found that most of these volunteers were moderate to high in self-esteem (Maslow, 1937b: 418). In order to find more low self-esteem women, he developed a test of self-esteem (Maslow, 1940b) which he used to identify potential subjects; he then persuaded these women to ‘decide to submit to interview’ (Maslow, 1942b: 266). Based on the information Maslow elicited in the interviews, he assigned each woman a score on a scale of self-esteem. He also scored both her attitude toward sex and her sex drive, with the latter rating based on such factors as the ease, intensity and frequency of climax in hetero=sexual acts and the number of everyday stimuli which were sexually arousing (Maslow, 1942b: 264). Although he calculated correlations among these scores, Maslow relied more on the qualitative relationships, the ‘relationships as they impressed the experimenter’ (Maslow, 1942b: 272), to draw his conclusion that a woman’s sexual attitudes and behavior were more closely related to her self-esteem than to her sex drive. Judging from the items in the self-esteem scale (Maslow, 1940b: 267–70), high self-esteem women are also male-identified. They prefer men for company in sports, intellectual activities and conversation, and consider most other women catty and petty. Not surprisingly, they dominate most of the

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women of their own age that they know; they also dominate most of the men of their own age that they know. In more general terms, high self-esteem women are more independent, socially poised, extroverted, relaxed and unconventional than low self-esteem women, who are timid, shy, modest, neat and retiring (Maslow, 1942b: 261). Low self-esteem women, however, were more honest than high selfesteem women (Maslow, 1942b: 261). Maslow does not seem to have fully realized the implications of this difference. He clearly believed that, since he had established good rapport and stressed the importance of telling the truth, his subjects were completely frank (Maslow, 1939: 5) and, as a result, his ratings were valid. Consequently, the possibility that his rating of self-esteem was simply a measure of a woman’s willingness to discuss sex with him, or that high self-esteem, male-identified women might have lied, exaggerated and distorted their sexual experiences (as did men) does not appear to have seriously influenced his interpretation of his results. Instead, he concluded that high self-esteem women were psychologically free and more natural, whereas low self-esteem women were inhibited and over-socialized (Maslow, 1939: 32).1 Maslow also concluded that among humans, and women in particular, dominance behavior is affected and inhibited by local and general cultural pressures and socialization as well as the specific situation (Maslow, 1939: 4); as a result, high self-esteem people might not always demonstrate their dominance. At the same time, however, being dominant is not always a sign of high self-esteem, since some people compensate for their low self-esteem by acting in a dominant way. Thus, Maslow had to differentiate between ‘true’ and ‘compensatory’ dominance behavior. Compensatory dominance behavior gives the impression of being ‘strained and unnatural . . . aggressive and louder than seems to be appropriate to the situation’ (Maslow, 1937b: 418), and occurs in people who have a ‘great craving’ for dominance, people who ‘feel weak but wish to appear strong’ (Maslow, 1937b: 422, 417). This craving occurs because of emotional or psychological insecurity. While insecure people feel isolated and rejected, are suspicious of others and crave power and status, secure people feel liked or loved, trust others and have a feeling of strength (Maslow, 1942a: 334–5). Maslow’s sense of the significance of emotional security had been reinforced by his experiences among the Blackfoot in 1938. Whereas the average person in North American society in general tended to be insecure (Maslow, 1942b: 269), Maslow saw the Blackfoot as very emotionally secure, a condition that he believed was due to the adults’ emphasis on instilling a sense of personal responsibility in children (Hoffman, 1988: 125). In addition, there was an almost perfect correlation between wealth and ability among ‘my Blackfoot Indians’ (Maslow, 1965: 137). Because of their emotional security, wealthy people were generous, giving away the goods that their ability had enabled them to acquire, and, as a result, they were admired and

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loved by others (Maslow, 1971: 204). Consequently, in Maslow’s perception (which may or may not have been accurate), dominance in Blackfoot society had positive connotations: capable people, appropriately rewarded for their capability, benefitted their society as a whole. Much as the less dominant in chimpanzee society had no reason to fear the more dominant, the less dominant in Blackfoot society had no reason to fear the more dominant. Indeed, they had every reason to praise and support them. The Blackfoot were also important for another reason. Maslow was originally a cultural relativist, arguing that ‘we must treat the individual first as a member of a particular cultural group, and only after this can we attempt to treat him [sic] as a member of the general human species’ (Maslow, 1937c: 409). However, he came to feel that ‘my Indians were first human beings and secondly Blackfoot Indians’ (Hoffman, 1988: 128, emphasis in the original). It was this feeling that led him to the concept of a ‘fundamental’ or ‘natural’ personality structure (Hoffman, 1988: 128), in other words, the universal theory of human motivation found in the needs hierarchy. In that hierarchy, emotional security is achieved through satisfaction of the love or belongingness needs. Satisfaction of the esteem needs comes through a willing and genuine acceptance of a given individual’s factual superiorities by both that individual and others. Hence, only the emotionally secure superior individual will develop the need to self-actualize. Self-actualization means taking one’s place among the elite who ‘enjoy responsibility’ and who are ‘parental or fatherly . . . stern as well as loving’ (Maslow, 1965: 131). A selfactualizer, because of his or her ‘deep feeling of identification, sympathy and affection’ for humanity in general, has a ‘genuine desire to help’ those ‘creatures whom he [sic] must regard with, if not condescension, at least the knowledge that he can do many things better than they can, that he can see things that they cannot see’ (Maslow, 1954: 217). The self-actualizing elite are able to provide this help through their clearer perception of reality, which makes their judgments a ‘partial basis for a true science of values, and consequently of ethics, social relations, politics, religion, etc.’ (Maslow, 1954: 204). Most important, only self-actualizers are ‘fully human’ (see, for example, Hoffman, 1996: 70).

Dominance and Motivation Theory Haraway (1978a: 21) has observed that the primatology of the 1930s represented a union between the political and the physiological (see also Sperling, 1991); Maslow’s needs hierarchy is the psychological outcome of that union. The psychological is based not just on the political but also on the physiological. This is explicit in the lowest of the needs in the hierarchy, the physiological needs, but also in the concept that the hierarchy itself is innate, and hence physiological or biological.

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The assumption that the needs hierarchy is innate leads to both the democratic and aristocratic premises in Maslow’s theory. An innate hierarchy of needs means that all people possess or have these needs: they are universal, ahistorical and not linked to gender, class or culture. At the same time, however, the biological basis of the needs hierarchy leads to its aristocratic premise. Just as only some people have the biological potential to become extremely tall while others do not, only some people have the biological potential to self-actualize while others do not. Whether or not the individual is able to develop this potential to self-actualize depends on the type of environment in which she or he lives. For Maslow, the ‘good’ society is one in which the ‘biological elite’ are given the opportunity to develop their superiority, but are protected from the ‘almost inevitable malice of the biologically nongifted’ (Hoffman, 1996: 71), who cannot accept the reality that their inferiority is a matter of biological chance. This opportunity for the elite to develop their potential is particularly crucial in organizations, because of the commitment to work in self-actualizing people’s lives: ‘These highly evolved individuals assimilate their work into the identity, into the self, i.e., work actually becomes part of the self, part of the individual’s definition of himself [sic]’ (Maslow, 1965: 1). Self-actualizers are the living embodiment of the Protestant work ethic, in that ‘Salvation Is a By-Product of Self-Actualizing Work and Self-Actualizing Duty ’ (Maslow, 1965: 6, capitals and emphasis in the original). Salvation is also available to the masses since proper management of the ways in which people work and earn their living ‘can improve them and improve the world and in this sense be a utopian or revolutionary technique’ (Maslow, 1965: 1). There are, however, limits to what proper management can achieve, in that it will be successful, and is appropriate, only when employees are already developed (Maslow, 1965: 15–33). In other words, there are limits to what even enlightened management can achieve if employees are incapable of growth. One reason for the intuitive appeal of Maslow’s theory is now apparent. In a ‘good’ organization, the truly superior will be able to rise to the upper levels of that organization, while the inferior will properly remain in the lower levels. Managers are entitled to their positions since they deserve them, just as subordinates deserve their positions. If managers have achieved their positions because of their recognized genuine superiority, then their dominance can be assumed to be ‘of the “chimpanzee” sort, older-brotherly, responsible, affectionate, etc.’ (Maslow, 1965: 18). By responsibly and affectionately directing the activities of subordinates, managers are enabling those subordinates to grow and develop. At the same time, however, a subordinate’s level of growth and development is ultimately dependent not on the manager’s behavior but on the limited potential that that subordinate was born with. Thus, Maslow’s hierarchy justifies managerial power in organizations while minimizing managerial accountability for what occurs in those organizations.2

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The incorporation of Maslow’s needs hierarchy into other motivational theories extends these effects, while at the same time reinforcing the apparent validity of Maslow’s theory. Many textbooks (see, for example, Hellriegel et al., 1995: 187, and Luthans, 1995: 154) explicitly equate the lower and higher needs in Maslow’s hierarchy with, respectively, the hygiene factors and motivators in Herzberg’s (1966) two-factor theory. While Herzberg (1982: 292–3) considered this comparison an act of ‘creativity’ on the part of textbook writers (who, according to Herzberg, are compelled to provide personal input when they write about other people’s theories), the comparison gives credence to both Maslow and Herzberg. Similarly, Porter and Lawler’s (1968) expectancy theory uses a modification of Maslow’s theory to specify the needs that determine which rewards for effective performance will be satisfying, valued and hence lead to greater future effort. Expectancy theory is an application of cognitive psychology, in that it explains behavior in terms of the individual’s perception of environmental events (that is, in terms of whether or not an environmental event is felt to satisfy a need) rather than in terms of the environment itself. However, cognitive psychology, like Maslow’s theory itself, is psychology as ideology. Focussing on the individual’s subjective reactions to external events deflects an examination of those external events and thus serves to perpetuate the status quo and the interests of the powerful (Sampson, 1981), in much the same way as does attributing differences in power to biological inevitability. The commonality between the biological and cognitive approaches is perhaps best illustrated by the moderating variable of growth need strength in Hackman and Oldham’s (1980) job design application of expectancy theory. Some people, they observe, have a strong need for self-development, while others do not. The latter ‘may not recognize the existence of such opportunities [for self-development], or may not value them, or may even find them threatening and balk at being “pushed” or stretched too far by their work’ (Hackman and Oldham, 1980: 85). Similarly, Lawler (1992: 83) cautions that employees who do not value the higher-order needs of achievement, competence and personal growth will be frustrated rather than motivated by the work structure of involvement-oriented organizations. Again, those who cannot or will not grow will resist enlightened management’s attempts to enable them to do so.

Primatology and Motivation Theory The fundamental problem with motivation theory’s use of Maslow’s hierarchy is not necessarily the concept of dominance as such since, in both human and monkey societies, some individuals are able to dominate other individuals. Indeed, the study of the maintenance of patterns of dominance and subordinance is an essential feature of the analysis of organizations.

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Nor is the problem necessarily the hierarchy’s basis in primatology data, since management theory has relied on insights drawn from other animal studies, as the example of organizational behavior modification illustrates. Skinner (see, for example, 1953) developed the principles of reinforcement theory in studies of rats and pigeons, and then applied these principles to understanding human behavior. Rather, the issue is the nature of the animal data on which Maslow based his understanding of dominance. His assumption that it was women’s selfesteem that enabled them to dominate others was based on his earlier conclusion that his monkeys’ confidence had enabled them to dominate others. However, his monkeys were both caged and isolated from one another except for the brief experimental periods they spent together. The methodology of Maslow’s monkey research led him to an individualistic conception of dominance, a conception which minimizes, if not ignores, the impact of the social setting and environment on the relationship between the more dominant and the less dominant. If we rely on a theory based on animal data that was collected more than 60 years ago, we are obligated to consider the accuracy and validity of that data. While Maslow assumed that ‘the behavior of caged animals differs in no fundamental way’ from that of uncaged animals, and that the behavior of animals in the zoo or laboratory was not ‘abnormal or perverted’ (1936a: 268, emphasis in original), we need to consider whether or not this is the case. In recent years, a major source of primatological data has been extended observations of free-living monkeys and apes in their natural habitats (for example, Goodall, 1990; Strum, 1987); as a consequence, primatologists’ understanding of dominance, aggression and competition has been significantly revised. The focus has shifted from explanations based on a given monkey’s size and physical strength to explanations based on that individual’s social skills. Concurrently, there has been growing attention to the means by which monkeys use cooperation as a means of maintaining the social systems in which they live (Fedigan, 1992: xix–xx). Most baboons and macaques, for example, live in multi-male, multifemale groups in which there are separate dominance hierarchies for each sex. Baboon and macaque societies are essentially female-bonded groups, since mothers and daughters form the permanent basis of the troop; at puberty, the males leave their original group and move to a new one (Napier and Napier, 1985: 71). Female hierarchies are relatively stable and primarily based on kinship, with the status of the mother being transferred to her daughters. While females rely on family members to maintain their position, they also form alliances with non-relatives (see, for example, Chapais, 1992; Datta, 1992). In the adult male hierarchy, while rank is determined by age and fighting ability, it is also determined by the length of time a male has been in the group and by his ability to form and maintain alliances with other members

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of the group. Dominant males are not isolated superiors, but rather are enmeshed in the ‘systems of social reciprocity they can actively construct’ (Strum, 1987: 152) through the exchange of social favors such as grooming or assistance in times of conflict. The formation of alliances or enduring cooperative relationships is a purposive act, involving complex social skills. Each individual monkey must recognize all of the others, be aware of and take into account its own relationship with each of those others, plus be aware of and take into account the relationships among the others (de Waal, 1982: 182) before it can begin to use those relationships for its own purposes. The experimental methods Maslow used did not permit him to see the social skills involved in establishing and maintaining dominance in nonhuman primate societies. As a result, he overestimated the autonomy of the dominant individual, and instead saw this individual as able to function independently and separately from others in the social setting. Concurrently, he underestimated the extent to which the dominant individual needed to pay attention to social links with others and use interpersonal skills in order to develop and maintain those links. His focus on the characteristics of the dominant individual in turn led to his belief that differences in the way those individuals behaved caused the differences in the nature of dominance in primate societies. However, the tendency of free-living monkeys and apes to form alliances, combined with the extent to which individuals are able to leave one group and enter another, provides an alternative explanation for these differences. The dominant individual in a group in which the less dominant are constrained from leaving can intimidate and oppress those subordinates, whereas the dominant individual whose subordinates can escape must behave more considerately (de Waal, 1996: 127). This environmental interpretation of differences in dominance style would seem to have more relevance for complex social settings such as organizations than does Maslow’s individualistic interpretation. Indeed, this new interpretation suggests another reason for both the initial and continuing intuitive appeal of Maslow’s theory. In times of economic growth and plentiful jobs, when employees are easily able to leave one organization for another, managers must treat those employees considerately, in order to ensure that they remain in the organization. These are precisely the conditions that existed in the 1960s, when Maslow’s theory entered the management literature. Organizations were able to recruit and retain employees by appearing to allow those employees the opportunity to develop their innate potential. In times of economic downturn, when jobs are not plentiful, managers can treat employees less considerately, threatening to discard them, or actually doing so through downsizing, restructuring, reengineering and rightsizing. However, these oppressive uses of control can be disguised by the contention that the remaining employees are ‘empowered’, and thus still apparently allowed to develop their innate potential.

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Perhaps a more intriguing aspect of current primatology’s understanding of dominance, however, is the possibility that it provides for another perspective from which to view and develop theories of motivation. What would be the form of a motivation theory in which people’s prime goal is to create and strengthen social bonds in order that they can survive individually and collectively? What would be the form of a motivation theory that is based on cooperation as a fundamental construct? What would be the form of a motivation theory that stresses the influence of the social and environmental setting rather than the needs and reactions of the individual? What implications would such theories have for managerial practices? Applying insights from a discipline as contentious and contested as primatology is not without danger. If we make use of such a field, we cannot ignore its debates and changes. The point is not that it is inappropriate to rely on principles drawn from animal studies, but rather that we need to recognize the source of these principles, and the ways in which they may reflect and reproduce values and assumptions under the guise of objective science. We cannot achieve this recognition when we adopt these principles and then repeat them, mantra-like, without critical analysis. How many more examples of Maslow and his monkeys does management theory contain?

Notes I especially want to thank Linda Fedigan for encouraging my exploration of the primatology literature. I appreciate the always valuable comments of Barbara Townley, and the supportive advice of the editors and reviewers of Organization. I also want to thank Karen Farkas for her careful reading of an earlier version of this paper, A.D. (Tony) Fisher for his advice about Blackfoot culture, and Hope Olson for her material support. 1. See Cullen (1994) for a more detailed critique of these studies and a discussion of their implications for the women in management literature. 2. Another reason for the intuitive appeal of Maslow’s theory may lie in the ways in which it reflects and reinforces the sexuality of organizations (Hearn and Parkin, 1987) through its conflation of self-actualization with masculinity, dominance and sexuality. However, this aspect of its appeal, and the implications for the analysis of gender in organizations, will not be developed in this paper.

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Maslow, A.H. (1937a) ‘The Comparative Approach to Social Behavior’, Social Forces 15: 487–90. Maslow, A.H. (1937b) ‘Dominance-Feeling, Behavior, and Status’, Psychological Review 44: 404 –29. Maslow, A.H. (1937c) ‘Personality and Patterns of Culture’, in R. Stagner Psychology of Personality, pp. 408–28. New York: McGraw-Hill. Maslow, A.H. (1939) ‘Dominance-Feeling, Personality and Social Behavior in Women’, Journal of Social Psychology 10: 3–39. Maslow, A.H. (1940a) ‘Dominance-Quality and Social Behavior in Infra-Human Primates’, Journal of Social Psychology 11: 313–24. Maslow, A.H. (1940b) ‘A Test for Dominance-Feeling (Self-Esteem) in College Women’, Journal of Social Psychology 12: 255–70. Maslow, A.H. (1942a) ‘The Dynamics of Psychological Security–Insecurity’, Character and Personality 10: 331– 44. Maslow, A.H. (1942b) ‘Self-Esteem (Dominance-Feeling) and Sexuality in Women’, Journal of Social Psychology 16: 259–94. Maslow, A.H. (1943) ‘A Theory of Human Motivation’, Psychological Review 50: 370–96. Maslow, A.H. (1954) Motivation and Personality. New York: Harper. Maslow, A.H. (1965) Eupsychian Management. Homewood, IL: Irwin. Maslow, A.H. (1971) The Farther Reaches of Human Nature. New York: Viking. Maslow, A.H. and Flanzbaum, S. (1936) ‘The Role of Dominance in the Social and Sexual Behavior of Infra-Human Primates: II. An Experimental Determination of the Behavior Syndrome of Dominance’, Journal of Genetic Psychology 48: 278–309. Maslow, A.H. and Mittelmann, B. (1941) Principles of Abnormal Psychology: The Dynamics of Psychic Illness. New York: Harper. Mitchell, V .F . and Moudgill, P . (1976) ‘Measurement of Maslow’s Need Hierarchy’, Organizational Behavior and Human Performance 16: 334 – 49. Moorhead, G. and Griffin, R.W. (1995) Organizational Behavior, 4th edn. Boston, MA: Houghton Mifflin. Napier, J.R. and Napier, P .H. (1985) The Natural History of the Primates. Cambridge, MA: MIT Press. Porter, L.W. and Lawler, E.E. III (1968) Managerial Attitudes and Performance. Homewood, IL: Irwin. Rowell, T.E. (1974) ‘The Concept of Social Dominance’, Behavioral Biology 11: 131–54. Sampson, E.E. (1981) ‘Cognitive Psychology as Ideology’, American Psychologist 36: 730–43. Shaw, R. and Colimore, K. (1988) ‘Humanistic Psychology as Ideology: An Analysis of Maslow’s Contradictions’, Journal of Humanistic Psychology 28(3): 51–74. Skinner, B.F. (1953) Science and Human Behavior. New York: Macmillan. Sperling, S. (1991) ‘Baboons with Briefcases vs. Langurs in Lipstick: Feminism and Functionalism in Primate Studies’, in M. di Leonardo (ed.) Gender at the Crossroads of Knowledge: Feminist Anthropology in the Postmodern Era, pp. 204 –34. Berkeley: University of California Press. Strum, S.C. (1987) Almost Human: A Journey into the World of Baboons. New York: Random House. Suomi, S.J. and LeRoy, H.A. (1982) ‘In Memoriam: Harry Harlow (1905–1981)’, American Journal of Primatology 2: 319– 42. de Waal, F. (1982) Chimpanzee Politics: Power and Sex among Apes. New York: Harper & Row. de Waal, F . (1996) Good Natured: The Origins of Right and Wrong in Humans and Other Animals. Cambridge, MA: Harvard University Press.

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Wahba, M.A. and Bridwell, L.G. (1976) ‘Maslow Reconsidered: A Review of Research on the Need Hierarchy Theory’, Organizational Behavior and Human Performance 15: 212–40. Wilson, C. (1972) New Pathways in Psychology: Maslow and the Post-Freudian Revolution. London: Victor Gollancz. Yerkes, R.M. (1939) ‘Social Dominance and Sexual Status in the Chimpanzee’, The Quarterly Review of Biology 14: 115–36. Yerkes, R.M. and Yerkes, A.W. (1929) The Great Apes: A Study of Anthropoid Life. New Haven, CT: Yale University Press. Zuckerman, S. (1932) The Social Life of Monkeys and Apes. London: Kegan.

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43 Maslow’s Theory of Motivation: A Critique Andrew Neher

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his critique will evaluate Abraham Maslow’s theory of motivation, including each of its basic propositions. Although other critics have addressed various aspects of Maslow’s theory, no one, as far as I know, has taken on Maslow’s basic theory in toto. Two decades after his death, Maslow is still revered as one of the founders and guiding lights of humanistic psychology. Unfortunately, humanistic psychologists have yet to probe the flaws in Maslow’s theory in any concerted or thorough fashion. Why is this? Maybe it stems from motivations such as loyalty to the cause, but it may also relate to the tendency of humanistic psychologists to be “accepting” rather than “critical.” Of course, Maslow is known outside of humanistic psychology circles. Maslow himself sought to apply his theory to fields in the borderlands of psychology, where it still wields influence in some quarters – for example in the fields of management (Maslow, 1967), religion (Maslow, 1964), and science (Maslow, 1969). In addition, Maslow is routinely cited when general psychology texts discuss humanistic psychology. Texts in “adjustment” courses, in particular, tend to pay him much attention, sometimes to the extent of recommending that students evaluate their own lives to see how well they conform to Maslow’s ideas concerning the “good life.” On the other hand – the field of management excepted (e.g., Huizinga, 1970) – Maslow is seldom cited in the research literature on motivation, which means that his theory, to a significant extent, lies outside the mainstream of testing and critical evaluation that is the lifeblood of any vital theory. Source: Journal of Humanistic Psychology, 31(3) (1991): 89–112.

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Thus there are many reasons to take a close look at Maslow’s theory and bring its flaws into the light of day. This article is a contribution to that effort.

Maslow’s Theory Outlined Most of Maslow’s basic theory is found in the 1970 edition of his book, Motivation and Personality, although I will draw from some of his other works from time to time. According to his theory 1. Each of us is endowed at birth with a full and, to an important extent, unique complement of needs that, allowed expression by our environment, will guide our growth in a healthy direction. 2. These needs function in a hierarchical manner. The bottom step of Maslow’s 5-step hierarchy, or pyramid, includes physiological needs (for food, water, and so on). Then come safety needs; next, needs for love and intimacy; then self-esteem needs; and, finally, at the apex of the pyramid, self-actualization (e.g., intellectual and esthetic) needs. By hierarchy is meant that needs lower on the pyramid must generally be satisfied before needs at higher levels are “activated.” For example, starving people (deprived on level one) will find it difficult to be very concerned about their relationships with others (needs on level three) until they are fed. 3. Needs on the first four levels are called deficiency-needs (or D-needs) because they drive us to gratify the need, at which point the need lapses in its importance to us until deprivation again motivates us to take action to satisfy the need. Self-actualization needs (on the fifth and highest level), on the other hand, are called being-needs (or B-needs) because, among other unique features, they sustain our interest without our being driven by feelings of deprivation. 4. The level of self-actualization, the end-point of the process outlined above, constitutes the highest level of human experience. To illustrate his theory, Maslow described a number of people he considered self-actualizers, including such well-known figures as Abraham Lincoln and Eleanor Roosevelt. All of these people, according to Maslow, share various personality traits (which Maslow subsumed under rubrics such as beingcognition and being-values). These include being relatively creative, spontaneous, able to see the “large picture,” nonjudgmental, and rich in emotional life; in particular, self-actualizers are more apt to experience euphoric heights of emotion that Maslow labeled peak experience. To summarize, we are born with certain needs, some of which, such as hunger, are prepotent in that they occupy our attention until they are satisfied. But such motivations are not what make us fully human. Only by living a life in which these lower needs are satisfied can we rise to our full human potential,

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becoming self-actualized, as we free ourselves to become involved in higher pursuits such as art, literature, and science, and to experience the finer human qualities of broad understanding, tolerance, and the sublime emotions. Stated in rough outline, Maslow’s theory finds ready acceptance with many people. The theory seems reasonable and fits many of our preconceptions: For example, of course hungry people are concerned with little else besides finding food. But as we take a closer look we will see that almost every aspect of Maslow’s theory is burdened with a multitude of problems. We will see that many of these problems stem from the extreme stands that his theory, as a close examination will show, tends to take. The problem of overstatement is not unique, of course, to Maslow. It is a common trait of theorists who attempt, as Maslow did, to develop a perspective in opposition to prevailing theories. In Maslow’s case, the prevailing theories of motivation stemmed from psychoanalysis on the one hand and behaviorism on the other. Thus we should not be surprised that Maslow overstated his case in an attempt to make his theory distinctive when compared with competing theories. Other problems involve some of Maslow’s more peripheral statements that contradict many of the assumptions of his own theory. To some extent, Maslow seemed to have second thoughts about his theory, but these modifications never filtered down to his general theoretical statements. This might have been intentional, in part, because these qualifications to his theory have the effect, as we shall see, of “watering it down” and making it less distinctive. But perhaps the most significant basis of these inconsistencies was Maslow’s tendency, which he himself recognized, to be impressionistic, rather than conceptually rigorous, in his thinking and writing (Daniels, 1982, pp. 62, 70–71). Finally, still other problems concern the internal logic of his theory.

Maslow’s Theory Critiqued Let us evaluate the various components of Maslow’s theory in the order that they were presented earlier. 1. Each of us is endowed at birth with a complete, and, to some extent, unique complement of needs that, allowed expression by our environment, will foster our growth in a healthy direction (Maslow, 1970, pp. 77–104). Few psychologists would disagree that our lower needs in general (hunger, need for intimacy, and so on) are innate. But many would question whether, in general, the higher needs (intellectual, esthetic) are innate as Maslow claimed (1970, pp. 100–101). Although there is good evidence for the innate nature of some of the higher needs (e.g., the curiosity drive; Eisenberger, 1972), others, such as esthetic motivations, are probably largely shaped by cultural experience. Maslow’s tendency to downgrade the role of the environment in

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forming the human psyche has been noted by several critics (e.g., Aron, 1977; Daniels, 1982; Geller, 1982; Smith, 1973) and seems to be related to his rejection of the behaviorist perspective, which traditionally committed the opposite error of viewing environmental influence as all-important (Maslow, 1970, pp. 88–89). According to Maslow, once [lower needs are met] each person proceeds to develop in his own style, uniquely, using these necessities for his own private purposes. In a very meaningful sense, development then becomes determined from within rather than from without. . . . The role of the environment is ultimately to permit him or help him to actualize his own potentialities [because] he “knows” better than anyone else what is good for him. (1968, pp. 34, 160, 198)

To sum up, Maslow believed that, given basic support and nurturance from the environment, our inborn needs are sufficient to foster our psychological growth in a healthy direction. Thus it is clear that Maslow is squarely in the camp of the nativists, who stress the role of hereditary influences in human experience. In this regard, he is in accord with many other humanistic psychologists (e.g., Carl Rogers) and, as a consequence, suffers along with them from a number of difficulties. If the most culture can do, or should do, is provide for basic needs and freedom of expression, then most of the structure of cultures around the world must be viewed as potentially disruptive. In particular, child-rearing practices may conflict with innate needs of children to develop in directions other than those sanctioned by the culture. As Maslow said, “Our human instincts [including our needs] are so weak that they need protection against culture, against learning – in a word, against being overwhelmed by the environment” (1970, p. 103). Of course, as Maslow admitted (1970, p. 278), our culture is relatively tolerant, but he believed that we still need to tip “the balance [even more] in favor of spontaneity, the ability to be expressive . . . creative, etc.” (1968, p. 198). Let us take Maslow at his word, and let us take language as our example. According to the widely accepted Sapir-Whorf hypothesis, the particular language we speak determines to some extent the way in which we are able to think about the world (Whorf, 1956). If this is so, then teaching our own language to our children has the effect, in part, of putting their thoughts in an intellectual straitjacket – perhaps, unfortunately, in ways that conflict with their innate needs to conceptualize the world in their own unique fashion. So perhaps we should “protect” our children from hearing our language so that they can create their own. But, of course, we know that, although children inherit a genetic ability to learn language that they hear in their environment (Piattelli-Palmarini, 1980), they do not inherit the ability to create, from scratch, their own language (Malson, 1972). And, if they could, can you imagine the problem of attempting to communicate with one another, each

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of us in a different language? Much the same could be said, of course, of a multitude of other cultural traits that serve as a common basis for human relations in any culture. One way to understand our need to learn the folkways of our culture is to remember that the trend in human evolution has been away from strong genetic programming. Instead, we develop our “humaness,” to a significant extent, through being socialized into the norms of our particular culture. In fact, our genetic heritage seems to consist, to a large degree, of a potential to adapt to any of the wide variety of cultures that have ever existed; that is, our genetic endowment seems very flexible in this regard. And, although we each inherit a unique mix of needs and potentials, these require for their development a context of cultural inputs (language, and so on) that are, at least initially, imposed upon us. This is because, as young children, our nervous systems are not sufficiently developed to allow us to choose from among these inputs. Of course, parents should be sensitive to their childrens’ unique individual needs, but it is hardly possible to tailor basic cultural inputs (language is a good example) to the individual. Naturally, as we mature most of us are increasingly able to choose the life experiences that best “fit” us, but these choices are a product of the unique mix of genes and culture that each of us embodies by the time we are old enough to make these choices. In sum, Maslow’s list of needs ignores considerations such as these. It does not include the need to learn language or any of the other cultural traits that create our humanness and bind us socially. To repeat, his theory implies that the imposition of cultural norms is unnecessary at best, and, at worst, destructive of our unique potential as individuals. In this regard, he and many other humanistic psychologists are in the mainstream of Western values that tend to glorify the individual. Maslow’s failure to acknowledge the need to learn cultural norms may have stemmed from more than one source. On the one hand, he may have assumed that, with the advent of pluralistic societies such as ours, we all need to pick and choose our own path, and that the best basis for this is the unique mix of needs we each inherit. But, as has already been pointed out, this assumption is undermined by the fact that we are helpless as children to “pick and choose” until we have already been socialized into the language patterns and other basic norms of our particular culture. Of course, if Maslow’s theory does apply only to pluralistic societies, then it is culture specific rather than universal in application. On the other hand, Maslow may have been reacting against the obvious failures of our own society, his solution being to base human development on the “wisdom” of the unique biological makeup of each of us rather than on bankrupt cultural priorities. However, a good argument can be made that extreme individualism, whether or not it is founded on the notions of individual biological uniqueness that Maslow favored, in fact, fosters much of the social alienation and dehumanization that plagues our society. One critic noted the “irony that those as deeply

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concerned about the human condition as . . . Maslow . . . should have developed a theory the practical recommendations of which sustain and strengthen the very dehumanization against which in part they are reacting” (Geller, 1982, p. 72). Thus the nativist position is more than just a theoretical issue. For example, we have all known parents who have hesitated to “put their own trip” on their child, for fear of violating their child’s unique nature, to the point where they became ineffective as parents. And we have all known children who have, in conformance with pop-psych beliefs, agonized over who the “real me” is as distinct from the “me whom my parents created.” But these hesitancies and agonies, of course, are predicated on the notion that there is a more or less complete, original “me” waiting to blossom given only a nurturing and accepting, but otherwise neutral environment. So whether or not the assumptions of Maslow – and other nativists such as Carl Rogers – are valid is a very significant question, with very real ramifications. Another difficulty with the nativist position concerns its internal logic. If all that we require to become self-actualized is that our culture provide for our basic needs and freedom of expression, then our genetic potential is indeed potent. As Maslow said, our “inner nature . . . tends strongly to persist” (1968, p. 190). But, if this is so, then why was Maslow, in agreement with many other humanistic psychologists, so fearful that our culture will misdirect us in ways that violate this potential? Elsewhere Maslow said that “this inner nature . . . is weak and delicate . . . and easily overcome by . . . cultural pressure” (1968, p. 4). Maslow seemed to want it both ways – a strong innate tendency to self-actualize on the one hand, but also a disturbing weakness in the face of cultural dictates on the other. But, of course, he cannot have it both ways. At least one assumption must be wrong – or, more likely, less extreme versions of both might be correct. A final issue related to Maslow’s nativist position concerns values rather than logic. Along with other nativists, Maslow maintained, in essence, that we have to live with whatever the genetic roll of the dice provides us, because environmental influences (other than providing for our needs) are viewed either as relatively insignificant or as potentially insensitive to our innate tendencies (Daniels, 1988, p. 25). Where behaviorists have traditionally said “You can become whatever you want, and we’ll show you how,” Maslow, and other nativist theorists, have said, “You can become what your native potential allows you to become, and nothing else.” Although the behaviorists are undoubtedly overly optimistic in their view, Maslow seems overly pessimistic. In this case, Maslow goes against the grain of Western values, which maintain that practically unlimited possibilities are open to any of us. To summarize, Maslow’s tendency to emphasize the role of our innate needs in directing the course of healthy psychological development, and his tendency to downgrade the importance of cultural input in this process, leads to a view of human development that is one-sided and consequently very

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difficult to support. Thus we start to see some of the problems that stem from Maslow’s tendency to take extreme stands. Now let us move ahead and examine the second component of Maslow’s theory. 2. Our needs function in a hierarchical fashion, so that our basic needs ( for food, etc.) are prepotent, in that generally they must be satisfied before we can feel “free” of them and move on to satisfy our higher needs (Maslow, 1970, pp. 35–51). Actually, in advanced societies our physiological and safety needs (the first two steps on Maslow’s need-pyramid) are often satisfied, whereas the next two steps – needs for love and for self-esteem – constitute stumbling blocks for many people. In simpler societies, on the other hand, the situation is often the reverse. In such societies, people may periodically go hungry and suffer from life-threatening illnesses, but nevertheless, unless these problems are severe (Turnbull, 1974), people in these societies typically exhibit strong social ties and a strong sense of self. In fact, it appears that a certain degree of hardship in meeting basic needs can bring people together and give them a sense of purpose as they cooperate to overcome adversity. Most of us can probably recall experiences of our own that illustrate this process. For example, many couples say that struggling together to make ends meet when they were young fostered strong bonds between them, compared with their later years when they had finally achieved a life of ease and comfort. If these examples are valid, they stand Maslow’s need-hierarchy on its head: In these instances, deprivation at lower-need levels (survival needs) seems to facilitate need satisfaction at higher levels (e.g., the achievement of intimacy) rather than hinder it as Maslow would predict. Aside from such anecdotal evidence, some researchers, particularly in the field of management, have attempted to test Maslow’s hierarchy in a more systematic fashion. In general, these researchers have wanted to determine if Maslow’s theory can clarify the factors involved in job choice and job satisfaction. Here is a sampling of these studies, many of which are summarized in Wahba and Bridwell (1979). Some of these studies have been designed to test Maslow’s particular ordering of needs in his hierarchy. Briefly, the results of these studies are equivocal; results range from some support (Graham & Balloun, 1973; Mathes, 1981; Wuthnow, 1978), to no support (Miner & Dachler, 1973), to outright refutation (Wofford, 1971). Other studies have attempted to test Maslow’s assertion that need satisfaction leads to a diminution of that need in the future. These studies show a similar spread, from some support (Alderfer, 1969; Graham & Balloun, 1973), to no support (Lawler & Suttle, 1972), to results that indicate that need-satisfaction leads to heightened salience of the need (Hall & Norigaim, 1968)! Obviously the research picture is rather equivocal. However, research of this nature seldom yields definitive answers and should not be considered, in

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and of itself, the last word. Thus let us take a closer look at Maslow’s assertion that “need gratification diminishes the strength of the need,” because, in spite of its quality of seeming obvious, I believe it is highly questionable. First of all, no one denies that need satisfaction leads to a temporary decrease in the strength of a need. But most needs are cyclical, in that they are satisfied for a time, only to resurface later. Hunger and sex are obvious examples. What Maslow meant is that, over the long term, the strength of a need that is readily and easily satisfied will decline. For example: “If a mother kisses her child often, the drive itself disappears and the child learns not to crave kisses” (Maslow, 1970, p. 63). As with much of Maslow’s theory, this statement seems reasonable at first glance. It certainly ties in with much of our experience, as well as with other theories, such as psychoanalysis, that are widely accepted: When we express our needs, we are less “bothered” by them. But there is another possibility. Behaviorists would probably maintain that kissing, for example, is usually more valued by adults than by children, partly because of the pleasures that have been associated with it on so many different occasions. And, strangely enough, we can probably all think of examples from our own experience that support this alternative perspective. So which is it? Over the long term, do needs “dry up” or “well up” when they are satisfied? Unfortunately, there is no ready answer to this question, and psychologists remain divided on the issue. If Maslow meant that we should oversatiate our needs (e.g., eat until we are sick of eating) then we would probably agree that needs would tend to “dry up,” but there is no indication that he had this in mind. The point is that Maslow’s assumption – that satisfying needs reduces their strength in the long run, which is so crucial to his theory as a whole – is much more tenuous than he indicated. It is important to keep in mind that Maslow put himself in such a tenuous position because he was intent on eliminating the lower needs, in this process, as a motivational force in our lives; this was his prescription for moving up the needs hierarchy to the level of self-actualization. At this point, we need to examine another of Maslow’s assumptions that is not obvious on first inspection – namely, that the highest level in his need hierarchy, self-actualization, is, ideally, autonomous. It is obvious that our motivations to engage in creative, intellectual, or esthetic pursuits (pursuits on the highest level of the hierarchy) may, in fact, stem from lower needs – such as needs to gain social recognition, enhance our self-esteem, or even, perhaps, to satisfy physiological survival drives. In general, both psychoanalysts and behaviorists would agree with this view, citing mechanisms such as sublimation on the one hand and conditioned associations on the other. Maslow himself made the point that “the cognitive capacities . . . are a set of adjustive tools, which have, among other functions, that of satisfaction of our basic needs. . . . Acquiring knowledge and systematizing the universe [are], in part, techniques for the achievement of basic safety in the world” (1970, pp. 47– 48). But, of course, it is central to Maslow’s theory that these

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lower motivations, when they are present, detract from the true essence of self-actualization. In Maslow’s theory, remember, the road to self-actualization requires having already satisfied these basic needs. This means that Maslow must, as he said, “distinguish the artistic and intellectual products of basically satisfied people from those of basically unsatisfied people” (1970, p. 46), to make sure their accomplishments are not contaminated by lower needs. Not an easy task. If the self-actualization needs are, ideally, autonomous, how then did Maslow explain the mechanism through which this occurs? His main theme, of course, was that the self-actualization needs evolved biologically (Maslow, 1970, pp. 100–101). The problem was that he was not clear how this came about. Now, our higher needs might have evolved to serve lower needs, and /or they might have evolved because they are adaptive in their own right. If they evolved to meet our lower needs, then we must somehow explain how, on a biological level, they have become autonomous. If they evolved because they are adaptive in their own right, we must postulate that creative, intellectual, and artistic endeavors facilitate survival in and of themselves and thus have been incorporated into the gene pool. As far as I know, Maslow never discussed these possibilities. Maslow’s chief explanation for the autonomous nature of the self-actualization needs invoked Gordon Allport’s (1937) notion of “functional autonomy [in which the higher need] develops only on the basis of the lower, but eventually, when well established, may become relatively independent of the lower” (Maslow, 1970, pp. 103–104). For example, consider the following scenario: Let us imagine that you have a natural talent for music for which you are praised (which satisfies social recognition and self-esteem needs) in your younger years. As you grow up, your interest in music itself is enhanced because of its association with social rewards, and thus you develop your musical skills more and more “for their own sake.” Also, behaviorists would predict this increasingly autonomous interest in music on the basis that the “schedule” of social reinforcement becomes intermittent and unpredictable. But, as reasonable as this scenario is, it is a poor fit with the rest of Maslow’s theory. It requires some initial degree of lowerneed deprivation, which violates his conception of the self-actualizing process, and, because it derives from an environmentalist perspective, it goes against the grain of his biological bias. Actually, it is questionable whether Maslow truly understood the implications of the functional autonomy theory. In sum, Maslow never adequately accounted, as far as I can determine, for the autonomous nature that he postulated for the self-actualization needs. Now let us address in greater detail Maslow’s belief that satiation of lower needs leads to self-actualization. This is such an important assertion that we need to be clear concerning what Maslow said about it: “Gratification of any basic need . . . is a move in the healthy direction” (1970, pp. 61–62), and “a man who is thwarted in any of his basic needs may fairly be envisioned simply as . . . less than fully human” (1970, p. 57). Seems pretty clear. Then

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what can we make of a statement such as “the complete absence of frustration, pain or danger is dangerous. To be strong, a person must acquire frustrationtolerance” (1968, p. 200). Obviously there is a contradiction here: Maslow said that thwarting of basic needs is unhealthy, but also that lack of frustration is unhealthy. Despite such contradictions, it is clear that Maslow’s theory favors a high level of need satisfaction. So let us go back to his basic theoretical position and see why, in fact, it does present great difficulties. Let us imagine what kind of circumstances would produce consistent gratification, remembering that partial gratification will produce less movement toward self-actualization. Using the hunger drive as an example, perhaps the only way that consistent gratification could be achieved is through eating small amounts of food almost continuously (although intravenous feeding would achieve a similar result). We can imagine similar conditions for other needs – for example, sexual gratification should be available just as soon as the urge arises. Do not make the mistake of dismissing this as farfetched. To the extent we allow ourselves to be hungry, or sexually unsatisfied, our efforts will be directed towards satisfying our lower needs rather than towards self-actualization. Following this logic, then, parents who want to raise selfactualized children should strive to meet their basic needs as soon as they arise, ideally before the children begin to feel much deprivation or motivation to make efforts to satisfy these needs. Now, if you are beginning to think that this approach might lead to problems, you are not alone. Researchers have found, not surprisingly, that parents who “pamper, indulge, and fawn over the youngster in such ways as to teach him that his every wish is a command to others” (Millon, 1969, p. 263) tend to raise children who are narcissistic, are exploitive of others, have little self-control, and lack competency skills (Millon, 1969, pp. 261–266). In fact, there are many threads of research and theory in psychology that postulate, contrary to Maslow, that some frustration and deprivation is necessary for healthy psychological development. Among these are (a) Robert White’s competence theory (1959), (b) Yerkes-Dodson’s law (Yerkes & Dodson, 1908), (c) Hans Selye’s eustress theory (1974), and (d) Alfred Adler’s compensation theory (Ansbacher & Ansbacher, 1959). In fact, these perspectives are far from esoteric; their essence can be found in any number of self-help books written for the general public (e.g., Bloomfield & Felder, 1985; Brown, 1983; Houston, 1981). In spite of their differences, all of these perspectives agree on one or more of the following points: (a) that a moderate amount of deprivation stimulates our creative potential; (b) that this keeps us motivated and interested in life; and (c) that this leads to a sense of competence that helps us deal with the vicissitudes of living. Nietzsche said it in a particularly pithy (and extreme) fashion: “What does not kill me makes me stronger.” In addition, research indicates that some degree of deprivation, and thus challenge, are necessary to keep us from feeling bored. In particular, this research indicates a connection between low levels of deprivation and

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psychosomatic illness (Goldberg, 1978). Note that this finding also conflicts with the widely noted position of Holmes and Rahe (1967), who, along with Maslow, believe that the less deprivation and stress (in their theory, stress that arises from having to adjust to change) the better. George Bernard Shaw’s memorable comment on the matter is certainly an overstatement, but it clearly states the alternative view to Maslow’s: “The only thing worse than not getting what you want is getting what you want.” Finally, it might be said that conditions that allow for consistent gratification of needs are probably only possible in advanced affluent societies such as ours. In fact, Maslow’s theory could be considered elitist in this regard (Smith, 1973, p. 29). This also makes it difficult to image the evolutionary conditions that would give rise to a self-actualization potential which could be realized only in a society that didn’t come into being until recently. So where did Maslow go wrong? His error, I think, lies in overstating his position. We can all agree that extreme need deprivation is ordinarily psychologically damaging. But this doesn’t mean that the opposite condition, extreme ease of need gratification, is psychologically healthy. As with many issues, a moderate position is the most defensible. Of course, as we have seen, Maslow did vacillate on this issue. This is understandable when we realize, on the one hand, how important his absolutist stand is to his theory as a whole. After all, if some deprivation is psychologically healthy, then not only does his theory lose much of its distinctiveness, but its chain of reasoning loses one of its crucial links: if we are deprived at lower-need levels, how then, in Maslow’s way of thinking, are we able to move up the need hierarchy and become fully self-actualized? On the other hand, as we have also seen, Maslow experienced great difficulty maintaining his absolutist stand in the face of so much opposing theory and research. Now we are ready to discuss the third component of Maslow’s theory. 3. The self-actualization needs differ qualitatively from the lower (or “deficiency”) needs in that they motivate us in the absence of a sense of deficiency – hence they are called “being” needs (Maslow, 1968, pp. 29–37). As Maslow said, being motivation involves a state “of desirelessness, purposelessness, [and] lack of D-need (deficiency-need)” (1971, p. 128). If Maslow were referring to the psychological state that often persists for a period following the gratification of a need, this would be an obvious statement. However, it is clear that he was describing a more or less ongoing level of functioning. Now we can grant that, for example, compared with eating a meal, there is a different feeling associated with creating art, writing literature, or getting involved in a favorite building project. Our involvement with these activities seems self-sustaining, persistent, and intrinsically rewarding, and this is certainly the quality that Maslow tried to capture in his theory. But we have seen that, when it comes to Maslow’s theory, initial impressions are often misleading. So let us take a closer look at this aspect of his theory.

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Let us begin by examining the logic of Maslow’s assumption that we can be motivated in the absence of a sense of deficiency. Another way to state this is that we can be motivated to gain or achieve something even though we don’t lack it in the first place. Not very logical. As Salvatore Maddi says, “In order to define a motive, you must specify a goal state that is to be achieved. . . . And once you define a goal, you are of necessity assuring that the person having the motive is in a deprived state until he reaches the goal” (1968, p. 83). Think about your own experiences with higher-level needs. Don’t you find yourself setting goals, perhaps very long-range goals, but goals that consist of something you lack at present? If you achieve your goals, don’t you typically set new goals for yourself, and the cycle repeats itself? Certainly this has a different quality than eating a meal, but the difference doesn’t seem to have to do with deficiency, as Maslow maintained. Rather, the difference seems to involve such matters as experiencing greater freedom to choose higher-level motivations, or challenges – deprivations if you will – that are practically unlimited in their potential scope. These characteristics of higher-order motivations might arise because a wide variety of such motivations can meet a multitude of lower-level needs or because these motivations have truly become functionally autonomous or both. In any case, the basis of the distinctiveness of the self-actualization needs seems not to hinge on the absence of a sense of deprivation. Maslow’s discontent with motivation based on deprivation stemmed from his rejection of the traditional behaviorist position, which postulated tension or drive reduction – that is, overcoming deprivation, especially with respect to basic needs – as the sole basis of motivation (Maslow, 1968, p. 38). Behaviorists traditionally ignored higher drives such as curiosity and exploration, which seem to involve pursuing challenges and thus heightened drive states (Berlyne, 1960). However, it now appears that these higher drives are capable of being satiated, at least in some species (Eisenberger, 1972). Because satiation implies a prior state of deprivation, these findings call into question Maslow’s assumption that these higher motives operate in the absence of feelings of deprivation. To sum up, what appears to be unique about higher-order needs is not the absence of feelings of deprivation, but rather a number of other characteristics, including the purposeful choosing of challenges, and thus deprivations, which can provide almost limitless motivation and satisfaction. Now we come to the final component of Maslow’s theory of motivation. 4. The level of self-actualization, which is the end-point of the process outlined above, constitutes the highest level of human experience (Maslow, 1970, pp. 149–180; Maslow, 1971). Let us start with a quotation from Maslow: “Western civilization has generally believed that the animal in us was a bad animal” (1970, pp. 82–83). So, to some extent, does Eastern civilization, and most important, so, to some extent, did Maslow. Where Maslow differed from both Western and Eastern traditions is in the route he favored to overcome

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our animal nature, by which he meant our basic needs that we share with other animals – needs for food, sex, and so on. You will remember that Maslow’s prescription runs as follows: ‘The easiest technique for releasing the organism from the bondage of the lower . . . needs is to gratify them” (1970, p. 61). Of course, we have already seen that it is questionable whether this approach is effective, but how does it compare with more traditional approaches? Now, traditionally in both East and West, the most common way to overcome lower needs is to deny and to suppress them. Of course, Maslow’s approach probably fits our modern-day affluent society much better, which often seems to believe that the best way to overcome temptation is to give in to it. But Maslow’s value judgment is the same as the traditional one – that a part of our basic biological makeup is sufficiently unworthy that it should be eliminated as an important concern in our lives (Daniels, 1988, p. 23). You may agree or disagree with Maslow’s value judgment (it makes little sense to me), but, for a theorist such as Maslow, who claimed to be taking his lead from basic biological characteristics, it seems strangely nonbiological. Why are these lower needs seen as unworthy (the term lower itself reinforces this assumption)? Maslow, in particular, considered them lower partly because he believed that they are basically selfish in nature (Maslow, 1968, p. 202). However, research in sociobiology has demonstrated that many lower drives, including the traditional archvillain, sex, are, biologically speaking, largely altruistic in nature. For example, animals will sometimes risk their own lives to conceive, or later to protect, their offspring (Wilson, 1980). Moving on to the characteristics of people who have attained selfactualization, Maslow once more had difficulty being consistent. We already know that ‘The perfectly healthy [self-actualized] man has no sex needs or hunger needs, or needs for safety, or for love, or for prestige, or self-esteem” (Maslow, 1970, p. 57). But elsewhere Maslow maintained that self-actualized people “tend to be good animals, hearty in their appetites and enjoying themselves without regret or shame or apology” (1970, p. 156). Of course, it makes no sense to say that people with no hunger needs are hearty in their appetites. This is yet another instance of Maslow contradicting Maslow. Maslow also granted that need satisfaction is not the only route to selfactualization: ‘There are apparently innately creative people in whom the drive to creativeness seems to be more important than any other counterdeterminant” (1970, p. 52). By this, Maslow meant that some people are chiefly motivated by higher-level needs even though they have failed to satisfy needs lower in the hierarchy. Examples would include artists or scientists who are so wrapped up in their work that they forgo eating, or sex, or meaningful relationships of any kind, for lengthy periods. Finally, Maslow admitted that his formula – satisfying lower needs is the way to achieve self-actualization – does not always work: “I have individual subjects in whom apparent basic-need-gratification is compatible with

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‘existential neurosis,’ meaninglessness, valuelessness, or the like” (1971, pp. 300–301). Maslow suggested that, to deal with this difficulty, he needed to modify his basic theory: “It is now more clear to me that gratification of the basic needs is not a sufficient condition for self-actualization” (1971, p. 300). And this is indeed a drastic modification. What, then, did Maslow propose as a sufficient condition for achieving self-actualization? Although he was far from clear on this point (Maslow, 1971, pp. 39, 301), he seems to have concluded that, because the potential for self-actualization is genetically based, some people will inherit it and some people won’t (Frick, 1982, pp. 32– 40). To expand on his reasoning, according to the principle of genetic variation, inherited needs are likely to be distributed more or less according to a normal curve, with some individuals demonstrating a high level of the need, others a low level, but most people a moderate level. This principle should apply as well to self-actualization needs, if they are indeed genetic in character. Thus some individuals would be expected to inherit a very low selfactualization potential. In the extreme case, for example, seeking to specify a process by which retarded individuals could function consistently at the level of higher motivations would probably be a futile endeavor. For such people to satisfy completely their lower needs might indeed be a misguided effort, because other motivations may not be available to sustain them. Thus Maslow recognized that a low genetic potential for self-actualization might account for the feelings of “meaninglessness” he said he observed in some people who were gratified in their basic needs. The problem is that this view clashes with other statements of his regarding self-actualization – for example, “What a man can be, he must be. He must be true to his own nature. This need we may call self-actualization” (1970, p. 46). This statement, of course, conveys quite a different conception of self-actualization; according to it, we would conclude that anyone can potentially become self-actualized. But, as we have just seen, Maslow elsewhere realized that his genetic theory in fact limits selfactualization to a favored proportion of the population. But, of course, Maslow cannot have it both ways. One of these positions must be wrong. Let us conclude this section on self-actualization with a look at the people Maslow cited as self-actualized. Remember, they include such well-known personalities as Abraham Lincoln and Eleanor Roosevelt. Now, according to Maslow, to be self-actualized, individuals should “have been satisfied in their basic needs throughout their lives, particularly in their earlier years” (Maslow, 1970, p. 53). Thus, achieving a high level of need satisfaction late in life won’t do; this situation fits the alternative “deprivation followed by fulfillment” model of human well-being, rather than Maslow’s “constant-fulfillment” model. Now, if you are familiar with the early lives of Abraham Lincoln and Eleanor Roosevelt, you know that they both had extraordinary challenges and deprivations to overcome. In other words, they fail to qualify as exemplars of Maslow’s theory. Why did Maslow include such individuals in his attempts to support his theory? The answer seems to be that Maslow chose

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his sample of self-actualizers on the basis of their adult traits, not their past life experiences (Maslow, 1970, pp. 149–180). Thus, unfortunately, instead of serving as a test of his theory (Does a consistently high level of need gratification produce self-actualized individuals?), his sample chiefly shows that if you look for people who meet any particular criteria of psychological health, you can probably find people who meet those criteria. For this reason, his demonstration of the traits of self-actualizers is “circular” and has little bearing on his theory. On the other hand, Maslow’s sample does demonstrate that some adults seem able to function much of the time at higher-need levels. However, most of the possible mechanisms for achieving self-actualization – we have discussed these in previous sections – are not encompassed by Maslow’s theory. In any particular instance, of course, it is difficult to know which of these mechanisms might be involved: for example, (a) gratification of lower needs in later life, (b) repression of lower needs, (c) a particularly strong genetic self-actualization potential, (d) a linkage between the two levels by which the pursuit of higher needs helps to meet lower needs, or (e) the achievement of functional autonomy of higher needs. Most likely, different combinations of these mechanisms operate in different people at different times. With respect to the traits of self-actualizers, you will remember that such people are said to be exceptionally creative, spontaneous, and nonjudgmental. However, in spite of the value Maslow seemed to attach to being nonjudgmental, Maslow’s theory is very judgmental – about what produces and what constitutes a self-actualized individual. In this, he is allied with other nativist theorists such as Carl Rogers. That is, because they postulate a more or less predetermined and unchanging human nature, they have a framework for judging whether or not people are pursuing the “correct path” to self-actualization. In contrast, behaviorists, for example, traditionally make no judgments about what an ideal human is like, because our human potential, in their view, is not fixed, but rather is infinitely malleable. Of course, either of these extreme positions is difficult to support. A final characteristic of self-actualizers deserves comment, and that is their ability to experience heights of emotion – what Maslow called peak experience, or what is more commonly referred to as mystical experience. Remember that, according to Maslow, people become self-actualized, and thus more likely to have peak experiences, when their lower needs have been met. However, as we have already said, both Eastern and Western traditions favor deprivation and suppression as a means of curtailing the lower needs, and this same approach, carried to an extreme, constitutes perhaps the most common path to mystical experience (Neher, 1990, pp. 107–121). At one point, and contrary to his theory, Maslow admitted that “higher needs may occasionally emerge, not after gratification, but rather after forced or voluntary deprivation, renunciation, or suppression of lower basic needs [as is]

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reported to be common in Eastern cultures” (1970, pp. 59–60). Probably all of us have experienced the ecstasy that can follow fulfillment after a long period of deprivation – for example, reunion with a loved one after a lengthy separation. But how do we make sense of deprivation practices of mystics, East and West, whose fulfillment, when it comes, seems to be in the form of transcendental feelings or visions of achieving oneness with a higher essence? St. Teresa’s accounts of ecstatic union with spiritual beings is probably the best-known example. Perhaps, as with much of experience, fulfillment is more a matter of expectation and perception than of external reality (Neher, 1990, pp. 122–130). Short of such extremes, most of us can remember when we have purposefully deprived ourselves of basic needs; going camping is a good example. Having to concern ourselves with providing shelter, keeping warm, and catching and preparing fish to eat may only prove what Cicero said: “Hunger is the best seasoning for meat.” But such experiences also seem to provide a connection with our primal roots (i.e., our basic needs) that can be very meaningful and invigorating. All these examples of purposeful need deprivation in the service of achieving apparently higher states of being tend, of course, to undermine further Maslow’s belief that satiating lower needs constitutes the most reasonable path to self-actualization and peak experience. To summarize, the problem here is not that the level of self-actualization is not worth attaining. The problems are that, first, there is a serious question whether its attainment is a consequence of the process Maslow advocated. In particular, the requirement that lower-level motivations must first be eliminated, through satiating them, is highly questionable on a number of grounds. And, second, there is good reason to believe that lower motivations are not always burdensome. In fact, they can make their own unique and significant contribution to our lives.

Conclusion With respect to the main outlines of his theory, Maslow certainly deserves credit for his general thesis: Undoubtedly, we do have a difficult time reaching the heights of experience if we are preoccupied with attaining the base essentials of life. However, many of the details of his theory need modification. In particular, the four components of the theory need some reworking. 1. We do inherit needs, but among these are needs that Maslow failed to acknowledge as necessary for developing as fully functioning humans. These needs involve the necessity for a great deal of cultural input, more than just what is necessary to gratify our lower needs. In particular, many

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higher needs undoubtedly require encouragement from the environment for their development. 2. There probably is some sort of need hierarchy, in that our basic needs are ordinarily more urgent in their demands than are higher-level needs. However, it is not clear that, in the long run, satisfying our lower needs diminishes their urgency, which Maslow felt was necessary for higher needs to emerge. In fact, for many reasons, a moderate level of need gratification seems to be more growth enhancing than the high levels of need gratification that Maslow favored. In addition, there is probably more linkage between various need levels than Maslow proposed. In particular, the higher needs may not be as autonomous as Maslow’s theory suggests. For example, if we could, we might often trace them to their origin, either in evolutionary or individual experience, in helping us meet lower needs. 3. Higher-level needs seem not to operate apart from a sense of deficiency, as Maslow proposed. However, higher needs certainly are distinctive in that, unlike lower needs, we are able to choose our higher motivations (or challenges, and thus deprivations) they are farthest because removed from essential survival needs. 4. The level of self-actualization, as Maslow described it, is unique to humans and is worthy of attainment. However, his widely cited sample of selfactualized individuals does not support his theory that a history of highlevels of satiation of basic needs, which is intended to eliminate them as motivations, is required for the attainment of self-actualization. In fact, there are many reasons to believe that “lower” motivations, far from always being a burden, can provide important fulfillments and satisfactions of their own. Nevertheless, there are a number of possible mechanisms, most of which Maslow’s theory fails to encompass, that may be involved in the achievement of self-actualization. In the face of these many problems, humanistic psychologists have a choice. They can ignore the difficulties, preserve Maslow’s teachings intact, and consequently run the risk of ideological atrophy as has happened, to some extent, in psychoanalysis. Or they can view Maslow’s theory as a serious scientific contribution that therefore deserves scrutiny and modification in the light of new insights and new information. The particulars of his theory aside, Maslow certainly deserves credit for a number of accomplishments. He attacked behaviorism, as well as psychoanalysis, at some of their most vulnerable points, and encouraged us to think about alternative ways of viewing motivation. And he encouraged us to devote more attention to the example of psychologically healthy individuals and what they can teach us about the positive aspects of living. There is little question that these are worthy accomplishments.

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References Alderfer, C. P . (1969). An empirical test of a new theory of human needs. Organizational Behavior and Human Performance, 4, 142–175. Allport, G. W. (1937). The functional autonomy of motives. American Journal of Psychology, 50, 141–156. Ansbacher, H., & Ansbacher, R. (1959). The individual psychology of Alfred Adler. New York: Basic Books. Aron, A. (1977). Maslow’s other child. Journal of Humanistic Psychology, 17(2), 9–24. Berlyne, D. E. (1960). Conflict, arousal, and curiosity. New York: McGraw-Hill. Bloomfield, H., & Felder, L. (1985). The Achilles syndrome: Transforming your weaknesses into strengths. New York: Random House. Brown, W. (1983). Welcome stress. Minneapolis, MN: Compcare. Daniels, M. (1982). The development of the concept of self-actualization in the writings of Abraham Maslow. Current Psychological Reviews, 2, 61–76. Daniels, M. (1988). The myth of self-actualization. Journal of Humanistic Psychology, 28(1), 7–38 Eisenberger, R. (1972). Explanation of rewards that do not reduce tissue needs. Psychological Bulletin, 77, 319–339. Frick, W. (1982). Conceptual foundations of self-actualization. Journal of Humanistic Psychology, 22(4), 33–52. Geller, L. (1982). The failure of self-actualization theory. Journal of Humanistic Psychology, 22(2), 56–73. Goldberg, P . (1978). Executive health. New York: McGraw-Hill. Graham, W., & Balloun, J. (1973). An empirical test of Maslow’s need hierarchy. Journal of Humanistic Psychology, 13(1), 97–108. Hall, D. T., & Norigaim, K. E. (1968). An examination of Maslow’s need hierarchy in an organizational setting. Organizational Behavior and Human Performance, 3,12–35. Holmes, T. H., & Rahe, R. H. (1967). The social readjustment rating. Journal of Psychosomatic Research, 11, 213–218. Houston, J. (1981). The pursuit of happiness. Glenview, IL: Scott, Foresman. Huizinga, G. (1970). Maslow’s need hierarchy in the work situation. Groningen, Netherlands: Wolters-Noordhoff. Lawler, E., & Suttle, J. L. (1972). A causal correlational test of the need hierarchy concept. Organizational Behavior and Human Performance, 7, 265–287. Maddi, S. (1968). Personality theories. Belmont, CA: Dorsey. Malson, L. (1972). Wolf children and the problem of human nature. New York: Monthly Review Press. Maslow, A. (1964). Religions, values, and peak experiences. Columbus, OH: Ohio State University. Maslow, A. (1967). Eupsychian management: A journal. Homewood, IL: Irwin-Dorsey. Maslow, A. (1968). Toward a psychology of being (2nd ed.). New York: Van Nostrand. Maslow, A. (1969). The psychology of science: A reconnaissance. New York: Harper & Row. Maslow, A. (1970). Motivation and personality (2nd ed.). New York: Harper & Row. Maslow, A. (1971). The farther reaches of human nature. New York: Viking. Mathes, E. (1981). Maslow’s hierarchy of needs as a guide for living. Journal of Humanistic Psychology, 21(4), 69–72. Millon, T. (1969). Modern psychopathology. Philadelphia: W. B. Saunders. Miner, J. B., & Dachler, H. P . (1973). Personal attitudes and motivation. Annual Review of Psychology, 24, 379– 402. Neher, A. (1990). The psychology of transcendence (2nd ed.). New York: Dover.

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Piattelli-Palmarini, M. (1980). Language and learning. Cambridge, MA: Harvard University Press. Selye, H. (1974). Stress without distress. Philadelphia: Lippincott. Smith, M. B. (1973). On self-actualization: A transambivalent examination of a focal theme in Maslow’s psychology. Journal of Humanistic Psychology, 13(2), 17–33. Turnbull, C. M. (1974). The mountain people. New York: Simon & Schuster. Wahba, M. A., & Bridwell, L. G. (1979). Maslow reconsidered: A review of research on the need hierarchy theory. In R. M. Steers & L. W. Porter (Eds.), Motivation and work behavior (pp. 47–55). New York: McGraw-Hill. White, R. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66, 297–333. Whorf, B. (1956). Language, thought, and reality. Cambridge, MA: MIT Press. Wilson, E. O. (1980). Sociobiology. Cambridge, MA: Harvard University Press. Wofford, J. C. (1971). The motivational bases of job satisfaction and job performance. Personnel Psychology, 24, 501–518. Wuthnow, R. (1978). An empirical test of Maslow’s theory of motivation. Journal of Humanistic Psychology, 18(3), 75–77. Yerkes, R., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18, 459– 482.

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44 Caught on Fire: Motivation and Giftedness Ann Robinson

I do not mean zeal without capacity, nor capacity without zeal. —Sir Francis Galton (1869)

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few years ago, a film based on real events surrounding a British team of Olympic runners found favor with movie goers. The film, Chariots of Fire, traced the development of two great athletes as they prepared for the 1924 Olympic games after the First World War. One long distance runner, Eric Liddell, was a devout missionary from Scotland; the other was Harold Abrahams, a young Jewish runner utterly absorbed by his sport. Despite early difficulties and experiences with ethnic prejudice, Abrahams went on to become an institution in British athletics. Liddell pursued a life in the church. The film is a revealing examination of zeal, or the eagerness to work. In the film, each of the two leading actors communicates the joy, the desire and the powerful identification with one’s talents that we have come to understand as integral to giftedness. Indeed, as a construct, motivation permeates our field (Feldhusen, 1986). We can trace its modern roots to the Victorian Sir Francis Galton who believed that great achievements called for both intellect and enthusiasm. Moving into the 1940’s, 1950’s and 1960’s, motivation was a focal point for researchers like White (1959) and McClelland (1961). Researchers began to refine their understanding of intrinsic and extrinsic motivation. Students who worked for personal feelings of satisfaction were intrinsically motivated and thought to be more likely to continue learning for its own sake than those Source: Gifted Child Quarterly, 40(4) (1996): 177–178.

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who achieved in school because of extrinsic rewards. Later achievement motivation researchers discriminated between task versus ego involvement. A student with task involvement learned because he or she was “carried away” with the activity itself. Ego-involved students were more likely to work in order to best others. Prominent figures in gifted education like E. Paul Torrance recognized the importance of “falling in love with an idea.” Here, motivation became an emotional state rather than a behavior or an action. More recently, Csikzentmihalyi conceptualized such feelings as flow – an optimal experience which transports the person beyond themselves. As classroom teachers, we have embraced Renzulli’s use of task commitment to describe the persistence necessary to the development of talent. The widespread use of task commitment by schools to define giftedness and to identify gifted students is testimony to the consensus that motivation counts in the real world. And count, it does. There can be no more valuable outcome for education than the love of learning. Unfortunately, in an attempt to categorize and to create handy taxonomies, educators have too often compartmentalized intellect and feeling. We speak or cognitive and affective domains as if they do not meet in the same individual. By artificially divorcing our cogitations from our passions, we have committed Descartes’ error – the belief that heart and mind are quite separate organs or entities. A thoughtful teacher observing a child happily, passionately, and zealously engaged in learning knows quite the contrary.

In This Issue First, Gottfried and Gottfried trace the development of academic intrinsic motivation from childhood through early adolescence. In their longitudinal study, gifted children were more likely than a comparison group to report higher motivation across all subject areas. The authors conclude that the enjoyment of learning is greater for gifted students and that motivation is important for the development of giftedness. Their contribution to our knowledge base includes the developmental finding that motivation in gifted children remains stable over time. Next, Chan examines the motivational orientations and metacognitive abilities of gifted children and their average achieving peers. Using the framework of attribution theory, she notes that gifted children have greater confidence in their feelings of control over success and failure in school than do their agemates. Gifted children are more likely to report that they can control the amount of effort they put into a school task and the strategies they use to learn them.

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In “Gifted and Non-Selected Children’s Perceptions of Academic Achievement, Academic Effort, and Athleticism,” Udvari and Rubin extend the landmark study by Tannenbaum (1962). They studied younger children and they introduced gender as a variable. Their results indicate that gifted children are more tolerant of “brilliant” peers than average children are, that neither group actively disparaged effort nor did they particularly reward it, and that athleticism continued to be the most important contributor to social acceptability. In the next article, Kurt Heller of Germany summarizes the considerable literature on gender differences in mathematics and the natural sciences from a motivational perspective. This important contribution to the knowledge base distills a vast literature with significant implications for the development of gifts and talents in girls and women. He examines the hypothesis that the lowered performance of girls and women may be due to their attributions about their abilities in these subject areas. To support his conjecture, he reviews empirical research which indicates that girls and women hold unrealistically low expectations of their abilities in mathematics and science. Then, Heller reports two studies of his own on attribution retraining programs which successfully modify attributions of high school and college women and which subsequently raise their level of achievement. Neither study reviewed has been accessible to English speaking scholars until now. What happens when motivation is diverted from a healthy course? How frequently is that likely to happen among gifted students? Is it truly unhealthy? In “The Incidence of Perfectionism in Gifted Students,” Parker and Mills explore these questions and conclude that gifted students and a comparison group of age peers do not differ significantly in the incidence of perfectionism. They also suggest that the anecdotal reportage of perfectionism among gifted youth may be the result of differential labeling. What is viewed as healthy effort among the general cohort may be viewed by others as unhealthy overachievement among gifted students. Finally, they urge the field to develop a more precise distinction between striving which stimulates excellence and striving which inhibits it. Our “In the Public Interest” shares the reflections of Pamela Clinkenbeard on what the literature on motivation and giftedness has to offer us as we set about developing the talents of our students in the schools. She notes that the studies which conceptualize motivation as a trait or state lead us to include measures or markers of motivation in the identification of gifted students. Leading us further, she points out that the field will benefit from viewing the motivation to learn as the outcome as well as an identification “input” of our programs and services. Finally, we close with two book reviews which contribute to our understanding of giftedness and motivation. First, Pat Haensly reviews Karen Arnold’s study of high school valedictorians. Her thoughtful review of Arnold’s thoughtful text poses an important question. To what extent does

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the traditional avenue of recognition for school achievement – class standing – divert talented young people from a life happily lived and creatively expressed? The review leads us to the fitting, final piece in this special issue on giftedness and motivation, Gary Davis’ review of a biography of E. Paul Torrance by Garnet Miller. Working through document analysis, interviews and extensive converations with Dr. Torrance himself, Millar has produced a portrait of a man who fell in love with an idea and made it a way of life.

References Csikzentmihalyi, M. (1991). Flow: The psychology of optimal experience. New York: Harper Perennial. Feldhusen, J. F. (1986). A conception of giftedness. In R. J. Sternberg & J. F . Davidson (Eds.), Conceptions of giftedness. Cambridge, England: Cambridge University Press. Galton, E. (1869). Hereditary genius: An inquiry into its laws and consequences. London: Macmillan & Co. McClelland, D. (1961). The achieving society. New York: The Pree Press. Renzulli, J. S. (1978). What makes giftedness? Re-examining a definition. Phi Delta Kappan, 60, 180–184, 261. Tannenbaum, A. (1962). Adolescents attitudes toward academic brilliance. New York: Teachers College Press. White, R. (1959). Motivation reconsidered: The concept of competence. Psychological Review, 66, 297–333.

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45 An Empirical Test of Maslow’s Theory of Motivation Eugene W. Mathes and Linda L. Edwards

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aslow’s (1970) theory of motivation suggests that there are five basic classes of needs and that they are hierarchically organized as follows: physiological, security, belongingness, esteem, and self-actualization. Each need level is prepotent to the next higher need level. This means that an individual initially attempts to satisfy his or her physiological needs, and only when they are satisfied does the individual attempt to satisfy security needs. Once security needs are satisfied the individual attempts to satisfy belongingness needs and so on. Although a number of studies have shown that satisfaction of physiological (Cofer & Appley, 1964), security (Maslow, Birsh, Honigmann, McGrath, Plason, & Stein, 1952), belongingness (Rogers & Dymond, 1954), and esteem needs (Maslow, 1939; 1940; 1942) facilitates self-actualization, there is no evidence demonstrating that these lower needs form the hierarchy specified by Maslow. The purpose of the study reported below was to test the hierarchical aspect of Maslow’s theory of motivation. To accomplish this end, student subjects (36 males, 76 females) were given self-report inventories: the Security-Insecurity Scale of Maslow et al. (1952); a belongingness scale devised by the authors; Rosenberg’s (1965) Self-Esteem Scale; and Shostrom’s (1965) measure of self-actualization, the Personal Orientation Inventory (POI). Physiological need satisfaction was not measured because it was assumed that the subjects’ physiological needs were satisfied. It was hypothesized that subjects scoring above the median on one of these measure of need satisfaction would obtain significantly higher average

Source: Journal of Humanistic Psychology, 18(1) (1978): 75–77.

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scores on all of the measures of need satisfaction further up the hierarchy than subjects scoring below the median. Specifically, three hypotheses were made: Hypothesis 1. Subjects scoring above the median on the measure of security need satisfaction would obtain significantly higher average belongingness satisfaction, esteem satisfaction, and self-actualization scores than subjects scoring below the median on the security measure. Hypothesis 2. Subjects scoring above the median on the measure of belongingness need satisfaction would obtain significantly higher average scores on the measures of esteem need satisfaction and self-actualization than subjects scoring below the median on the belongingness measure. Hypothesis 3. Subjects scoring above the median on the measure of esteem need satisfaction would obtain a significantly higher average score on the measure of self-actualization than subjects scoring below the median on the esteem measure.

To test the first hypothesis, subjects were split into secure and insecure groups by means of a median split of Security-Insecurity Scale scores. The average scores of these two groups on the Belongingness, Self-Esteem, and POI scales were then compared by means of t tests. As Table 1 shows, Hypothesis 1 was entirely supported by the women’s data but only partially supported by the men’s. Although the secure men scored significantly higher on the POI than the insecure men, significant differences were not found for the other two scales. To test the second hypothesis, subjects were split into belonging and nonbelonging groups by means of a median split of Belongingness Scale scores. The average scores of these two groups on the Self-Esteem and POI scales were then compared by means of t tests. As Table 1 shows, Hypothesis 2 was not supported.

Table 1: Mean satisfaction scores of subjects scoring above and below the median on lower level need satisfaction measures Women

Belonging Self-Esteem POI

Self-Esteem POI

POI

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Men

Insecure ss

Secure ss

p

Insecure ss

Secure ss

p

18.58 48.37 96.52

21.16 60.47 106.00

.0013 .0011 .0054

19.28 53.89 92.61

21.11 55.22 107.50

n.s. n.s. .0006

Unloved ss

Loved ss

p

Unloved ss

Loved ss

p

52.74 98.42

56.11 104.11

n.s. n.s.

53.50 99.50

57.11 102.50

n.s. n.s.

Low Self-Esteem

High Self-Esteem

p

Low Self-Esteem

High Self-Esteem

p

98.08

104.45

n.s.

98.22

n.s.

101.44

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To test the third hypothesis, subjects were split into high and low self-esteem groups by means of a median split of Self-Esteem scale scores. The average scores of these two groups on the POI were then compared by means of a t test. Table 1 shows that Hypothesis 3 was not supported. The results of this study suggest that Maslow’s hierarchical theory of motivation should be modified to include only two or three levels. Security was shown to be a prerequisite to self-actualization, while belongingness and esteem were shown not to be essential prerequisites.

References Cofer C. N., & Appley, M. H. Motivation: Theory and research. New York: Wiley, 1964. Maslow, A. H. Dominance-feeling, personality and social behavior in women. Journal of Social Psychology, 1939, 10, 3–39. Maslow, A. H. A test for dominance-feeling (self-esteem) in women. Journal of Social Psychology, 1940, 12, 255–270. Maslow, A. H. Self-esteem (dominance feeling) and sexuality in women. Journal of Social Psychology, 1942, 16, 259–294. Maslow, A. H. Motivation and personality (Revised ed.). New York: Harper and Row, 1970. Maslow, A. H., Birsh, E., Honigmann, I., McGrath, F., Plason, F., & Stein, M. Manual for the security-insecurity inventory. Palo Alto, Calif.: Consulting Psychologists Press, 1952. Rogers, C. R., & Dymond, R. F . (Eds.). Psychotherapy and personality change. Chicago: University of Chicago Press, 1954. Rosenberg, M. Society and the adolescent self-image. Princeton, N.J.: Princeton University Press, 1965. Shostrom, E. L. A test for the measurement of self-actualization. Educational and Psychological Measurement, 1965, 24, 207–218.

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46 Meaningfulness, Commitment, and Engagement: The Intersection of a Deeper Level of Intrinsic Motivation Neal Chalofsky and Vijay Krishna

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he managerial and popular literature has been increasingly referring to the “baby boomers” in America (the disproportionately large generation born just after World War II) nearing retirement age and questioning the meaning and purpose of their work and their lives. At the same time, their children, Generations X and Y, have started their careers asking the same questions. The classic motivation theorists and humanistic psychologists clearly supported the notion that individuals have an inherent need for a work life that they believe is meaningful (Alderfer, 1972; Herzberg, Mausner, & Snyderman, 1959; Maslow, 1943, 1954, 1971; McClelland, 1965; McGregor, 1960; Rogers, 1959, 1961). Maslow (1971) wrote that individuals who do not perceive the workplace as meaningful and purposeful will not work up to their professional capacity. There is a long history of research and discourse about what motivates employees and the relationship between job satisfaction and performance/productivity. The need or content theories of the 1960s and 1970s and their emphasis on the individual gave way to the reinforcement and person–environment interaction theories of the 1970s through the 1990s and their emphasis on performance, organizational systems, and productivity. Most of the research, therefore, has been in relation to these theories. The resurgence of interest of intrinsic factors such as meaning, purpose, spirituality, and commitment Source: Advances in Developing Human Resources, 11(2) (2009): 189–203.

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and the recent introduction of engagement has resulted in an increase in both the popular and scholarly literature concerning the role of work as a motivator in the organization (Csikszentmihalyi, 1990; Fox, 1994; Lockwood, 2007; Meyer & Herscovitch, 2001). Employee commitment and engagement have emerged as very important constructs in organizational research on account of their favorable relationship with employee behaviors that promote organizational retention and performance. According to Porter (1968), commitment involves the willingness of employees to exert higher efforts on behalf of the organization, a strong desire to stay in the organization, and accept major goals and values of the organization (as cited in Porters, Steers, Mowday, & Boulin, 1974). A number of studies have shown a positive correlation between employee commitment and job performance (Hunter & Thatcher, 2007; Pool & Pool, 2007). Angle and Perry (1981) showed in their research that organizational commitment correlates positively with employees’ and organization’s ability to adapt to unforeseeable events. Studies also suggest that organizational commitment supports organizational citizenship behaviors that are central to flatter organizations, effective teams, and empowerment (Dessler, 1999). Kanter (1968) in her study of the 19th century American utopian societies, such as the Shakers, showed that the commitment-producing strategies distinguished successful from unsuccessful societies: “commitment is central to the understanding of both human motivation and system maintenance” (p. 499). According to Senge (1993), personnel commitment is one of the key requirements to become a learning organization. Be it a utopian society or a learning organization, commitment is seen as one of the key factors for organizational survival and growth. Despite the tremendous interest that organizational commitment research generates (Beck & Wilson, 2000), questions about the process and determinants of organizational commitment remain unanswered (Cohen, 2003; Meyer & Herscovitch, 2001). One of the possible reasons for this lack of a clear understanding of the motivational processes is because of the separation of the intrinsic aspects of motivation from the organizational and contextual factors that affect its development. Although there has been some research that suggests that employee engagement is related to workforce efficiency and productivity, very little empirical research exists that explains the processes through which engagement develops. Engagement has been defined as “the extent to which employees commit to something or someone in their organization, [and] how hard they work and how long they stay as a result of that commitment” (Corporate Leadership Council, 2004). The purpose of this article is to explore a deeper level of intrinsic motivation, meaningfulness, and to discuss the connections between meaning of work and meaning at work, represented by the concepts of employee commitment and engagement as organizational and contextual factors. A holistic approach to workplace motivation that combines the intrinsic aspects of

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work motivation with the contextual and organizational factors has not been developed in the literature. This approach is important because although motivation is an individual and personal process, it is also significantly influenced and shaped by the contextual and organizational factors. Hence, while studying motivational factors, it is necessary to consider both the individual and the organizational factors that affect its development. This article attempts to fill this gap by generating a conceptual frame of a deeper level of motivation, namely, meaningfulness or meaningful work, and outlines the connection between meaning of work and meaning at work that is expressed in terms of employee commitment and engagement. This article seeks to contribute to the organizational behavior field by linking these streams of research and conceptual development that have not been connected previously. The integrative approach adopted in this article provides a new perspective on the connections between workplace motivation, employee commitment, and employee engagement.

Conceptual Background In preindustrial society, work was performed in the same community setting where people lived. Consequently, people knew one another closely and saw the connection between their work and how that work benefited the rest of the community. The work of an individual was intricately tied to the wellbeing of the self and the community. There was no separation of work from self, community, and life. The twin forces of reduction in agricultural work and rise of mechanical work meant more people becoming wage earners who were working for others (Brisken, 1996). In 1860, half the working population was self-employed; by 1900, two thirds were wage earners. Work became governed by the clock, by uniform standards, and by supervisors. “Reason demanded that workers subordinate their own experience of natural rhythms to the logic of efficiency” (Brisken, 1996, p. 100). The industrial era separated work from the community and created the bureaucracy to house, organize, and control work. There was little or no contact between the organization where employees worked and the community where they lived. Work was no longer an integral part of community life; it was detached, separated, and contained within specific buildings and times. In bureaucracies, hierarchies separated executives from workers, and internal competition forced workers against workers as they fought to move up the increasingly narrow upper levels of the organization. Wall Street further separated the owners from the employees. Now there are people who commute from New York or Boston to Washington and beyond, as well as people all over the globe who work in virtual teams and even virtual organizations. Consequently, people are not only moving work further away but are further away from the rest of their

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lives. As work has become separated from the community and life, it has lost its original sense of meaning as an integral aspect of human existence. One hypothesis is that motivation only became an issue because meaning disappeared when the work became separated from the rest of life and community. “As a consequence motivation theories have become surrogates for the search for meaning” (Sievers, 1984, p. 3). There is very little research based on the premise that meaningful work is lost when work becomes separated from being a natural and integral part of the community. In the 1960s and 1970s, the classic motivation theorists and humanistic psychologists clearly supported the notion that individuals have an inherent need for a work life that they believe is meaningful (Alderfer, 1972; Herzberg et al., 1959; Maslow, 1943, 1971; McGregor, 1960; Rogers, 1959, 1961). Maslow (1971) wrote that individuals who do not perceive the workplace as meaningful and purposeful will not work up to their professional capacity. They theorized that individuals are motivated to take certain actions based on fulfilling needs believed to be inherent in all humans. These theorists all proposed that as these needs move from the basic survival needs to higher-order needs, they become more intrinsic and reflective in nature. The higher-order needs reflect life values: working toward a higher cause, meaningfulness, and life purpose. Maslow (1971) expressed these values as being values, referred to as B-values. B-values included truth, transcendence, goodness, uniqueness, aliveness, justice, richness, and meaningfulness. Maslow believed that individuals have the potential to reach what he called self-actualization, which is the process of developing one’s potential, of expressing oneself to the fullest possible extent in a manner that is personally fulfilling. It is not an end-state but an ongoing process of becoming. Near the end of his life, Maslow wrote of people who seemed to transcend self-actualization. He labeled this phenomenon “Theory Z” after McGregor’s (1960) “Theories X and Y.” In this state, people are devoted to a task, vocation, or calling that transcends the dichotomies of work and play. Maslow (1971) viewed this as a dynamic process of expanding the capabilities of the self to virtually unlimited potential. Also noteworthy were the thoughtful concepts from Rogers (1961), Locke (1975), and Ackoff (1981). Rogers believed that people find purpose when they experience freedom to be exactly who they are in a fluid and changing manner. Locke (1975) wrote that people strive to attain goals to satisfy their emotions and desires. Ackoff (1981) described purpose and meaning as progress toward an ideal that converts mere existence into significant living by making choice meaningful.

Meaning of Work In the late 1990s and early 2000s, spirituality and meaning at work emerged as a reaction to the loss of job security, as well as other factors (Darling & Chalofsky, 2004). One set of events was the environmental

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disasters of Chernobyl, the chemical pollution at Bhopal, and the big oil spills off the coasts of Canada and Europe. These sparked an increase in the collective conscious about corporate social responsibility. The second set of events was the ethics scandals by Enron, Worldcom, and others. There have been a host of books, articles, and other media questioning our misuse of this planet, the role of work in capitalist societies, and our moral, ethical, and spiritual stance around life’s meaning and purpose (Holbecke & Springnett, 2004). In the past several years, organizations had been attempting to attract and retain highly qualified workers in advance of a projected labor shortage and amid increasing global competition. More recently, the economic downturn that began in 2007/2008 has been causing tremendous turmoil in employment. Yet new young professionals are still expressing a preference to work for socially responsible, ethically driven organizations that allow the “whole self” to be brought to work. And the “baby boomers” in America have been going through midlife and early retirement questioning the meaning and purpose of work in their lives, especially those who went through the downsizings of the 1990s (both the ones who lost their jobs and the survivors). When you ask these people about how they feel about work, according to one consulting group, they talk about a sense of loss; a lack of purpose, trust, and commitment; a loosening of emotional ties to the workplace; and a questioning of whether their work is worthwhile (Holbecke & Springnett, 2004). According to the Society for Human Resource Management’s (2008b) workplace forecast report, 4 of the 10 key themes identified were the following: • The implications of increased global competitiveness, especially the need for an educated and skilled workforce • Demographic changes, especially the aging of the workforce, the impending retirement of the baby boom generation, and the greater demand for work / life balance • Growing need to develop retention strategies for current and future workforce • Demographic shifts leading to a shortage of high-skill workers • Other findings from their survey that were relevant include the following: • Growth in the number of employees with caring responsibilities (elder care, child care, and both elder care and child care at the same time) • Generational issues – recognizing and catering to groups such as Generation Y (born 1980–2000), Generation X (born 1965–1980), and so on As mentioned earlier, the United States and the rest of the world were going through a chaotic economic decline, and even before the economic turmoil fully emerged, employees identified job security as their top concern (Society for Human Resource Management, 2008a). The Society for Human Resource

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Management study identified contributors to employee job satisfaction, and the rest of the top four were the following: benefits, compensation, and feeling safe in the work environment. The top four contributors to job satisfaction were actually not satisfiers, based on Herzburg, but basic hygiene factors, or lower-order Maslow’s hierarchy levels. And they were rated high, at least in part, because of the dismal economic situation. So to call them contributors to satisfaction, or motivational factors, is a misnomer. But five out of the top 10 contributors to job satisfaction are motivational: • • • • •

Opportunities to use skills and abilities Relationship with immediate supervisor The work itself Meaningfulness of job Flexibility to balance life and work issues

What all these findings point to is the American workforce’s desire to be part of an organization that is going to take care of them and help them take care of their families, support their growth through skill and knowledge development, understand their need to have some work–life balance, and use their skills and abilities in a way that is meaningful.

Motivation and Meaning The literature refers to values as intrinsic motivators to performing a task and deriving satisfaction from the accomplishment of that task (or job). Although the emphasis may be on the congruence of the task with our beliefs, objectives, and anticipated rewards, motivation is seen as focused on the accomplishment of the task. The common assumption is that we are motivated by values based on result or outcome. Meaning, on the other hand, is more deeply intrinsic than values, suggesting three levels of satisfaction: extrinsic, intrinsic, and something even deeper. This level of intrinsic motivation is about the meaning of the work itself to the individual. Csikszentmihalyi (1990), in his attempt to define meaning, readily acknowledged the difficulty the task presents by suggesting that any definition of the term would undoubtedly be circular. However, he pointed to three ways in which the word may be defined, two of which are (a) having a purpose or the significance of something and (b) the intentions one holds. Similarly, Dirkx (1995) subscribed to the theory that work is one of the ways that a mature adult cares for oneself and others. This was expressed by respondents in the Schaefer and Darling (1996) study, who defined work as an opportunity for service to others and not distinct from the rest of life. The term may also be definitive of one’s uniqueness and a way of expressing one’s self in the world.

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The significance of Csikszentmihalyi’s research was how intrinsically motivated people are driven by the work itself rather than by the accomplishment of the task. He included people in a wide range of occupations and activities and discovered a particular kind of experience where people’s performance seemed effortless. They described the feeling of being able to continue forever in their task and wanting to learn additional skills to master more demanding challenges. The fun, sense of mastery, and the potential for growth of self was what he labeled flow. In addition, they were disappointed when the work was finished because they were no longer in the flow state. This flow state was very similar to Maslow’s peak experiences at the self-actualization level. The work itself is but one aspect of Chalofsky’s (2003) construct of meaningful work. Chalofsky identified three themes: sense of self, the work itself, and the sense of balance. These themes represent a deeper level of motivation than the traditional intrinsic values of a sense of accomplishment, pride, satisfaction of finishing a task, and praise from a supervisor. This emerging new paradigm links back to some of the work of the content theorists but takes their thinking and the concept of intrinsic motivation to a deeper evolutionary level.

Sense of Self The idea of people needing to bring their whole selves (mind, body, emotion, and spirit) to their work is critical to finding meaning in work. People often fail to bring their whole selves to work out of fear of rejection, prejudice, or misunderstanding. “We work hard to create physical safety in our workplaces. Can we also create mental, emotional, and spiritual safety – safety for the whole person?” (Richards, 1995, p. 87). Mitroff and Denton (1999), in their groundbreaking study of spirituality in the workplace, found that the word that best described what people were feeling was a loss of interconnectedness, and what upset them the most was not being able to bring their complete selves into the workplace. For those people who felt adrift spiritually, their work and the workplace ceased to be a source to find deeper meaning, satisfaction, and connection. Helping individuals integrate their work and spiritual lives might mean that the time people spend working in their lifetime are more joyful, balanced, and meaningful and spiritually nourishing (Gibbons, 2007). These more fulfilled individuals might then return to their families, friends, and communities contented, refreshed, and ready to contribute. Because of this integration, one might expect that these people might be more ethical and more productive workers – which would benefit their employers. Moreover, a values-based organization culture might help businesses to become humane, socially active, and environmentally responsible.

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Before one can bring the whole self to work, one has to first be aware of one’s own values, beliefs, and purpose in life. The sense of self also includes constantly striving to reach one’s potential and believing in one’s ability to reach that potential. And it includes an alignment between one’s purpose in life and the purpose for the work. Fulfillment, in part, comes from feeling that what we do on this earth makes a difference to other people. In fact, Maslow’s (1971) views expressed in the Farther Reaches of Human Nature would warrant the term selfless-actualization rather than self-actualization (Greene & Burke, 2007). His last work espoused human development beyond the self in self-actualization. Maslow’s (1971) message was that people must ultimately move from a focus on self to a focus and concern for other people to achieve the highest level of human nature. People who move beyond self-actualization “are, without a single exception, involved in a cause outside of their skin: in something outside of themselves, some calling or vocation” (p. 42). Meeting the self-actualization needs focuses on achieving a personal identity and complete acceptance of self and then moving beyond to a higher connection with others.

The Work Itself In the not-so-distant past, managers made decisions about the structure and process of work activities, in the name of efficiency (Thomas, 2000). Jobs were broken down into tasks, which involved certain competencies, and specific and measurable objectives. But work has now changed dramatically. Organizations have realized that they need to rely more and more on workers to make decisions about how the work should get accomplished. This requires more worker autonomy, flexibility, empowerment, continuous learning, risk taking, and creativity. Thomas captures what the research has demonstrated with his list of the four most critical intrinsic rewards: sense of meaning and purpose, sense of choice, sense of competence, and sense of progress. Although the work itself relates back to both Maslow’s self-actualization and Alderfer’s growth levels, and to an extent Herzberg’s motivators, the focus is on carrying out one’s life purpose through the work itself. “This is what I was meant to do.” It is not about productivity or other end state. It is about working and growing as a never-ending process. Professionalism is a related concept about taking pride in your work, a commitment to quality, a dedication to the interests of the client (be they internal or external), and a sincere desire to help. The premise of Good Work (Gardner, Csikszentmihalyi, & Damon, 2001) also speaks to professionalism but expands the concept to include ethics and social responsibility. They define good work as “work of expert quality that benefits the broader society” (p. ix). And people know that they are doing good work because it feels good. This may sound too simple, but people know when the work they are doing is good and meaningful. It is about trusting both one’s judgment and

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one’s intuition. The more we know ourselves, the more we can evaluate and change our professional behavior, our moral and ethical judgment, and how our performance affects those around us.

Sense of Balance To paraphrase a Zen Buddhist saying, work and pleasure should be so aligned that it is impossible to distinguish one from the other. The sense of balance at its ideal is that life is so integrated that it does not matter whether what one is doing so long as it is meaningful. But given that most of us do not live in an ideal world, a sense of balance concerns the choices we make between the time spent at paid work, unpaid work (work at home, with family, as a volunteer), and at pleasurable pursuits, such that no one area of our lives is so dominant that we cease to value the other areas. All work and no play is stressful, overwhelming, and usually results in our health, family, and social lives suffering – even when the work is meaningful. All play and no work quickly becomes boring and meaningless. We also need to balance the nourishing of our different selves (mental, physical, emotional, and spiritual) because, in the less than ideal world, we do not have the luxury of meeting all our needs through one major activity. So we need to take the time to learn, to keep fit, to reflect, to meditate or pray, and to give to others. Again, because we usually worry most about doing our paid work, we do not take the time to care for ourselves. And when we do not take care of ourselves, we usually cannot be there for others. So we end up running on the proverbial treadmill until we finally realize we are not meeting our own or anyone else’s needs. The statistics we read in the media on work-related stress, people being overweight and less than physically fit, depression, divorce, and even workplace violence speak for themselves. Employees today are defining success on their own terms and some are opting out of the corporate rat race. Instead of living to work, people are working to live. They are tired of the inflexibility of standard work hours and the lack of concern for work–family balance and are leaving corporate positions in favor of more flexible career options. Meaningful work is not just about the meaning of the paid work we perform; it is about the way we live our lives. It is the alignment of purpose, values, and the relationships and activities we pursue in life. It is about living our lives and performing our work with integrity. It is about integrated wholeness.

Meaning at Work Meaning at work implies a relationship between the person and the organization or the workplace, in terms of commitment and engagement. Richards (1995) talked about the situation that when there is meaning at work, “[only

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then] will our work become more joyful [and] our organizations will flourish with commitment, passion, imagination, spirit, and soul” (p. 94). As noted earlier, commitment involves the willingness of employees to exert higher efforts on behalf of the organization, a strong desire to stay in the organization, and accept major goals and values of the organization (as cited in Porters et al., 1974).

Commitment The primary drivers of commitment are identification with the organization’s goals and values, congruence between individual and organizational goals, and internalization of organizational values and mission. The term work commitment refers to a broader concept than organizational commitment and includes the different forms commitment can take in the workplace. According to Morrow (1993), there are five universal forms of work commitment, namely, (a) work ethic endorsement, (b) career commitment, (c) affective organizational commitment, (d) continuance organizational commitment, and (e) job involvement. The third form refers to an affective or psychological bonding that binds an employee to his/her organization. The primary drivers of this form of commitment are identification with the organizations goals and values, congruence between individual and organizational goals, and internalization of organizational values and mission. Of all the forms of commitment, affective commitment has been found to have the strongest positive relationship with desirable outcomes (Eisenberger, Huntington, Hutchison, & Sowa, 1986). Organizations that want to foster affective commitment must in turn show their commitment to the employees by providing supportive work environments. The research that has examined the relationship between perception of organizational support and organizational commitment has found a consistent positive relationship between them. Perception of organizational support states that “employees form a global belief concerning the extent to which the organization cares about them and values their contribution to the organization” (Aselage & Eisenberger, 2003, p. 492). Employees will be loyal to their organization if their organization values and appreciates them (Tyler, 1999, as cited in Fuller, Barnett, Hester, & Relyea, 2003). Organizations that are committed to employee development, their well being, and their need for actualization tend to have employees with high commitment (Dessler, 1999). Paul and Anantharaman (2004), in their research study, found that of all the human resource management variables that correlate with commitment, the human resource development variables of (a) career development, (b) development-oriented appraisal, (c) comprehensive training, and (4) employee-friendly work environment have the strongest correlation. In a study on culture and employee-friendly/ humane organizations, Chalofsky (2008) found that there was an interdependent relationship based

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on the values of the organizational culture. Although no organization can be all things to all people, the organizations that were studied work hard to recognize and support employees’ work, family, leisure, personal, and community needs. They knew that if work–life balance is provided, then more of the whole employee will be able to focus (and wants to focus) on their work. Employees of the organizations are not there just because they have great benefits. The benefits are a result of the culture, because the culture values employees. In turn, employees have an overwhelming commitment to their organizations. It is all intertwined and synergistic. This was evident by the overwhelming alignment between the organizations’ missions and their commitment to their employees, customers, suppliers, and community. The organization supports the whole person, and the whole person is engaged in the organization.

Engagement Employee engagement has emerged as the most recent “business driver” of organizational success (Lockwood, 2007). A number of consulting companies (e.g., Gallup, Blessing-White) have surveyed their clients and have found a concern that the majority of employees are not engaged in their work and their organizations. One survey (Blessing-White, Inc., 2005) found that some of those employees who are not engaged may care about the organization and their work, but did not feel there is a good fit between their capabilities and their tasks. Others were not dissatisfied enough to leave the organization but were biding their time and not committed to either their work or the organization. The rest are actively looking to leave the organization. Engaged employees, on the other hand, work harder, are more committed, and are more likely to go “above and beyond” the requirements and expectations of their work (Lockwood, 2007). Engaged employees tend to feel that their work actually positively affects their physical health and their psychological well-being (Crabtree, 2005). The findings of Blessing-White, Inc. (2006) were similar: Engaged employees were proud to work in their organizations and trusted their immediate managers. Overall, their emotional connections were positive. Emotionally based commitment to the work and the organization results in higher levels of engagement and commitment based on developmental, financial, or professional rewards (Corporate Leadership Council, 2004).

Conclusion: Meaningfulness, Commitment, and Engagement One of the primary challenges organizations are facing today concerns motivating employees to carry out broader and more proactive roles. The current workforce is becoming more emergent and less traditional. An

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emergent workforce is driven by opportunity as against a traditional work force that believes that tenure dictates growth (Campbell, 2002). Hence, organizations will need to develop novel approaches to motivation to retain an emergent workforce. Given the current state of the economy, it may seem that hiring and retention are not as important as they were thought to be several years ago. But organizations that want to be sustainable and successful over the long term need to still consider how to attract and grow high performing and committed employees. In view of the ineffectiveness of extrinsic motivational factors in fostering employee commitment and engagement, and the limited impact of traditional intrinsic factors in isolation, this article develops a conceptual framework of the relationship between commitment and engagement and a deeper level of intrinsic motivation, namely, meaningful work. This article builds on the premise that people with the highest levels of productivity and fulfillment view themselves as inseparable from their work (Mohrman & Cohen, 1995), are intrinsically motivated by the work itself (Csikszentmihalyi, 1990), and are professionally committed to and engaged with the organization. This approach combines the individual aspect of motivation emanating from a psychological perspective to a contextual dimension of motivation that highlights the importance of workplace environment and culture. Although the commitment construct has been researched for more than four decades, the research pertaining to engagement is of recent origin. Most of the engagement literature at this time is primarily based on survey results generated by consulting companies rather than empirical research. More research needs to be conducted concerning engagement as a viable construct and the relationship between engagement, commitment, and meaningfulness. The connections of the concepts of meaningful work, employee commitment, and engagement can give human resource development practitioners and managers powerful tools to develop workplace strategies that can greatly improve employee satisfaction, fulfillment, and loyalty. Organizational productivity, retention, and sustainability will be enhanced, and individuals will feel good about their work and how it affects the rest of their lives.

References Ackoff, R. L. (1981). Creating the corporate future: Be planned or be planned for. New York: Wiley. Alderfer, C. P . (1972). Existence, relatedness and growth: Human needs in organizational settings. New York: Free Press. Angle, H. L., & Perry, J. L. (1981). An empirical assessment of organizational commitment and organizational effectiveness. Administrative Science Quarterly, 26, 1–13. Aselage, J., & Eisenberger, R. (2003). Perceived organizational support and psychological contracts: A theoretical integration. Journal of Organizational Behavior, 24, 491–509.

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Beck, K., & Wilson, C. (2000). Development of affective organizational commitment: A cross-sequential examination of change with tenure. Journal of Vocational Behavior, 56, 114–136. Blessing-White, Inc. (2005). Employee engagement report 2005. Princeton, NJ: Author. Blessing-White, Inc. (2006). Employee engagement report 2006. Princeton, NJ: Author. Brisken, A. (1996). The stirring of the soul in the workplace. San Francisco: Jossey-Bass. Campbell, B. (2002). The high cost of turnover: Why holding on to your employees can improve your bottom line. Black Enterprise, 33(5), 61. Chalofsky, N. (2003). An emerging construct for meaningful work. Human Resource Development International, 6, 69–83. Chalofsky, N. (2008). Work-life programs and organizational culture: The essence of workplace community. Organization Development Journal, 26, 11–18. Cohen, A. (2003). Multiple commitments at work: An integrative approach. Hillsdale, NJ: Lawrence Erlbaum. Corporate Leadership Council. (2004). Driving performance and retention through employee engagement. Washington, DC: Author. Crabtree, S. (2005). Engagement keeps the doctor away. Gallup Management Journal. Retrieved November 12, 2007, from http://gmj.gallup.com/content/14500/EngagementKeeps-Doctor-Away.aspx Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper Perennial. Darling, J., & Chalofsky, N. (2004). Spirituality in the workplace. In M. Marquardt (Ed.), Encyclopedia of life support systems (EOLSS). Oxford, UK: EOLSS. Retrieved February 5, 2009, from http://www.eolss.net/outlinecomponents/HumanResources-Management.aspx Dessler, G. (1999). How to earn your employees’ commitment. Academy of Management Executive, 13, 58 –67. Dirkx, J. (1995). Earning a living or building a life? Reinterpreting the meaning of work in the practice of workplace education. Paper presented at the Academy of Human Resource Development Conference, San Antonio, TX. Eisenberger, R., Huntington, R., Hutchison, S., & Sowa, D. (1986). Perceived organizational support. Journal of Applied Psychology, 71, 500 –507. Fox, M. (1994). The reinvention of work: A new vision of livelihood for our time. New York: Harper Collins. Fuller, J. B., Barnett, T., Hester, K., & Relyea, C. (2003). A social identity perspective on the relationship between perceived organizational support and organizational commitment. Journal of Social Psychology, 143, 789–791. Gardner, H., Csikszentmihalyi, M., & Damon, W. (2001). Good work: When excellence and ethics meet. New York: Basic Books. Gibbons, P . (2007). Spirituality at work: A pre-theoretical overview. Retrieved September 8, 2008, from http://www.paulgibbons.net Greene, L., & Burke, G. (2007). Beyond self-actualization. Texas State University, School of Health Administration. Retrieved September 22, 2008, from http://ecommons.txstate. edu/cgi/viewcontent.cgi?article=1001&context=sohafacp Herzberg, F ., Mausner, B., & Snyderman, B. B. (1959). The motivation to work. New York: Wiley. Holbecke, L., & Springnett, N. (2004). In search of meaning in the workplace. Unpublished report, Roffey Park Institute, London. Hunter, L. W., & Thatcher, S. M. (2007). Feeling the heat: Effects of stress, commitment, and job experience on job performance. Academy of Management Journal, 50, 953–968. Kanter, R. M. (1968). Commitment and social organization: A study of commitment mechanisms in utopian communities. American Sociological Review, 33, 499–517.

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Locke, E. A. (1975). Personnel attitudes and motivation. Annual Review of Psychology, 26, 457– 498. Lockwood, N. R. (2007). Leveraging employee engagement for competitive advantage: HR’s strategic role (SHRM Research Quarterly Report). Alexandria, VA: Society for Human Resource Management. Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50, 370–396. Maslow, A. H. (1954). Motivation and personality. New York: Harper. Maslow, A. H. (1971). The farther reaches of human nature. New York: Penguin. McClelland, D. C. (1965, November / December). Achievement motivation can be developed. Harvard Business Review, 43, 7–16. McGregor, D. (1960). The human side of enterprise. New York: McGraw-Hill. Meyer, J. P ., & Herscovitch, L. (2001). Commitment in the workplace: Toward a general model. Human Resources Management Review, 11, 299–326. Mitroff, I., & Denton, E. (1999). A study of spirituality in the workplace. Sloan Management Review, 40, 83–92. Mohrman, S. A., & Cohen, S. G. (1995). When people get out of the box: New relationships, new systems. In A. Howard (Ed.), The changing nature of work (pp. 365– 410). San Francisco: Jossey-Bass. Morrow, P . (1993). The theory and measurement of work commitment. Greenwich: CT: JAI Press. Paul, A. K., & Anantharaman, R. N. (2004). Influence of HRM practices on organizational commitment: A study among software professionals in India. Human Resource Development Quarterly, 15, 77– 88. Pool, S., & Pool, B. (2007). A management development model: Measuring organizational commitment its impact on job satisfaction among executives in a learning organization. Journal of Management Development, 26, 353–369. Porters, L. W., Steers, R. M., Mowday, R. T., & Boulin, P. V . (1974). Organizational commitment, job satisfaction, and turnover among psychiatric technicians. Journal of Applied Psychology, 59, 603–609. Richards, R. (1995). Artful work: Awakening joy, meaning, and commitment in the workplace. San Francisco: Berrett-Koehler. Rogers, C. (1959). A theory of therapy, personality, and interpersonal relationships as developed in the client-centered framework. In S. Koch (Ed.), Psychology: A study of science ( Vol. 3, pp. 184–256). New York: McGraw-Hill. Rogers, C. (1961). On becoming a person. Boston: Houghton Mifflin. Schaefer, C. & Darling, J. Contemplative Disciplines in Work and Organizational Life,” High Tor Alliance, Spring Valley, NY, 1996. Senge, P . (1993). The fifth discipline: The art and practice of the learning organization. New York: Doubleday. Sievers, B. (1984). Motivation as a surrogate for meaning (Arbeitspapiere des Frachbereichs). Wupprtal, Germany: Bergische Universitat. Society for Human Resource Management. (2008a). Job satisfaction survey report. Alexandria, VA: Author. Society for Human Resource Management. (2008b). Workplace forecast. Alexandria, VA: Author. Thomas, K . (2000). Unlocking the mysteries of intrinsic motivation. OD Practitioner, 32(4), 27–30.

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47 Motivation and Human Growth: A Developmental Perspective M.S. Srinivasin

Introduction

M

otivation is a subject of perennial interest in management, psychology and leadership. However, most modern motivational theories suffer from two inadequacies – a lack of sufficient attention to the higher motives of the mental, moral and spiritual being in humans; and a too-heavy insistence on performance rather than on growth. What is not recognized fully is that motivation can be a means or lever of human development in the organization. A human being is not merely a knowledge, skill and productivity engine created solely for filling the coffers of an organization or meeting its bottom line and deadlines. It is a complex living entity with a sacred essence, created for a higher purpose. Most wisdom-traditions of the world agree that this higher purpose is a progressive unfolding of the human potential, culminating in fully blossomed flowers of humanity. This article provides a conceptual framework for understanding the process of motivation from an evolutionary and developmental perspective.

Hierarchy of Motives Equality of humans may be a spiritual truth, but is not yet an actual fact of life because individuals are at various levels of development. Needs, values and attitudes of individuals depend on their nature and the level of their inner Source: Journal of Human Values, 14(1) (2008): 63–71.

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development. The task or challenge of corporate leadership is, therefore, to understand intuitively this inner spirit of an employee and provide him with an individualized motivational programme that matches his unique needs. But how is this motivational level of each individual employee to be determined? This is where the importance of the well known ‘need hierarchy of motives’ model of Abraham Maslow comes in. This model identifies five basic human needs and arranges them in an ascending order. They are: first, biological ones for sex, survival and other physical needs; second, those for material and emotional security; third, social needs for affection, autonomy, achievement, status, recognition and attention; and, finally, the highest need of all, self-actualization. According to Maslow, as each of these needs become substantially satisfied, the next needs become dominant. So the right motivation requires a clear understanding of these motivational needs of each individual and focus on satisfying them (Robins 1997: 214). This need hierarchy model of Maslow, after a powerful initial impact on management thinkers and professionals, later went out of favour for supposedly better theories. Maslow’s idea was criticized on many points. For example, it was accused of ignoring the cultural factor; of lacking empirical validity; and that the needs are parallel rather than hierarchical. All these criticisms can be valid, for no concept or theory can hope to explain or encompass the incredible complexity of human nature and its motives. But Maslow’s need hierarchy model has two plus points over other modern motivational theories. First, it recognizes the process of evolution, viewing the human being as an evolving entity, moving progressively towards higher and higher levels of motivation; second, its intuition or idea is broader and more comprehensive than other modern theories. However, from the viewpoint of Indian spiritual vision, Maslow’s model has two flaws. First, it ignores or fails to articulate clearly the higher intellectual, moral and spiritual motives in man; and, second, from a holistic perspective, it needs to be integrated with a comprehensive vision of human development. This is where the Indian vision of human development can rectify and complement Maslow’s model.

Evolution and Motivation: The Indian Paradigm According to Indian thought, there are four stages in the evolution of humans that takes them towards their spiritual goal. Every human being begins the evolutionary journey as a physical entity driven by biological and security needs. He progresses to becoming a vital being with emotional and vital needs.1 There are two sub-stages in the evolution of the vital human. First, he becomes someone who lives predominantly in his emotional and pragmatic mind with its need for mutuality, harmonious relationship, enjoyment

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and pragmatic adaptation to life. In the need hierarchy of Maslow these social needs constitute only one part of our emotional needs. At the next stage, the vital human becomes a person of strong will and abundant vital energy, the leader or the warrior type, with needs for power, achievement, conquest, expansion, name and fame. These ‘esteem’ needs, are again, one part of the needs of the human type of will and power in Maslow ‘s theory. Alexander and Napoleon are archetypal vital men of power, while in the corporate world, great and successful entrepreneurs and executives like Carnegie and Ford of the old economy, and Gates and Grove of the new economy, are predominantly vital men. As the person progresses further, he becomes the intellectual, moral and artistic type of personality with intellectual, ethical and aesthetic needs for knowledge, values, ideals and vision; in other words, the mental human.2 He looks beyond physical and vital needs, seeking to understand higher aims, values and laws of life, and trying to organize it according to these higher verities. Socrates and Plato, Tagore and Leonardo da Vinci, Einstein, Confucius and Gandhi are different types of mental men who have reached the higher plateaus of the human mind. One of the major aims of the social philosophy and practices of ancient Indian and Chinese civilization is to create a society governed by the mental and moral motives of dharma. As the mental human reaches the highest peak of intellectual, ethical and aesthetic development, he becomes aware of a spiritual reality beyond the mind and awakens to this highest spiritual need for self-realization, truth and God. He begins to become the spiritual human. The Vedic and Upanishadic sages, St Francis of Asisi, Meister Eckhart, and modern age sages like Sri Aurobindo, Vivekananda and Ramana Maharishi are different types of accomplished spiritual men. We must note here that the stages of an individual’s evolution depend mainly on the dominant temperament and motives that shape and drive his life, and not on academic status or mental development. In the process of evolution, mind and vital need develop simultaneously, although some vital persons may be at a transitional stage from the vital to the mental phase of development. Take for example someone like Andy Grove of Intel, the microchip giant. He started his career as a brilliant research engineer with a doctorate in chemical engineering, did some outstanding research work in fluid mechanics and semiconductor physics, and wrote six books. But when we look at his later life as CEO of Intel, we can see his dominant temperament and motives are that of the vital human, with an aggressive push for power, dominance, achievement, name and fame. There four types or stages in human evolution can be placed in a corresponding four-fold motivational spectrum. At the lower end of the spectrum are the outwardly motivated who need the stimulus of external reward or punishment to remain active. At the higher end first come the self-motivated who feel an intrinsic joy in work and, therefore, need no external stimulus to remain motivated. Next come the ethically motivated who feel the need to

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contribute or serve a higher moral or social cause. The ethically awakened individual seeks not only joy in work, but a higher meaning as well. The last and the highest is spiritual motivation, which develops when the individual is awakened to his spiritual self beyond his body and mind. Let us now try to relate these four stages of evolution to their motivation spectrum. The physical human who is bound to the needs and instincts of the body is at the lowest level of the motivation spectrum. For his higher evolution and development, his vital and emotional being have to be awakened by external motivators like the need for wealth, power, enjoyment and success. The vital man is capable of self-motivation and self-dedication to a higher moral or spiritual cause. When he awakens to these higher motives and dedicates himself to a higher ideal, he not only accelerates his own higher evolution, but also becomes a dynamic instrument for the higher evolution of the collectivity. The vital being, inspired by higher values, can be a very effective and heroic leader and crusader for manifesting these higher values in the outer life. Some of the Indian kings like Ashoka, Shivaji and Akbar, and statesmen of the West like Winston Churchill and Abraham Lincoln belong to this category. However, if there is a lack of sufficient mental or spiritual illumination in the mind, the vital man can become an aggressive and intolerant tyrant, forcefully championing a narrow dogmatic idea. Similarly, when the mental human awakens to the spiritual realm may blossom into a high thinker, sage or a saint sowing luminous, kindly or inspiring ideals in the consciousness of people. But if there is a lack of strength in the will or vital force, the mental or moral individual will be ineffective as a leader. So, to fully realize moral and spiritual potentialities, both vital and mental humans must pursue a mental, moral and spiritual education and discipline, leading to a deepening, widening and refinement of mind and heart, linking their consciousness and will to a spiritual inspiration and energy. One such discipline is the karma yoga or yoga of action of the Indian scripture, the Bhagavad Gita. A main principle of this discipline, which has direct relevance for the corporate world, is to renounce the eager and anxious seeking of rewards of action and concentrate all our energies on the present, on the work to be done. If we have faith in God, we may add to this a consecration of all our activities to the divine power. The karma yoga path of the Gita leads to motiveless action, driven not by human motives – vital, mental or moral – but by a universal spiritual force, transcending the individual and collective ego. Thus, Indian spiritual vision links motivation with human development in an integrated perspective. This scheme provides a broad and general framework for understanding and identifying the process of motivation in an evolutionary perspective. However, as mentioned earlier, human evolution is a complex process that cannot be rammed into any mental formula. We are at once a physical, vital, mental and a spiritual being. The motives and impulses of all these parts exist simultaneously within us although some of

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them may be dormant, weak or unmanifest.3 The stage of our inner development depends on the most dominant, conscious or manifest part of our personality. For example, if the dominant part is vital we are in the second, vital stage of development. We also admit that this Indian scheme of human evolution is only one among many other possible formulas. Other schemes with different systems of classification are also possible and equally valid, but the Indian concept is preferable because we find it integral, embracing all the fundamental elements constituting the human organism.

Beyond Job Satisfaction This brings us to one of the major objectives of modern motivational strategies – job satisfaction. Job satisfaction happens when the nature of work and the rewards received for this work match the motivational needs of an employee. But mere job satisfaction cannot be the highest ideal for an evolving human being. In an evolving world, growth and progress is an eternal law and a higher need. Anything that does not grow disintegrates and perishes. So we have to create a work culture that consciously promotes and accelerates the progressive evolution of the individual by awakening in him the dormant higher needs. So the aim of motivational strategy has to be not only to satisfy the employee’s present needs, but also to awaken higher needs. This means the physical being has to be awakened to his vital and mental needs, and helped to become the vital and mental being; the vital human to his mental, moral and aesthetic needs to bring the light of a higher culture to his life of raw desire and ambition; and the mental or moral individual to his highest spiritual goal. The need for this evolutionary transition to higher needs is indicated by a lack of interest in the needs and activities of the present stage of development, and a growing interest in the needs and activities of higher stages. Here is an example from the Harvard Business Review illustrating this transition. Mark was a star at the large West Coast Bank where he had worked for three years. He had an MBA from a leading business school and he had distinguished himself as a skilled lending officer. He excelled in every work task the bank gave him. He was smart and knew no other way to approach than to give it his all. The bank paid Mark well and senior managers had every intention of promoting him. But over time Mark grew more and more unhappy. He was seriously considering leaving the organization. Fortunately for both Mark and the bank, after consulting a counsellor, he was able to identify the cause of his unhappiness: he was no longer interested in his present job, which involved number crunching and interaction with customers. He wanted a more intellectually stimulating job. Using this insight, he was able to find a new assignment that required conceptual and analytical thinking, making him happy and satisfied (Butler and Waldrop 1999).

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It is very difficult to say with precision or certainty what the psychological factors behind Mark’s motivational problem were. One could be a shift in his life-motives from the vital to the mental level. However, sometimes this awakening to higher motives may express itself not in the professional life of the person, but in his hobbies and extra-professional interests. For example, it was reported in a leading business journal that a top executive from a big business house was very much interested in the field of unified theory in physics and in his spare time read every available book on the subject.

The Corporate World in the Motivation Map We are now in a better position to relate the motivational process sketched so far to the present state of the corporate world. Our modern age represents a rapid and increasing ‘vitalization’ and ‘mentalization’ (terms coined by Sri Aurobindo) of the human mass. So the pure physical type of personality satisfied with basic minimum needs are becoming fewer and fewer, for in the hyper-competitive and charged atmosphere of the corporate world, with its new thrust towards empowerment, knowledge, innovation and relentless chasing of deadlines, there is not much scope for the physical human. However, most of the shop floor and clerical workforce in the corporate world may perhaps live predominantly in their physical consciousness, but with a growing awakening to vital and mental needs. Moving up to the managerial cadre, we have some interesting insights on executive motivation from two psychologists, Timothy Butler and James Waldrop, as elaborated in their article in the Harvard Business Review. According to these two Harvard psychologists, most executives in business are driven by seven basic ‘business core functions’ related to their deeply embedded life-interests or needs. They are: application of technology; enterprise control; managing people and relationships; quantitative analysis; counselling and mentoring; theory development and conceptual thinking; and influencing through language and ideas (ibid. 1999.) The first four factors are predominantly needs of the vital and pragmatic mind, while the last three are needs of the thinking and communicating mind. But this classification is based on the expression of life needs of people in their professional life. For a better understanding of the motivational level of people, we have to take into consideration the nature of their extra-professional activities. Moreover, there are probably a considerable number of people in the corporate world who are seeking a moral and spiritual fulfilment or meaning in and through work. For example, the US Academy of Management recently launched a new magazine, Journal of Management, Spirituality and Religion, focusing on these higher needs and broader issues emerging in the management community.

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However, motivation is not only individual, but also collective. Just as an individual, the collectivity can also move up the motivational ladder in the course of its natural evolution. Contemporary business is perhaps in such a state of evolutionary transition towards some higher mental and moral needs. The first major change is what we may call the people-knowledge factor, a shift in the strategic motive of business from reliance on a mechanical and mass application of technology to the living knowledge or creativity of people or individual employees. As Michael Burns, chairman and CEO of Mercer Human Resource Consulting points out: ‘The last decade has been technology fuelled productivity. Now is the turn of the knowledge-economy’ (Burns 2007). And knowledge economy is people-centric. Christopher Barret of the Harvard Business School explains: We can’t just manage by systems which are invariably defined in financial terms, we need to focus on people and on developing, managing and building our capacities through them. . . . Because they are the ones with the expertise and that is replacing capital as the scarce strategic resource. The new model, of the Individualized Corporation that we have evolved requires companies to leverage individual competencies, capacities, knowledge and skills. This is going to be the source of competitive advantage. (1999: 61)

Barret gives the following example of ISS, a Denmark-based firm which is in the cleaning business: It is a business with minute margins, so they have to focus on costs. They could have regarded their employees as labourers who were asked to go and do their job, directed in the classical hierarchal form. But what they did instead was to create individual teams that worked together on cleaning contracts. . . . Then they engaged in education . . . where they took the front-line people through a series of training sets. The first obviously was teaching them how to clean properly. The second was to work together in a team. Third, they started focusing on quality. Fourth they got their teams to focus on customer service and listening to customers. Fifth, the teams were taught to read financials. Eventually the teams became interested in what the customer wanted and became capable of interpreting data. This is innovation. You get costs down by driving responsibility down the organization, creating entrepreneurial initiative and leveraging ideas across the organization – it’s a different philosophy. (ibid.)

The second factor is the growing interest in ethics. There are two important features in the emerging ethical debate in business. First is the recognition of the motivational power of ethics. As former CEO of Johnson & Johnson, James Burke, says: Here we believe strongly in three things, decentralization, managing for the long-term, and the ethical principles embodied in our Credo. Credo is the sort of thing that inspires the best in people. I think that all of us have

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a basic moral imperative hidden somewhere in us. In some people it is more central to their being, but it’s always there. To tap that well-spring creates energy that you can’t get elsewhere. (1986: 19)

The second feature is the growing demand for fairness and transparency. As founder of Infosys N.R. Narayana Murthy states: ‘Investors, customers, employees and vendors have all become more discerning and are demanding greater transparency and fairness in all dealings’ (Skaria 1999: 25). This shows that the corporate world as a whole is becoming more sensitive to ethical issues. The third factor is the concept of corporate social responsibility (CSR), which is spreading fast in the business community. CSR seems to be the new fad in business and management. As a columnist in the business section of a leading Indian daily points out: Call it guilt cleansing or genuine concern for the downtrodden; the fact is that from single-minded devotion to bottom line till a few years ago, corporations are increasingly putting their mind and money to the bottom of social pyramid. Philanthropy indeed is fast becoming an integral part of corporate culture. Today nearly every major corporate house is supporting some cause or social initiative. And they are no longer taking it as charity but as a responsibility. In today’s world being a good and responsible corporate citizen is as important as increasing your business. ( Vishwajeet 2006)

For example, in India most of the major players in the new economy like Satyam, Wipro, Infosys, and Dr Reddy’s Laboratory have their charitable trusts working on social causes. In the US, two icons of the new economy, Bill Gates and Andy Grove, have their own foundations.

The Path Ahead These mental and moral needs emerging in the corporate mind hold great promise for the future evolution of business, but these needs have to be explored to their highest potential. This requires a deep insight into the psychological and spiritual sources of knowledge and ethics, and which must be harnessed for the higher evolution of business. If businesses can do this, it will give a quantum thrust to their future evolution. This higher evolution is not a matter of idealism, but a crucial choice that will determine the future status of individuals and collectivities. Tex Gunning (2007), a vice-president of the Unilever Group, in his valedictory address to the CII national summit on corporate social responsibility, said: Many companies did not exist more than 60 to 70 years because they do not evolve. . . . Earning money was essential but it was not the essence of life. Companies have to create social capital, economic capital, spiritual capital and intellectual capital. Companies that don’t create this kind of

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wealth would be dissolved or swept away. We have to act now out of choice or have change forced on us.

These prophetic words from the mind of a top business executive display an instinctive recognition of what Sri Aurobindo perceived with a more conscious, enlightened and far-seeing vision in the beginning of the twentieth century. ‘In the next stage of human progress,’ said Sri Aurobindo, ‘it is not a material but a spiritual, moral and psychological progress that has to be made . . . [and] whatever race or whatever country that seizes on the lines of these evolution and fulfills it will be the leader of humanity’ (Sri Aurobindo 1972a, 1972b). In the scheme of nature, whatever that does not evolve either becomes extinct or has to play second fiddle to the leaders who surge ahead. However, there is one more important factor related to this higher evolution, which we would like to briefly touch upon before concluding our discussion. Human motivation or action has an inner intent as well as an outer content. The word ‘motive’ is normally used to describe mainly the inner intent. For example, if I become moral out of fear of hell in the life after death or because of karmic consequences, then my motivation is ethical only in the outer content and not in the inner intent, which is still the vital motive of fear. In this sense, the mental and moral needs emerging in business are very much mixed. There is a change only in the outer content, but not much in the inner intent of still vital needs like productivity, competitive advantage, and the pressure of outer circumstances. However, our human organism is ‘psychosomatic’. Our body and mind, thoughts, feelings and actions have a mutual interaction and influence. An outer action, when it is done with sincerity, persistence and conviction, has corresponding inner results. For example, someone who becomes moral out of vital or material needs may one day become conscious of the inherent joy of virtue and as a result the lower needs may drop away. Or else, as he grows mentally, he may awaken to the fact that ethics is an integral part of the higher laws of life, and as a result, a corresponding change may occur in the inner motives of action. For instance, the modern environmental movement is the result of such a mental awakening to the laws of physical nature. When there is a similar awakening to the psychological and spiritual ecology of universal nature, and when these higher laws of life are implemented and institutionalized in the corporate life, then it will give a decisive thrust to the higher evolution of the collective life of humanity. The corporate mind in business has to consciously strive for this higher awakening.

Notes 1. We use the term ‘vital’ to denote that part of our consciousness that is the source of our emotions, passions, enthusiasm, energy, and the dynamic will for action and execution.

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2. We use the word ‘mental’ for that part of our consciousness that houses our intellectual, ethical and aesthetic intelligence. A human being can achieve his full development, or in other words, become the true mental human, only when he develops fully all the potentialities of his higher mental nature made of the rational, ethical and aesthetic being, and govern the rest of his nature with this higher element in him. This is the reason why in our scheme of human development we have placed the mental above the vital in the evolutionary ladder. Beyond the perfection of ‘humanhood’ there is what we may call the perfection of ‘soulhood’, which can be achieved only by realizing our spiritual nature beyond mind. 3. Even when we are fully awakened to the higher needs and try to organize our life around them, lower needs are still present – perhaps very much suppressed and held down, but not mastered. Therefore, they can cast their overt or covert influence over our actions. So the intellectual, the artist and the saint can still be swayed by vital motives like name and fame and power and wealth. This is the reason why the path of yoga in which the seeker makes a conscious effort to rise beyond the mind into the spiritual consciousness is so difficult. Even after we have kindled the fire of aspiration and kept it burning, our lower nature may still throw its smoke and dust and filth into the sacred flame and disturb the inner sacrifice, or even extinguish the flame. This fact of the inner life is symbolically conveyed in Indian mythology in the image of titanic beings disturbing the fire-sacrifice rituals of the rishis.

References Barret, Christopher (1999), ‘Interview: Create a Purpose to Engage People’, Business Today, 7 May, 61–69. Burke, James (1986), Interview, in Thomas R. Horton, ed., What Works For Me, pp. 16–25 (New York: Random House). Burns, Michael (2007), ‘Interview: Now it is the Turn of the Knowledge Economy’, Business Today, 15 June. Butler, Timothy and James Waldrop (1999), ‘Job Sculpting: The Art of Retaining Your Best People’, Harvard Business Review, September–October, 41–63. Robins, Stephen (1997), Organizational Behavior (New Delhi: Prentice-Hall). Gunning, Tex (2007), ‘Corporates Should have a Conscience’, Hindu, 16 June. Skaria, George (1999) ‘The Well-governed Corporation’, Business Today, 21 November, 25–31. Sri Aurobindo (1972a), Collected Works: Bande Matharam (Pondicherry: Sri Aurobindo Ashram). ———. (1972b). Collected Works: Supplement (Pondicherry: Sri Aurobindo Ashram).

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efore Charles Darwin’s theory gained influence in the social and behavioral sciences, the traditional philosophical and theological views distinguished human motivation from animal motivation as something governed by the “free will,” as opposed to by instinct. The growing acceptance of Darwinian ideas resulted in three major innovations in psychology, which led to a segregation rather than integration of approaches. First, McDougall (1908) argued that a set of basic instincts and drives guides not only animal but also human behavior. His approach is reflected in modern ethological approaches to fundamental behavioral systems, such as aggression (Bischof, 1985; Lorenz, 1966), parenting (Bischof, 1985; Bowlby, 1969), and foraging (L. Tinbergen, 1960; N. Tinbergen, 1951). Second, simultaneously with McDougall’s (1908) ideas about human motivational drives, Sigmund Freud developed his psychodynamic theory, which conceptualizes behavior and cognition as influenced by latent and unconscious drives of the individual. This approach found its continuation in personality conceptions of motivation and their specific diagnostic instruments, namely, projective tests (McClelland, 1971; Murray, 1938). Third, the ability to adjust instinctual behavior to changing environmental conditions is a key feature of human behavior, which should be precedented by early forms of intelligent behavior in related animal species. The pioneer of comparative research in learning (i.e., associative) capacity was Thorndike (1898). His groundbreaking work, together with James’s (1890) conception

Source: American Behavioral Scientist, 43(6) (2000): 1015–1029.

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of “habit,” laid the foundation for behaviorism, which unfortunately dominated psychology at the expense of all other approaches for nearly three decades. In consequence of the excessive and prolonged domination of psychology by behaviorism, human motivation appeared to be an unworthy domain of psychological research. Nevertheless, the field made important advances in terms of adopting models of instrumentality of behavior (Vroom, 1964) and of decision rationality by way of combining the expectations about outcomes with perceived outcome value as determinants of human motivation and thus behavioral investment. Atkinson (1957) combined this expectancy-value approach with an interindividual-difference construct of motive strength, thus creating a predictive model of motivated behavior. However, the model became ever more cognitive and thus segregated from ethological and comparative approaches to motivation. The human-animal gap widened even more with the rise of attributional theory in motivation (Kelley, 1967; Weiner, 1972), which may have been, in part, a reaction to the overdominance of behaviorism. The modern revival of human motivation in psychology (e.g., see J. Heck-hausen & Dweck, 1998) was largely associated with the cognitive paradigm and its integration with an interindividual-difference approach to motives (H. Heck-hausen, 1991). This course of scientific evolution has largely bypassed the issue of evolutionary precursors of motivated human behavior. At the same time, comparative psychology has focused on cognitive phenomena to the exclusion of phenomena of motivational engagement and disengagement.

Why Should Evolutionary Psychology Be Interested in Motivation? Evolutionary psychology has thus far paid little attention to phenomena of motivation and emotion (see review in Schneider & Dittrich, 1990), and has mostly focused on the cognitive functioning involved in social exchange (e.g., Cosmides & Tooby, 1992), risk perception (e.g., Gigerenzer, Todd, & the ABC Research Group, in press; Rode & Wang, 2000 [this issue]), foraging and food preferences (Stephens & Krebs, 1986; Rozin, 2000 [this issue]), mate choice (Buss, 1994; Todd, 2000 [this issue]), and parenting (e.g., Keller, 2000 [this issue]; Mann, 1992). An evolutionary approach to motivation and emotion must first ask the question of how the organism can direct its behavior to seek favorable and avoid harmful environments and outcomes (Schneider & Dittrich, 1990). Hypothetically, one might postulate either of two extreme types of mechanisms: The first is fixed stimulus-response patterns, which are preadapted by genetically transferred programs of behavior, what Mayr (1974) referred to as “closed behavior programs.” The alternative mechanisms would be one

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that directly guides the organism’s behavior in view of the requirements of maximizing inclusive fitness (Hamilton, 1964; Wilson, 1975), a view promoted by radical sociobiologists. Both these extreme alternative mechanisms seem unlikely to play a key role in human behavioral regulation. Fixed or “closed behavior programs” (Mayr, 1974) are not flexible enough to effectively guide the behavior of a species living in a highly complex material and social environment. Intentional pursuit of ultimate goals of reproductive fitness would exceed the capacity of a central regulating mechanism, in terms of both the complexity and coordination of subsystems. Instead of such extreme models, an approach that integrates the operation of “open behavioral programs” (Mayr, 1974) or behavioral modules (Cosmides & Tooby, 1994; Rozin, 1976) and more general processes of behavior direction associated with emotional states and motivational tendencies (Hamburg, 1963; Plutchik, 1980; Scherer, 1984) is more promising. Evolutionary psychology has furnished an impressive range of research programs in various domain-specific modules preadapted to solve specific tasks involved in the optimization of inclusive fitness (Cosmides & Tooby, 1994; Fodor, 1983; Rozin, 1976; Tooby & Cosmides, 1992). However, little attention has been invested and consequently no consensus has been achieved with respect to the regulation of behavior across domains. In complex situational settings that afford more than one module of behavior (e.g., foraging and mate selection), the organism needs to manage cross-domain trade-offs. Moreover, in a mobile species capable of highly varied and flexible behavior, the attainment of proximate goals may require prolonged effort even in the absence of immediate situational affordances. This constellation of challenges for behavioral regulation requires mediational mechanisms, which help the organism select the most appropriate behavior given a certain combination of need state, environmental opportunity, and expected control. Emotional mediation between situational affordances and the organism’s responses provides an overall directionality to behavior, and thus enables the organism to activate behavior that tightly fits its specific needs and the environmental opportunities. An example is sexual excitement in rhesus monkeys that facilitates a variety of behavior patterns ranging from mounting to grooming or even masturbating, depending on the presence and behavior of a potential mate. Emotional mediating also allows the organism to put learning experiences acquired within its own ontogenesis to use, rather than having to rely on phylogenetically evolved preadapted and fixed stimulusresponse connections. Learning the relation between a certain behavior and a certain desired (or feared) outcome makes it possible to bring behavior under the control of anticipated consequences. In sum, motivational and emotional mechanisms might provide the missing link to the environmentneed fit in the activation and deactivation of behavioral and cognitive modules. In this way, behavioral regulation by motivation may be part of a multilevel architecture of the mammal and, indeed, the human mind.

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How Could Motivational Psychology Profit from an Evolutionary Perspective? Motivational psychology has started out with the great complexity involved in adult human action. The first motivational research was focused around the concepts of volition and the free will (Ach, 1910; James, 1890; Wundt, 1896). What could be more cerebral, and thus discrepant, from the regulation of behavior in animals? However, phenomena of volition in human motivation did not suddenly occur with modern man. Motivational mechanisms, including those of volition and the free will had evolutionary precursors. Evolution can not invent solutions to environmental or regulative challenges because it is not teleologically guided. Therefore, nature needs to work with what evolution has already brought about in previously evolved species. This is as true for behavioral programs as it is for older brain structures and for basic body plans of anatomy (Rumbaugh, Savage-Rumbaugh, & Washburn, 1994). It is known from comparative psychological research that various complex psychological mechanisms can be traced to simpler, more basic processes in nonhuman species (e.g., Leger, 1992; Roitblad, 1987). To be sure, the evolutionary heritage is not necessarily the best solution for present problems but merely the best solution selected in the phylogenetic past, given the constraints of already existing canalizations of phylogeny at the time. Although evolutionary psychology, with its present emphasis on specific cognitive modules involved in foraging, decision making, and risk-related behavior focuses on those modules believed to be a product of hunter-gatherer evolution (Barkow, Cosmides, & Tooby, 1992), I would argue that key modules involved in the motivational regulation of human behavior go back as far as early mammal or even vertebrate evolution. To arrive at a model of the origins and evolution of motivational processes, I should start with a task analysis of survival and reproductive fitness in terms of its motivational implications. The basic survival functions involve the internal regulation of body metabolism by way of breathing, cardiovascular functioning, and balancing of substances, as well as regulatory challenges involving control of the immediate environment to attain food, liquid, and shelter; avoid predators; seek a mate; reproduce; and (in some species) care for offspring. It can probably be said that for most invertebrates, these challenges of inclusive fitness are mastered by way of closed-behavior programs (Mayr, 1974), which comprise genetically fixed stimulus-response connections (e.g., hunger triggers species-typical foraging behavior, followed by consummatory activity). However, even in some invertebrates and lower vertebrates, these stimulus-response connections are modifiable by need states in the sense that higher need lowers the threshold for the stimulus-typical response (Ewert, 1976; Kravitz, 1988). Thus the animal may, for example, react with sexual behavior even to objects that are remotely similar in appearance to conspecifics. This modification of response threshold may be the very earliest form of

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flexibilization of the fixed stimulus-response connections in closed behavior programs (Mayr, 1974). However, these closed connections between need states, behavior, need-relevant stimulus, and responses provide no degree of freedom for multiple behavioral options and the adaptation of behavioral means to variations in the environment. In vertebrate – and especially mammal – evolution, open behavior programs evolved that provide greater degrees of freedom to flexibly adapt behavior to environmental conditions for foraging, predator avoidance, reproduction, and other challenges. An example is adaptively varied insect foraging patterns in birds across an array of food patches with varying food availability, familiarity, and under conditions of high (with hatchlings) versus low need states (e.g., Krebs, 1980; McFarland, 1977; L. Tinbergen, 1960). The evolutionary precursors of emotional processes probably evolved hand in hand with the transition from vertrebrates, which fed by filtering nutritious particles out of water, to those actively searching for larger individual pieces of nutrients. The latter need to regulate their movement patterns, whereas the former had no choice. Recent work on the first steps in the evolution of neocortical structures at the transition between invertebrate and vertebrate strata (e.g., Appendicularia) shows that extrapyramidal neocortical structures resemble those dedicated to visual orientation in more complex species and occur strictly contingent with the ability to move in the water, rather than being fixed to a certain place. A transition species even showed an ontogenetic contingent with the juvenile form moving about and endowed with a minute and basic neocortical structure, which the stationary adult form loses. Emotional states may have come about with the emergence of neocortical structures, which allowed the secondary projection and integration of sensory input and motor programs with the vegetative and endocrinological systems that had evolved even earlier for the maintenance of internal bodily equilibrium. The types of species that are associated with this milestone in the evolution of motivational regulation are reptiles. It has been shown with contemporary caimans (Keating, Kormann, & Horel, 1970) that artificial stimulation of certain central cortical areas elicited directed-flight behavior, including the circumvention of obstacles and involving heavy breathing and vocalizations. Thus, these reptiles exhibited all the constitutional aspects of motivated behavior and both vegetative and motoric behavioral patterns of emotional responses. Comparative research in learning patterns provides strikingly convergent evidence for the transition from fixed to emotionally mediated (in the broadest sense) connections between behavior and environmental events. Species differences in the response to changes in reinforcement incentives (food pellets) reflect probably the earliest step in this evolutionary advance. Bitterman’s (1975) classical comparative study of learning revealed that whereas fish and certain turtle species exhibit a direct relation between resistance to extinction and magnitude of reinforcement (i.e., resistance to extinction increases with

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magnitude of reinforcement), mammals such as rats show an inverse relation between extinction and reinforcement (resistance to extinction decreases with magnitude of reinforcement). These findings may be interpreted as an impressive illustration of the mediating effects of emotional states. In species with more sophisticated neuronal systems, behavior changes do not simply mirror changes in reinforcement. Higher developed species instead react to the change in incentives by disproportionately decreasing the operant behavior after decreases in incentives and disproportionately increasing it after increases in incentives. It is an intriguing question whether this phylogenetic transition may be associated with the evolution of reptiles and thus converge with the transition to earliest forms of behavior motivated by emotional states (for findings on caiman behavior, see Keating et al., 1970). In mammals, emotional reactions are found that mediate between stimulus and reaction and provide a general directionality of behavior, for example, in terms of appetence with regard to favored and needed food or avoidance with regard to predators or superior rivals. This general directionality of behavior then allows the specific behavioral means to be adjusted in accordance to the specific affordances of the environment. Such emotional mediators can become effective incentives of behavior, not only via conscious expectations but also by way of Pavlovian conditioning of emotional responses to stimulus constellations. This way, certain situations and behavioral patterns become marked emotionally, and are thus incorporated into internal mental representations and modifiable by learning (Schneider & Dittrich, 1990). Hence, even without any insight into the ultimate goals of behavior in terms of reproductive fitness, the organism is steered toward maximizing inclusive fitness in the various domains of survival and reproduction. The major motivational systems of prosocial (altruistic) behavior, aggression, affiliation, power, and achievement lend directionality and dynamics to behavior by way of need (push) and incentives (pull) and involve motivespecific emotions (H. Heckhausen, 1991). Although the completeness of this list may be debated and various longer lists have been proposed, the systems mentioned play a key role in regulating behavior by way of a hidden agenda that maximizes reproductive fitness while being experienced by the organism as highly need- and situation-specific motivators of behavior. A telling case in point is altruistic behavior, which is costly to the individual, yet holds benefits for inclusive fitness, and thus is an ultimate goal for adaptation (Hoffman, 1981). The mediation between proximate incentives and this ultimate goal is provided by empathic affective experiences, which motivate the individual to invest altruistic behavior in ameliorating distress in others. As altruism researcher Hoffman (1981) notes, Empathy may be uniquely suited for bridging the gap between egoism and altruism, since it has the property of transforming another person’s misfortune into one’s own feeling of distress. . . . an aversive state that may often best be alleviated by helping the victim, (p. 133)

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Basic Motivational Modules as Domain-General Regulators of Human Behavior A common feature of all motivated behavior is that the organism attempts to achieve outcomes in the environment by its own activity. In activities such as trying to find food, winning a mate, or struggling with a rival, the organism strives for control in terms of bringing about desired outcomes and preventing unde-sired ones. I therefore argue that the most fundamental and universal of motivational modules should relate to this basic endeavor to control the environment (J. Heckhausen & Schulz, 1995, in press). The strive for control should also be shared with the broadest range of species and go back the furthest into the phylogenetic past; at least as far back as to those species that first acquired a notable flexibility in their behavior programs (Gallistel, 1990; Rumbaugh & Sterritt, 1986). From a functionalistic perspective, one would hypothesize a set of basic motivational modules that would together favor an overall preference for controlling the environment and maximizing one’s resources and capacities for control (J. Heckhausen & Schulz, in press; Schulz & Heckhausen, 1997). Because of the dearth of comparative psychological research into motivational processes, one has to rely on reasoning about functional requirements of behavior regulation in active, complex, and resource-needy organisms such as mammals. First, one might expect a selectively enhanced attentional readiness and sensitivity to detect contingencies between behavior and external stimuli. Such a module for detecting behavior-event contingencies would help the organism to generally learn about its effectiveness to bring about events in the environment and identify specific behavioral patterns as causes for certain desired or dreaded outcomes. Second, control striving is promoted by an inherent preference for behavior-event contingencies. By inherent, it is meant that the preference holds even when there is no reinforcement with regard to a specific need, such as hunger, thirst, and so on. There is ample evidence for this assumption both with regard to humans and to other mammals (see review in Rumbaugh et al., 1994; White, 1959). Animals of various mammal species have been shown to become listless and depressed when experiencing uncontrollable negative events (Overmier & Seligman, 1967). Operant conditioning studies with mammals show that behavior-event contingencies are preferred to event-event contingencies even in the absence of consummatory behavior (see review in White, 1959). Chimpanzees favor objects that can be moved, changed, or made to emit sounds and light (Welker, 1956); monkeys spend hours solving mechanical puzzles (Harlow, 1953); and both children and rats prefer response-elicited rewards to receiving the same rewards without having to respond (Singh, 1970; see also aversion of freeloading phenomenon, Osborne, 1977). These preferences for behavior-event contingencies

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are already in place at the very beginning of life. Even human neonates are able to detect behavior-event contingencies (Janos & Papousek, 1977; Papousek, 1967). Papousek (1967) found, for example, that very young infants learned head movements contingent on acoustic signals and milk reinforcement. Even after complete satiation, when the milk had lost its reinforcing potential, signals elicited prompt head movements and pleasure on the occurrence of the expected contingent presentation of the milk bottle.1 The third motivational module, which would favor control behavior, is a tendency to repeat responses when they have led to desirable consequences. This is the classical behaviorist notion of the “law of effect” (Thorndike, 1898) and operant conditioning (Skinner, 1938), which has been shown to hold for an extensive variety of vertebrate species, ranging from fish to birds, rats, monkeys, and humans. However, as discussed above, there appear to be interesting interspecies differences in the response to changes in reinforcers, so that species with elaborated neocortex structures exhibit enhanced reactions to shifts in incentives. The fourth motivational module is an asymmetric pattern of affect reactions to negative and positive changes in the environment. This asymmetry in affective responses is closely related to the basic forms of affective transformations discussed in the previous paragraph. Frijda (1988) has proposed “the laws of emotion” that humans affectively respond to negative change more strongly than to positive change. After a change for the worse, the negative emotions are stronger and typically last much longer than the positive emotions that follow a change for the better. In terms of control behavior, an interesting fact is that positive emotions of pride, feeling satisfied with the environment, and so on would hardly motivate the individual to become active to change the environment. In contrast, a negative emotion after a negative change motivates the individual to do something to change the environment to get rid of the noxious situation. Thus, the asymmetry in responding emotionally to positive and negative changes leads to a selective promotion of control behavior directed at changing the environment. Bitterman’s (1975) findings on interspecies differences may suggest the transition in phylogeny when this asymmetry evolved. The fifth motivational module involved in promoting control behavior is curiosity and exploration. Those species that operate based on open-behavior programs (Mayr, 1974) rely heavily on the acquisition of experience and knowledge during each organism’s ontogenesis. Experience and knowledge acquisition is, of course, most promoted when the organism exposes itself to novel situations. It is striking how similar and almost stereotypical mammal species with more complex neocortices are with regard to their typical exploratory behavior; they gaze at, walk around, sniff, touch, and manipulate an unknown object or animal (Schneider, 1996). It would seem likely that curiosity and exploration is a universal motivational system in higher mammals. An organism can only profit from the experiences of exploration when they are stored in

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some kind of mental representation, as expectancies, schemata, and so on. With greater neocortical capacity came the ability to store more complex schemata about object relations and causal connections. Violations of expectancies can then become instigators of curiosity and elicit exploratory behavior. This phenomenon of a preference for moderate discrepancy has been widely researched in the wake of Helson’s (1964) adaptation-level theory (McClelland, 1953). As a sixth component of motivational regulation, humans exhibit a perception of personal control, mastery, and self-efficacy (Bandura, 1982; Harter, 1974; Watson, 1966; White, 1959). The developmental origin of this mastery perception is a generalized awareness of behavior-event contingency (Watson, 1966) that emerges during the first 2 or 3 years of life and provides a motivational resource for active control attempts (J. Heckhausen, 1989; see also review in J. Heckhausen & Schulz, 1995). Such a generalized conception of one’s own competence, efficacy, and control enables the organism to view activities directed at attaining outcomes in the environment as opportunities to experience and test competence, thus creating a motivational resource for overcoming difficulties and pursuing effortful activities, even in the face of obstacles or long-term delays of gratification. Anticipated selfreinforcement then, is the missing link to adult human achievement motivation (H. Heckhausen, 1991). Unfortunately, very little is known about the potential nonhuman primate or even mammal precursors of such generalized concepts. Rumbaugh and Sterritt (1986) suggested that perceptions of control may have had both proximate reinforcement value as a buffer against anxiety with overwhelmingly novel stimulation and ultimate reproductive advantage by facilitating the development of new activities and experiences. With regard to the phylogenetic availability of the phenomenon of perceived control, it should be taken into account that perceived control most likely requires an awareness of self, which phylogentically did not evolve before the higher primates. In the great apes, however, self-recognition seems to be present (Gallup, 1970, 1979), and thus a notion of one’s own competence may play a role as a motivating factor of control activities, as well. In addition to these modules that would steer an organism toward selecting activities directed at achieving goals in the environment, there are probably other facilitative processes that help to focus attention and behavior on a chosen goal of control. Among these should be mechanisms of intentionbased priming, which enhance the salience of goal-relevant cues and benefits while degrading irrelevant and particularly conflicting goals and their respective cues. Modern approaches to human motivation (Gollwitzer, 1990; H. Heckhausen & Gollwitzer, 1986; H. Heckhausen, 1991; Kuhl, 1984) have put such long-forgotten volitional processes back into the larger field of motivational psychology. Moreover, recent models of control behavior and developmental regulation have addressed self-regulatory processes as part of motivational engagement and disengagement (J. Heckhausen, 1999; J. Heckhausen &Schulz, 1993,1998; Schulz & Heckhausen, 1996).

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The motivational modules discussed so far all are directed at engaging the organism with goals of controlling the external world. However, control when striving for a particular goal may become dysfunctional when the goal turns out to be unattainable or the costs for striving become excessive and harm other, more important goal pursuits. Under such presumably not uncommon circumstances, the organism needs to disengage from a control goal so as to avoid wasting behavioral and motivational resources in futile goal pursuits, or become frustrated (a consequence of emotional-laden goal commitment) or even depleted in self-esteem and hopefulness (for those species that can construct a conception of their own competence). Deactivating behavior programs is not a challenge uniquely encountered by humans. All behavior that is to some extent flexible and involves choosing among options can go awry and should be susceptible to deactivation. Activities such as searching for food on a particular patch, chasing prey, courting a potential mate, or fighting a rival can turn out futile and thus wasteful or even directly destructive. Thus, mechanisms that promote engagement in goal pursuits need to be balanced by those allowing disengagement. Such mechanisms seem to be in place. Animals do not follow a prey until they collapse from exhaustion, they do not exploit a patch until collecting the last grain of food, or fight a superior rival until they are killed. Instead, there seem to be discontinuous mechanisms of goal deactivation that allow the animal to switch from complete engagement to disengagement in a sudden, discrete manner. The mechanisms involved in such deactivation of goal pursuit can be seen as the building blocks for human self-regulation of goal pursuit and coping with failure and losses. They enable the individual to switch behavioral and motivational resources over to comparatively more promising goal pursuits, and to avoid frustration with blocked goals. In addition to these two important functions of goal disengagement, humans also have to compensate for the negative consequences of failure experiences on self-esteem and general self-related conceptions of competence. Self-protective processes of reinterpreting failure or loss (e.g., by self-serving causal attributions; Snyder, Stephan, & Rosenfield, 1978) are probably unique to humans, although they do not rely on conscious processing of information. In fact, they may be all the more effective the less intentional they are (Brandtstädter & Renner, 1992; Brandtstädter, Wentura, & Greve, 1993).

Summary and Conclusion The history of psychology has disconnected motivational and comparative perspectives that had once inspired each other. After the prolonged reign of behaviorism, both comparative and motivational psychology have become dominated by a strong emphasis on cognitive processes at the expense, and

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to the exclusion of, affect-related processes. This has made it difficult to formulate and pursue an evolutionary approach to human motivation. However, evolutionary psychology should be keenly interested in motivational issues, given that problems of behavior and self-regulation are not resolved by merely addressing cognitive skills and modules. Instead, organisms with a substantial neocortex and behavioral flexibility require mechanisms that mediate between environmental challenges and behavior and allow adjustment of behavioral means in accordance with a complex and changing environment. From the point of view of motivational psychology, the paradigm of evolutionary psychology can provide a good approximation to the likely phylogenetic origins of specific motivational processes in other primates, mammals, and vertebrates. The problems of behavior regulation share common features across an impressive range of different species, and may have led to the selection of a few basic modules involved in affecting change in the environment. The set of potential motivational modules discussed promotes control behavior directed at the environment and is broadly applicable across domains of functioning and tasks involved in reproductive fitness.

Note 1. A related but functionally distinct issue is the preference for self-determination or self-controlled selection of goals for behavior. For example, Washburn and Rumbaugh (1991) report that rhesus monkeys perform better on tasks that they had selected themselves than on tasks assigned to them. A similar argument is made by Deci and Ryan (1985; Ryan, Kuhl, & Deci, 1997) with regard to basic psychological needs in humans. This preference for autonomy is a most interesting phenomenon with regard to those species that live in hierarchical social structures. Choosing one’s own behavioral goals is counteracting the dominance of high-status individuals. It may have benefits for the individual, but certainly not for group stability.

References Ach, N. (1910). Über den Willensakt und das Temperament [On acts of will and temperament]. Leipzig, Germany: Quelle & Meyer. Atkinson, J. W. (1957). Motivational determinants of risk-taking behavior. Psychological Review, 64, 359–372. Bandura, A. (1982). Self-efficacy mechanisms in human agency. American Psychologist, 37, 122 –147. Barkow, J. H., Cosmides, L., & Tooby, J. (Eds.). (1992). The adapted mind: Evolutionary psychology and the generation of culture. New York: Oxford University Press. Bischof, N. (1985). Das Rätsel Ödipus [The Oedipus mystery]. München, Germany: Piper. Bitterman, M. E. (1975). The comparative analysis of learning. Science, 188, 699–709. Bowlby, J. (1969). Attachment and loss: Attachment (Vol. 1). New York: Basic Books. Brandtstädter, J., & Renner, G. (1992). Coping with discrepancies between aspirations and achievements in adult development: A dual-process model. In L. Montada, S.-H. Filipp, & M. R. Lerner (Eds.), Life crises and experiences of loss in adulthood (pp. 301–319). Hillsdale, NJ: Lawrence Erlbaum.

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49 The Debate about Rewards and Intrinsic Motivation: Protests and Accusations Do Not Alter the Results Judy Cameron and W. David Pierce

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ur research (Cameron & Pierce, 1994) has clearly touched a nerve. The results of our meta-analysis indicate that rewards can be used effectively to enhance or maintain an individual’s intrinsic interest in activities. These findings are challenging to those who espouse the view that rewards and reinforcement are generally detrimental to a person’s intrinsic motivation. Our article has drawn criticism because the data from approximately 100 experiments show that there is only one small negative effect of reward, an effect that is highly circumscribed and easily avoided. This finding is disconcerting to those who contend that the negative effects of reward are substantial, generalized and occur across many conditions. Our analysis of 20 years of research is the most extensive review of the literature on rewards and intrinsic motivation to date. Because of its thoroughness, the data, analysis, and conclusions must be taken seriously. Faced with the evidence, researchers who have argued that rewards produce harmful effects under a wide range of conditions are put in a difficult position. One option they can take is to reanalyze the data in an attempt to show that rewards have strong negative effects on intrinsic motivation. Our data are readily available for additional analyses, and our procedures are clearly outlined in the original article. Failing this option, a second strategy is to suggest that the findings are invalid due to intentional bias, Source: Review of Educational Research, 66(1) (1996): 39–51.

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deliberate misrepresentation, and inept analysis. Our critics have chosen the second strategy. Lepper, Keavney, and Drake (1996); Ryan and Deci (1996); and Kohn (1996) have responded to the results of our meta-analysis by accusing us of asking inappropriate questions, omitting important moderator variables, excluding critical experiments, and contradicting other reviews on the topic. In addition, they criticize our meta-analytic procedures and decisions as flawed. In response to these criticisms, we show that all relevant studies were included in our analyses and that the questions and reward conditions we assessed expand on previous reviews to provide a more comprehensive picture of the effects of rewards on intrinsic motivation. We answer the statistical concerns of our critics and show that our analysis is appropriate, accurate, and robust. Most importantly, we show that none of the objections raised by our critics negates our findings. The results and conclusions of our meta-analysis remain important, especially for those involved in education and other applied settings. An issue of prime concern to educators is how to use rewards effectively to promote learning without disrupting students’ intrinsic interest. Contrary to Ryan and Deci’s (1996) claim that our “theoretical position acknowledged no conditions under which one should expect negative effects” (p. 33), our results provide important clarifications about the conditions under which rewards produce positive or negative effects on intrinsic motivation. Of primary importance in classroom situations is the finding that rewards can be used to maintain or enhance students’ intrinsic interest in schoolwork. Verbal praise and performance feedback increase the value of an activity. When tangible rewards are offered contingent on level of performance or are given unexpectedly, students remain motivated in the subject area. A slight negative effect can be expected when a teacher offers a tangible reward without regard to the students’ level of performance. Under this condition, when the rewards are withdrawn, students will continue to like their schoolwork as much as others, but they may spend slightly less time on it in a free period. This negative effect can be easily prevented by offering students rewards for successful solution of problems, completion of work, or for attaining specified levels of performance on particular tasks. The point is that teachers can reward the level and quality of students’ work without disrupting motivation and interest in learning. These conclusions are not altered by the comments of Kohn, Ryan and Deci, and Lepper et al. In the following commentary we address our critics’ concerns. Our response is organized in two sections; the first deals with the general issues that have been raised by our critics, and in the second we focus on specific statistical criticisms.

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General Issues The Overall Question One issue of contention involves our decision to begin our meta-analysis by investigating the overall effect of reward on intrinsic motivation (overall effect hypothesis). Lepper and his colleagues state that “to ask about the ‘overall’ or ‘in general’ effects of rewards or reinforcers is to pose a fundamentally meaningless question” (p. 7). They argue that the question is senseless and misleading, a view echoed by Kohn and by Ryan and Deci. We maintain that the overall effect hypothesis is central to an understanding of this area of research. One reason is practical. Many educators, parents, and administrators have adopted Kohn’s (1993) position that overall, rewards and incentive systems are harmful. In the present context, this stance means that rewards negatively affect students’ intrinsic interest, a question of overall effect. Others involved in education are still open to the possibility that rewards may be beneficial. A classroom teacher who wishes to implement an incentive system is first of all interested in whether rewards disrupt intrinsic interest in the subject matter. Of course, it may be advantageous to target particular subgroups or implement additional measures, but the question of the overall effect of reward is crucial to one’s teaching strategy. Another reason to address the main effect hypothesis is that academic journals, introductory textbooks, newspapers, and some of our critics continue to point to the overall negative or harmful effects of reward and reinforcement. In a prominent scientific journal, Nature, we learn that “it has been repeatedly shown that if people are rewarded for performing a task they find intrinsically pleasurable, they do it less, not more” (Sutherland, 1993, p. 767). A major introductory psychology textbook informs us that when an extrinsic reward is given, the motivation becomes extrinsic and the task itself is enjoyed less. When the extrinsic rewards are withdrawn, the activity loses its material value. . . . The moral is: A reward a day makes work out of play. (Zimbardo, 1992, p. 454, italics in the original)

Even in this issue of Review of Educational Research, Kohn asserts that “there is more than adequate justification for avoiding the use of incentives to control people’s behavior, particularly in a school setting” (p. 3). These examples are but a small sample of the claims made about the overall effects of reward. Many university students, educators, and parents have been exposed to this negative main effect assumption and base their own understanding and use of rewards on it. Social policy in our schools and other institutions reflects these beliefs. Because of this, an analysis of the general effects of reward is warranted.

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In their critiques of our meta-analysis, Lepper et al. and Ryan and Deci indicate that they and others have long recognized that the negative overall effect hypothesis is incorrect. Nonetheless, numerous writers interpret the research findings as indicative of an overall negative effect and decry the use of rewards in educational and work settings (e.g., see Kohn, 1993). As a result, many parents, teachers, and others are reluctant to use rewards – any rewards – under any circumstances! Lepper and his colleagues suggest that reversing this incorrect conclusion will be harmful. They imply that we are trying to propagate our own myth – that rewards have no negative effects. We do not want to add any more myths to this research area. So let us be clear in stating that our research demonstrates that rewards have either positive or negative effects depending on the way they are administered. Importantly, the only negative effect of reward on intrinsic motivation occurs under a circumscribed set of conditions, namely, when rewards are tangible and promised to individuals without regard to any level of performance.

The Role of Moderator Variables A major focus of our meta-analysis was to assess the effects of various moderator variables. The moderators we included (type of reward, reward expectancy, and reward contingency) were chosen because of their theoretical and practical importance in the literature on intrinsic motivation as well as replication over a number of experiments. Our results indicate that the detrimental effects of reward are limited and depend on multiple moderators. All of our critics, Lepper et al., Ryan and Deci, and Kohn, are concerned that we failed to assess the impact of additional important moderators. The implication of their comments is that decremental effects of reward occur under numerous conditions and are far more widespread than our analysis suggests. Interestingly, however, as we describe below, an analysis of additional moderators would, in fact, show the opposite. Lepper et al. point to studies that assessed the impact of initial task interest and reward salience on intrinsic motivation. Other moderator variables hypothesized to influence intrinsic motivation include reward attractiveness, presence or absence of the experimenter, task difficulty, reward magnitude, and so on. It is critical to point out that the few studies designed to investigate the impact of these moderators typically begin with the one condition that produces a negative effect. Furthermore, such moderators have been shown to enhance, mitigate, or reverse the negative effects of expected, tangible, noncontingent reward. For example, Ross (1975) found that salient rewards make the negative effect of tangible, expected, noncontingent reward greater. McLoyd (1979), on the other hand, demonstrated that individuals offered a noncontingent, tangible reward experienced an increase in intrinsic motivation when the task was less interesting, while Williams’s (1980) research

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indicated that the negative effects of tangible, expected, noncontingent reward could be offset by offering attractive rewards. In other words, the variables we have not assessed are moderators that have typically been added to the conditions that produce the single negative effect of reward found in our meta-analysis. Thus, an analysis of studies that included moderators that increase the negative effects of expected, tangible, noncontingent reward would serve to place further restrictions on the circumstances under which rewards undermine intrinsic motivation. That is, the negative effect phenomenon may be even more circumscribed than our data indicate, a finding contrary to the implications hinted at by our critics. Presently, however, there is no way to assess the theoretical or applied importance of these moderator variables. This is because only one or two studies have replicated the same moderator procedures on a common dependent measure of intrinsic motivation. If the effects of moderators such as reward salience, reward attractiveness, and so on were systematically replicated, a subsequent meta-analysis could be conducted to determine the conditions that moderate the negative effect on intrinsic motivation of tangible, expected, noncontingent rewards when they are removed. Of course, such an analysis would simply extend our findings and show that tangible, expected, noncontingent rewards produce negative effects on intrinsic motivation only when other conditions are present. For example, in terms of reward attractiveness, Williams’s (1980) research shows that when tangible, expected, noncontingent, unattractive rewards are given, intrinsic motivation decreases; the same reward condition with attractive rewards does not produce a decrement. Although present theoretical accounts (e.g., cognitive evaluation theory, the overjustification hypothesis) may be able to organize such circumscribed effects, the theories would become less and less generalizable. In applied settings, negative effects of reward on intrinsic motivation would depend on so many conditions that there would be little need for concern. Both Kohn (1996) and Ryan and Deci (1996) raise the question of moderators in the context of our finding that verbal praise produces positive effects both on the free time students spend on tasks and on attitude measures of intrinsic motivation. Specifically, they claim that verbal praise directed at controlling student behavior has negative effects on intrinsic motivation, whereas informational praise does not. We did not conduct an analysis on the control-informational dimension of verbal reward because these variables appear in only one or two studies. In addition, most research on this topic has been conducted without adequate no-feedback control groups (e.g., Ryan, 1982). Until a sufficient number of experiments with control groups are conducted, a meta-analysis of conditions that have few replications would not be reliable or beneficial to our understanding of reward and intrinsic motivation. We note, however, that although there are so few studies on this topic, the effects of controlling and informational verbal reward

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were analyzed in a recent meta-analysis by Tang & Hall (1995). They found no significant effects on either of these dimensions. In sum, although our meta-analysis was designed to assess the effects of several moderators on reward and intrinsic motivation, Lepper et al., Ryan and Deci, and Kohn have suggested that many additional important moderators were omitted. As we have shown, an analysis of additional moderators would not alter our conclusions or change any of the results of our metaanalysis. That is, negative effects of reward on intrinsic motivation are highly conditional and occur solely in the presence of multiple moderators. In educational settings, negative effects can be avoided by praising students for their work and making tangible rewards contingent on performance.

Our Findings in Context Both Ryan and Deci and Lepper et al. argue that our findings contradict previous narrative reviews and other meta-analyses of reward and intrinsic motivation. Lepper et al. are not consistent on this point, and in a later section of their critique they concede that “other recent meta-analyses, . . . as well as numerous previous narrative reviews, have reached exactly [our] conclusion” (p. 7). In this section, we show that our results are in accord with other summaries of reward and intrinsic motivation and that our review advances the knowledge in this area. We briefly comment on three other meta-analyses on this topic (Rummel & Feinberg, 1988; Tang & Hall, 1995; Wiersma, 1992). The most recent meta-analysis on rewards and intrinsic motivation, conducted by Tang and Hall (1995), was designed to test several theoretical propositions about the overjustification effect. Fifty studies were included, largely a subset of the experiments examined in our review. One analysis concerned assessing the effects of expected, tangible, task-contingent (noncontingent) reward on the free time measure of intrinsic motivation. Tang and Hall found a negative effect, as did we. Also, in accord with our findings, they found no detrimental effect with unexpected, tangible reward. It is difficult to compare our findings on the effects of verbal reward on free time with their study, because their analysis included only two effect sizes (their result was not significant). Tang and Hall (1995) reported a negative effect on the free time measure for performance-contingent reward, whereas we found no significant effect. This difference in findings is due to Tang and Hall’s classification of performance-contingent reward as well as to their omission of several relevant studies. Of the seven studies that Tang and Hall analyzed as performance contingent, six are actually task-contingent reward procedures, as defined by Deci and Ryan (1985). We used Deci and Ryan’s definitions and identified 10 studies of performance-contingent reward; overall, there was no evidence of a negative effect. Additional measures of intrinsic motivation (e.g., attitude toward task) that we examined were not reported by Tang and Hall.1

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The meta-analyses by Wiersma (1992) and Rummel and Feinberg (1988) were discussed in our original article (Cameron & Pierce, 1994). Wiersma analyzed 20 studies, and Rummel and Feinberg analyzed 45 studies. We cannot compare our findings with those of Rummel and Feinberg, because they averaged over different dependent measures of intrinsic motivation. Our meta-analysis shows that this is inappropriate, because the free time and attitude measures do not necessarily covary with the same experimental treatment. In addition, in both Rummel and Feinberg’s and Wiersma’s analyses, many of the effect sizes reported came from studies where one reward condition was compared to another reward condition. The lack of a no-reward group makes a comparison of findings problematic. Wiersma does, however, report effect size estimates for six experiments on free time that compared a no-reward condition to an expected, tangible, noncontingent reward condition. Though we have not conducted a meta-analysis on his results, we computed the average of the six independent effects sizes and found a negative effect, a finding compatible with our original conclusions. All in all, our findings for rewards that are tangible, expected, and noncontingent are consistent with other meta-analyses. Our research, however, went beyond an analysis of the one negative reward procedure and assessed the effects of reward under a variety of conditions. In terms of other reward procedures (e.g., verbal reward, performance-contingent reward) and other measures of intrinsic motivation (e.g., attitude toward a task), we failed to find any detrimental effects on intrinsic motivation. That is, our study showed that most reward procedures can be used to maintain or enhance intrinsic motivation; the negative effect other reviews have detected is only a small part of a larger picture. Thus, our meta-analysis provides a more complete account of the effects of rewards on intrinsic motivation.

The Completeness of Our Review A criticism put forward by Kohn, as well as by Ryan and Deci, is that we failed to include several critical experiments in our meta-analysis. The implication is that had such studies been included, our results would have been different. Kohn cites a number of studies that he believes we have overlooked. Most of these studies were located in our original search and were not included in our meta-analysis because of the lack of an adequate no-reward control condition. In addition, as we reported in our original article, our metaanalysis included studies published up to and including 1991. The studies from the period 1992–1994 cited by Kohn (Boggiano et al., 1992; Freedman, Cunningham, & Krismer, 1992; Gottfried, Fleming, & Gottfried, 1994) were, of course, not included. Of these, Freedman et al. varied the amount of reward but had no nonreward control group. The article by Boggiano et al. reported past research in order to develop a theory or model of students’

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achievement patterns. Gottfried et al. examined parental motivational practices; their study did not include any of the reward conditions or dependent measures that we analyzed in our meta-analysis. Earlier studies by Birch, Marlin, and Rotter (1984) and Fabes, Fultz, Eisenberg, May-Plumlee, and Christopher (1989) concerned food preferences and prosocial behavior, respectively. Clearly, all these studies are off topic. Other papers that Kohn cites as missing are, in fact, included in our analyses (a list of all studies is presented in Cameron & Pierce, 1994, pp. 399–403). In contrast to Kohn, Lepper et al. charge us with including too many “bad” studies. An essential criterion of a reliable meta-analysis, however, is that all the studies done in a field are examined, independently of one’s own theoretical position and the degree to which the results of any particular study may be promising. We have met this criterion. In fact, our meta-analysis on the effects of rewards on intrinsic motivation is the most comprehensive review of this literature to date. The results are based on a large number of studies, and, to our knowledge, no relevant published studies were omitted. Due to the large sample of studies included in our analyses, any single study that may have been overlooked would not alter the conclusions. Overall, our results were based on all the available evidence, and the findings are central to an understanding of the effects of rewards on intrinsic motivation.

Meta-Analytic Issues In addition to the general criticisms discussed above, Lepper and his associates object to our use of meta-analysis for assessing the research on the effects of rewards on intrinsic motivation. In particular, they contend that the distributions of effect sizes in our article indicate that meta-analytic tests should not have been conducted. In accord with Ryan and Deci (1996) and Kohn (1996), they further suggest that the statistical procedures used in our meta-analyses must be flawed. Specifically, they criticize the technique of aggregating effect sizes within a single study when moderator variables are present. In this section, we respond to our critics’ meta-analytic and statistical concerns. We show that our analyses are appropriate, that the data are approximately normal and homogeneous, that inclusion or exclusion of outliers does not alter the results, and that our procedures yield correct estimates for the effects of rewards on intrinsic motivation at each level of analysis.

The Appropriateness of Meta-Analysis There are two main issues that concern Lepper et al. with regard to our use of meta-analytic techniques for assessing the effects of rewards on intrinsic motivation. First, they suggest that the apparent normality of our distributions for the

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critical measures of intrinsic motivation (free time, attitude) is deceptive. Their second concern is that the data are not homogeneous (equal spread of effect sizes) and that meta-analytic tests should therefore not have been performed. As Lepper et al. acknowledge (p. 13–14), our distributions of effect sizes approximate a normal shape. However, they attribute the normality of these distributions to the inclusion of “pure zero cases” and random estimates. They argue that our inclusion of “pure zero cases” in our graphic portrayal of effect sizes (Cameron & Pierce, 1994, Figures 1 and 2) guarantees a normal distribution around the value of zero. Pure zero cases refer to studies that did not provide sufficient information to calculate effect sizes or random estimates (4 cases for free time and 17 cases for attitude). The truth is that we did not include pure zero cases in these figures. This is clearly stated on pages 379 and 384 of our original article. The normality of the distributions centering around zero is not due to pure zero cases. Thus, Lepper and his associates need not be concerned. In terms of our use of random estimates of effect sizes, our procedure is innovative and may be more appropriate than merely assigning a zero effect to the experiment or omitting the study itself. The procedure depended on the information available in each study. When t or F values were nonsignificant and were reported as less than some value (e.g., < 1), a random number between 0.01 and that value was selected; and an effect size was then calculated. In other cases, t or F values were not available, but means or directions of means were reported. In these situations, a random number between 0.01 and the critical value of t or F at p = .05 was drawn, and an effect size was then calculated. (For more information, see Cameron & Pierce, 1994, p. 376). With regard to the normality of our distributions, it is important to note that the direction of effect for random estimates was always known. If more studies had had negative effects, the distribution would have been pulled in that direction. The actual shape of the distribution shows that positive and negative effect sizes occurred with similar frequency. This is based not on our use of random estimates but on the actual direction of effects reported in such studies. In other words, the use of random estimates in no way biases the results toward an average zero effect size. The normality of the distributions centering around zero is not due to this, and, again, there is no need for concern. The point is that the effect size distributions approximated a normal shape, and meta-analytic tests could be used with confidence. Although Lepper et al. agree that our distributions are normal, they argue that our data are heterogeneous (lacking equal spread) and therefore inappropriate for meta-analysis. Our decision to use meta-analytic procedures involved a consideration of several issues. Initially, we were concerned with the normality of the distribution of effect sizes. We showed that the distributions were approximately normal and reported the degree of kurtosis and skewness of the free time distribution in the original article (p. 381). Next we considered the results of the Q test for homogeneity. It is well known that this

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test is liberal in the sense that the null hypothesis (homogeneity) is too often rejected (Hunter, Schmidt, & Jackson, 1982). Because of this problem, we set the critical value of Q farther out on the chi-square distribution, just below the value at the .01 level (that is, p > .01). Homogeneity was achieved by excluding extreme effect sizes. The exclusion of outliers is not unusual and is recommended by Hedges (1987) as a method for obtaining more equal spread of the effect sizes. To assess any biases due to the removal of outliers, we reported all analyses with extreme values included and excluded. In addition, we identified the studies with extreme values and discussed the conditions that may have led to these atypical results. Inspection of our original article shows that the results do not change to any extent by excluding outliers. The validity of our meta-analysis is also increased by the use of the CL statistic (McGraw & Wong, 1992). CL is another way to express effect size. Importantly, McGraw and Wong conducted 118 tests (simulations) to show that the CL statistic is robust with respect to violations of normality and homogeneity. Because of this, we used CL in all our analyses and reported results identical to those of the other meta-analytic tests. In sum, the distribution of effect sizes for the critical measures of intrinsic motivation approximated a normal shape. The normality was not due to the inclusion of “pure zero cases” or random estimates as Lepper and his associates have suggested. Homogeneity of effect sizes was achieved by excluding outliers. All results were reported with outliers included and excluded; our findings were not altered to any extent by the exclusion of outliers. In addition, given our use of the CL statistic, we are confident that our analyses are appropriate and that the results are accurate and valid.

Aggregation of Effect Sizes in Meta-Analysis Lepper et al., Ryan and Deci, and Kohn are critical of the method of aggregating effect sizes within a study to yield a single estimate for each meta-analytic test. They contend that such procedures yield inaccurate estimates of the effects of reward on intrinsic motivation. Underlying this criticism is the supposition that the effects of important moderators and interactions were not detected in our analyses. Again, the implication of these comments is that negative effects of reward are more prevalent than our results communicate. In response to this concern, we first note that aggregation of effect sizes within a study is a common procedure in meta-analysis that avoids violation of the assumption of independence (Cooper, 1989; Hedges & Olkin, 1985). The procedures for aggregation are clearly described in our original article (pp. 376–377). It is important to point out that a serious statistical violation occurs when more than one effect size from an individual experiment is

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entered into a single meta-analysis. Typically, in such cases, a control group is compared with more than one experimental treatment within a study, several effect sizes are calculated, and each is entered into a single meta-analytic test. The major problem is that the effect sizes are not independent (errors among observations are correlated). If the dependencies in such data were properly accounted for, the error term would become larger and mean effect sizes would become smaller. Another problem is that a particular study will contribute more weight to the overall meta-analytic outcome than a study yielding only one effect size. Other meta-analyses on reward and intrinsic motivation favored by Lepper et al. (p. 5) have violated the assumption of independence by entering several (sometimes over 10) effect sizes from one study into a single meta-analytic test (e.g., Rummel & Feinberg, 1988; Tang & Hall, 1995). The implication is that conclusions based on these meta-analyses could be incorrect. The way to achieve independence and at the same time retain effect sizes for an analysis of the impact of various moderators is to (a) aggregate them into a single estimate for an overall analysis of the effects of rewards on intrinsic motivation and (b) conduct further analyses of the effects of various moderator variables. For factorial designs, the main effect of reward is entered into an analysis of the overall effects of reward; interaction effects that have been replicated in a sufficient number of experiments are then analyzed separately. These are the procedures we used in our meta-analyses. As we indicated previously, the moderators we analyzed (reward type, reward expectancy, and reward contingency) were chosen because of their theoretical and applied importance as well as replication. Lepper et al. are concerned that aggregation of the moderators (rather than separate analyses) yields inaccurate estimates of the effects of reward on intrinsic motivation (p. 11–13). As mentioned earlier, the moderators not assessed in our analyses (e.g., presence of experimenter, reward attractiveness, salience, distraction, etc.) have appeared in only one or two studies, and in these studies they have been added to the tangible, expected, noncontingent reward condition to decrease, mitigate, or increase the negative effect. In terms of such studies, it is possible to obtain an unbiased estimate of the effect size of tangible, expected, noncontingent reward. When the results are pooled across all studies, the effects of any additional moderators are averaged out. That is, although any one of these manipulations may push intrinsic interest up (e.g., reward attractiveness) or down (e.g., surveillance, reward salience) in a given study, their effects are expected to cancel out across many studies. In other words, the best estimate of the effect size of tangible, expected, noncontingent reward when additional moderators are present is the average of all the comparisons of the rewarded conditions with nonrewarded control groups. Of course, additional meta-analyses could be conducted on the effects of these moderators if they were sufficiently replicated. As we pointed out,

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however, because they are added to the one reward procedure that produces a reliable negative effect, the results would show that decremental effects of reward on intrinsic motivation depend on even stricter conditions than our analysis indicates. This is demonstrated in Lepper et al.’s analysis of three factorial experiments (Calder & Staw, 1975; Loveland & Olley, 1979; McLoyd, 1979) that crossed initial task interest (high, low) with reward (reward, no reward). Lepper et al. (p. 10) show that in these three studies, rewarding activities with high intrinsic interest yields a large negative effect size. In contrast, rewarding a task with low initial interest produces a positive effect size. In each of these studies, the reward procedure involved tangible, expected, noncontingent (or task-contingent) rewards – the one procedure that produces a negative effect on the free time measure of intrinsic motivation. Thus, if Lepper et al.’s analysis is reliable, the results indicate that tangible, expected, noncontingent rewards are harmful only when delivered for more interesting tasks. It is worth mentioning here, however, that a study excluded in Lepper et al.’s analysis (Mynatt et al., 1978) also crossed task interest with tangible, expected, noncontingent reward but found positive effects of reward for both low- and high-interest tasks. Given that there are so few studies of the interest variable, the results from this one study could substantially alter Lepper et al.’s conclusions about the importance of level of task interest when rewards are tangible, expected, and noncontingent. In summary, the procedures used in our meta-analysis yield correct estimates for the effects of reward on intrinsic motivation at each level of analysis. Our critics have implied that analyses of additional moderators and interactions would yield more general negative effects of reward on intrinsic motivation. However, as we have shown, further analyses would actually reveal that positive effects of reward are more general and that decremental effects of reward occur under even more restricted circumstances than our results indicate.

Conclusion A prominent view in education and social psychology is that rewards decrease a person’s intrinsic motivation. Our meta-analysis of 20 years of research suggests that this view is incorrect. The findings from approximately 100 studies indicate that rewards can be used effectively to enhance or maintain intrinsic interest in activities. The only negative effect of reward occurs under a highly specific set of conditions, circumstances that are easily avoided. Not surprisingly, these results have not been well received by those who argue that rewards produce negative effects on intrinsic motivation under a wide range of conditions. In response to the findings, Lepper, Keavney, and Drake (1996), Ryan and Deci (1996), and Kohn (1996) have suggested that the questions asked in our meta-analysis were inappropriate, that critical studies were excluded,

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that important negative effects were not detected, and that the techniques used in our meta-analysis were unsuitable. In this response, we have shown that the questions asked are fundamental to an understanding of the relationship between rewards and intrinsic motivation and that our meta-analytic techniques are appropriate, robust, and statistically correct. Our meta-analysis includes all relevant studies on the topic, and the results clearly show that negative effects of rewards occur under limited conditions. All told, the results and conclusions of our meta-analysis are not altered by our critics’ protests and accusations. Our findings have important practical implications. In applied settings, the results indicate that verbal rewards (praise and positive feedback) can be used to enhance intrinsic motivation. When tangible rewards (e.g., gold stars, money) are offered contingent on performance on a task or are delivered unexpectedly, intrinsic motivation is maintained. A slight negative effect of reward can be expected when tangible rewards are offered without regard to level of performance. Under this condition, when the rewards are withdrawn, individuals report as much interest in the activity as those in a nonrewarded group, but they may spend slightly less time on it in a free period.2 This negative effect can be prevented by rewarding people for completing work, solving problems successfully, or attaining a specified level of performance. In other words, rewards can be used effectively in educational and other applied settings without undermining intrinsic motivation.

Notes 1. Tang and Hall (1995) reported effect sizes for questionnaire measures of intrinsic motivation. The studies they analyzed used questionnaire items to index attributions of causality; moral obligation; attitude toward the task; perceptions of luck, ability, effort, and difficulty; feelings of competence; negative affect; self-esteem; and so on. Tang and Hall combined the effect sizes of all these measures and reported metaanalyses based on this composite index. They did not examine attitude toward the task separately, as we did. Thus, we cannot compare our findings on the attitude measure of intrinsic motivation. 2. It may be informative to consider how serious the negative effect of expected, tangible, noncontingent reward on free time really is. How much less time would students spend on academic subjects if a teacher implemented this reward procedure and then removed it? Results from our meta-analysis indicate that the average effect size for a comparison between people who receive an expected, tangible, noncontingent reward and nonrewarded individuals on time on task following withdrawal of reward is –0.26. In the original experiments, time on task was typically measured over an 8-minute period. In order to convert the effect size of –0.26 to real time, one needs to know the pooled standard deviation of rewarded and nonrewarded groups. Because many researchers reported only t or F statistics, we will use a well-designed study by Pretty and Seligman (1984) to estimate a pooled standard deviation. Their study reported two experiments with large sample sizes and readily available statistical information. Both experiments compared a condition of expected, tangible, noncontingent reward (N = 30) with a nonrewarded control group (N = 30) on 8 minutes of free time. The pooled standard deviation was 2.6 minutes.

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Using this estimate of error, we are able to convert the negative effect size from the meta-analysis into real time. An effect size of −0.26 would mean that in an 8-minute period, the average individual who is promised a noncontingent, tangible reward will spend about 41 seconds less time on the task when the reward procedure is withdrawn than the average nonrewarded individual. Given this result, what would happen if a teacher implemented this incentive procedure in a reading program and then removed it? According to the estimate, students who are offered gold stars for reading would spend about 3 minutes, 25 seconds less time reading in a 40-minute free-choice period than students not given the incentive. Of course, this is a hypothetical example, but it does illustrate the magnitude of this negative effect size in terms of real time.

References Birch, L. L., Marlin, D. W., & Rotter, J. (1984). Eating as the “means” activity in a contingency: Effects on young children’s food preference. Child Development, 55, 431–439. Boggiano, A. K., Shields, A., Barrett, M., Kellam, T., Thompson, E., Simons, J., & Katz, P. (1992). Helplessness deficits in students: The role of motivational orientation. Motivation and Emotion, 16, 271–296. Calder, B. J., & Staw, B. M. (1975). Self-perception of intrinsic and extrinsic motivation. Journal of Personality and Social Psychology, 31, 599–605. Cameron, J., & Pierce, W. D. (1994). Reinforcement, reward, and intrinsic motivation: A meta-analysis. Review of Educational Research, 64, 363–423. Cooper, H. M. (1989). Integrating research: A guide for literature reviews (2nd ed.). Beverly Hills, CA: Sage. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Fabes, R. A., Fultz, J., Eisenberg, N., May-Plumlee, T., & Christopher, F. S. (1989). Effects of rewards on children’s prosocial motivation: A socialization study. Developmental Psychology, 25, 509–515. Freedman, J. L., Cunningham, J. A., & Krismer, K. (1992). Inferred values and the reverseincentive effect in induced compliance. Journal of Personality and Social Psychology, 62, 357–368. Gottfried, A. E., Fleming, J. S., & Gottfried, A. W. (1994). Role of parental motivation practices in children’s academic intrinsic motivation and achievement. Journal of Educational Psychology, 86, 104 –113. Hedges, L. (1987). How hard is hard science, how soft is soft science? The empirical cumulativeness of research. American Psychologist, 42, 443–55. Hedges, L., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic. Hunter, J. E., Schmidt, F. L., & Jackson, G. B. (1982). Meta-analysis: Cumulating research findings across studies. Beverly Hills, CA: Sage. Kohn, A. (1993). Punished by rewards. Boston: Houghton Mifflin. Kohn, A. (1996). By all available means: Cameron and Pierce’s defense of extrinsic motivators. Review of Educational Research, 66, 1– 4. Lepper, M. R., Keavney, M., & Drake, M. (1996). Intrinsic motivation and extrinsic rewards: A commentary on Cameron and Pierce’s meta-analysis. Review of Educational Research, 66, 5–32. Loveland, K. K., & Olley, J. G. (1979). The effect of external reward on interest and quality of task performance in children of high and low intrinsic motivation. Child Development, 50, 1207–1210.

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McGraw, K. O., & Wong, S. P. (1992). A common language effect size statistic. Psychological Bulletin, 111, 361–365. McLoyd, V. C. (1979). The effects of extrinsic rewards of differential value on high and low intrinsic interest. Child Development, 50, 1010–1019. Mynatt, C., Oakley, D., Arkkelin, D., Piccione, A., Margolis, R., & Arkkelin, J. (1978). An examination of overjustification under conditions of extended observation and multiple reinforcement: Overjustification or boredom? Cognitive Therapy and Research, 2, 171–177. Pretty, G. H., & Seligman, C. (1984). Affect and the overjustification effect. Journal of Personality and Social Psychology, 46, 1241–1253. Ross, M. (1975). Salience of reward and intrinsic motivation. Journal of Personality and Social Psychology, 32, 245–254. Rummel, A., & Feinberg, R. (1988). Cognitive evaluation theory: A meta-analytic review of the literature. Social Behavior and Personality, 16, 147–164. Ryan, R. M. (1982). Control and information in the intrapersonal sphere: An extension of cognitive evaluation theory. Journal of Personality and Social Psychology, 43, 450 – 461. Ryan, R. M., & Deci, E. L. (1996). When paradigms clash: Comments on Cameron and Pierce’s claim that rewards do not undermine intrinsic motivation. Review of Educational Research, 66, 33–38. Sutherland, S. (1993). Impoverished minds. Nature, 364, 767. Tang, S., & Hall, V. (1995). The overjustification effect: A meta-analysis. Applied Cognitive Psychology, 9, 365–404. Wiersma, U. J. (1992). The effects of extrinsic rewards in intrinsic motivation: A metaanalysis. Journal of Occupational and Organizational Psychology, 65, 101–114. Williams, B. W. (1980). Reinforcement, behavior constraint, and the overjustification effect. Journal of Personality and Social Psychology, 39, 599–614. Zimbardo, P. G. (1992). Psychology and life (13th ed.). New York: Harper Collins.

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50 A Comprehensive Expectancy Motivation Model: Implications for Adult Education and Training Kenneth W. Howard

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otivating adult learners has always been a critical concern of adult education theorists and practitioners. Motivation has been defined as a hypothetical mechanism which controls goal-directed behavior (Reykowski, 1965). Various theoretical frameworks have been used to explain motivation in the context of adult education. Of these various frameworks, perhaps Maslow’s (1943) five-stage self-actualization model has been the most prominent (Gilmore, 1974). In recent years, expectancy theory has begun to gain popularity as a model for understanding educational motivation. Derived from social learning theory generally and cognitive or field theory specifically, it views people as purposeful beings who interact proactively with their environments based on their expectancies about the likelihood that their efforts will result in outcomes that they value. In other words, they choose to perform in ways that they believe are likely to benefit them (McMillan, 1980). Such a model has relevance to adult education and training, not only as a means of increasing learner motivation and performance in the learning situation, but also for refining enrollment strategies, reducing dropout rates, and insuring that learning has a practical application for the learner.

Source: Adult Education Quarterly, 39(4) (1989): 199–210.

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The Development of Expectancy Theory Expectancy theory has its origins in the theories of Lewin (1938) and Tolman (1932), who postulated that human behavior was a result of the interaction of the individual and the environment, in the context of a specific situation, and that individuals develop beliefs about the probability of various possible outcomes of their behaviors, preferring some outcomes over others. Julian Rotter (1954, 1971), a social learning theorist, expanded on Lewin’s ideas regarding expectancy and motivation by adding elements of stimulus-response theory, suggesting that behaviors are motivated by the interaction of three factors: expectancy, reinforcement value, and the specific psychological situation. Some outcomes hold greater reinforcement value than others because they satisfy stronger needs. Specific situational cues (e.g., novelty of the situation, other people present) may alter expectancy or reinforcement values. Building on the work of Lewin, Tolman, and others, Vroom (1964a) developed valence-instrumentality-expectancy ( VIE) theory in its classical form. He postulated that the force of motivation behind any behavior was a product of valence, instrumentality, and expectancy. He defined expectancy as the individual’s subjective estimation of the likelihood of successfully performing a particular behavior, instrumentality as the individual’s subjective estimation of the likelihood that the behavior would be rewarded, and valence as the positive or negative value that the individual placed on the reward. Vroom (1964b) spelled out three basic assumptions underlying VIE theory: (a) that anticipation of reward energizes individual behavior, (b) that perceived value of various outcomes gives direction to individual behavior, and (c) that learned connections develop between behavior and outcome expectancy. Expectancy theory originated as a theory of work motivation and job satisfaction (Vroom, 1964a). Hence, most early applications of the theory were focused on business and industry, as were most early expectancy research studies. Yet despite the fact that expectancy theory had become the dominant motivation model in industry, it had been largely ignored by educators and educational administrators (Wright, 1985). However, in recent years adult education theorists have begun to recognize that expectancy theory has significant implications for adult education, particularly in accounting for the importance of barriers (internal or external) in predicting dropout (Darkenwald, 1981). Swedish theorists Rubenson and Hoghielm (1976, 1978) adapted Vroom’s VIE theory to explain and predict dropout from adult education. This was later refined by Borgstrom (1980). Their model described Force ( Vroom’s force of motivation), the strength of which determines if the individual completes or drops a course, as resulting from valence (the extent to which the individual regards a course as a fruitful means of satisfying perceived needs) and expectancy (the extent to which the individual feels capable of completing or coping with a course).

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Results of Expectancy Research Researchers have tested aspects and variations of expectancy theory with a variety of adult populations involved in both traditional and non-traditional educational settings, including: undergraduate university students (Arvey & Dunnette, 1980; Butler & Womer, 1985; Constantinople, 1967; Henson, 1976; Mitchell & Knudson, 1971; Mitchell & Nebeker, 1973; Polczynski & Shirland, 1976; Schmitt, 1975); adult GED students (Darkenwald, 1987; Moore & Davies, 1984); community college students (Malloch & Michael, 1981; Pritchard & DeLeo, 1973); graduate students (Miskel, DeFrain, & Wilcox, 1980), and public school teachers (Miskel, DeFrain, & Wilcox, 1980; Wright, 1985). Expectancy theory has also undergone extensive research in business and industry settings, in addition to the limited research in educational settings described above. Excellent comparative analyses of these studies and their results have been compiled by Heneman and Schwab (1972) and House, Shapiro, and Wahba (1974). Results of expectancy research have been mixed: the expectancy basis for motivation is supported but the individual elements of the theory are not consistently supported. Certainly, a simple, multiplicative model as proposed by Vroom cannot be supported. For example, while some studies support a multiplicative relationship between the VIE process variables (Lawler, 1968), others support an additive relationship (Feldman, 1974), others support both under different conditions (Butler & Womer, 1985), and others support neither an additive nor a multiplicative relationship (Hackman & Porter, 1968; Pritchard & Sanders, 1973). Some have found significant effects for all of the VIE process variables; others have found significant effects for some of the variables but not others (Arvey & Dunnette, 1980; House, Shapiro, & Wahba, 1974; Malloch & Michael, 1981; Moore & Davies, 1984; Pritchard & DeLeo, 1973). Although most studies agree that some combination of the VIE process variables is predictive of effort, a large number of studies have demonstrated that the VIE process variables alone are not predictive of performance (House, Shapiro, & Wahba, 1974). These studies have identified various other variables (e.g., ability, self-esteem, various personality traits) which either intervene between motivation and performance or influence the VIE process variables (Arvey & Dunnette, 1980; Butler & Womer, 1985; Darkenwald, 1987; House, Shapiro, & Wahba, 1974; Henson, 1976; Malloch & Michael, 1981; Mitchell & Knudson, 1971; Mitchell & Nebeker, 1973; Moore & Davies, 1984). These mixed results can be traced to two major areas: problems with research methodology and the lack of a sufficiently comprehensive model that better describes the complex relation between both the expectancy process variables and the other variables. Because several researchers have cogently and thoroughly addressed methodological problems in expectancy research (Butler & Womer, 1985; House, Shapiro, & Wahba, 1974), these are not discussed in detail in this paper. A few theorists (Graen, 1969; Lawler, 1973)

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have either commented on the need for a more complex model of expectancy motivation or have suggested specific additions or changes to existing models. While these have been positive contributions, none have proved sufficiently comprehensive. Therefore, this paper addresses the need for a comprehensive model of expectancy motivation.

A Comprehensive Expectancy Motivation Model A comprehensive expectancy motivation model must meet three criteria. First, it must accurately describe the dynamics of the fundamental process variables. Second, it must place expectancy motivation in the context of a cycle that explains not only the influence of expectancy motivation on the actual behavior of individuals but also the influence of actual performance, reward, and need satisfaction on expectancy motivation. Third, it must describe the influence of other variables on the motivation process. This paper proposes such a model.

The Primary Expectancy Motivation Variables In this model, motivation is seen as the product of four primary process variables (see Figure 1): effort-performance (E-P) expectancy, performancereward (P-R) expectancy, reward-need satisfaction (R-N) expectancy, and valence ( V ). E-P expectancy is defined as an individual’s perception of the likelihood that his or her effort will lead to successful performance of a specific behavior(s) in a specific situation. P-R expectancy is defined as the perception of the likelihood of being rewarded for successful performance. R-N expectancy is defined as the perception of the likelihood that those rewards will meet important personal needs. Valence ( V ) is defined as the value the individual places on the object (e.g., performance, reward, or need satisfaction) of any of the above expectancies. Motivation

E1

E2

Effort

Performance

E P Expectancy

E3

Reward

P R Expectancy

Need Satisfaction

R N Expectancy

Figure 1: The primary expectancy motivation variables

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For example, an individual’s motivation in a learning situation would be high if that person: (a) perceived a high likelihood of performing successfully in the classroom and transferring those behaviors to the job (E-P), ( b) perceived that improved job performance was likely to be rewarded by recognition from co-workers and supervisor (P-R), (c) perceived a high likelihood that recognition would meet basic acceptance needs (R-N), and (d) placed a high value ( V ) on each of the above. How a situation is viewed varies among individuals who will have different expectancies and valences. For example, a learner with internal perceived locus of control would be more likely to value (and expend effort towards) intrinsic (i.e., built-in, learner-centered) than extrinsic (i.e., educator-administered) rewards.

Expectancy Motivation as a Dynamic Process Figure 2 illustrates the dynamic nature of the expectancy motivation process variables. The model is a cyclical one: The outcomes of motivation (i.e., effort, performance, reward, and need satisfaction) affect the individual’s level of motivation on a continuous basis. Initial motivation is based on an individual’s subjective prediction of the probability of performance, reward, and need satisfaction. However, initial motivation results in actual effort, which in turn may result in actual performance, reward, and need satisfaction. Based on these observed results, the individual tests the accuracy of initial predictions (i.e., expectancies) and revises current E-P, P-R, and R-N expectancies. In any given situation, motivation directly influences only the amount of effort a person will expend towards performing required behaviors (e.g., learning tasks). Actual effort is the only variable directly related to motivation; the rest are indirectly related. Actual effort may or may not result in successful performance. Initial success (or progress) may increase a person’s E-P expectancy and, thereby, motivation to continue efforts toward performing subsequent tasks. Similarly, initial lack of success results in lower E-P expectancy. Successful performance of

Motivation E-P

P-R R-N

Effort

Performance

Reward

Need Satisfaction

Figure 2: Expectancy motivation as a dynamic process

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initial learning tasks motivates the learner to work toward subsequent learning tasks. Conversely, poor performance of initial learning tasks may lead to lower motivation on subsequent learning tasks. Continued poor performance may lower E-P expectancy to the point that the learner may decide that continued effort is wasted and drop out of the learning activity. Actual performance may or may not result in rewards. In a given situation, consistent reward for successful performance improves an individual’s P-R expectancy. Similarly, lack of reward (or inconsistent or inequitable rewards) results in lowered P-R expectancy. Reinforcement of newly learned behavior improves the learner’s P-R expectancy and therefore increases that person’s motivation to continue in the activity. If learned behavior is not reinforced, P-R expectancy and resultant motivation are decreased. Actual rewards may or may not meet the individual’s needs. If rewards satisfy the individual’s needs, that person’s R-N expectancy – and resulting motivation – will be increased. Learning programs tailored to unique learner needs result in higher motivation. This model implies that performance would have a stronger impact on satisfaction than satisfaction would have on performance. In other words, successful performance in a learning situation results in increased learner satisfaction (performance-reward-need satisfaction), setting up a cycle of reinforcement which becomes stronger over time.

The Influence of Other Variables on Expectancy Motivation As Figure 3 illustrates, expectancies are not only modified by ongoing feedback in the current situation but also by the individual’s past experience. Personal experience in similar situations provides the individual with a basis for determining E-P, P-R, and R-N expectancies. Observed experience (e.g., knowledge obtained by directly observing others’ experiences in similar situations) and communicated experience (e.g., shared information from others about their experiences in similar situations) are other sources. Repeated exposure to similar situations develops an individual’s knowledge, skills, and abilities (KSAs). An individual with moderate motivation and a high skill level will probably perform better than one with moderate motivation but a lower skill level. An individual whose effort frequently results in successful performance will have higher self-esteem than one who experiences frequent failures. Lower self-esteem translates as lower E-P expectancy and, therefore, lower motivation. P-R and R-N expectancies are similarly influenced by the individual’s past experience in similar situations. Personality variables also influence expectancy motivation. Finally, uncontrollable environmental forces sometimes interfere with actual performance and reward. A turbulent environment decreases E-P and P-R expectancies.

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Self Esteem

Past Experience

Ability (KSAs)

Motivation E-P P-R R-N

Effort

Environ. Conditions

Performance

INT/EXT

Reward

Need Satisfaction

Figure 3: A comprehensive expectancy motivation model

Implications for Adult Education and Training Increasing and maintaining learner motivation is a fundamental concern of adult educators. One might broadly conceptualize learning situations as having three stages, each with a different motivational focus: Pre-Learning (i.e., the period immediately prior to the learning situation), Learning (i.e., the actual learning situation), and Post-Learning (i.e., the period immediately following the learning situation). In the Pre-Learning stage, prospective learners must be motivated to become initially involved in learning. In the Learning stage, learners must be motivated to continue and take an active part in learning activities. In the Post-Learning stage, learners must be motivated to apply what they have learned. In each stage the same internal process variables – E-P, P-R, and R-N expectancies, and Valence – determine the level of motivation. The implications of this model are clearest for planned, structured adult education programs with specific learning objectives. Increased planning and structure provide the adult educator with more opportunities to manipulate the expectancy variables. Similarly, increased structure and specific learning objectives increase the ability of the learner to formulate clear expectancies regarding the learning situation. However, even in less structured learning situations or learning situations with broader goals (e.g., liberal education programs, self-directed learning projects) the same principles still apply: Learners that believe the learning goals are achievable and will result in personal rewards that meet their individual needs will be more motivated than those who do not. Similarly, learners involved in self-directed learning activities can plan and structure their learning according to the principles of the Comprehensive Expectancy Motivation Model in order to maximize their motivation in the context of their learning projects.

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Pre-Learning: Motivating Initial Involvement To motivate learners to become involved in a specific learning project, the adult educator must persuade them that: (a) the learning tasks are within their ability to perform, given reasonable effort (increased E-P expectancy); (b) successful performance of these tasks will be rewarded, both in the learning situation and in practical application, (increased P-R expectancy); and (c) the reward will satisfy their needs (increased R-N expectancy). The individual’s perception of the situation – not the objective reality – influences motivation at this stage, since the person has no direct experience to go on. This means that the learners will formulate expectancies based on past experience in similar learning programs, and particularly on what others have said about the learning program in question. Marketing can be a key factor in maximizing motivation. Brochures should clearly describe learning objectives and demonstrate how they translate into improved performance (E-P). They should clearly state the minimum experience and the KSA levels for which a program is designed (E-P). In the case of industry-based programs, they should describe organizational sanctions or incentives in support of programs (P-R); supervisors should be made aware of programs so that they can encourage appropriate employees to attend (E-P and P-R). Word-of-mouth marketing from employees currently or previously involved in similar programs can also be motivating (E-P, P-R, and R-N). This assumes that program objectives are, in fact, based on assessed needs related to typical tasks, and that the learning program is designed in such a way that successful performance in the learning program is analogous and transferable to practical performance. It also suggests that in-house trainers should actively work with management to build support for job-related education, as well as for specific learning programs. One way of maximizing job relevance, organizational support, and effective word-of-mouth marketing would be to involve representatives of targeted groups in the design of programs. Another option would be to include a representative cross-section of staff on an advisory committee.

Learning: Motivating Continued Involvement During this stage learner motivation is much more fluid and may be influenced by actual experience in the learning situation. Learners’ initial motivation may decrease if their experience in the learning situation leads them to believe: (a) that they cannot perform the learning tasks (E-P), (b) that learning task performance will not translate to performance on the job (E-P), (c) that performance will not be rewarded in either the learning situation or in the practical settings (P-R), or (d) that the rewards will not satisfy their needs (R-N). If motivation drops significantly, they may become uninvolved or

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may drop out altogether. The learner’s perception is still the only thing that counts. However, now we are dealing with the learner’s perception of his or her own actual learning experience. Adult educators should attempt to build success into learning designs. The curriculum should build on skills that learners already possess. Learning should be in steps that are challenging, yet achievable, and the tasks related to the practical setting. A variety of opportunities for performing should be offered, allowing for different learning styles and incorporating both intrinsic and extrinsic rewards. Successful performance builds learner motivation directly, through experience in the learning situation, and indirectly, by building self-esteem. The adult educator should attempt early in the program to engage the individual learners in explicit goal setting, focusing on clarifying expectancies regarding the learning situation. Specifically, the focus should be on whether the program will accomplish the learner’s goals (R-N) and whether the learner can – with reasonable effort – achieve the learning objectives (E-P). Learning contracts are ideal for use in such goal-setting activities. Learning contracts, though strongly validated by field practice, have been criticized for their lack of a theoretical base (Polczynski & Shirland, 1976). Expectancy theory would appear to provide a strong theoretic basis for contract learning. The adult educator should be alert at this stage for adaptations necessary to bring the learning activities in line with overall learner expectancies. On the other hand, adult educators should not devote excessive time to unrelated warm-up exercises, ice breakers, and strategies aimed at making learners feel good about themselves, since the model presented here does not support the assumption that such strategies will improve motivation to learn. Rather, it suggests that learner practice be encouraged as early in the program as possible. Learner practice should be followed with immediate, constructive feedback from the adult educator and other learners. Clear ground rules for feedback, set early in the program, allow for reinforcement of learner expectancies that effort will in fact result in successful performance on learning tasks. Such feedback can also help shape performance in the learning situation into performance that can be more easily transferred to real-life situations which are more likely to reward learners in ways that satisfy their needs. Effective problem-solving methods, imparted early in the program, can help maintain learner motivation by providing the learner with the tools to improve performance, which, in turn, will both directly and indirectly raise expectancy levels.

Post-Learning: Motivating Application of Learning In the Post-Learning stage, the learners must be motivated to apply the skills learned. Learners’ motivation may decrease if they develop the perception

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that: (a) learning task performance will not translate to actual performance (E-P), (b) actual performance will not be rewarded (P-R), or (c) that the rewards will not satisfy their needs (R-N)- These issues need to be addressed toward the end of the program. Learners’ problem-solving strategies should also be refined and action and contingency plans developed for implementing their new skills. Conscious planning is helpful in maintaining motivation. The problem of maintaining motivation can also be dealt with by breaking up the program into a series of sessions, interspersed with opportunities for practical application. This gives the learner the opportunity to “phase in” actual performance in small, achievable steps, thus undergirding self-esteem and building an objective, experiential foundation on which to base expectancies. Finally, the adult educator should encourage learners to form support groups during the Post-Learning period. In job-related training, the adult educator should attempt to educate learners’ supervisors to the need to reinforce successful performance during this period through constructive feedback and by suggesting opportunities to use the new skills.

Testing the Comprehensive Expectancy Motivation Model The Comprehensive Expectancy Motivation Model presented in this paper suggests a number of hypotheses regarding motivation that can be tested empirically: 1. The learner’s expectancies would change and become more accurate and consistent with continuing experience in any situation. 2. The expectancies of individuals with prior experience in similar situations would be more accurate and consistent than those of others who had not. 3. Successful performance in a learning situation would increase the learner’s E-P expectancy; failure to perform would decrease it. 4. Consistent reward in a learning situation would increase the learner’s P-R expectancy; lack of rewards or inconsistent rewards would decrease it. 5. Lack of fit between rewards and the learner’s perceived needs would decrease the learner’s R-N expectancy. 6. Successful performance would have a greater impact on learner satisfaction than learner satisfaction would have on performance. 7. Expectancy and ability combined would be a better predictor of successful performance than either would separately. At the start of any learning situation, expectancy motivation would predict effort, while ability would be more strongly correlated with performance. However, with increased experience in any given situation the correlation between expectancy motivation and performance would become stronger.

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8. Learner practice should be encouraged as early in the program as possible since the model does not support the assumption that warm-up exercises, ice breakers, and other strategies aimed at making the learners feel good about themselves improve motivation to learn. The Comprehensive Expectancy Motivation Model provides a framework that encompasses and explains the dynamic relationships among most of the commonly observed adult learning principles. Adult educators have long observed that adults are more motivated to learn when involved in setting their own learning goals, when given opportunities for relevant practice, when the “payoff” of learning is immediate, and so forth. This paper has described how these principles can be integrated into a single, predictive model that can be tested empirically.

References Arvey, R, & Dunnette, M. (1980). Task performance as a function of perceived effortperformance and performance-reward contingencies (Technical Report No. 4003). Washington, DC: Office of Naval Research. Borgstrom, L. (1980). Drop-out in municipal adult schools in the context of allocation policy. In R. Hoghielm and K. Rubenson (Eds.), Adult education for social change (pp. 105–130). Stockholm: Stockholm Institute of Education. Butler, Jr., J., & Womer, N. (1985). Hierarchical vs. non-nested tests for contrasting expectancy-valence models: Some effects of cognitive characteristics. Multivariate Behavioral Research, 20, 335–352. Constantinople, A. (1967). Perceived instrumentality of the college as a measure of attitudes toward college. Journal of Personality and Social Psychology, 5(2), 196 –201. Darkenwald, G. (1981). Retaining adult students. Columbus, OH: National Center for Research in Vocational Education. Darkenwald, G. (1987). Dropout as a function of discrepancies between expectations and actual experiences of the classroom social environment. Adult Education Quarterly, 37, 152–163. Gilmore, R. (1974). Expectancy beliefs, ability, and personality in predicting academic performance. Journal of Educational Research, 156(4), 28–37. Graen, G. (1969). Instrumentality theory of work motivation: Some experimental results and suggested modification. Journal of Applied Psychology, 53, 2. Feldman, J. (1974). Note on the utility of certain weights in expectancy theory. Journal of Applied Psychology, 59(6), 727–730. Hackman, J., & Porter, L. (1968). Expectancy theory predictions of work effectiveness. Organizational Behavior and Human Performance, 3, 417– 426. Heneman, H., & Schwab, D. (1972). Evaluation of research on expectancy theory predictions of employee performance. Psychological Bulletin, 78(1), 1–9. Henson, R (1976). Expectancy beliefs, ability, and personality in predicting academic performance. Journal of Educational Research, 70, 41– 44. House, R., Shapiro, H., & Wahba, A. (1974). Expectancy theory as a predictor of work behavior and attitude: A re-evaluation of empirical evidence. Decision Sciences, 5, 481–506.

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Lawler, E. (1968). A correlation-causal analysis of the relationship between expectancy attitudes and job performance. Journal of Applied Psychology, 52, 462– 468. Lawler, E. (1973). Motivation in work organizations. Monterey, CA: Brooks-Cole. Lewin, K. (1938). The conceptual representation and the measurement of psychological forces. Durham, NC: Duke University Press. Maslow, A. (1943). A theory of human motivation. Psychological Review, 50, 370–396. Malloch, D., & Michael, W. (1981). Predicting student grade point average at a community college from scholastic aptitude tests and from measures representing three constructs in Vroom’s expectancy theory model of motivation. Educational and Psychological Measurement, 41, 1127–1135. McMillan, J. (1980). Social psychology and learning. In J. H. McMillan (Ed.), The social psychology of school learning. New York: Academic Press. Miskel, C, DeFrain, J., & Wilcox, K. (1980). A test of expectancy work motivation in educational organizations. Educational Administration Quarterly, 16(1), 70–92. Mitchell, T, & Knudson, B. (1971). Instrumentality theory predictions of students attitudes towards business and their choice of business as an occupation. Journal of Applied Psychology, 57, 61–67. Mitchell, T., & Nebeker, D. (1973). Expectancy theory predictions of academic effort and performance. Journal of Applied Psychology, 57, 61–67. Moore, R., & Davies, J. (1984). Predicting GED scores on the bases of expectancy, valence, intelligence, and pretest skill levels with the disadvantages. Educational and Psychological Measurement, 44, 483– 490. Polczynski, J., & Shirland, L. (1976). Expectancy theory and contract grading combined as an effective motivational force for college students. Journal of Educational Research, 70, 238–241. Pritchard, R., & DeLeo, R. (1973). Experimental test of the valence-instrumentality relationship in job performance. Journal of Applied Psychology, 57, 264 –270. Pritchard, R., & Sanders, M. (1973). The influence of valence, instrumentality, and expectancy on effort and performance. Journal of Applied Psychology, 57, 55–60. Reykowski, J. (1965). Motivation as a component of the regulatory system of behavior. In M. Jones (Ed.), Human Motivation (pp. 71–85). Lincoln, NE: University of Nebraska Press. Rotter, J. (1954). Social learning and clinical psychology. Englewood Cliffs, NJ: PrenticeHall. Rotter, J. (1971). Clinical psychology. Englewood Cliffs, NJ: Prentice-Hall. Rubenson, K. (1976). Recruitment in adult education: A research strategy. Stockholm: Stockholm Institute of Education. Rubenson, K, & Hoghielm, R. (1978). The teaching process and study dropouts in adult education. Stockholm: Stockholm Institute of Education. Schmitt, N. (1975). A causal-correlational analysis of expectancy theory hypotheses. Psychological Reports, 37, 427– 431. Tolman E. (1932). Purposeful behavior in animals and men. New York: Appelton-CenturyCrofts. Vroom, V. (1964a). Work and motivation. New York: John Wiley. Vroom, V. (1964b). Some psychological aspects of organizational control. In W. W. Cooper (Ed.), New perspectives in organizational research (pp. 72–86). New York: John Wiley. Wright, R. (1985). Motivating teacher involvement in professional growth activities. The Canadian Administrator, 24(5), 1–6.

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51 The Academic Motivation Scale: A Measure of Intrinsic, Extrinsic, and Amotivation in Education Robert J. Vallerand, Luc G. Pelletier, Marc R. Blais, Nathalie M. Brière, Caroline Senécal and Evelyne F. Vallières

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ne of the most important psychological concepts in education is certainly that of motivation. Indeed, much research has shown that motivation is related to various outcomes such as curiosity, persistence, learning, and performance (for a review of the literature see Deci and Ryan, 1985). In light of the importance of these consequences for education, one can easily understand the interest of researchers for motivation in educational settings. Several conceptual perspectives have been proposed in order to better understand academic motivation (see The Educational Psychologist, 1991, Issue 4, for a complete number devoted to academic motivation). One useful perspective posits that behavior can be intrinsically motivated, extrinsically motivated, or amotivated (Deci and Ryan, 1985, 1991). This theoretical approach has generated a considerable amount of research and appears rather pertinent for the field of education (see Deci and Ryan, 1985; Deci, Vallerand, Pelletier, and Ryan, 1991). This approach is detailed below.

Intrinsic Motivation In general, intrinsic motivation (IM) refers to the fact of doing an activity for itself, and the pleasure and satisfaction derived from participation (Deci, Source: Educational and Psychological Measurement, 52 (1992): 1003–1017.

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1975; Deci and Ryan, 1985). An example of IM is the student that goes to class because he or she finds it interesting and satisfying to learn more about certain subjects. Deci and Ryan posit that IM stems from the innate psychological needs of competence and self-determination. Thus, activities that allow individuals to experience such feelings will be engaged in again freely out of IM. While most researchers posit the presence of a global IM construct, certain theorists (Deci, 1975) have proposed that IM might be differentiated into more specific motives. Unfortunately, these authors have not indicated which types of IM follow from the more general IM construct. More recently, a tripartite taxonomy of intrinsic motivation has been postulated ( Vallerand, Blais, Brière, and Pelletier, 1989). This taxonomy is based on the IM literature which reveals the presence of three types of IM that have been researched on an independent basis. These three types of IM can be identified as IM to know, to accomplish things, and to experience stimulation. These types of IM are described more fully below. Intrinsic motivation to know (IM-to know). This type of IM has a vast tradition in educational research. It relates to several constructs such as exploration, curiosity, learning goals, intrinsic intellectuality, and finally the IM to learn (e.g., Gottfried, 1985; Harter, 1981). To the above perspectives which are more specific to the realm of education, may be added others that are more global such as that of the epistemic need to know and understand, and that of the search for meaning (see Vallerand et al., 1989). Thus, IM-to know can be defined as the fact of performing an activity for the pleasure and the satisfaction that one experiences while learning, exploring, or trying to understand something new. For instance, students are intrinsically motivated to know when they read a book for the sheer pleasure that they experience while learning something new. Intrinsic motivation toward accomplishments (IM-to accomplish things). This second type of IM has been studied in developmental psychology as well as in educational research under concepts such as mastery motivation (Harter, 1981). In addition, other authors have postulated that individuals interact with the environment in order to feel competent, and to create unique accomplishments (Deci, 1975; Deci and Ryan, 1985, 1991). Finally, to the extent that individuals focus on the process of achieving rather than on the outcome, achievement motivation can be seen as being subsumed under the umbrella of IM-to accomplish things. Thus, IM-to accomplish things can be defined as the fact of engaging in an activity for the pleasure and satisfaction experienced when one attempts to accomplish or create something. Students who extend their work beyond the requirements of a term paper in order to experience pleasure and satisfaction while attempting to surpass themselves display IM toward accomplishments. Intrinsic motivation to experience stimulation (IM-to experience stimulation). Finally, IM-to experience stimulation is operative when someone engages in

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an activity in order to experience stimulating sensations (e.g., sensory pleasure, aesthetic experiences, as well as fun and excitement) derived from one’s engagement in the activity. Research on the dynamic and holistic sensation of flow, on feelings of excitement in IM, on aesthetic stimulating experiences, and peak experiences is representative of this form of IM (e.g., Csikszentmihalyi, 1975). Students who go to class in order to experience the excitement of a stimulating class discussion, or who read a book for the intense feelings of cognitive pleasure derived from passionate and exciting passages represent examples of individuals who are intrinsically motivated to experience stimulation in education.

Extrinsic Motivation Contrary to IM, extrinsic motivation (EM) pertains to a wide variety of behaviors which are engaged in as a means to an end and not for their own sake (Deci, 1975). Recently, Deci, Ryan and their colleagues (Deci and Ryan, 1985, 1991) have proposed that three types of EM can be ordered along a selfdetermination continuum. From lower to higher levels of self-determination, they are: external regulation, introjection, and identification1. External regulation corresponds to EM as it generally appears in the literature. That is, behavior is regulated through external means such as rewards and constraints. For instance, a student might say: “I study the night before exams because my parents force me to.” With introjected regulation, the individual begins to internalize the reasons for his or her actions. However, this form of internalization, while internal to the person, is not truly self-determined since it is limited to the internalization of past external contingencies. Thus, the individual might say: “I study the night before exams because that’s what good students are supposed to do.” To the extent that the behavior becomes valued and judged important for the individual, and especially that it is perceived as chosen by oneself, then the internalization of extrinsic motives becomes regulated through identification. The individual might say, for instance: “I’ve chosen to study tonight because it is something important for me.”

Amotivation In addition to intrinsic and extrinsic motivation, Deci and Ryan (1985) have recently posited that a third type of motivational construct is important to consider in order to fully understand human behavior. This concept is termed amotivation. Individuals are amotivated when they do not perceive contingencies between outcomes and their own actions. They are neither intrinsically nor extrinsically motivated. When amotivated individuals experience feelings of incompetence and expectancies of uncontrollability. They perceive their

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behaviors as caused by forces out of their own control. They feel undeceived, and start asking themselves why in the world they go to school. Eventually they may stop participating in academic activities. Although scales assessing motivation toward education do exist, no scale currently allows to assess all constructs discussed above. Harter’s (1981) Intrinsic vs Extrinsic Orientation Scale pits IM against EM on the same continuum and thus prevents an independent assessment of these two constructs. In addition, it does not measure the different types of EM and amotivation. Gottfried’s (1985) Children Academic Intrinsic Motivation Inventory assesses only intrinsic interest toward learning in various subjects (e.g., reading, social sciences) as well as toward school in general. Thus, it does not measure the different types of IM, EM, or amotivation. Furthermore, while Ryan and Connell (1989) have recently developed a scale that does assess IM, identification, introjection, and external regulation, the psychometric properties of this scale have not been fully presented. In addition, this scale does not include the different types of IM or amotivation. Finally, it should be noted that all of the above scales are aimed at elementary and beginning high-school students. No existing scale seems to assess motivation toward post-secondary studies within the present theoretical framework. In light of the importance of conducting research on academic motivation with an instrument based on a valid theoretical conceptualization, and the fact that no scale to date seems to assess IM, EM, and amotivation toward post-secondary studies, Vallerand et al. (1989) developed and validated in French the Echelle de Motivation en Education (EME). This scale is made up of seven subscales of four items each assessing the three types of IM (IM to know, to accomplish things, and to experience stimulation), three types of EM (external, introjected, and identified regulation), and amotivation. In the EME, motivation is operationalized as the underlying “why” of behavior (Deci and Ryan, 1985) and focus on the perceived reasons for engaging in the activity. Thus, the scale asks the question “Why do you go to college?” and items represent possible answers to that question, thus reflecting the different types of motivation. Here are some sample items from the scale: Amotivation subscale, “Honestly I don’t know; I really feel that I’m wasting my time in college”; External Regulation, “In order to get a more prestigious job later on”; Introjected Regulation, “To prove to myself that I can do better than just a high-school degree”; Identified Regulation, “Because eventually it will allow me to enter the job market in a field that I like”; IM-to know, “Because I experience pleasure and satisfaction while learning new things”; IM-Accomplishment, “For the pleasure I experience while surpassing myself in my studies”; IM-Stimulation, “For the high feeling that I experience while reading on various interesting subjects.” Preliminary (Daoust, Vallerand, and Blais, 1988; Vallerand and Bissonnette, in press) and validation studies ( Vallerand et al., 1989), which involved

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more than 3,000 students, revealed that the EME has satisfactory internal consistency levels (a mean alpha score of .80), as well as high indices of temporal stability (a mean test-retest correlation of .75) over a one-month period. Results of a confirmatory factor analysis (with LISREL) also confirmed the seven-factor structure of the EME. Finally, the construct validity of the scale was supported by a series of correlational analyses among the seven subscales, as well as between these scales and other psychological constructs relevant to education, such as interest toward school, time spent in academic activities, being distracted in class, academic satisfaction, positive emotions in the classroom, and nihilism toward education. These findings replicated the results reported earlier on the role of the different IM, EM, and amotivation in various educational outcomes. In addition, earlier versions as well as the current version of the EME were able to predict dropout behavior in high school and junior college (see Vallerand et al., 1989). The French version of the EME therefore appears to represent a reliable and valid measure of IM, EM, and amotivation in education. Because the EME was initially validated in French, it was thus not available to researchers conducting research with English-speaking students. In light of the psychometric qualities of the EME, the findings it has yielded, and the importance of assessing motivation from a sound theoretical perspective, it was decided to cross-culturally validate the EME in English. To validate a scale into another language involves much more than translation (Brislin, 1986; Vallerand, 1989). In addition to appropriate translation, one must conduct research in order to show that this new version of the scale shares the same psychometric properties as the original scale. Thus, the overall purpose of the present study was to translate the scale in English and to conduct initial assessment of its psychometric properties.

The Current Investigation Purpose A four-fold purpose guided this investigation: (a) to translate the EME in English using appropriate cross-cultural procedures, (b) to replicate the sevenfactor structure of the AMS through confirmatory factor analysis (with LISREL), (c) to assess the reliability (internal consistency and temporal stability) of the seven subscales, and (d) to assess whether the results from the Vallerand et al. (1989) study which revealed that females reported higher levels of IM to know, IM to experience stimulation, identification, and introjection, but lower levels of amotivation than males, would be replicated with a population of English-speaking students.

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Method Translation of the EME in English In line with recent approaches to cross-cultural scale translation (Brislin, 1986; Vallerand, 1989), three steps were taken. First, the scale was translated from French to English. This was done with the parallel back-translation procedure (Brislin, 1986). Back translation first involves translating the scale from the original to the target language by a bilingual individual. This translation is then translated back to the original language by another bilingual individual without the use of the original scale. To the extent that the original scale is appropriately retranslated, this method provides an initial assessment of the adequacy of the translated version of the scale. The parallel back-translation procedure necessitates the use of two independent back translation sequences. This approach is preferred to the single back-translation method because it prevents the occurrence of certain biases that could result from the two specific bilingual individuals used in the back translation. In this study, four bilingual individuals (two social psychologists and two graduate students in social psychology) well cognizant of Deci and Ryan’s (1985) motivation theory conducted the parallel back-translation procedure. This led to two preliminary English versions of the AMS that were evaluated in the next phase. In the second phase, the items produced by the two back-translations were thoroughly assessed by a committee. The committee was formed of the individuals who participated in the back translation procedures and the authors of the original version of the scale (the EME). The committee selected the items that had been retranslated appropriately, that is which had retained the original meaning, and that had been conveyed in acceptable English. Once the 28 English items were selected the committee prepared the scale format and instructions so that they be identical to the ones used with the original French-Canadian version. Thus, the experimental version of the English AMS lists 28 items that may represent reasons why students go to college. These reasons are scored on a 7-point scale anchored by the end point “Not at all” (1) to “Exactly” (7) with a midpoint at 4 (“Moderately”). Third and final, a pretest was conducted with 10 junior-college students in order to determine whether the AMS was clear and formulated in a language to which post-secondary studies students can relate ( Vallerand, 1989). Students were asked to read the AMS and to verbalize any questions they may have with the items or instructions. This led to some minor modifications with the instructions.

Procedures The AMS was completed by 745 university students from the province of Ontario. This sample was composed of 484 females and 261 males with a

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mean age of 21.0 years. In order to assess the temporal stability of the AMS, a second sample of 57 university students (27 males and 30 females) with a mean age of 19.3 years also completed the AMS twice over a one-month period. Students were informed that we were interested in better understanding the reasons why they go to the university. To this end, we asked students to complete the AMS. Students were told that they did not have to complete the questionnaire but that their collaboration would be very much appreciated. Subjects completed the AMS in class at the beginning of the period.

Statistical Analyses The various statistical analyses conducted dealt with the confirmatory factor analysis (with LISREL), the internal consistency (Cronbach alphas), test-retest correlations of the seven subscales, and the analysis of variance on the means of the subscales in order to test for sex differences.

Results and Discussion Confirmatory Factor Analysis The data were subjected to a confirmatory factor analysis with LISREL VI (Jöreskog and Sörbom, 1984). This analysis tests the extent to which the theoretical model, in this case the seven-factor model corresponding to the seven subscales, adequately represents the covariance matrix of the data. The fitting function estimated by the procedure was assessed through several indices, namely a chi-square statistic, the Goodness of Fit Index (GFI), the Adjusted Goodness of Fit Index (AGFI), and the Normed Fit Index (NFI) being the most widely used. These indices vary from 0 to 1 where 1 indicates a perfect fit for the model. In the initial model, seven factors were postulated. These factors corresponded to the seven subscales and were made up of the four corresponding items. No cross-loadings were postulated. Although the confirmatory factor analysis of the initial measurement model yielded fit values of .89 for the NFI, .87 for the AGFI, and .89 for the GFI, the model did not reach statistical nonsignificance (c2 = 1228.27, df = 329, p < .001). Correlations between pairs of measured-variable residuals were added to the model on the basis of the inspection of the modification indices. This resulted in 26 correlated residuals added to the model. With these additions the fit indices for the final measurement model showed that the model fits the data reasonably well, NFI = .93, AGFI = .91, GFI = .94, although the model did not reach statistical nonsignificance (c2 = 748.64, df = 303, p ≤ .001).

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This improvement in fit was highly significant, difference in c2 = 479.63, df = 26, p < .001. In order to assess whether the inclusion of these theta delta values in the model could bias the interpretation of the model, the initial parameter estimates from the initial model were correlated with those from the final model. Results from the correlations involving the lambda x parameters yielded a .99 correlation value, while those including the lambda x and phi parameters indicated a .98 correlation value. These results underscore the fact that including the additional parameters in the model did not bias interpretation of the model. In sum, results from the confirmatory factor analysis replicated the findings obtained with the original French-Canadian version (the EME), and confirmed the seven-factor structure of the AMS. Loadings from the final model, which were all significant, are presented in Table 1.

Reliability The internal consistency of the subscales was assessed with the use of the Cronbach alpha. Values appear in the first column of Table 2. It can be seen that values varied from .83 to .86, except for the Identification subscale which had an alpha value of .62. These findings are remarkably similar to those obtained with the original version of the scale (EME) where values varied from .76 to .86, except for the Identification subscale which had a value of .62. Overall, considering the fact that these subscales are made up of 4 items, they appear to display adequate levels of internal consistency equivalent to that obtained with the original scale. In order to assess the temporal stability of the AMS, a second sample of 57 university students completed the AMS twice over a one-month period. Results from the test-retest correlations appear in the last column of Table 2. It can be seen that correlations are fairly high ranging from .71 to .83, with a mean test-retest correlations of .79. These results are once again very similar to those obtained with the French-Canadian version (the EME), and support the temporal stability of the English version of the scale. In addition, the alpha values for the pretest and posttest appear in Table 2. It can be seen that these values are quite acceptable varying from .72 to .91 at the pretest, and from .78 to .90 at the posttest. The alpha values for the identification subscale were of .72 and .78 at the pretest and posttest, respectively thereby further supporting the reliability of that subscale. In sum, overall these results provide support for the internal consistency and the temporal stability of the AMS.

Analyses of Variance on the Subscale Means Means of the seven subscales as a function of sex appear in Table 3. A sex X scale repeated measure analysis of variance, with repeated measures on the

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Amotivation 1 Amotivation 2 Amotivation 3 Amotivation 4 External Regulation 1 External Regulation 2 External Regulation 3 External Regulation 4 Introjected Regulation 1 Introjected Regulation 2 Introjected Regulation 3 Introjected Regulation 4 Identified Regulation 1 Identified Regulation 2 Identified Regulation 3 Identified Regulation 4 Intrinsic Motivation-Knowledge 1 Intrinsic Motivation-Knowledge 2 Intrinsic Motivation-Knowledge 3 Intrinsic Motivation-Knowledge 4 Intrinsic Motivation-Accomplishment 1 Intrinsic Motivation-Accomplishment 2 Intrinsic Motivation-Accomplishment 3 Intrinsic Motivation-Accomplishment 4 Intrinsic Motivation-Stimulation 1 Intrinsic Motivation-Stimulation 2 Intrinsic Motivation-Stimulation 3 Intrinsic Motivation-Stimulation 4

1.059 0.750 1.025 0.940

Amotivation

1.143 1.024 1.139 1.262

External regulation

1.384 1.321 1.398 1.225

Introjected regulation

Table 1: Standardized loadings from the confirmatory factor analysis (LISREL)

0.582 0.808 0.749 0.783

Identified regulation

0.953 0.918 1.223 1.226

Intr.Mot. knowledge

1.198 1.174 1.261 1.292

Intr.Mot. accomplishment

0.878 1.424 1.449 1.445

Intr.Mot. stimulation

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Table 2: Internal consistency values (Cronbach alpha) and test-retest correlations of the AMS 7 subscales: Samples 1 and 2

Amotivation External Regulation Introjected Regulation Identified Regulation IM-to Know IM-Accomplishment IM-Stimulation

Alpha sample 1 (n = 745)

Alpha pretest sample 2 (n = 57)

Alpha posttest sample 2 (n = 57)

Test-retest correlations sample 2 (n = 57)

.85 .83 .84 .62 .84 .85 .86

.91 .85 .76 .72 .85 .90 .88

.88 .89 .83 .78 .90 .87 .84

.83 .83 .73 .71 .79 .83 .80

Table 3: Means (and standard deviations) for males and females on the AMS: Sample 1 Subscales Amotivation External Regulation Introjected Regulation* Identified Regulation* Intrinsic Motivation – Knowledge* Intrinsic Motivation – Accomplishment* Intrinsic Motivation – Stimulation*

Males (n = 261)

Females (n = 484)

6.74 (3.96) 21.78 (4.79) 16.0 (5.82) 21.60 (3.57) 18.89 (4.22) 15.93 (5.03) 12.21 (5.33)

6.51 (4.14) 21.80 (5.27) 17.80 (5.81) 22.19 (3.98) 20.46 (4.74) 17.52 (5.39) 13.83 (5.75)

* Females scored significantly higher (p < .01) than males.

scale factor, revealed the presence of main effects for sex, F(1, 743) = 21.10, p < .001, and scale, F(6, 738) = 1035.18, p < .001. The latter main effect revealed that all subscales differed from each other except for the Introjection and IM to Accomplish subscales, and the identification and external regulation subscales, respectively. The most important forms of motivation for the students in this sample were, in decreasing order: identification, external regulation, IM to know, introjection, IM toward accomplishments, IM to experience stimulation, and amotivation. However, these main effects must be interpreted in light of the significant sex X scale interaction, F(6, 738) = 3.87, p < .001. Results from the simple main effects revealed that female students scored higher than males on the 3 IM subscales (knowledge, accomplishment, and stimulation), as well as on the Identification and Introjection subscales. However, no sex differences were found on the other subscales (all Fs > 4.03, ps > .05).

General Discussion The purpose of the present study was to cross-culturally validate the English version of the EME. Results revealed that the AMS has adequate levels of reliability and factorial validity, very much in line with those of the original French-Canadian version. With respect to the reliability of the scale, results from this study revealed that the internal consistency of all subscales was adequate, typically ranging in the .80s, with the exception of

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the Identification subscale which yielded values of .62 in the large sample, and .72 and .78 with the second sample used to assess the temporal stability of the scale. Finally, it should be reiterated that all AMS subscales displayed acceptable levels of temporal stability with a mean test-retest correlation value of .79 over a one-month period. These last results support the contention that the AMS measures students’ rather stable motivational orientations toward education. With respect to the validity of the AMS, the present results are also very encouraging on at least three accounts. First, results from the confirmatory factor analysis confirmed the seven-factor structure of the AMS and thus provided some support for the factorial validity of the scale. Second, results from the confirmatory factor analysis and the pattern of means of the IM subscales yielded preliminary support for the discriminant validity of the three IM subscales. Finally, gender differences on the various subscale means generally reproduced findings from the original study (Vallerand et al., 1989). The only difference between these two studies is that in the Vallerand et al. study (1989) females were also less amotivated than males and there was no sex differences on the IM Accomplishment subscale (although the means were in the predicted direction). These differences between the results from the Vallerand et al. and this study could be due to several factors including distinctions between the French and EnglishCanadian cultures, the motivation of university students (this study) and junior-college students (the Vallerand et al., 1989 study), as well as specificities (e.g., age, socio-economic background) of the samples used in the present and Vallerand et al. (1989) studies. Future research is needed in order to more fully understand these sex differences. However, one thing seems rather clear: In line with past research in education (e.g., Daoust et al., 1988; Vallerand and Bissonnette, in press; Vallerand et al., 1989) it appears that female students display a more self-determined motivational profile than male students. Overall, the findings from the series of studies replicated the results obtained with the French-Canadian version (EME). It now appears that preliminary support exists for the reliability and some elements of validity of the AMS. Although these findings are indeed very encouraging, they must nevertheless be perceived as being only preliminary in nature. A complete assessment of the psychometric properties of the scale will necessitate additional research. In that perspective, recent research of ours (Vallerand, Pelletier, Blais, Brière, Senécal, and Vallieres, in press) has shown that the AMS has elements of concurrent and construct validity. Specifically, it was found that the scale was correlated as hypothesized with other motivational scales such as that of Gottfried (1985). In addition, the AMS correlated as predicted from cognitive evaluation theory (Deci and Ryan, 1985) with motivational antecedents and consequences. Future research in that direction would therefore appear fruitful.

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In addition, it seems appropriate to reiterate that the operational definition of the AMS directly reflects the conceptual definition of intrinsic/extrinsic motivation which refers to one’s perceived reasons for engaging in a given activity (the “why” of behavior), be they for the activity itself or for reasons lying outside the activity. Such an equivalence between the conceptual and operational definition of motivation should lead to more meaningful research. Furthermore, it should also be noted that contrary to other unidimensional instruments (e.g., Gottfried, 1985), the AMS assesses several types of motivation in a multidimensional fashion. These types of motivation go beyond the usual IM/EM distinction and allow a finer analysis of the motivational forces in education, thereby opening the door to innovative research. In sum, even though the AMS represents a recent scale whose evaluation should be pursued in future research, results from the present study provide support for the adequacy of its psychomometric properties. Not only does the AMS represent an adequate cross-cultural adaptation of the original FrenchCanadian version (the EME), but it represents a reliable and valid scale in its own right. The psychometric properties of the AMS, as well as the flexibility allowed through its multidimensional structure, should make it a useful tool in motivation research in educational settings.

Note 1. Deci and Ryan (1985) also include integrated regulation as one type of extrinsic motivation. However, integrated regulation was not initially included in the Echelle de Motivation en Education (EME) and therefore is not assessed in the Academic Motivation Scale (AMS). Two major reasons supported this initial decision. First, pilot data revealed that integrated regulation did not come out as a perceived reason for participating in educational activities. Second, factor analyses on experimental forms of the EME revealed that integrated regulation did not distinguish itself from identified regulation. The above findings may have been due to a host of potential factors including the fact that young adults may be too young to have achieved a sense of integration with respect to school activities. Future research would appear necessary on this issue.

References Brislin, R. W. (1986). The wording and translation of research instruments. In W. Lonner and J. Berry (Eds.), Field methods in cross-cultural research (pp. 137–164). Beverly Hills, CA: Sage. Csikszentmihalyi, M. (1975). Beyond boredom and anxiety. San Francisco: Jossey-Bass. Daoust, H., Vallerand, R. J., and Blais, M. R. (1988). Motivation and education: A look at some important consequences. Canadian Psychology, 29 (2a), 172. (abstract). Deci, E. L. (1975). Intrinsic motivation. New York: Plenum Press. Deci, E. L. and Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press.

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Deci, E. L. and Ryan, R. M. (1991). A motivational approach to self: Integration in personality. In R. Dienstbier (Ed.), Nebraska Symposium on motivation: Vol. 38. Perspectives on motivation (pp. 237–288) Lincoln, NE: University of Nebraska Press. Deci, E. L., Vallerand, R. J., Pelletier, L. G., and Ryan, R. M. (1991). Motivation in education: The self-determination perspective. The Educational Psychologist, 26, 325–346. Gottfried, A. E. (1985). Academic intrinsic motivation in elementary and junior high school students. Journal of Educational Psychology, 77, 631–645. Harter, S. (1981). A new self-report scale on intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Developmental Psychology, 17, 300–312. Jöreskog, K. G. and Sörbom, D. (1984). LISREL VI. Chicago, IL: National Educational Resources. Ryan, R. M. and Connell, J. P. (1989). Perceived locus of causality and internalization: Examining reasons for acting in two domains. Journal of Personality and Social Psychology, 57, 450–461. Vallerand, R. J. (1989). Vers une méthodologie de validation trans-culturelle de questionnaires psychologiques: Implications pour la recherche en langue française (Toward a cross-cultural validation methodology for psychological scales: Implications for research conducted in the French language). Canadian Psychology, 30, 662–680. Vallerand, R. J. and Bissonnette, R. (in press). Intrinsic, extrinsic, and amotivational styles as predictors of behavior: A prospective study. Journal of Personality. Vallerand, R. J., Blais, M. R., Brière, N. M., and Pelletier, L. G. (1989). Construction et validation de l’Echelle de Motivation en Education (EME) [Construction and validation of the Echelle de Motivation en Education (EME)]. Canadian Journal of Behavioral Sciences, 21, 323–349. Vallerand, R. J., Pelletier, L. G., Blais, M. R., Brière, N. M., Senécal, C., and Vallières, E. F. (in press). On the assessment of intrinsic, extrinsic, and amotivation in education: Evidence on the concurrent and construct validity of the Academic Motivation Scale. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT.

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52 Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again Edward L. Deci, Richard Koestner and Richard M. Ryan

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old stars, best-student awards, honor roles, pizzas for reading, and other reward-focused incentive systems have long been part of the currency of schools. Typically intended to motivate or reinforce student learning, such techniques have been widely advocated by some educators, although, in recent years, a few commentators have questioned their widespread use. The controversy has been prompted in part by psychological research that has demonstrated negative effects of extrinsic rewards on students’ intrinsic motivation to learn. Some studies have suggested that, rather than always being positive motivators, rewards can at times undermine rather than enhance self-motivation, curiosity, interest, and persistence at learning tasks. Because of the widespread use of rewards in schools, a careful summary of reward effects on intrinsic motivation would seem to be of considerable importance for educators. Accordingly, in the Fall 1994 issue of Review of Educational Research, Cameron and Pierce (1994) presented a meta-analysis of extrinsic reward effects on intrinsic motivation, concluding that, overall, rewards do not decrease intrinsic motivation. Implicitly acknowledging that intrinsic motivation is important for learning and adjustment in educational settings (see, e.g., Ryan & La Guardia, 1999), Cameron and Pierce nonetheless stated that “teachers have no reason to resist implementing incentive systems in the classroom” (p. 397). They also advocated abandoning Deci and Ryan’s (1980) cognitive Source: Review of Educational Research, 71(1) (2001): 1–27.

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evaluation theory (CET), which had initially been formulated to explain both positive and negative reward effects on intrinsic motivation. In the Spring 1996 issue of RER, three commentaries were published (Kohn, 1996; Lepper, Keavney, & Drake, 1996; Ryan & Deci, 1996) arguing that Cameron and Pierce’s meta-analysis was flawed and that its conclusions were unwarranted. In that same issue, Cameron and Pierce (1996) responded to the commentaries by claiming that, rather than reanalyzing the data, the authors of the three commentaries had suggested “that the findings are invalid due to intentional bias, deliberate misrepresentation, and inept analysis” (p. 39). Subtitling their response “Protests and Accusations Do Not Alter the Results,” Cameron and Pierce stated that any meaningful criticism of their article would have to include a reanalysis of the data. Subsequent to that interchange, Eisenberger and Cameron (1996) published an article in the American Psychologist summarizing the Cameron and Pierce (1994) meta-analysis and claiming that the so-called undermining of intrinsic motivation by extrinsic rewards, which they said had become accepted as reality, was in fact largely a myth. We do not claim that there was “intentional bias” or “deliberate misrepresentation” in either the Cameron and Pierce (1994) meta-analysis or the Eisenberger and Cameron (1996) article, but we do believe, as Ryan and Deci argued in 1996, that Cameron and Pierce used some inappropriate procedures and made numerous errors in their meta-analysis. Therefore, because we believe the problems with their meta-analysis made their conclusions invalid, because we agree that a useful critique of their article must involve reanalysis of the data, and because the issue of reward effects on intrinsic motivation is extremely important for educators, we performed a new metaanalysis of reward effects on intrinsic motivation (Deci, Koestner, & Ryan, 1999). Our meta-analysis included 128 experiments, organized so as to provide a test of CET, much as Cameron and Pierce had done. The new metaanalysis, which we summarize in this article, showed that, in fact, tangible rewards do significantly and substantially undermine intrinsic motivation. The meta-analysis provided strong support for CET and made clear that there is indeed reason for teachers to exercise great care when using reward-based incentive systems. The new meta-analysis was published in Psychological Bulletin (Deci et al., 1999). Included in that article was an appendix table (here reproduced with permission as Table 1a) listing every study in the meta-analysis and explaining exactly where errors were made by Cameron and Pierce, how our meta-analysis corrected their errors, and what studies were included in ours that had been overlooked or omitted by them. The table allows interested readers to see for themselves exactly how it is that Cameron and Pierce’s meta-analysis and our meta-analysis arrived at such different conclusions. In the seven years since the publication of Cameron and Pierce’s (1994) article, academics, school administrators, and classroom teachers from

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many countries have spoken to us about the article, making it clear that the conclusions of the article had been widely disseminated and that the issue of reward effects is of considerable interest to educators around the world. Given the great importance of this issue for education, then, the current article is intended to set the record straight for the many readers of RER. In this article, we provide a brief description of CET, because it has guided much of the research in the field. This is followed by a summary of the methods and results of our meta-analysis and, finally, a discussion of the relevance of the results for education.

Cognitive Evaluation Theory CET proposes that underlying intrinsic motivation are the innate psychological needs for competence and self-determination. According to the theory, the effects on intrinsic motivation of external events such as the offering of rewards, the delivery of evaluations, the setting of deadlines, and other motivational inputs are a function of how these events influence a person’s perceptions of competence and self-determination. Events that decrease perceived self-determination (i.e., that lead to a more external perceived locus of causality) will undermine intrinsic motivation, whereas those that increase perceived self-determination (i.e., that lead to a more internal perceived locus of causality) will enhance intrinsic motivation. Furthermore, events that increase perceived competence will enhance intrinsic motivation so long as they are accompanied by perceived selfdetermination (e.g., Ryan, 1982), and those that decrease perceived competence will diminish intrinsic motivation. Finally, rewards (and other external events) have two aspects. The informational aspect conveys self-determined competence and thus enhances intrinsic motivation. In contrast, the controlling aspect prompts an external perceived locus of causality (i.e., low perceived self-determination) and thus undermines intrinsic motivation. As noted, CET applies not only to reward effects but to the effects of various other external factors such as evaluations (Smith, 1975), deadlines (Amabile, DeJong, & Lepper, 1976), competition (Deci, Betley, Kahle, Abrams, & Porac, 1981), and externally imposed goals (Mossholder, 1980), as well as to the general climate of classrooms, schools, and other interpersonal settings (e.g., Deci, Connell, & Ryan, 1989; Deci, Schwartz, Sheinman, & Ryan, 1981). In this article, however, we focus only on CET as an explanation for reward effects. In making predictions about reward effects on intrinsic motivation, CET analyzes the type of reward and the type of reward contingency to determine whether the reward is likely to be experienced as informational or controlling. The theory acknowledges that in some cases both the informational and

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controlling aspects will be somewhat salient, so, in those situations, additional factors are taken into account in making predictions. We begin our discussion of CET’s reward-effect predictions by distinguishing between verbal rewards and tangible rewards, considering verbal rewards first and then moving on to tangible rewards.

Verbal Rewards Although we do not usually use the term verbal rewards, preferring instead to speak of “positive feedback,” we do use that term here in order to include the positive-feedback studies within the general category of reward effects. Verbal rewards typically contain explicit positive performance feedback, so CET predicts that they are likely to enhance perceived competence and thus enhance intrinsic motivation. In the meta-analysis, we tested the hypothesis that verbal rewards would enhance intrinsic motivation. Nonetheless, verbal rewards can have a significant controlling aspect leading people to engage in behaviors specifically to gain praise, so verbal rewards have the potential to undermine intrinsic motivation. The theory therefore suggests that the interpersonal context within which positive feedback is administered can influence whether it will be interpreted as informational or controlling. As used here, the term interpersonal context refers to the social ambience of settings, such as classrooms, as they influence people’s experience of self-determination (Deci & Ryan, 1991). When studied in laboratory experiments, the interpersonal climate is usually manipulated in terms of the interpersonal style used by the experimenter when providing the feedback (e.g., Ryan, 1982; Ryan, Mims, & Koestner, 1983). An interpersonal context is considered controlling to the extent that people feel pressured by it to think, feel, or behave in particular ways. Verbal rewards administered within such a context are thus more likely to be experienced as controlling rather than informational. For example, CET suggests that if a teacher uses an interpersonal style intended to make students do what he or she wants them to, verbal rewards administered by that teacher are likely to be experienced as controlling. In a supplemental meta-analysis involving five studies, we tested the prediction that controlling positive feedback would lead to less intrinsic motivation than informational positive feedback.

Tangible Rewards Unlike verbal rewards, tangible rewards are frequently offered to people as an inducement to engage in a behavior in which they might not otherwise engage. Thus, according to CET, tangible rewards will tend to be experienced as controlling, and as a result they will tend to decrease intrinsic motivation.

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The meta-analysis tested the hypothesis that, overall, tangible rewards would decrease intrinsic motivation. In order for tangible rewards to be experienced as controlling, however, people would need to be engaging in the behavior for the rewards; that is, they would need to expect that the behavior would lead to the rewards. If tangible rewards are given unexpectedly to people after they have finished a task, the rewards are less likely to be experienced as the reason for doing the task and are thus less likely to be detrimental to intrinsic motivation. The meta-analysis tested the hypothesis that unexpected tangible rewards would not undermine intrinsic motivation, whereas expected tangible rewards would. Expected tangible rewards can be administered through various contingencies; that is, they can be made contingent upon different aspects of taskrelated behavior. In making more refined predictions about the effects of expected tangible rewards on intrinsic motivation, CET takes account of task contingency. Ryan et al. (1983) specified three types of reward contingencies: task-noncontingent rewards, which do not require engaging in the activity per se but are instead given for some other reason such as simply participating in the experiment; task-contingent rewards, which require doing or completing the target activity; and performance-contingent rewards, which require performing the activity well, matching a standard of excellence, or surpassing a specified criterion (e.g., doing better than half of the other participants). A further distinction has been made between task-contingent rewards that specifically require completing the target task (herein referred to as completioncontingent rewards) and those that require engaging in the activity but do not require completing it (herein referred to as engagement-contingent rewards). We (e.g., Deci & Ryan, 1985) have considered the completion-contingent and engagement-contingent rewards to constitute the single category of taskcontingent rewards because the effects of these two reward contingencies have seemed to be remarkably similar; however, we separated them for this metaanalysis in order to evaluate whether the effects of completion-contingent and engagement-contingent rewards are, in fact, the same. Because task-noncontingent rewards do not require doing, completing, or doing well at the target task, there is no reason to expect these rewards to be experienced as either informational or controlling with respect to the task. Accordingly, the meta-analysis tested the hypothesis that intrinsic motivation would not be affected by these rewards. Engagement-contingent rewards specifically require that people work on the task, so the rewards are likely to be experienced as controlling the task behavior. Because these rewards carry little or no competence affirmation, they are unlikely to increase perceived competence, and thus there will be nothing to counteract the negative effects of the control. Thus, the metaanalysis tested the hypothesis that engagement-contingent rewards would undermine intrinsic motivation.

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Completion-contingent rewards require that people complete the task to obtain the rewards, so the rewards are likely to be experienced as even more controlling than engagement-contingent rewards. However, with completioncontingent rewards, receipt of the rewards conveys competence if the task required skill and the person had a normative sense of what constitutes good performance on the task. To the extent that the rewards do represent competence affirmation, this implicit positive feedback could offset some of the control. Still, averaged across different types of tasks, the competenceaffirming aspect of completion-contingent rewards is not expected to be strong relative to the controlling aspect, so we tested the hypothesis that completioncontingent rewards would undermine intrinsic motivation at a level roughly comparable to that of engagement-contingent rewards. Parenthetically, because the category of task-contingent rewards is composed of engagement-contingent and completion-contingent rewards, we also expected this larger category to yield significant undermining of intrinsic motivation. Finally, performance-contingent rewards are linked to people’s performance, so there is even stronger control. People have to meet a standard to maximize rewards, and thus there is a strong tendency for these rewards to undermine intrinsic motivation. However, performance-contingent rewards can also convey substantial positive competence information when a person receives a level of reward that signifies excellent performance. In those cases, there would be a tendency for performance-contingent rewards to affirm competence and, thus, to offset some of the negative effects of control. In the meta-analysis, we tested the hypothesis that performance-contingent rewards would undermine intrinsic motivation, but we also expected that other factors would influence the effects of these rewards on intrinsic motivation. One such factor is whether or not the level of reward implies excellent performance. Thus, we examined the hypothesis that performance-contingent rewards would be more undermining of intrinsic motivation if the rewards did not convey high-quality performance. Another factor that is expected to influence the effects of performancecontingent rewards is the interpersonal context (as was the case with verbal rewards). If the interpersonal climate within which these rewards are administered is demanding and controlling, the rewards are expected to be more undermining of intrinsic motivation. Although few studies have manipulated the interpersonal context of performance-contingent rewards, Ryan et al. (1983) compared a performancecontingent rewards group in which the rewards were administered in a relatively controlling manner and one in which they were administered in a relatively non-controlling manner. As predicted, the controlling administration of performance-contingent rewards led to undermining of intrinsic motivation relative to the noncontrolling administration. In terms of education, this is a particularly important finding because it suggests that when rewards are used in the classroom, it is important that the climate of the

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classroom be supportive rather than controlling so that the students will be less likely to experience the rewards as controlling.

Method Our meta-analytic strategy (Deci et al., 1999) involved a hierarchical approach in which the results of 128 experiments were examined in two separate meta-analyses. The first involved 101 of the studies that had used a free-choice behavioral measure of intrinsic motivation, and the second involved 84 of the studies that had used self-reported interest as a dependent variable. In a hierarchical meta-analysis, one begins with the most general category and reports the composite effect size. If the set of effects is heterogeneous, then one proceeds to differentiate the overall category into meaningful subcategories in an attempt to achieve homogeneity of effects within the subcategories. Thus, in both meta-analyses (i.e., with the two dependent measures), we began by calculating the effects of all rewards on intrinsic motivation and then systematically differentiated the reward conditions. Only after we had exhausted all possible moderator variables did we discard outliers to create homogeneity within subcategories. Using this approach, we ended up discarding only about 4% of the effects as outliers, whereas Cameron and Pierce (1994) had discarded approximately 20% of the effects as outliers. In the differentiation, studies were first separated into those that examined verbal rewards versus those that examined tangible rewards. Then tangible rewards, which have been extensively studied, were analyzed as follows. The effects of rewards that were unexpected versus expected were examined separately. Studies of expected tangible rewards were then separated into four groups, depending on what the rewards were contingent upon. The groups were as follows: task noncontingent (rewards that did not explicitly require working on a task), engagement contingent (rewards that did require working on the task), completion contingent (rewards that required finishing a task), and performance contingent (rewards contingent upon a specified level of performance at a task). As described subsequently, because the performance-contingent reward effects on the free-choice measure were heterogeneous, that category was further differentiated. Finally, in categories in which the effect sizes were heterogeneous after all theoretically based differentiations had been completed, we compared the effects of the reward types on schoolchildren versus college students, an issue that had not been considered previously but emerged from an inspection of the data and seemed very important in terms of the educational relevance of the results. Inclusion criteria for studies that spanned the period 1971 to 1996 were the following. First, because intrinsic motivation is pertinent to tasks that

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people experience as interesting and because the field of inquiry has always been defined in terms of reward effects on intrinsic motivation for interesting tasks, we included only studies or conditions within studies if the target task was at least moderately interesting (i.e., if it either was not defined a priori as a boring task by the experimenter or did not have a prereward interest rating below the midpoint of the scale). In contrast, Cameron and Pierce (1994) had aggregated across boring and interesting tasks without even addressing the issue in their article. Second, the analyses included only studies that assessed intrinsic motivation after the rewards had been clearly terminated, because while the reward is in effect participants’ behavior reflects a mix of intrinsic and extrinsic motivation. Cameron and Pierce, however, included assessments which they called intrinsic motivation but which had been taken while the reward contingency was still in effect. Third, studies were included only if they had an appropriate no-reward control group. Cameron and Pierce had made numerous comparisons based on questionable selections of control groups, at times even using inappropriate control groups when appropriate ones were available. In conducting the meta-analyses, we used Cohen’s d as the measure of effect size. It reflects the difference between the means of two groups divided by the pooled within-group standard deviations, adjusted for sample size (Hedges & Olkin, 1985). The mean of the control group was subtracted from the mean of the rewards group, so a negative d reflects an “undermining effect,” whereas a positive d reflects an “enhancement effect.” Means, standard deviations, t tests, F tests, and sample sizes were used to calculate d values. For any study in which insufficient data were provided to calculate an effect size, we assigned an effect of d = 0.00, and we included those imputed values in all analyses. All effect-size computations and summary analyses were done with DSTAT (Johnson, 1993), a meta-analytic software program. Each calculation of a composite effect size is accompanied by a 95% confidence interval (CI) (for additional methodological details, see Deci et al., 1999).

Results Effects of All Rewards Although the early discussions of extrinsic reward effects on intrinsic motivation (e.g., deCharms, 1968) tended to consider extrinsic rewards as a unitary concept, even the very first investigations of this issue differentiated the concept. Deci (1971, 1972b) distinguished between tangible rewards and verbal rewards (i.e., positive feedback), reporting that tangible rewards decreased intrinsic motivation, while verbal rewards increased it. Furthermore, Deci (1972a) differentiated task-contingent rewards from task-noncontingent

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rewards, finding that task-contingent rewards decreased intrinsic motivation but task-noncontingent rewards did not, and Lepper, Greene, and Nisbett (1973) distinguished between rewards that were expected and those that were unexpected, finding that expected rewards decreased intrinsic motivation but unexpected rewards did not. Accordingly, given that different rewards and different reward contingencies seem to have different effects on intrinsic motivation, aggregating across all types of rewards meta-analytically is, in a sense, a meaningless endeavor, because the outcome will depend primarily on how many studies of each type of reward or reward contingency are included in the meta-analysis (Ryan & Deci, 1996). Nonetheless, because Cameron and Pierce (1994) calculated the effect of all rewards on intrinsic motivation in their meta-analysis, we also calculated it for comparative purposes. The effect of all types of rewards across all relevant studies revealed significant undermining for the freechoice behavioral measure of intrinsic motivation (k = 101; d = –0.24; CI = –0.29, –0.19),1 although the overall effect for the self-report measure was not significant. These and other major results are summarized in Table 1.

Table 1: Major results of the meta-analysis of the effects of extrinsic rewards on free-choice intrinsic motivation and self-reported interest, shown as Cohen’s composite d, with k effects included Free-choice behavior

All rewards Verbal rewards College Children Tangible rewards Unexpected Expected Task noncontingent Engagement contingent College Children Completion contingent Performance contingent Maximal reward Not maximum reward Positive feedback control Negative feedback control

Self-reported interest

d

k

−0.24* 0.33* 0.43* 0.11 −0.34* 0.01 −0.36* −0.14 −0.40* −0.21* −0.43* −0.44* −0.28* −0.15* −0.88* −0.20* −0.03

101 21 14a 7a 92 9a 92 7a 55 12a 39a 19a 32 18a 6a 10a 3a

d

k

0.04 0.31*

84 21a

−0.07* 0.05 −0.07* 0.21 −0.15*

70 5a 69 5a 35a

−0.17* −0.01

13a 29a

a

These categories were not further differentiated and are homogeneous. Some of the studies used to determine the overall composite effect size (i.e., for all rewards) in each meta-analysis had multiple reward conditions, so the sums of the numbers of effect sizes in the most differentiated categories of each metaanalysis are greater than the numbers in the all-rewards category. There were 150 effect sizes in the most differentiated categories for the free-choice analyses, of which 6 were removed as outliers, and there were 114 effect sizes in the most differentiated categories of the self-report analyses, of which 6 were removed as outliers. * Significant at p < .05 or greater.

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As already mentioned, we expected that all rewards would not affect intrinsic motivation in a uniform way, and thus we both expected and found that the set of effects for the all-rewards category was heterogeneous. Consequently, we proceeded with more differentiated analyses of specific types of rewards, based on both theoretical and empirical considerations. We first separated studies of verbal rewards from those of tangible rewards.

Verbal Rewards (Positive Feedback) We first tested the CET prediction that, on average, verbal rewards would enhance intrinsic motivation. Twenty-one studies examined the effects of verbal rewards on free-choice intrinsic motivation, and 21 examined its effects on self-reports of interest. Results indicated that verbal rewards enhanced intrinsic motivation: for the behavioral measure, d = 0.33 (CI = 0.18, 0.43), and for self-reports, d = 0.31 (CI = 0.19, 0.44). However, there are two important caveats to this general finding. First, because the set of effect sizes for verbal-reward effects on free-choice behavior was heterogeneous, we inspected the studies to determine whether there was any obvious pattern in the results. We noticed that the effects of verbal rewards on schoolchildren appeared to be different from the effects on college students, so we conducted separate analyses for schoolchildren and college students. It turned out that verbal rewards enhanced free-choice intrinsic motivation for college students (k = 14; d = 0.43; CI = 0.27, 0.58) but not for children (k = 7; d = 0.11; CI = 0.11, 0.34), a point that is very important when thinking about educational practices. Second, CET has emphasized that although positive feedback can enhance intrinsic motivation, it can actually undermine intrinsic motivation if it is administered with a controlling interpersonal style. Five studies examined the administration of verbal rewards with an informational versus controlling interpersonal style, so we did a supplemental analysis of these studies. The results indicated, as hypothesized, that although informationally administered verbal rewards enhanced intrinsic motivation (d = 0.66; CI = 0.28, 1.03), controllingly administered verbal rewards undermined intrinsic motivation (d = –0.44; CI = –0.82, –0.07). To summarize, research indicates that verbal rewards (i.e., positive feedback) tend to have an enhancing effect on intrinsic motivation; however, verbal rewards are less likely to have a positive effect for children than for older individuals. Furthermore, verbal rewards can even have a negative effect on intrinsic motivation if the interpersonal context within which they are administered is controlling rather than informational.

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Tangible Rewards Next, we tested the CET prediction that, overall, tangible rewards (including material rewards, such as money and prizes, and symbolic rewards, such as trophies and good player awards) would decrease intrinsic motivation, because tangible rewards are frequently used to persuade people to do things they would not otherwise do, that is, to control their behavior. The meta-analysis included 92 tangible reward studies with a free-choice measure and 70 with a self-report measure. As predicted by CET, results indicated that, on average, tangible rewards significantly undermined both free-choice intrinsic motivation (d = –.34; CI = –0.39, –0.28) and self-reported interest (d = –0.07; CI = –0.13, –0.01). Of course, we have regularly argued that a full understanding of the effects of tangible rewards requires a consideration of additional factors such as reward contingency and interpersonal context, but these results do highlight the general risks associated with the use of tangible rewards as a motivator. Because age effects had emerged for verbal rewards, we also compared the effects of tangible rewards in studies of children versus college students. This revealed that even though tangible rewards significantly undermined intrinsic motivation for both groups, the undermining effect was significantly greater for children than for college students on both behavioral and selfreport measures of intrinsic motivation. The real-world implications of this pattern of results are extremely important. There is great concern about children’s motivation for school work, as well as for other behaviors such as sports, art, and prosocial activities, and a study conducted by Boggiano, Barrett, Weiher, McClelland, and Lusk (1987) indicated that adults tend to view salient extrinsic rewards as an effective motivational strategy for promoting these behaviors in children. However, the age-effect analyses indicate that, although tangible rewards may control immediate behaviors, they have negative consequences for subsequent interest, persistence, and preference for challenge, especially for children. In summary, the age effects that emerged from our meta-analysis indicate that tangible rewards have a more negative effect on children than on college students and that verbal rewards have a less positive effect on children than on college students.

Unexpected Rewards and Task-Noncontingent Rewards We next tested the CET prediction that unexpected rewards would not be detrimental to intrinsic motivation, whereas expected rewards would. The reasoning was that if people are not doing a task in order to get a reward, they are not likely to experience their task behavior as being controlled by the reward. The meta-analysis supported the hypothesis. Nine studies of free-choice behavior revealed no undermining (d = 0.01; CI = –0.20, 0.22), and five studies of self-reported interest revealed similar results (d = 0.05; CI = –0.19, 0.29).

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In contrast, analyses of expected rewards did yield undermining for both free-choice behavior (k = 92; d = –0.36; CI = –0.42, –0.30) and self-reported interest (k = 69; d = –0.07; CI = –0.13, –0.01). It is interesting in this regard to note that verbal rewards are generally unexpected, and that may be one of the reasons they do not typically have a negative effect on intrinsic motivation. According to CET, rewards not requiring task engagement should be unlikely to affect intrinsic motivation for the task because the rewards are not given for doing the task. Although relatively few studies of tasknoncontingent rewards have been done, the meta-analysis revealed no evidence that these rewards significantly affected either measure of intrinsic motivation (k = 7; d = –0.14; CI = –0.39, 0.11, for free-choice behavior and k = 5; d = 0.21; CI = –0.08, 0.50, for self-reported interest).

Engagement-Contingent Rewards Engagement-contingent rewards are offered explicitly for engaging in an activity. When children were told they would get a good player award for working on an art activity (Lepper et al., 1973), the reward was engagement contingent. Similarly, when college students were told they would receive a reward if they performed a hidden-figures activity, the reward was engagement contingent (Ryan et al., 1983). In neither case was there a performance requirement: Participants did not have to finish the task or do well on it; they simply had to work on it. More studies have used engagement-contingent rewards than any other reward contingency, and that is particularly true for studies of children. Results of the meta-analyses confirmed that engagementcontingent rewards significantly diminished intrinsic motivation measured in both ways (k = 55; d = –0.40; CI = –0.48, –0.32, for free-choice and k = 35; d = –0.15; CI = –0.25, –0.06, for self-reports). Furthermore, the undermining on the free-choice measure, while significant for both children and college students, was significantly stronger for children than for college students. The strength of the undermining on self-reports did not differ for the two groups.

Completion-Contingent Rewards The first study of reward effects on intrinsic motivation in humans (Deci, 1971) employed completion-contingent rewards. In it, participants were offered $1 for each of four puzzles they completed within a specified amount of time. As already mentioned, the pressure associated with the completion-contingent rewards was greater than that associated with engagement-contingent rewards, but we expected this to be offset somewhat by the implicit competence affirmation provided by the reward. Overall, we predicted an undermining effect for this category of rewards comparable to that for engagement-contingent rewards (Ryan et al., 1983).

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Twenty studies examined completion-contingent reward effects on freechoice behavior, and 15 examined effects on self-reports. Analyses revealed that completion-contingent rewards significantly undermined intrinsic motivation for both dependent measures. Because the effects for these rewards on free-choice behavior were heterogeneous and there were no age effects, we had to remove one outlier to achieve homogeneity. With the outlier removed, the results were as follows: k = 19; d = –0.44; CI = –0.59, –0.30. For selfreports, the effects were also heterogeneous, and again there were no age effects; thus, we had to remove two outliers. With these outliers removed, we also found significant undermining by the completion-contingent rewards (k = 13; d = –0.17; CI = –0.33, –0.00, for self-reports).2 As expected, the effects of engagement-contingent and completion-contingent rewards were virtually identical.

Task-Contingent Rewards In the first taxonomy of reward contingencies, Ryan et al. (1983) included task-contingent rewards, and Cameron and Pierce included the category in their meta-analysis. Because the task-contingent reward category is simply the aggregate of engagement-contingent rewards and completion-contingent rewards, this category is redundant. However, for comparative purposes, we mention it here. Task-contingent rewards undermined intrinsic motivation assessed with both measures (k = 74; d = –0.39; CI = –0.46, –0.32, for free choice and k = 48; d = –0.12; CI = –0.20, –0.04, for self-reports). Again, the undermining tended to be worse for children.

Performance-Contingent Rewards From the standpoint of CET, performance-contingent rewards are the most interesting type of tangible rewards. Performance-contingent rewards were defined by Ryan et al. (1983) as rewards given explicitly for doing well at a task or for performing up to a specified standard. Examples of performancecontingency studies include the Ryan et al. study, in which all participants in the performance-contingent-rewards condition received $3 for “having done well at the activity,” and the Harackiewicz, Manderlink, and Sansone (1984) study, in which participants received a reward because they were said to have performed better than 80% of other participants. According to CET, performance-contingent rewards have the potential to affect intrinsic motivation in two ways, one quite positive and one quite negative. Performance-contingent rewards can maintain or enhance intrinsic motivation if the receiver of the reward interprets it informationally, as an affirmation of competence. Yet, because performance-contingent rewards are often used as a vehicle to control not only what the person does but how well he

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or she does it, such rewards can easily be experienced as very controlling, thus undermining intrinsic motivation. According to CET, it is the relative salience of the informational versus controlling aspects of performance-contingent rewards which determines their ultimate effect on intrinsic motivation. In most experiments examining performance-contingent rewards, all participants receive rewards as if they had done very well (which, of course, does not happen in the real world). Therefore, these studies do not address the effects of receiving only partial rewards or no rewards under performance contingencies, a circumstance that is more common in the real world and would undoubtedly diminish both perceived competence and perceived self-determination and accordingly have a very negative effect on intrinsic motivation. There can thus be little doubt that research on the effects of performance-contingent rewards markedly underestimates the negative effects of this type of reward, since it has focused largely on people who succeed at the contingency. In contrast, a real-world contingency in which only those achieving above the 80th percentile receive a reward, if veridically applied, would mean that 80% of participants would end up getting no reward and, implicitly, receiving negative competence feedback. The meta-analyses for the overall effects of performance-contingent rewards included 32 studies with a free-choice measure and 30 with a selfreport measure. Performance-contingent rewards significantly undermined free-choice behavior (d = –0.28, CI = –0.38, –0.18), whereas results for the self-report studies were not significant. We did not do further analyses of studies with the self-report measure because the set of effects was homogeneous with only one outlier removed. However, the effects for the free-choice measure were quite heterogeneous. Consequently, we separated the effects into four categories based on the following two considerations. First, different studies of performance-contingent rewards have used different control groups; specifically, some have used control groups in which participants received neither rewards nor feedback, whereas others have used control groups in which participants received no rewards but did receive the same feedback conveyed by the rewards to the participants who received rewards. In this latter instance, for example, if the rewards were given for doing better than 80% of the participants, participants in a no-reward control group that received feedback would have been told that they did better than 80% of the participants. To examine the combined effects of performance-contingent rewards and the feedback inherent within them, one would compare the rewards condition with a no-rewards, no-feedback condition. On the other hand, to examine the effects of the rewards per se, independent of the feedback conveyed by them, one would compare the rewards group with a no-rewards group that received comparable feedback. Second, although the definition of performance-contingent rewards used in the majority of studies involves giving rewards to all participants as if they

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had performed well, some studies gave rewards in a way that conveyed to some or all of the participants that they had not performed well. These participants got less than the maximum available rewards, thus indicating that their competence was not optimal. For example, in a study conducted by Rosenfield, Folger, and Adelman (1980) that involved a feedback control group, rewarded participants got a small reward for performing in the bottom 15% of all participants, and the corresponding control group received the comparable “negative” feedback without the reward. Clearly, this and other such studies are quite different from the more typical studies of performancecontingent rewards in which all participants receive the same maximum reward for having done well. Studies involving different types of control groups and different levels of performance were aggregated without comment by Cameron and Pierce (1994). In our meta-analysis, however, because performance-contingent reward effects were not homogeneous, we examined four categories of performance-contingent rewards rather than simply discarding outliers as Cameron and Pierce had done. The four categories were as follows: effects involving no-feedback control groups in which everyone received the maximum possible rewards, effects involving no-feedback control groups in which all participants did not receive the maximum possible rewards, effects involving comparable-feedback control groups in which all participants received positive feedback, and effects involving comparable-feedback control groups in which all participants received negative feedback. With the free-choice measure, for studies that compared no-feedback control groups and participants who received the maximum possible rewards, there was significant undermining (k = 18; d = –0.15; CI = –0.31, –0.00).2 For studies with no-feedback control groups in which all participants did not receive the maximum possible rewards, there was also significant undermining (k = 6; d = –0.88; CI = –1.12, –0.65). The same was true for studies with comparable-feedback control groups in which everyone received positive feedback (k = 10; d = –0.20; CI = –0.37, –0.03). However, for the three studies with comparable-feedback control groups in which participants received negative feedback, there was not a significant effect for reward versus no reward. The group in which at least some participants got less than the maximum possible rewards and the control group received no feedback stands out and deserves special mention. This represents the type of performance-contingent rewards that one would typically find in the real world, in that here rewards are a direct function of performance. Those who perform best get the largest rewards, and those who perform less well get smaller rewards or no rewards. The analysis showed that this type of reward had the largest undermining effect of any category used in the entire meta-analysis (d = –0.88), indicating clearly that rewarding people as a direct function of performance runs a very serious risk of negatively affecting their intrinsic motivation.

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Summary of the Primary Analyses To summarize the primary findings from the meta-analyses, when free-choice behavior was used as the dependent measure, all rewards, all tangible rewards, all expected rewards, engagement-contingent rewards, completioncontingent rewards, task-contingent rewards, and performance-contingent rewards significantly undermined intrinsic motivation. Only verbal rewards enhanced intrinsic motivation in general, but verbal rewards did undermine intrinsic motivation if they were given with a controlling interpersonal style. The undermining of intrinsic motivation by tangible rewards was worse for children than for college students, and the enhancement by verbal rewards was weaker for children than for college students. The most damaging reward contingency was the commonly used one of performance-contingent rewards in which not all participants receive maximum rewards. When self-reported interest served as the dependent measure, all tangible rewards, all expected rewards, engagement-contingent rewards, completion-contingent rewards, and task-contingent rewards significantly undermined intrinsic motivation. Verbal rewards enhanced self-reported interest.

Supplemental Analyses To further clarify the limiting conditions and moderator effects of rewards, we performed two supplemental analyses. First, to determine whether the undermining of intrinsic motivation is simply a transitory phenomenon, we examined the effects of tangible rewards on the free-choice behavior of children, dividing the studies into three groups: those for which intrinsic motivation was assessed immediately after the reward was terminated, those for which it was assessed a few days later, and those for which it was assessed at least a week later. Analyses indicated that timing of the dependent measure did not affect the results. For all three groups, the composite effect sizes were between –0.40 and –0.53, all statistically significant. If anything, the undermining was strongest in the studies in which the measure was taken at least a week after the rewards were given. Second, although our primary meta-analyses included only studies for which the target activity was initially interesting, whereas Cameron and Pierce collapsed across interesting and dull tasks without analyzing task effects, we conducted a set of analyses to consider this issue empirically. In our first analysis, we included data from the dull-task conditions and repeated the overall meta-analysis. For the free-choice analyses, every undermining effect that had appeared when only initially interesting tasks were included also appeared after the dull-task conditions were added in; for the self-report analyses, all except one of the effects that had indicated significant

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undermining when only interesting tasks were used were again significant when the dull-task conditions were included. The one exception for selfreport studies was that the inclusion of the dull-task data led the undermining of self-reported interest in the completion-contingent condition to drop to nonsignificance. In our second analysis, we examined the 13 studies that had included both interesting and dull tasks, assessing the effects of tangible rewards separately for interesting and dull tasks. For the 11 studies with a free-choice measure, results indicated a large undermining by rewards in the interestingtask conditions (d = –0.68; CI = –0.89, –0.47) but not in the dull-task conditions (d = 0.18; CI = –0.03, 0.39). For 5 studies with self-reports, there was also significant undermining with the interesting task (d = –0.37; CI = –0.67, –0.07) but not the dull task (d = 0.10; CI = –0.09, 0.40). In summary, it is clear that rewards do not undermine people’s intrinsic motivation for dull tasks because there is little or no intrinsic motivation to be undermined. But neither do rewards enhance intrinsic motivation for such tasks. From our perspective (see, e.g., Ryan & Deci, 2000; Ryan & Stiller, 1991), the issue of promoting self-regulation of uninteresting activities is addressed with the concept of internalization rather than reward effects on intrinsic motivation. In other words, if a task is dull and boring, the issue is not whether the rewards will lead people to find the task intrinsically interesting because rewards do not add interest value to the task itself. Rather, the issue is how to facilitate people’s understanding the importance of the activity to themselves and thus internalizing its regulation so they will be selfmotivated to perform it.

Summary and Conclusions To summarize, results of the meta-analysis make clear that the undermining of intrinsic motivation by tangible rewards is indeed a significant issue. Whereas verbal rewards tended to enhance intrinsic motivation (although not for children and not when the rewards were given controllingly) and neither unexpected tangible rewards nor task-noncontingent tangible rewards affected intrinsic motivation, expected tangible rewards did significantly and substantially undermine intrinsic motivation, and this effect was quite robust. Furthermore, the undermining was especially strong for children. Tangible rewards – both material rewards, such as pizza parties for reading books, and symbolic rewards, such as good student awards – are widely advocated by many educators and are used in many classrooms, yet the evidence suggests that these rewards tend to undermine intrinsic motivation for the rewarded activity. Because the undermining of intrinsic motivation by tangible rewards was especially strong for school-aged children, and because studies have linked

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intrinsic motivation to high-quality learning and adjustment (e.g., Benware & Deci, 1984; Ryan & Grolnick, 1986), the findings from this meta-analysis are of particular import for primary and secondary school educators. Specifically, the results indicate that, rather than focusing on rewards for motivating students’ learning, it is important to focus more on how to facilitate intrinsic motivation, for example, by beginning from the students’ perspective to develop more interesting learning activities, to provide more choice, and to ensure that tasks are optimally challenging (e.g., Cordova & Lepper, 1996; Deci, Schwartz, et al., 1981; Harter, 1974; Reeve, Bolt, & Cai, 1999; Ryan & Grolnick, 1986; Zuckerman, Porac, Lathin, Smith, & Deci, 1978). In these ways, we will be more able to facilitate the type of motivation that has been found to promote creative task engagement (Amabile, 1982), cognitive flexibility (McGraw & McCullers, 1979), and conceptual understanding of learning activities (Benware & Deci, 1984; Grolnick & Ryan, 1987). The results of the meta-analysis also provided strong support for CET. Specifically, the predictions made by CET, based on an analysis of whether reward types and reward contingencies are likely to be experienced as informational or controlling, were uniformly supported and were particularly strong for the behavioral measure. Thus, although Cameron and Pierce argued that CET should be abandoned and stated that there is no reason for teachers to resist using rewards in the classroom, it is clear that CET provides an excellent account of reward effects and that there is, in fact, good reason for teachers to think carefully about when and how to use rewards in the classroom.

Appendix A list of each study used in our meta-analyses. A (D) indicates an unpublished dissertation. The second column indicates types of rewards and/or reward contingencies, followed by whether participants were children or undergraduates, followed by whether the dependent measure was free-choice behavior or self reported interest. (Codes appear in Notes to the Appendix.) Finally, we explain whether our treatment of the study and results differed from Cameron and Pierce’s. If a study was coded the same, the same control groups were used in the comparisons, and the effect sizes we reported did not differ from the effect sizes Cameron and Pierce reported by more than 0.10 in either direction, we noted that the study was the same in the two meta-analyses. If there was a difference, we explained what it was.

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Variables

E, 1, F, S E, 2, S V, E, 1, F

V, 2, S C, 2, F, S E, 2, S E, C, 2, S P, 2, S V, 2, F, S V, 2, F, S E, P, 1, F E, 1, F E, C, P, 1 ,F

E, 2, F

Study

Amabile et al., 1986, Exp. 1 Amabile et al., 1986, Exp. 3 Anderson et al., 1976

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Anderson & Rodin, 1989 Arkes, 1979 Arnold, 1976 Arnold, 1985 Bartelme, 1983 (D) Blanck et al., 1984, Exp. 1 Blanck et al., 1984, Exp. 2 Boggiano & Ruble, 1979 Boggiano et al., 1982 Boggiano et al., 1985

Brennan & Glover, 1980

(Continued )

Same.1 Same. This had multiple no-reward control groups. We selected the one recommended as appropriate by the study’s authors and comparable to ones used for other studies in this meta-analysis. C. & P.2 used a control group that the authors said was inappropriate, in which the experimenter avoided eye contact with the young children and ignored their attempts to interact, even though there were just the two people in the room. The study’s authors said that this condition was uncomfortable, even painful, for both the children and experimenter. Not surprisingly, that group showed free-choice intrinsic motivation that was considerably lower than any other group. Nearly the same.3 Both meta-analyses treated the composite dependent variable as self-report. Same. Same. Same. Excluded, type I.4 Same for free-choice; nearly the same for self-report. Excluded, type II.5 Excluded, type II. Same. The study’s authors crossed reward contingency with salience of reward. They referred to the two reward contingencies as task contingent and performance contingent, and C. & P. coded them that way, treating the task-contingent conditions as engagement contingent.6 However, the salience manipulation in the task-contingent condition changed the contingency. In the low-salience group, rewards were given for simply working on the puzzles, which makes them engagement contingent, but in the high salience group, rewards were given for each puzzle “completed,” which makes them completion contingent. This was engagement contingent because participants got rewards if they “work with the Soma puzzle for at least 8 minutes,” but C. & P. coded it task noncontingent. Further, C. & P. combine two control groups, including one that had not worked on the task for the same amount of time as the rewards group during the experimental period, but we used only the control group that had worked on the task for the same amount of time.

Comparison with Cameron & Pierce’s (1994) analysis

Table 1a: Studies used in our meta-analyses compared with Cameron and Pierce (1994)

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E, P, 1, F, S C, 2, F, S V, 1, S C, D, 2, S

E, P, D, 1, F V, P, 2, F, S V, 2, F, S N, P, 1, F, S P, D, 2, F, S

V, E, 1, F, S C, 2, F, S V, 2, F, S N, 2, F V, C, 2, F V, 2, F E, 1, F E, 1, F, S V, P, 1, F, S

N, 2, F, S

V, E, P, 2, S U, C, D, 1, F P, 2, F C, P, 1, F

C, 1, F

Study

Brewer, 1980 (D) Brockner & Vasta, 1981 Butler, 1987 Calder & Staw, 1975

Chung, 1995 Cohen, 1974 (D) Crino & White, 1982 Dafoe, 1985 (D) Daniel & Esser, 1980

Danner & Lonky, 1981, Exp. 2 Deci, 1971, Exp. 1 Deci, 1971, Exp. 3 Deci, 1972a Deci, 1972b Deci et al., 1975 DeLoach et al., 1983 Dimitroff, 1984 (D) Dollinger & Thelen, 1978

Earn, 1982

Efron, 1976 (D) Eisenstein, 1985 Enzle et al., 1991 Fabes, 1987, Exp. 1

Fabes, 1987, Exp. 2

Table 1a: (Continued)

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Excluded, type I. Same. Nearly the same. This study provided monetary rewards for completing a set of puzzles, thus making it completion contingent, but C. & P. coded it engagement contingent. Also, C. & P. collapsed across interesting and dull tasks.7 Excluded, type III.8 Excluded, type I. Same. Excluded, type I. In this study, participants were told “they could win up to $2 depending on how quickly they correctly assembled the puzzles.” This conveyed that the rewards depended on doing well relative to a standard and not just on finishing the puzzles. Thus, we coded it performance contingent, but C. & P. coded it completion contingent. Also, C. & P. collapsed across interesting and dull tasks. Nearly the same. Same. Same. Same. Same. Excluded, type II. Same. Excluded, type I. This had three tangible rewards groups, a verbal rewards group, and a control group. C. & P. inappropriately collapsed across verbal and tangible rewards, and they did not use the free-choice data. Rewards were given “simply for participating in the study” which makes it task noncontingent, but C. & P. coded it engagement contingent. Excluded, type I. Excluded, type II. Excluded, type II. Same for the performance-contingent condition. For the other condition, participants were given rewards “when they finished” a block construction, making it completion contingent, but C. & P. coded it engagement contingent. This study used the same procedure as the completion-contingent condition in Fabes (1987, Exp. 1), making it completion contingent, but C. & P. coded it engagement completion.

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E, 1, F, S E, 1, F, S

E, 1, F C, 2, F V, C, P, 1, F, S C, 2, F

U, E, P, 1, F

E, D, 1, F

C, 1, F

E, C, D, 2, S

V, E, P, 1, S

P, 1, S P, 2, F, S U, P, 2, F, S

P, 2, F, S P, 1, S

E, D, 2, F, S E, P, 1, F E, P, 1, F

V, IC, 1, S V, 2, F, S

Fabes et al., 1986 Fabes et al., 1988

Fabes et al., 1989 Feehan & Enzle, 1991, Exp. 2 Goldstein, 1977 (D) Goldstein, 1980 (D)

Greene & Lepper, 1974

Griffith, 1984 (D)

Griffith et al., 1984

Hamner & Foster, 1975

Harackiewicz, 1979

Harackiewicz & Manderlink, 1984 Harackiewicz et al., 1984, Exp. 1 Harackiewicz et al., 1984, Exp. 2

Harackiewicz et al., 1984, Exp. 3 Harackiewicz et al., 1987

Hitt et al., 1992 Hyman, 1985 (D) Karniol & Ross, 1977

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Kast & Connor, 1988 Koestner et al., 1987

(Continued )

Excluded, type III. Excluded, type I. Same except we coded the performance-contingent conditions for whether participants got the maximum rewards with implicit positive feedback or less than maximum rewards with implicit negative feedback. Excluded, type II. Same.

Excluded, type II. Same for free-choice, but C. & P. did not include the self-report. In this study, children selected a face ranging from frown to smile to reflect how much they enjoyed the task, a procedure that is common for obtaining self-report data from young children. Excluded, type II. Excluded, type II. Excluded, type I. Excluded, type I. This included competition conditions but we did not use those because competition has a complex effect on intrinsic motivation (Reeve & Deci, 1996). Same for the two unexpected groups and the engagement-contingent group, but C. & P. exclude the performancecontingent group. Excluded, type I. To be comparable to most other studies in this meta-analysis, we included only participants who worked in the individual context. Children were rewarded for finishing reading a passage up to the bookmark, which makes it completion contingent, but C. & P. coded it engagement contingent. (The McLoyd, 1979 study used the same instructions and C. & P. did code it completion contingent.) Same coding for completion contingent. In engagement contingent, participants were paid “75 cents for the 20 minute task,” but C. & P. coded it as task noncontingent. Also, C. & P. collapsed across interesting and dull tasks. Same for verbal rewards. Nearly the same for engagement contingent. C. & P. excluded the two performancecontingent rewards groups. Same. Same. Same coding, but C. & P. made an error in the self report effect size for performance contingent, showing it as enhancement when in fact it was undermining with a d = –0.16. Same. Same.

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Variables

N, 1, S

U, 1, S C, 1, S

P, 1, S

P, 2, F, S U, E, 1, F

E, 1, F C, 2, F, S C, 2, F, S E, D, 1, F C, P, 2, F, S

C, 2, S C, D, 1, F E, 1, F, S E, 1, F, S E, 1, F, S E, 1, F, S E, D, 1, F E, D, 1, F E, 1, F E, 1, F, S N, E, 1, F, S

Study

Kruglanski et al., 1971

Kruglanski et al., 1972 Kruglanski et al., 1975, Exp. 1

Kruglanski et al., 1975, Exp. 2

Lee, 1982 (D) Lepper et al., 1973

Lepper et al., 1982, Exp. 3 Liberty, 1986, Exp. 1 (D) Liberty, 1986, Exp. 2 (D) Loveland & Olley, 1979 Luyten & Lens, 1981

McGraw & McCullers, 1979 McLoyd, 1979 Morgan, 1981, Exp. 1 Morgan, 1981, Exp. 2 Morgan, 1983, Exp. 1 Morgan, 1983, Exp. 2 Mynatt et al., 1978 Newman & Layton, 1984 Ogilvie & Prior, 1982 Okano, 1981, Exp. 1 Okano, 1981, Exp. 2

Table 1a: (Continued)

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Rewards were given “because you have volunteered for this study …” so they were task noncontingent, but C. & P. coded them engagement contingent. Same. Participants were rewarded either for the number of coin flips they guessed correctly or for the number of block constructions they completed correctly, making it completion contingent, but C. & P. coded it performance contingent. It explored moderation by endogenous versus exogenous rewards. There were two reward groups and two control groups. In one pair, people worked on a stock market game and earned cash after each trial for good investments. The control group was the same as the experimental group except they were told they had to give back their earnings, so it was not a reasonable no-reward control group. In the other pair of conditions, money was not mentioned to the no-reward control group. We excluded the pair of conditions without a proper control group, but C. & P. collapsed across the two pairs of conditions. Excluded, type I. Same coding. Same effect sizes for engagement contingent. C. & P. made an error in calculating the effect size for unexpected rewards. Excluded, type II. Excluded, type I. Excluded, type I. Same coding, but C. & P. collapsed across interesting and dull tasks. Same for performance contingent. In the other rewards condition participants were paid after each of three puzzles they solved, so it was completion contingent, but C. & P. coded it as engagement contingent. Same. Coded the same, but C. & P. collapsed across interesting and dull tasks. Same on free-choice; nearly the same on self-report. Same. Same on free-choice; nearly the same on self-report. Same. Coded the same, but C. & P. collapsed across interesting and dull tasks. Excluded, type II. Same. Excluded, type II. Excluded, type II.

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V, U, P, 1, F

V, U, P, 1, F

E, P, 1, F, S E, 1, F, S E, P, 2, F, S P, 2, F, S V, IC, 2, F N, E, 1, F

E, 1, F C, 2, F

V, U, E, 2, F, S U, E, 2, F, S E, 1, F P, 2, F, S

E, 1, F, S E, 1, F, S N, E, 1, F

IC, 2, F

Orlick & Mosher, 1978

Pallak et al., 1982

Patrick, 1985 (D) Perry, et al., 1977 Picek, 1976 (D) Pittman et al., 1977 Pittman et al., 1980 Pittman et al., 1982, Exp. 1

Pittman et al., 1982, Exp. 2 Porac & Meindl, 1982

Pretty & Seligman, 1984, Exp. 1 Pretty & Seligman, 1984, Exp. 2 Reiss & Sushinsky, 1975, Exp. 1 Rosenfield et al., 1980

Ross, 1975, Exp. 1 Ross, 1975, Exp. 2 Ross et al., 1976

Ryan, 1982

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Extrinsic Rewards and Intrinsic Motivation

(Continued )

Same coding for verbal and unexpected. In performance contingent, children got rewards “if you do a good job today and tomorrow on the balance board,” but C. & P. coded it as completion contingent. There were discrepancies in the effect sizes. Same for verbal and unexpected. C. & P. did not report how they coded the tangible expected rewards condition, which was performance contingent. Excluded, type I. Excluded, type II. Excluded, type I. Same coding, but C. & P. used only self-report. We also used free-choice persistence, calculated as the number of trials. Same except that C. & P. did not do an analysis of informational versus controlling positive feedback. Same codings and nearly the same free-choice effects. C. & P. imputed a self-report value of 0.00, but participants were not asked how interesting or enjoyable they found the activity. Nearly the same. C. & P. coded this engagement contingent, but participants received $1.50 for each puzzle solved. C. & P. reported a comparison for 40 experimental and 20 control participants, but there were only 50 participants in the study. We calculated the reward effect size based on a comparison of the rewarded groups with neutral and extrinsic mind sets versus the non-rewarded groups with neutral and extrinsic mind sets, because that comparison provided corresponding reward versus no-reward conditions. Same for unexpected and engagement contingent. Nearly the same for verbal on free-choice. Same. Same. This study had performance-contingent, completion-contingent, and task-noncontingent groups, and a control group with feedback comparable to that in performance contingent. There was no appropriate control group for completion contingent or task noncontingent. It also crossed tangible rewards with positive versus negative feedback. C. & P. reported a verbal effect for positive versus negative feedback, and then they collapsed across feedback to examine tangible-reward effects. We did a moderator analysis of rewards signifying positive versus negative feedback. C. & P. listed a performance-contingent self report d = 2.80, but the correct d was 0.22. For free-choice, there was a modest discrepancy. Same for free-choice; they did not include self-report. Nearly the same for free-choice; they did not include self-report. Same for engagement contingent. In the other group, children were rewarded “for waiting,” which is task noncontingent, but C. & P. coded it engagement contingent. We included this study only in the supplemental meta-analysis of Informational versus Controlling verbal rewards. C. & P. excluded it.

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Variables

V, E, P, IC, 2, F, S

P, 2, F, S

V, 2, S V, 2, S V, 2, S E, 1, F, S V, 2, F, S E, P, 1, F, S V, U, P, 2, F, S E, D, 1, F

P, 2, F, S

C, 1, F C, 2, S

N, E, 1, F E, 1, F

P, 2, S E, 2, F V, E, 2, F, S E, P, 2, F, S

Study

Ryan et al., 1983

Salancik, 1975

Sansone, 1986 Sansone, 1989 Sansone et al., 1989 Sarafino, 1984 Shanab, 1981 Shiffman-Kaufman, 1990 (D) Smith, 1975 (D) Smith, 1980 (D)

Smith & Pittman, 1978

Sorensen & Maehr, 1976 Staw et al., 1980

Swann & Pittman, 1977, Exp. 1 Swann & Pittman, 1977, Exp. 2

Taub & Dollinger, 1975 Thompson et al., 1993 Tripathi & Agarwal, 1985 Tripathi & Agarwal, 1988

Table 1a: (Continued)

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Same on verbal and engagement contingent. There were two performance-contingent groups, one informational and one controlling. There were three no-reward control groups, one with informational positive feedback, one with controlling positive feedback, and one with no-feedback. We compared performance-contingent both to comparablefeedback controls and no-feedback controls in the moderator analyses. C. & P. did only the comparable-feedback comparisons. Also, C. & P. did not do an informational-controlling comparison. Same coding. C. & P. collapsed across positive and negative feedback conditions, but we did a moderator analysis for positive versus negative. Same. Same. Same. Same. Same. Excluded, type I. For comparability with other studies, we used only data from the 10-day assessments. Excluded, type I. Excluded, type I. In this study, there was also a condition called positive feedback, but the statements were not competence feedback. Same for self-report. C. & P. imputed a score of 0.00 for free-choice performance, even though means and significance tests were reported. Excluded, type II. Participants got a $1 reward for completing 15 puzzles, making it completion contingent, but C. & P. coded it engagement contingent. Same. There were two engagement-contingent groups, an engagement-contingent plus verbal-rewards group, and two no-reward control groups. There was not a control group for the engagement plus verbal group. We compared the two engagement to the two control groups, but C. & P. used all three reward groups. Same. Excluded, type III. Nearly the same. Same for engagement contingent on free-choice. For performance contingent, there were two tasks, with free-choice data reported for only one. Both we and C. & P. used the data for the one task and assigned d = 0.00 for the other, but C. & P. averaged the effects whereas we combined them meta-analytically. In the self-report data, C. & P. combined the engagement and performance conditions so it is unclear which analysis they were used in.

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P, 2, S C, 2, F, S

E, P, 2, F, S E, 1, F, S E, D, 2, F, S N, C, 2, S

E, 2, F, S V, 1, F

Weinberg & Jackson, 1979 Weiner, 1980

Weiner & Mander, 1978 Williams, 1980 Wilson, 1978 (D) Wimperis & Farr, 1979

Yuen, 1984 (D) Zinser, 1982

Same. Same. This study had pre-post data for a rewards group and a control group. C. & P. did pre-post analyses for the rewards group and ignored the control group. We compared the rewards group to the control group with pre-post analyses. We coded it completion contingent, but C. & P. did not code it. Same. Participants received $.25 for each anagram completed, which makes it completion contingent, but C. & P. coded it performance contingent. Same. Same. Excluded, type I. In one group, participants received $1.75 for being in the study, making it task noncontingent, but C. & P. coded it engagement contingent. In the other, participants “were paid for each model or subunit completed,” making it completion contingent, but C. & P. coded it performance contingent. Excluded, type I. Same.

Note: (D) = Unpublished Dissertation; V = Verbal Rewards; U = Unexpected Tangible Rewards; N = Task-Noncontingent Rewards; E = Engagement-Contingent Rewards; C = Completion-Contingent Rewards; P = Performance-Contingent Rewards; D = Dull-Task condition included in study and used in supplemental meta-analysis; IC = Informational versus Controlling comparison was made in supplemental meta-analysis. The code of 1 means the participants were children and the code of 2 means they were undergraduates. Finally, F means that the free-choice dependent measure was used and S means that the self-report measure was used. 1 Same means that Cameron and Pierce and we coded the study the same, used the same control groups, and found effects sizes that did not differ from each other by more that 0.10 in either direction. 2 C. & P. refers to Cameron and Pierce. 3 Nearly the same means the studies were coded the same and the same control groups were used, but that the effect sizes were different by more than 0.10, probably due to differences in estimation of standard deviations. If the discrepancy is large, we make note of that. 4 “Excluded, type I” refers to dissertations, and Cameron and Pierce excluded all dissertations. 5 “Excluded, type II” refers to studies that Cameron and Pierce excluded for no apparent reason. 6 Cameron and Pierce (1994) did not use the term “engagement-contingent.” When we say they coded a reward engagement-contingent, it means that they coded it as both “task-contingent” and what they referred to as “not contingent using a behavioral definition.” Because the intersection of those two codes is equivalent to our engagementcontingent code, we say that they coded it as engagement-contingent to minimize confusion for the reader. Similarly, they did not use the term completion-contingent, but what they coded as both “task-contingent” and “contingent using a behavioral definition” is equivalent to what we call completion-contingent. 7 These studies used both interesting and uninteresting tasks. We excluded the uninteresting tasks from the primary meta-analyses and included them in the supplemental metaanalysis concerned with initial task interest. Cameron and Pierce collapsed across the interesting and dull tasks even though it has been firmly established in the literature that initial task interest interacts with reward effects. 8 “Excluded, type III” refers to studies that Cameron and Pierce excluded because they were published after Cameron and Pierce’s cut-off date.

V, 1, S V, 2, S C, 1, F

Vallerand, 1983 Vallerand & Reid, 1984 Vasta & Stirpe, 1979

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Notes 1. The value k represents the number of effects considered in calculating a composite effect size. Because, for any given calculation, the data were aggregated across all relevant conditions within a study in order to ensure independence of effect sizes, k also represents the number of studies that were included in the calculation of a composite effect size. The value d represents the composite effect size corrected for reliability (Hedges & Olkin, 1985). In regard to CIs, if both endpoints are on the same side of 0.00, it indicates that the mean for the reward groups is significantly different from the mean for the no-reward groups. 2. Although one end of the CI appears to be 0.00, it was actually slightly negative and was rounded to 0.00. A significance test indicated that the composite effect size was significant.

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Ryan, R. M., & La Guardia, J. G. (1999). Achievement motivation within a pressured society: Intrinsic and extrinsic motivations to learn and the politics of school reform. In T. C. Urdan (Ed.), Advances in motivation and achievement: The role of context ( Vol. 11, pp. 45–85). Greenwich, CT: JAI Press. Ryan, R. M., Mims, V., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45, 736–750. Ryan, R. M., & Stiller, J. (1991). The social contexts of internalization: Parent and teacher influences on autonomy, motivation and learning. In M. L. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement ( Vol. 7, pp. 115–150). Greenwich, CT: JAI Press. Smith, W. E. (1975). The effect of anticipated vs. unanticipated social reward on subsequent intrinsic motivation. Unpublished doctoral dissertation, Cornell University, Ithaca, NY. Zuckerman, M., Porac, J., Lathin, D., Smith, R., & Deci, E. L. (1978). On the importance of self-determination for intrinsically motivated behavior. Personality and Social Psychology Bulletin, 4, 443–446.

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53 Beyond the Rhetoric: Understanding Achievement and Motivation in Catholic School Students Janine Bempechat, Beth A. Boulay, Stephanie C. Piergross and Kenzie A. Wenk

I

n the early 1980s, James Coleman’s work on the academic advantage associated with Catholic school membership generated a variety of research studies that examined the nature and extent of these early findings (Coleman, Hoffer, & Kilgore, 1982). Since then, a growing literature has documented that low-income students of color in Catholic high schools tend to outperform their peers in public schools in virtually every measure of preand post-secondary achievement, including GPA, SAT scores, enrollment in higher-track coursework, and high school completion (Bryk, Lee, & Holland, 1993; Carbonaro, 2003; Ellison & Hallinan, 2004; Morgan, 2001; Sander & Krautman, 1995). More recently, research on college acceptance has found that, on average and relative to their public school peers, students who graduate from Catholic high schools are more likely to attend college and be admitted to more selective colleges (Altonji, Elder, & Taber, 2005; Eide, Goldhaber, & Showalter, 2004). The research on what has come to be called the “Catholic school advantage” cannot be taken lightly. It is known that urban Catholic schools advance their students’ achievement with far fewer resources and curricula and pedagogy that are not necessarily on the cutting edge of educational research (Bempechat, Drago-Severson, & Boulay, 2002; Cattaro, 2002a).

Source: Education and Urban Society, 40(2) (2008): 167–178.

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Furthermore, inner-city Catholic schools achieve the greatest success with students who are the most disadvantaged and at risk for school failure, for both demographic and public policy reasons (Ilg, Massucci, & Cattaro, 2004; Peterson & Walberg, 2002). In other words, students at risk – those who are poor, whose first language is not English, who are members of an ethnic minority, and whose own parents have limited educations – are the most likely to suffer the negative consequences of the resurgence of school segregation and the increasing use of school promotion examinations (Heubert, 2002; Orfield, Frankenberg, & Lee, 2002). Yet enduring concerns about self-selection, although not inappropriate, have made it commonplace to attribute the higher achievement of Catholic school students to factors having nothing to do with pedagogy. Because Catholic school are schools of choice, students who enroll may be smarter, be better off materially, and have parents who are themselves better educated and therefore more motivated to ensure academic excellence in their children (Chubb, 2005; Goldberger & Cain, 1982). It could also be the case that administrators select the most well-behaved students and expel the most disruptive from their midst, making teachers’ jobs that much more manageable (Hoxby, 2003; Salganik & Karweit, 1982). Despite increased evidence of negative selection (i.e., that Catholic schools educate under- rather than over-achieving students; Sander, 2001), the positive outcomes associated with Catholic school enrollment are seemingly routinely dismissed. This is regrettable because something impressive is going on in urban Catholic schools, something from which we can and should want to learn (Cattaro, 2002b). As educators and students of educational reform, our goal is to look beyond outcome variables to probe the underlying factors that motivate students to achieve. The purpose of this article is to suggest new directions for research that go beyond an enumeration of outcome scores. Specifically, we present a program of research that we designed to build grounded theory on achievement and motivation in urban Catholic high school students. We first provide the reader with a brief background on the research context in which our work has evolved.

The Motivational Underpinnings of Success Research in achievement motivation has demonstrated that students’ beliefs about what it takes to do well in school are better predictors of their performance than even achievement test scores (Grant & Dweck, 2003). Beliefs about reasons for success and failure are particularly powerful because they predict the extent to which students will persist in the face of difficulty (Weiner, 2005). For example, students who tend to attribute poor performance to internal factors within their control, such as lack of effort, are more likely to feel ashamed and work harder for the next assignment or test.

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In contrast, students who tend to implicate external factors over which they have little or no control, such as a difficult test or a teacher who does not like them, are more likely to believe that investing more effort for the next test will be of little consequence for their ultimate performance (Weiner, 1994). To the extent that urban Catholic school students are outperforming their public school peers academically, is it possible that they adhere to beliefs that place them at an advantage motivationally? We asked more than 1,000 public and Catholic school fifth and sixth graders to read short scenarios that described success or failure experiences and to indicate the extent to which effort (e.g., “I’m careful in my work”), ability (e.g., “Everyone knows I do math badly”), or external factors (e.g., “The teacher likes me”) could explain the outcomes, if they themselves had actually lived through these experiences. Overall, we found that, relative to their public school peers, African American and Latino Catholic school students attributed success and failure to causes that were helpful for learning (Bempechat et al., 2002). For example, these students were much less likely than their public school peers to believe that success could be because of external factors, such as luck or an easy test. Relative to their public school peers, the Catholic school African American students were much less likely to believe that failure could be because of external factors, such as being disliked by the teacher or having studied the wrong material. Again, this is a helpful belief because it implies that failure is controllable and potentially avoidable. This first phase of our work provided evidence that, at the elementary school level, Catholic school students seem to hold more adaptive beliefs about learning than do their public school peers. However, these findings were limited by the very method we chose to employ. Because we used a questionnaire, the particular learning beliefs that students responded to and the way in which they responded (a 5-point scale) were dictated by us from the outside (an etic perspective). However, what we as researchers feel are important constructs may not match what students believe to be important (an emic perspective; Strauss, 1987). We thus launched a 4-year, longitudinal investigation of the ways in which adolescents in Catholic high schools conceptualize and speak about learning, achievement, and motivation in the context of their educational experiences. Our goal is to build a grounded theory by focusing on the issues that students raise in response to open-ended and semistructured interviews (Bempechat, 2003).

Developing Grounded Theory: A Longitudinal Investigation We designed our study to address the following research question: How do low-income adolescents of color construct meaning about learning, achievement, and motivation? More specifically, how do these students conceptualize

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the role that education plays in their lives, present and future? How do they perceive and interpret teachers’ goals for them? What are the ways in which family and peers foster or inhibit their school progress? At each of two urban Catholic high schools, we have been following a group of 20 students, half females and half males, all of whom come from lowincome families. These students are not exemplary pupils. Many are struggling to stay in good academic standing. When we began this study, half of the students were 9th graders and half were 10th graders. At Sienna High, the students are African American. At Norman High, the students are of Dominican descent. With the schools’ and parents’ permission, we conducted two individual interviews with each student in the spring of 2000. With few exceptions, each student was interviewed by the same member of our research team. Each interview lasted about 45 minutes, or one school period. We audiotaped all the interviews for later transcription, and all students were assured that their comments and opinions would remain anonymous. They also understood that they were free to refuse to answer any question and knew that they had the option of withdrawing from the study at any time. We designed the first interview to elicit open-ended descriptions of students’ learning experiences. Our goal was to let the students dictate the topics of discussion. We were very concerned about not putting words into the students’ mouths. We probed their responses by asking them to say more or give examples to illustrate the points they were making. After an initial reading of these interview transcripts, we developed the second interview as a semistructured questionnaire designed to examine perceived parent and peer support for learning and to ask students to speak about the meanings of academic-related words such as learning, motivation, success, failure, ability, and effort. After we reviewed the material from both interviews, we raised common themes in a focus group interview at the end of the school year. During a breakfast meeting at Norman High and a pizza lunch at Sienna High, the research team members asked the students to comment on issues that emerged in many of the interviews. Following conventions of qualitative analyses, we first read each interview to get a sense of what each individual student was expressing – his or her own beliefs, concerns, and questions. We then read “across” the interviews, paying particular attention to words and phrases that the students used frequently and spontaneously. The themes that emerged – culture of caring, personal responsibility, and adaptive achievement beliefs – provide distinct categories, but the reader will see that they are very much interrelated.

“You Need to Work Up”: The Culture of Caring The students we interviewed described their school as a caring environment, where teachers take a deep interest in both their academic and psychosocial well-being. Many students described the Sienna and Norman High Schools’

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“family,” even though Norman High has an enrollment of 1,100 students. Enrique described his perceptions in terms of his adjustment to the school: I thought it was going to be a lot harder for me, and it’s like, everybody here at the school is just like one big family, they try to help out a lot. But the teachers, they help any time they can, like if they see you falling off, like, in the beginning, I was doing, I was like doing pretty good. But once the basketball season started, I kinda fell off a little bit. The teachers were like, “I see your grades dropping a little, you need to work up.” So I started staying after, started to keep working. . . . It’s like they care for you so much at this school, they make sure they don’t want nobody, you know, to fall down in their grades and fail and not be able to, you know, reach their goals in life.

The students described their teachers variously, as “nice,” “mean,” “cool,” boring,” and “strict.” Regardless of the characterization, students’ overall comments describe a faculty who deeply care about and believe in their ability to learn. Abel experienced this as being pushed to higher levels, whereas for Darnell, in particular, this realization came through the fact that many teachers know who he is: Umm, the teachers here are like really cool. They, they’re not narrow minded about just one thing. They’re for everybody, they help everybody. If you’re, you’re doing bad or something, or you need help. They are there, like, to put you up to the other level that you should be in. (Abel) There’s more people like, that know your name, like teachers, some teachers … they might like know, like know the kids that really want to succeed in life. And they won’t really know the kids, um, that really like, have low grades—they won’t know their name, and they really wouldn’t care about cuz it’s only the people that have to care about. (Darnell)

“It Shows If You Don’t Have Effort”: Personal Responsibility in Learning The theme of personal responsibility emerged in the many comments that students made about the importance of effort and the necessity of setting goals. For many students, effort can be a double-edged sword that leaves them unsure about how much and when they should invest effort in their learning (Covington & Dray, 2002). Most students recognize that effort will enhance their academic performance. Yet they also realize that if they have to try hard, this implies that they probably are not smart. We did not hear this view from any of the students we interviewed. Quite to the contrary, the students spoke about the importance of persistence, as in Juana’s comments: Like, because I mostly think that effort is something that you put in, and it shows if you have effort, it shows if you doesn’t have effort, don’t have

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effort. Because, umm, if you … let’s say you learn something but you don’t get it and you give up. You know, there’s no effort there. It’s learning something that you don’t get because you don’t get it automatically. Umm, it really plays a part in school because . . . also, umm, if you don’t have effort . . . in school you’re not going to get everything, you’re not like a genius, you know. Albert Einstein didn’t get everything, you know. Umm, but like if you have effort and you want to do something so bad, like, it often turns into ability. Because if you have ability and you have effort, to do something bad you’re going to eventually. . . . Well, it depends on how much time it takes, but you’re going to eventually make a difference in your school, work, and whatever you do.

The notion that effort – an unstable quality – can be eventually transformed into ability – a more enduring trait – is notable because it is more common among very young children (Nicholls, 1978; Nicholls, Nolen, & Thorkildsen, 1995). As early as the second grade, most students begin to view the relationship between effort and ability as compensatory. In other words, they begin to endorse the view that the harder they have to try, the “dumber” they must be. A great deal of research attention has been paid to classroom factors that can promote the mature view that Juana articulates (Cheung & Rudowicz, 2003; Eccles, Roeser, Vida, Fredricks, & Wigfield, 2006; Schunk & Pajares, 2002). Nadia talked about her desire to make it on the honor roll, noting that her failure thus far is her own doing: [It’s a challenge] being on the honor role. [laughing] I can’t get grades good enough to get on the honor role. . . . I ain’t studying hard. I want it, just to be on it, cause like, ever since I was like in sixth grade and up I’ve never been on the honor role. But, like, in elementary school I was on the honor role a lot. And now no more.

Indeed, the students who conveyed some dissatisfaction with their performance blamed only themselves, mirroring findings from our questionnaire study of elementary school students.

“Failure Is Not Really Something Bad”: Adaptive Achievement Beliefs For many students, the experience of failure can be debilitating and lead to learned helplessness and feelings of inability. Educators have found that many students can be helped by reorienting their perception of failure toward the belief that mistakes and setbacks are a natural part of learning (Lepper, Corpus, & Iyengar, 2005). In this context, Margarita’s comments about failure demonstrate that she values seeing the positive in what many consider a negative experience:

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What [failure] means, umm … it’s not really something bad. It something that you need to try again. Umm, even if you fail at something that you want to do, don’t, don’t give up. Never, never give up. Umm, it’s something that probably takes motivation away from you. But, at the same time it wants you . . . umm, brings you new ideas to your head to break the obstacles. Like can I try this again, can I do something else. Umm, want me to move on our something. Like, it’s not really a bad term because we people are like saying failure, they are like, oh! you’re a failure. It’s not really bad because you have so many chances in this life and you can always try again. And, umm, I think, like let’s say I was to fail at something. I would try my hardest to do it again. To . . . like . . . make sure that something that I did wrong is fixed. Or because failure is the best way of learning, because when you fail something you learn, you learn more, because you want to succeed. And if you fail again, you even learn more. But like, and then at the same time when you fail and then you succeed it brings like … if you just succeed it’s just like I succeed, but if you fail and then you succeed, like, you learn more because you’re like, I failed at something but then I didn’t give up. . . . And then I got it right. That’s what I think [failure] means for me.

We found that, even when speaking about academic challenges, many students fell back on effort as a means of strategizing their way out of difficulty. Notably, Hector can articulate a strategy for coping with challenge even when he dislikes the work in question: Chemistry class, umm, that’s the toughest class for any first year. To be honest with you, that’s a tough class. And, and you know, I’m not going to lie to you but I can’t stand that class. I hate, I hate that class. [laughing] And, umm, it’s tough man. You have to memorize the periodic tables, the atoms. You know, stoichiometry, this and that, it’s tough man. I mean, but like I said you got to show perseverance and never say never. And, go for it. And that’s what I try to do.

Learning from Catholic Schools The most interesting finding of this research is that, when given the opportunity to express their views, these Catholic school students focused on their teachers’ commitment to them as learners and articulated mature and sophisticated views about their learning. The level of support and care that these students expressed has been reported in previous research on Catholic schools (Nelson & Bauch, 1997). Importantly, this finding dovetails not only with Noddings’s (2005) work on the positive psychosocial influence of caring adults in students’ lives but also with Wentzel’s (2002) recent research on social motivation. Her research has revealed that students who feel cared for and who have supportive teachers who mentor them tend to do better in school, both

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academically and socially. Furthermore, they tend to be supportive of their peers and more prosocial in and out of the classroom (Wentzel, 2004). However, we are struck by the degree to which educational goals and expectations were clearly communicated and understood by all students, an observation that has been made in previous work on Catholic school pedagogy (Hill, Foster, & Gendler, 1990). This is even more compelling when we consider that many of the students we interviewed fit the literature’s definition of those who are at risk for school failure (RAND, 2005). The students we interviewed perceived that their teachers not only hold them to high standards but also offer the support they need to meet these standards. For these students, the standards are not mysterious – they are clear, are unambiguous, and apply to everyone. In setting such goals, teachers are communicating the belief that all students have what it takes to achieve at the level expected of them. It is certainly the case that in Catholic schools, as in public schools, children become increasingly aware of who learns faster or who is “smarter” (Marsh, Hau, & Craven, 2004). Nonetheless, the message that all teachers can promote is that despite differences in rates of learning, everyone can and will learn. As all of us who study education reform know, higher standards in and of themselves do not guarantee higher achievement – they must be accompanied by ongoing support (Heubert, 2002). This support, according to the students we interviewed, was both emotional and academic. Teachers provided ageappropriate, pragmatic suggestions that helped them focus their efforts in ways that were likely to improve their performance. From the perspective of achievement motivation theory, the suggestions themselves can help to foster a sense of control over how well they do in school. Furthermore, to the extent that the teachers are offering up strategies for dealing with difficulty, they may be modeling persistence, a component of motivation that is critical for school success (Eccles et al., 2006). Finally, the students in our study perceived the standards and support they received to be ongoing, ebbing and flowing with variations in their performance. In other words, these students understood that their teachers would not tolerate performance that did not meet their definition of an acceptable standard. These students knew that they could not rest on previous laurels without being taken to task, should the quality of their work deteriorate. According to these students, their teachers appeared to be relentless in their pursuit of highquality work from their pupils. As Nicholls (1978; Nicholls et al., 1995) and others have shown, this insistence serves to communicate an unwavering belief in students’ ability to master the required work, a conviction that is a powerful motivator for all students (EdSource, 2006; Rosenthal, 2002). We cannot know the extent to which the adaptive beliefs about learning, endorsed by Margarita, Hector, and their peers, were fostered by the teachers’ pedagogical styles or were the result of factors having nothing to do with the school. Our goal remains the identification of influences that the students themselves perceive as influential. In this regard, it appears that these students appreciate teachers who

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believe in them, who closely monitor their progress, and who provide a variety of emotional and academic supports to help them excel in school. In our view, the more we understand about how students think about and interpret their educational experiences, the better equipped we are to develop models of intervention that promise success for all students. Our hope is that the important lessons that we can glean from the success of Catholic schools will not be lost in the ongoing debate over self-selection.

Authors’ Note The authors gratefully acknowledge the ongoing support of Sr. Kathleen Carr, CSJ, superintendent of schools, Archdiocese of Boston; Robert J. McCarthy, president, David Paskind, associate principal, Sister Ellen Powers, CSJ, former president/principal, and the faculty and students of North Cambridge Catholic High School; and David M. DeFillippo, principal, Christopher Sullivan, assistant principal, and the faculty and students of Central Catholic High School, Lawrence. This work was supported in part by a Spencer Foundation Small Grants Award.

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Eccles, J. S., Roeser, R., Vida, M., Fredricks, J., & Wigfield, A. (2006). Motivational and achievement pathways through middle childhood. In L. Balter & C. S. Tamis-LeMonda (Eds.), Child psychology: A handbook of contemporary issues (2nd ed., pp. 325–355). New York: Psychology Press. EdSource. (2006). Similar students, different results: Why do some schools do better? Palo Alto, CA: Author. Eide, E. R., Goldhaber, D. D., & Showalter, M. H. (2004). Does Catholic high school attendance lead to attendance at a more selective college? Social Science Quarterly, 85(5), 1335–1352. Ellison, B. J., & Hallinan, M. T. (2004). Ability grouping in Catholic and public schools. Catholic Education: A Journal of Inquiry and Practice, 8(1), 107–129. Goldberger, A., & Cain, G. (1982). The causal analysis of cognitive outcomes in the Coleman, Hoffer, and Kilgore report. Sociology of Education, 55, 103–122. Grant, H., & Dweck, C. S. (2003). Clarifying achievement goals and their impact. Journal of Personality and Social Psychology, 85(3), 541–553. Heubert, J. (2002). First, do no harm. Educational Leadership, 60(4), 26–30. Hill, P. T., Foster, G. E., & Gendler, T. (1990). High schools with character. Santa Monica, CA: RAND. Hoxby, C. (2003). The economics of school choice. Cambridge, MA: National Bureau of Economic Research Conference Report. Ilg, T. J., Massucci, J. D., & Cattaro, G. M. (2004). Brown at 50: The dream is still alive in urban Catholic schools. Education and Urban Society, 36(3), 355–367. Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivational orientation in the classroom: Age differences and academic correlates. Journal of Educational Psychology, 97(2), 184 –196. Marsh, H. W., Hau, K., & Craven, R. (2004). The big-fish-little-pond effect stands up to scrutiny. American Psychologist, 59(4), 269 –271. Morgan, S. L. (2001). Counterfactuals, causal effect heterogeneity, and the Catholic school effect on learning. Sociology of Education, 74, 341–374. Nelson, M. D., & Bauch, P. A. (1997, March). African American students’ perceptions of caring teacher behaviors at Catholic and public schools of choice. Paper presented at the American Educational Research Association, Chicago. Nicholls, J. G. (1978). The development of the concepts of effort and ability, perception of own attainment, and the understanding that difficult tasks require more ability. Child Development, 49, 800 – 814. Nicholls, J. G., Nolen, S. B., & Thorkildsen, T. A. (1995). Big science, little teachers: Knowledge and motives concerning student motivation. In J. G. Nicholls & T. A. Thorkildsen (Eds.), Reasons for learning: Expanding the conversation on student-teacher collaboration (pp. 5–20). New York: Teachers College Press. Noddings, N. (2005). Care and moral education. In H. S. Shapiro & D. E. Purpel (Eds.), Critical social issues in American education: Democracy and meaning in a globalizing world (pp. 297–308). Mahwah, NJ: Lawrence Erlbaum. Orfield, G., Frankenberg, E. D., & Lee, C. (2002). The resurgence of school segregation. Educational Leadership, 60(4), 16–20. Peterson, P. E., & Walberg, H. J. (2002). Countering the negative effect of poverty on learning. Chicago: Heartland Institute. RAND. (2005). Children at risk: Consequences for school readiness and beyond. Santa Monica, CA: Author. Rosenthal, R. (2002). The Pygmalion effect and its mediating mechanisms. In J. Aronson (Ed.), Improving academic achievement: Impact of psychological factors on education (pp. 25–36). San Diego, CA: Academic Press. Salganik, L., & Karweit, N. (1982). Voluntarism and governance in education. Sociology of Education, 55, 152–161.

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Sander, W. (2001). The effects of Catholic schools on religiosity, education, and competition (Occasional Paper NCSPE-OP-32). New York: Teachers College. Sander, W., & Krautman, A. C. (1995). Catholic schools, dropout rates and educational attainment. Economic Inquiry, 33(2), 217–233. Schunk, D. H., & Pajares, F. (2002). The development of academic self-efficacy. In A. Wigfield & J. S. Eccles (Eds.), Development of achievement motivation (pp. 15–31). New York: Academic Press. Strauss, A. (1987). Qualitative analysis for social scientists. Cambridge, UK: Cambridge University Press. Weiner, B. (1994). Integrating social and personal theories of achievement strivings. Review of Educational Research, 64(4), 557–573. Weiner, B. (2005). Motivation from an attributional perspective and the social psychology of perceived competence. In A. J. Elliot & C. S. Dweck (Eds.), Handbook of competence and motivation (pp. 73–84). New York: Guilford. Wentzel, K. R. (2002). Are effective teachers like good parents: Teaching styles and student adjustment in early adolescence. Child Development, 73(1), 287–301. Wentzel, K. R. (2004). Understanding classroom competence: The role of social-motivational and self-processes. In R. V. Kail (Ed.), Advances in child development and behavior ( Vol. 32, pp. 231–241). San Diego, CA: Elsevier.

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54 Dimensions of School Motivation: A Cross-cultural Validation Study Dennis M. McInerney and Kenneth E. Sinclair

I

n a multicultural society such as Australia, educators are concerned with the school performance of children from various minority groups. Within the context of Australian education, aboriginal children appear particularly disadvantaged with regard to academic achievement and school retention, whereas the children of certain migrant minority groups appear, in the latter part of the century, to be performing particularly well. In recent studies, Mclnerney (1989, 1990, 1991a, 1991b; Mclnerney & Sinclair, 1991) has examined a range of factors that are considered influential in determining the success or otherwise of particular groups within school settings, and in particular the studies have focused on key variables that predict school retention for these groups. In a study of aboriginal, migrant, and Anglo-Australian students (Mclnerney, 1988, 1989), a hypothesized set of influential background variables was examined using the Facilitating Conditions Questionnaire (FCQ). Parental influence emerged as the major discriminating variable for those aboriginal children who continued with school. It was also apparent that the child’s feelings toward school and the perceived support the child received from teachers and friends to continue with school, were also critical variables distinguishing the aboriginal school-leaver and nonleaver. Other variables such as negative peer influence and perceived value of school appeared to be not important as discriminant variables. Although parental influence emerged as the most important discriminant variable for the nonaboriginal groups, affect to school and the positive influence of teachers and peers on the child’s decision Source: Journal of Cross-Cultural Psychology, 23(3) (1992): 389– 406.

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making appeared less important. For these groups the perceived value of school and negative peer influence appeared relatively more important. Convergent evidence for the importance of parental influence on the child’s decision to continue with school was obtained in a further study with the same sample using the Behavioural Intentions Questionnaire (Mclnerney, 1990). In addition to external factors such as parental encouragement and peer influence, factors intrinsic to the person, such as desire for achievement, competitiveness, and self-reliance also play an important role in influencing a student’s application to learning and schooling. In the international literature a key construct used to examine differential school performance across cultural groups has been achievement motivation. However, the methodological and conceptual difficulties involved in measuring and defining achievement motivation for cross-cultural use have been discussed in a large number of publications (see Davidson & Thomson, 1980; De Vos, 1968, 1973; De Vos & Caudill, 1973; Draguns, 1979; Maehr, 1974; Maehr & Nicholls, 1980; Pedersen, 1979). A theoretical model with clear and significant implications for methodological improvements in cross-cultural research on achievement motivation is Maehr’s Personal Investment Model (Braskamp & Maehr, 1983; Maehr, 1984; Maehr & Braskamp, 1986), which provides the framework for the present study. Three critical components are designated by this model in determining an individual’s personal investment (or motivation) in a specific situation. The first is Sense of Self, which refers to the more or less organized collections of perceptions, beliefs, and feelings related to who one is. Sense of Self is presumed to be composed of a number of components such as sense of competence, sense of autonomy, and sense of purpose, each contributing to the motivational orientation of the individual. The second component, Personal Incentives, refers to the motivational focus of activity, especially what the person defines as “success” and “failure” in a particular situation. Among possible personal incentives are task goals (e.g., experiencing adventure, novelty, or working to understand something), ego goals (e.g., doing better than others), socialsolidarity goals (e.g., pleasing others and making others happy), and extrinsicreward goals (e.g., working for a prize or reward of some kind). Each of these components is subdivided into two facets described in Figure 1. The third component, Perceived Alternatives, refers to the behavioral alternatives that a person perceives to be available and appropriate (in terms of the individual’s sociocultural norms) in a given situation. Each of these components may be influenced by the design of the task, the personal experience and access to information of the individual, and the sociocultural context. In summary, personal investment or motivation in a particular task or behavior is a function of the sense of self, the feelings toward the behavior or task, the personal incentives operating, and the perceived options available. Each of the dimensions, Maehr maintains, is significant in any individual or situation interaction and has been considered, at some time, important in explaining and interpreting the differential performances and motivation of

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Personal Investment = Sense of Self + Affect + Personal Incentives SR (self-reliance) SE (self-esteem) GD (goal directed)

Ego Extrinsic Social Solidarity Task Rewards

– Competitiveness (co) – Power (pw) – Recognition (rc) – Token rewards (tn) – Social concern (sc) – Affiliation (af) – Task involvement (ta) – Striving for excellence (ex)

ACTION POSSIBILITIES

Figure 1: Dimensions of Maehr’s personal investment model

various cultural groups in school settings. The purpose of the present article is to describe the construction and validation of an instrument entitled the Inventory of School Motivation (ISM), which is based on the Personal Investment Model. The scale was developed (a) to test the “sense of self” and “personal incentives” dimensions of the Maehr model, (b) to test the applicability of the model and instrument in cross-cultural settings, and (c) to provide an instrument for measuring dimensions of motivation in classroom settings.

Method Subjects In total, 2,152 subjects were surveyed comprising 492 aboriginal students, 487 migrant-background students, and 1,173 Anglo students drawn from Year 7 to Year 10 in 12 NSW high schools. There were approximately equal numbers of males and females.

Materials Inventory of School Motivation (ISM) A presurvey of adult community members of the three groups was undertaken to ensure the cultural relevance of the items. An instrument was devised to evaluate the nature of school motivation for aboriginal-, Anglo-, and migrantbackground children. For the ISM, questions were written to measure the following 11 dimensions of the Maehr model: self-reliance (e.g., I can do things as well as most people at school), self-esteem (e.g., at times I feel that I’m no good at anything at school), goal directed (e.g., it is good to plan ahead

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to complete my schooling), competitiveness (e.g., winning is important to me), power (e.g., I often try to be the leader of a group), recognition (e.g., having other people tell me that I did well is important to me), token rewards (e.g., getting merit certificates would make me work harder at school), social concern (e.g., it is very important for students to help each other at school), affiliation (e.g., I try to work with friends as much as possible at school), task involvement (e.g., the more interesting the school-work the harder I try), and striving for excellence (e.g., I try hard to make sure that I am good at my schoolwork). Items were measured by a Likert-type scale, from strongly agree (1) to strongly disagree (5). There were 100 questions in the final pool of items in the Inventory of School Motivation. There were approximately 9 questions targeted on each dimension of the model. The questions were randomly assigned throughout the form and contained 24 negative items to guard against response bias. Items comprising the questionnaire are found in the appendix.

Procedure Administration of the Survey Each survey session began with a standardized explanation of the purpose of the survey and a request for the support of the students in completing the survey accurately. To ensure that procedures adopted for the survey were standardized from school to school, to avoid any difficulties students might have completing the survey due to poor reading skills, and to ensure that the majority of students completed the questionnaire in the available time, the chief researcher read the questionnaire (including the standardized directions) aloud while students filled in their responses. Students who experienced difficulties in answering questions or who required other assistance simply raised their hand and one of the research assistants went to their aid. In this way the procedure of the survey was not interrupted.

Statistical Analyses Preliminary Data Reduction and Statistical Analysis Factorial Study 1. Preliminary analysis consisted of determining whether the designed instrument had construct validity for the full group as well as for each of the separate groups, aboriginal, Anglo, and migrant. As the Maehr model hypothesizes, 11 dimensions relating to sense of self and personal incentives, a principal axis factor analysis with orthogonal (varimax) rotation setting the NFACTORS parameter at 11 was performed on the data for the full group and each separate group. Pairwise deletion of missing data was used to maximize the amount of data available for each analysis.

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Factor analysis of the set of 100 items for the full group (N = 2,152, M = 1,042, F = 1,110) resulted in 10 theoretically interpretable factors accounting for 98.2% of the variance in these items (although the last three factors consisted of doublets). Factors were named based on the content of the items with factor loadings that exceed .30. From this analysis it was apparent that for the full group of subjects the Inventory of School Motivation gave broad support for the existence of several discrete parameters that may influence student motivation in school settings, even though this analysis failed to find all of the 11 separate dimensions hypothesized in the Maehr model. The following dimensions were demonstrated: Self-Esteem, Self-Reliance, Affiliation, Social Concern, and Power (defined by group leadership). To a lesser extent, the existence of the dimensions Token Rewards and Competition was supported. The items designed by Maehr to measure ego and extrinsic rewards (viz., competitiveness, power, recognition, and token rewards) formed one general factor that we termed Extrinsic Motivation. Task rewards (viz., task involvement and striving for excellence) formed one factor that we called Intrinsic Motivation. It also included items written to measure Goal-Directed behavior. In order to assess the cross-cultural validity of the model and its reliability, a further series of principal axis factor analyses were performed on the three groups in the sample, aboriginal, Anglo, and migrant. In each case a varimax solution was chosen and the NFACTOR parameter was set to 11. Key dimensions of the Maehr model, Intrinsic Motivation, Extrinsic Motivation, SelfEsteem, Self-Reliance, Affiliation, Social Concern, and Power (group leadership), emerged again as major factors. The consistency of the findings across the four groups argues very strongly for the reliability of the ISM as well as for its construct validity. It gives strong support to the theoretical model from which it is derived. The ability of the model to illustrate characteristics of specific relevance to each group indicates its validity for use in a cross-cultural context.

Discussion The similarity of the factor pattern matrices across the three groups argues strongly for the etic validity of the constructs, whereas the differences that emerged in the composition of factors in the several groups support the emic validity of the scales derived from the constructs. It remains to demonstrate the relative importance of these dimensions for each group in determining performance level in educational settings. All scales were analyzed by means of the reliability subprogram of the SPSS package (Nie & Hull, 1981) for each group. Cronbach’s alphas were calculated for each scale. In general there was a high degree of reliability for each of the scales analyzed (with the majority being in excess of .70). Factor score variables were produced to represent the factors for each of the groups in later analyses. Reliability estimates are presented in Table 2.

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The Significant Predictors Multiple Regression and Intention to Complete Schooling A series of stepwise multiple-regression analyses (based on listwise deletion of missing data) were conducted to ascertain which variables were of most significance for each of three groups in predicting school performance (in particular motivation to continue with school beyond the minimum school-leaving age). The criterion variable was the expressed intention of the subject to continue with school and complete the Higher School Certificate (the final year of study in NSW schools). The predictor variables included were scales derived from the factor analyses (earlier described). Table 2 presents the list of predictor variables for each of the three groups in the study. Because results from multiple-regression analyses can be severely affected by intercorrelations among the predictor variables, each of the predictor variables was correlated with each other using the Pearson correlation program from SPSS (Nie, Hull, Jenkins, Steinbrenner, & Bent, 1975). The pattern of correlations among the factor score scales indicated very low levels of intercorrelation (␶ < .14).

Further Model Testing and Data Reduction Factorial Study 2. To test the model further and to reduce the number of items comprising the ISM (it was intended to develop a set of [composite] scales that might be used to assess the motivational characteristics of students within school settings), the data were subjected to a further set of principal axis factor analyses with varimax rotation. It was felt that limiting the NFACTOR parameter to 11 may have prevented a number of other salient dimensions of the Maehr model from emerging in earlier analyses. Consequently, for each group (Anglo, aboriginal, and migrant), a further factor analysis was performed without any limitation on the number of factors to be obtained. These factor analyses of the ISM clearly identified important dimensions of the Maehr model, with the pattern of factor loadings providing support for the scales that the ISM was designed to measure. However, the unrestricted factor analyses generated more factors supportive of the Maehr model than the analyses based on an a priori restriction of the NFACTOR to 11 and they also generated a large number of trivial and poorly defined factors. In an attempt to remove them and to reduce the item set from the 100 original variables to a set more manageable for general classroom purposes, each factor analysis was scrutinized carefully in order to isolate those items that did not factor out for a particular group on any factor (there were only a small number of these), and those items that loaded on poorly defined or trivial factors. Through this procedure it was possible to select, for further analysis, items of greatest relevance to each particular group.

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The reduced set of items for each group was subjected to a principal axis factor analysis using varimax rotation. Pairwise deletion of missing data was utilized. Table 1 presents a comparison of the factor structure for the three groups. Items defining each factor are included. Table 2 presents the multiple regression results for the three groups on the ISM for the intention to complete the Higher School Certificate. These

Table 1: Factor patterns for the ISM across three groups and items defining each factor for each group Aboriginal Intrinsic rewards (f1)a Extrinsic rewards (f2) Self-reliance (f3) Affiliation (f4) Competition (f5) Recognition (f6) Social concern (f7) Self-esteem (f8) Goal directed (f10) Power (f11) Confidence (aboriginal) (f9) – –

Migrant

Anglo

Intrinsic rewards (f1) Extrinsic rewards (f2) Self-reliance (f11) Affiliation (f5) Competition (f7) Recognition (f3) Social concern (f6) Self-esteem (f4) Goal directed (f10) Power (f9) – Token reward (f8) –

Intrinsic rewards (f1) Extrinsic rewards (f2) Self-reliance (f5) Affiliation (f6) Competition (f7) Recognition (f3) Social concern (f8) Self-esteem (f4) Goal directed (f10) Power (f9) – – Success (f11)

Defining Items Scale

b

Anglo

Aboriginal

Migrant

Intrinsic

7, 13, 16, 22, 30, 33 34, 40, 44, 56, 63, 66 68, 70, 79, 89

4, 7, 9, 11, 12, 13,16, 22 28, 30, 33, 34, 38, 39, 40 48, 54, 56, 57, 63, 69, 75 79, 83, 89, 96

Extrinsic

3, 15, 18, 27, 32, 52, 53 65, 88

8, 14, 15, 23, 24, 27, 32, 41 3, 6, 15, 18, 27, 32 44, 53, 65, 72, 73, 78, 91 53, 65, 88, 94

Recognition

12, 17, 20, 23, 24, 28 41, 73, 91

3, 6, 17

8, 12, 20, 23, 28, 41 24, 50, 73, 91

Self-esteem

45, 55, 67, 77, 80, 81 82, 95, 98

45, 77, 81, 100

45, 55, 57, 67, 70, 82

7, 13, 40, 56, 59, 60 63, 66, 69, 74, 75, 79 83, 84, 89, 93

Self-reliance

31, 59, 60, 69, 75, 83, 97

18, 60, 61, 66, 74, 90

31, 97

Affiliation

35, 36, 37, 42, 47

35, 36, 37, 42, 47

35, 36, 37, 42, 47, 61 1, 2, 14, 43, 76, 99

Competition

1, 2, 14, 43, 76

1, 2, 76

Social concern

10, 29, 46, 61, 74, 85

10, 21, 29, 46

10, 21, 29, 46, 85

Power

62, 71, 86, 94

86, 88

62, 71, 86

Goal directed

54, 84, 87

59, 87, 84

22, 38, 39, 48

Success

90, 93

–

–

Token

–

–

72, 78, 80, 90, 95, 98

Confidence

–

80, 95, 98

–

a. b.

Order of factor. Items are listed if they loaded 0.3 or greater on the factor.

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935 90 44 67 72 75 68 54 71 *** 54 – –

1 2 3 4 5 6 7 8 10 11 9 – –

fnum

2

Aboriginal (n = 492)

627

419** –002 102* –060 –039 –047 –014 014 372** –036 162** – –

beta

3

3 – –

2

4

1

ord

4

88 91 60 72 82 85 63 72 74 75 – *** –

alpha 1 2 11 5 7 3 6 4 10 9 – 8 –

fnum

Migrant (n = 487)

Groups

591

423** –036 053 –095* 223** 084* 121** 091* 185** 026 – –087* –

beta

– 6 –

5 2 7 4 6 3

1

ord 1 2 5 6 7 3 8 4 10 9 – 11

***

fnum

89 87 81 71 80 84 63 79 76 66 –

alpha

Anglo (n = 1,173)

665

–105*

241** –052* 262** –070** 032 051* 125** 152** 429** –046 –

beta

6

9 5 4 1 10

3 8 2 7

ord

Note: 1. Reliability coefficients (Cronbach’s alpha); 2. Order of factor; 3. Standardized beta weights; 4. Order of importance of the significant predictor variables; 5. All coefficients are presented without decimal points. *p < .05. **p < .01. ***Reliability not available due to limitation of the Reliability program (Nie & Hull, 1981). A minimum of three items is required to constitute a scale.

Multiple R

alpha

Predictor variables

1

Intrinsic rewards Extrinsic rewards Self-reliance Affiliation Competition Recognition Social concern Self-esteem Goal directed Power Confidence (ab) Token reward (mig) Success (Anglo)

Factor score scale

Table 2: Sets of beta weights and multiple-correlation coefficients for each group (aboriginal, migrant, anglo) on predictor variables drawn from the inventory of school motivation (ISM) and intention to complete the higher school certificate

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results indicate the usefulness of the ISM in determining the salient predictors of intentional behavior for the three groups studied. For each group (aboriginal, migrant, and Anglo), the combined set of culturally determined predictor variables developed from the personal investment theoretical framework was significantly related to the criterion variable. The multipleregression analyses therefore indicate the usefulness of the ISM in explaining and describing the nature of motivation for students from different cultural backgrounds in school settings, given the adequacy of the ISM for the three groups in the first place. In essence, the major correlates of intention for the nontraditional aboriginal students in this study were level of intrinsic motivation, desire to complete schooling, or lack of it, and level of confidence and self-reliance. Factors often alleged to be important determinants of aboriginal motivation in the school setting such as affiliation, social concern, self-esteem, and recognition did not emerge as important predictors in this study. A greater number of predictors was important for the migrant group, with eight scales significantly related to the criterion. Intrinsic Motivation, Competition, Goal Directed (to have a better future), and Social Concern accounted for most of the explained variance in the criterion variable. After Intrinsic Motivation, Goal Directed (to have a better future) and Competition emerged as the two most significant factors. This interesting finding supports the notion that many migrant children do well in Australian schools because of encouragement by their parents to work hard for a better future, and therefore to compete. Other variables that were expected to be significant for the aboriginal group but were not (viz., affiliation, social concern, recognition, and self-esteem) attained significance for the migrant group. For the Anglo group, all variables were found to be significantly related to the intention to complete schooling except for Competition. The most important predictor variable was Goal Directed (to complete schooling), followed by Self-Reliance and Intrinsic Motivation. Extrinsic Motivation was found to be significantly and negatively related to the intention to complete schooling. To the extent that the Anglo student is reward dependent, the less likely he or she is to hold the intention to finish school.

Summary Although direct numerical comparisons across the groups are not possible as each regression equation is based on a different set of predictor variables, some generalizations can be made. For all groups, Intrinsic Motivation appears to be a major predictor. It was the single most important predictor for the aboriginal and migrant group, whereas for the Anglo group Goal Directed (to complete schooling) emerged as the single most important

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predictor followed by Intrinsic Motivation. For all groups, Goal-Directed motivation was a significant predictor but the nature of the goal direction varied across groups. The goal was school completion for the Anglo and aboriginal groups, and it was pinpointed as the student’s desire to complete schooling and to do better than his or her parents for the migrant group. A narrower range of predictors was significant for the aboriginal group. Apart from the two intrinsically oriented scales, aboriginal motivation to continue schooling is largely determined by feelings of self-reliance within the school setting. An attributional model of motivation thus appears particularly salient to this group of students. Attribution theory (Weiner, 1974) maintains that children who perceive that they lack ability (internal stable and uncontrollable factor) or perceive that the situation is beyond them (external, stable, uncontrollable factor) will withdraw from the task. These feelings of inadequacy may become intractable and lead to learned helplessness in school situations (Dweck & Goetz, 1978). Extrinsic Motivation emerged as a low level predictor for the Anglo group, being negatively related to the intention of completing school. There was a negative, though nonsignificant, relationship between extrinsic motivation and intention to complete school for the other two groups. Clearly, to the extent students say they intend to complete schooling, they are less dependent on external rewards. Conversely, those children who perceive little value in schooling and/or dislike it are likely to be reward dependent to keep them at the task of learning. Competition was not an important predictor for either the aboriginal or the Anglo groups; however, it was the second most important predictor for the migrant group. Power Motivation (indicated through a desire to be group leader) was not an important predictor for any group. Motivational characteristics such as Affiliation and Social Concern often claimed to be important for the aboriginal group, emerged as more important predictors for the migrant and Anglo groups throwing into stark relief the cluster of variables that was found to be significant for the aboriginal group.

Prediction and Behavior: Are They Related? Discriminant Analyses and Returning to School As a final test of the validity of the Inventory of School Motivation, a series of discriminant analyses (with stepwise variable selection and minimization of Wilks’s lambda) using the significant predictors from the initial analyses was performed with a subset of the data on those subjects who had continued

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with school or left it before completing the Higher School Certificate. In other words, we set out to examine the value of the predictor variables identified and discussed earlier in distinguishing between those who remained at school and those who left after Year 10. Subjects consisted of 658 Anglo students (M = 313, F = 345), 283 migrant students (M = 154, F = 129), and 85 aboriginal students (M = 42, F = 43). Analyses with the Anglo group indicated that all of the predictor variables except Affiliation were retained in the analysis. The most important of these variables (based on standardized canonical discriminant coefficients) were Goal-Directed (school), Self-Reliance, and Success Motivation. Using this discriminant analysis, 72% of the sample were correctly classified as being at school or having left school ( p < .001). Analyses with the migrant group indicated that the best set of predictors was Intrinsic Motivation, Recognition, Self-Esteem, Affiliation, Competition, Token Reward, and Power. This combination of variables correctly classified 63% of the sample as being at school or having left (p < .001). Aboriginal analyses indicated that the best set of predictor variables was Goal Directed (school), Self-Reliance, and Confidence. Using this combination of variables, 70% of the sample were correctly classified (p = .002). Table 3 presents the comparison of the major discriminant variables for each group studied.

Table 3: A comparison of the discriminant variables drawn from the inventory of school motivation across three groups (aboriginal, anglo, and migrant) on continuing with school or leaving school after year 10 Standard canonical discriminant coefficients Discriminant variables

Aboriginal

Anglo

Goal directed Self-reliance Confidence Competition Intrinsic Power Token Self-esteem Recognition Social concern Extrinsic Affiliation

.962* .449 .405

.697 .485 .379 .403 .173 .207

% of group correctly classified

70%**

.232 .121 .199 .096

Migrant

.579 .551 .331 .305 .216 .189

.267 72%**

63%**

*Standardized canonical discriminant function coefficients indicate the relative importance of the variable to the discriminant equation. The higher the number the more important the variable. **Significant at the .001 level.

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Discussion The pattern of discriminant variables for each group bears comparison. The major discriminant variables for the Year 10 aboriginal and Anglo groups are strikingly similar and stand in marked contrast to the pattern established for the migrant group. In the former case a self-efficacy model explains behavior; that is, an aboriginal or Anglo child who feels confident, is self-assured, and has a sense of purpose in schooling continues with schooling. An interesting difference between these two profiles should be highlighted, however. First, the range of variables relevant to the Anglo group is much greater, suggesting a more complex interplay of factors in the Anglo child’s decision to continue with school. In the case of the aboriginal children there is clear evidence that the explanatory base for their decision making is much narrower and relates very much to feelings of confidence and assurance within the school setting; this finding is of great importance. Contrary to expectations, competition was not found to be an important discriminant variable for the Anglo group. The pattern of discriminant variables for the aboriginal group appears even more telling when compared with the migrant group. In this latter case, the significant variables are rewards and competition. This pattern gives clear support to the hypothesis that the children of migrants are more competitive, independent, and desirous of proving their capacity to obtain rewards, both through self-satisfaction and extrinsic modes (such as recognition, marks, and power through group leadership), than Anglo or aboriginal children. The success rate of migrant children at school and their retention levels increasingly appears better than norms established for the nonmigrant groups. It should be noted that Goal Directed (to improve one’s life-style), which was an important predictor variable for this group, was not a discriminant variable. It is possible that those migrant children who leave school hope to obtain an occupation that will enable them to do better than their parents, even though they may lack the competitive drive and intrinsic motivation that characterizes their non-leaving confreres. Variables, which according to generally held beliefs about aboriginal students should have been discriminant variables, such as Affiliation, Social Concern, Competition, and Self-Esteem, did not emerge as such. Given the adequacy of the dimensions in the first place there seems little justification for emphasizing these variables in any analysis of aboriginal student performance at school. Greater attention should be given to investigating the development of school confidence and self-reliance in aboriginal students as well as the development of a positive sense of the value of schooling.

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Appendix: Items Comprising the Inventory of School Motivation Predicted factors: (ta) working for the inherent interest (ex) striving for excellence (co) competitiveness (pw) power (afi) affiliation (sc) social concern (re) recognition (tn) token rewards (gd) goal directed (sr) self-reliance (se) self-esteem 1. I want to do well at school to be better than my classmates. 2. Winning is important to me. 3. I try to do well at school to please my teachers. 4. I like being given the chance to do something again to make it better. 5. I often try new things on my own. 6. I work hard it. school for rewards from the teacher. 7. I want to do well at school to show that I can do it. 8. I work best in class when I can get some kind of reward. 9. The more interesting the schoolwork the harder I try. 10. It is very important for students to help each other at school. 11. I don’t mind working a long time at schoolwork that I find interesting. 12. Having other people tell me that I did well is important to me. 13. I try hard to make sure that I am good at my schoolwork. 14. I am happy only when I am one of the best in class. 15. I work hard at school for presents from my parents. 16. I try to do well at school to please my parents. 17. Praise from my teachers for my good schoolwork is important to me. 18. I don’t often make mistakes at school. 19. I am always getting into trouble at school. 20. Getting a reward for my good schoolwork is not very important to me. 21. I like to help other students do well at school. 22. I want to do well at school so that I can have a good future. 23. Praise from my friends for good schoolwork is important to me. 24. Getting merit certificates would make me work harder at school. 25. Students shouldn’t depend on their friends for help with schoolwork. 26. I usually do the wrong things at school.

27. I like my teacher to show my work to the rest of the class. 28. I like to be encouraged for my schoolwork. 29. I care about other people at school.

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30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66. 67. 68. 69. 70. 71. 72.

Motivation

When I get good marks I work harder at school. I can do things as well as most people at school. I work hard because I want the teacher to take notice of what I say. I like to see that I am improving in my schoolwork. I need to know that I am getting somewhere with my schoolwork. I do not like working with other people at school. I can do my best work at school when I am working with others. I try to work with friends as much as possible at school. I aim my schooling toward getting a good job. I want to do well at school so that I have something better to look forward to than my parents. I work hard to try to understand something new at school. At school I work best when I am praised. I do better work by myself at school. Coming first is very important to me. Getting good marks is everything for me at school. At times I feel that I’m not good at anything at school. I enjoy helping others with their schoolwork even if I don’t do so well myself. When I work in groups at school I don’t do my best. I try hard to do well at school so I can get a good job when I leave. Not doing better than my friends in class is important to me. Having people notice my good schoolwork is not really important to me. I just do my schoolwork day by day without thinking about the future. I try to do well at school to please my friends. I like my schoolwork to be compared with others. It is good for me to plan ahead so I can do well at school. I feel I always need help with difficult schoolwork. When I am improving in my schoolwork I try even harder. Marks are the best way to know that you’ve done well at school. No one pays much attention to me at school. I am bright enough to continue my schooling to the Higher School Certificate. I like to think things out for myself at school. I don’t worry about other students, I just do my own work. I often try to be the leader of a group. Most of the time I feel that I can do my schoolwork. Kids usually pick on me at school. I work hard because I want to feel important in front of my school friends. I don’t need anyone to tell me to work hard at school; I do it myself. I often think that there are things I can’t do at school. The harder the problem the harder I try. On the whole I am pleased with myself at school. How I get on with other students is more important than how I get on with my schoolwork. At school I don’t like being in charge of a group. Getting rewards of money would make me work harder at school.

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73. 74. 75. 76. 77. 78. 79. 80. 81. 82. 83. 84. 85. 86. 87. 88. 89. 90. 91. 92. 93. 94. 95. 96. 97. 98. 99. 100.

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I want to be praised for my good schoolwork. As long as I am doing my own work well other students don’t matter much. I am very confident at school. I work harder if I’m trying to be better than others. I wish I had a little more confidence in my schoolwork. Praise for good work is not enough, I like a reward. I try hard at school because I am interested in my work. Trying hard at school is not much fun if the competition is too strong. I often worry that I am not very good at school. Other students have to help me a lot with my work. I think that I can do quite well at school. I work hard at school so that I can go on to Year 12. It makes me unhappy if my friends aren’t doing well at school. It is very important for me to be a group leader. It is good to plan ahead to complete my schooling. I work hard at school because I want the class to take notice of me. I am always trying to do better in my school work. Things hardly ever bother me at school. Praise from my parents for good schoolwork is important to me. If I’m working alone, difficult schoolwork doesn’t bother me. I succeed at whatever I do at school. I work hard at school so that I will be put in charge of things. I only like to do things at school that I feel confident at. I often forget the time when I’m working on something interesting at school. I think I’m as good as everybody else at school. I always choose easy work for myself to do at school so that I don’t have too much trouble. I don’t like trying to be better than someone else. I don’t like being told my marks.

Authors’ Note This research was supported, in part, by two grants from the Australian Institute of Aboriginal Studies. We would like to thank Don Apearritt and Jenny Tjugiarto for their invaluable assistance. Requests for reprints should be sent to Dennis Mclnerney, School of Education and Language Studies, University of Western Sydney, Macarthur, P .O. Box 555, Campbelltown, NSW 2560, Australia.

References Braskamp, L. A., & Maehr, M. L. (1983). Personal investment: Theory, assessment and application. Revision of a paper present at AERA, Montreal, Canada. Davidson, A. R., & Thomson, E. (1980). Cross-cultural studies on attitudes and beliefs. In H. C. Triandis & R. Brislin (Eds.), Handbook of cross-cultural psychology (Vol. 5). Boston: Allyn & Bacon.

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De Vos, G. A. (1968). Achievement and innovation in culture and personality. In E. Norbeck, D. Price-Williams, & W. M. McCord (Eds.), The study of personality. An interdisciplinary appraisal. New York: Holt, Rinehart & Winston. De Vos, G. A. (Ed.). (1973). Socialisation for achievement: Essays on the cultural psychology of the Japanese. Berkeley: University of California Press. De Vos, G. A., & Caudill, W. (1973). Achievement, culture and personality: the case of Japanese-Americans. In G. DeVos (Ed.), Socialisation for achievement. Essays on the cultural psychology of the Japanese. Berkeley: University of California Press. Draguns, J. G. (1979). Culture and personality. In A. J. Marsella, R. G. Tharp & T. J. Ciborowski (Eds.), Perspectives on cross-cultural psychology. New York: Academic Press. Dweck, C. S., & Goetz, T. (1978). Attributions and learned helplessness. In J. H. Harvey, W. J. Ickles, & R. F. Kidd (Eds.), New directions in attribution research ( Vol. 2). Hillsdale, NJ: Lawrence Erlbaum. Maehr, M. L. (1974). Culture and achievement motivation. American Psychologist, 29, 887– 896. Maehr, M. L. (1984). Meaning and motivation. Toward a theory of personal investment. In R. Ames & C. Ames (Eds.), Research on motivation in education: Vol. 2. Student motivation. Orlando, FL: Academic Press. Maehr, M. L., & Braskamp, L. A. (1986). The motivation factor: A theory of personal investment. Lexington, MA: Lexington. Maehr, M. L., & Nicholls, J. C. (1980). Culture and achievement motivation: A second look. In N. Warren (Ed.), Studies in cross-cultural psychology ( Vol. 2). London: Academic Press. Mclnerney, D. M. (1988). The psychological determinants of motivation of urban and rural nontraditional Aboriginal students in school settings: A cross-cultural study. Unpublished doctoral dissertation presented to the University of Sydney, Australia. Mclnerney, D. M. (1989). A cross-cultural analysis of students’ motivation. In D. M. Keats, D. Munro, & L. Mann (Eds.), Heterogeneity in cross-cultural psychology. Lisse: Zwets & Zeitlinger. Mclnerney, D. M. (1990). The determinants of motivation for urban Aboriginal students: A cross-cultural analysis. Journal of Cross-Cultural Psychology, 21, 474 – 495. Mclnerney, D. M. (1991a). The key determinants of motivation of urban and rural nontraditional Aboriginal students in school settings: Recommendations for educational change. Australian Journal of Education, 35, 154 –174. Mclnerney, D. M. (1991b). The behavioural intentions questionnaire. An examination of face and etic validity in an educational setting. Journal of Cross Cultural Psychology, 22, 293–306. Mclnerney, D. M., & Sinclair, K. E. (1991). Cross-cultural model testing: Inventory of School Motivation. Educational and Psychological Measurement, 51, 123–133. Nie, N., & Hull, C. (1981). SPSS update. New York: McGraw-Hill. Nie, N., Hull, C., Jenkins, J., Steinbrenner, K., & Bent, D. (1975). SPSS: Statistical package for the social sciences (2nd ed.). New York: McGraw-Hill. Pedersen, P. (1979). Non-western psychology; the search for alternatives. In A. J. Marsella, R. G. Tharp and T. J. Ciborowski (Eds.), Perspectives on cross-cultural psychology. New York: Academic Press. Weiner, B. (1974). Achievement motivation and attribution theory. Morristown, NJ: General Learning Press.

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55 Achievement Motivation in Children of Three Ethnic Groups in the United States Manuel Ramirez III and Douglass R. Price-Williams

I

n a recent article on achievement motivation, Maehr (1974) points out that McClelland’s well-known work in this area (1961) has given minimal attention to the fact that motives to achieve may be actualized in different ways in different cultures. He states: The important principle is that achievement and achievement motivation must be understood in terms of the sociocultural context in which they are found, as well as in terms of generalized descriptions of achieving norms or abstract constructions of psychological processes (p. 894).

In addition, Maehr points out that: Much of the research that attempts to understand the motivational patterns of ethnic and cultural groups involves placing children in a ‘middle-class-biased’ performance setting and then observing behavior (p. 894).

Maehr suggests that we would do well to pursue an ethnographic approach to the study of achievement motivation in cross-cultural research. He argues for an experimental anthropology of motivation. Gallimore, Weiss, and Finney (1974) agree with this point of view.

Source: Journal of Cross-Cultural Psychology, 7(1) (1976): 49–60.

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In reviewing research on delay of gratification, they note: A methodological problem common to many cross-cultural, cross-ethnic investigations is the use of behavior observation classifications irrelevant or inappropriate to one or more groups about which comparative statements are made (p. 78).

De Vos (1968) has indicated that McClelland’s definition of achievement motivation is based on a Western view of psychodynamics – that it is dependent on a conception of human behavior as individualistically motivated. To support his argument, De Vos cites the importance of affiliation in motivation among “successful” Japanese, indicating that in Japan, striving for success is more often motivated by a concern for the reaction of others than by the pursuit of what in the West is considered self-satisfaction. A similar orientation toward achievement was also observed among Japanese Americans (Caudill and De Vos, 1956). Gallimore, Weiss, and Finney (1974) have noted that affiliation is critical to achievement among Hawaians. These investigators observed that Hawaiian parents socialized their children to be attentive to the concerns and expectations of others, and that this type of training makes children more responsive to affiliation and social rewards. In particular, Gallimore, Boggs, and Jordan (1974) found that young Hawaiians regard contributions to and continuing affiliation with the family system as more important goals than personal achievement and independence as these are represented by McClelland’s conceptualization of achievement motivation. The fact that Hawaiian culture and socialization emphasize identification with the family may be the critical variable in understanding why McClelland’s measures for n Achievement may not be appropriate for them. McClelland’s definition of n Achievement is consonant with socialization that encourages children to view themselves as individuals separate from their families. It is not likely that measures based on his definition would be appropriate for assessment of achievement motivation in most Mexican-American and Black children. We hypothesize that many MexicanAmerican and Black children, like Hawaiians and Japanese, are socialized to identify themselves with their family and ethnic group, and to cooperate for attainment of mutual goals: socialization in Mexican-American and Black cultures has strong affiliation components. Recent research by Gray (1975) supports this hypothesis for MexicanAmerican children. Using a questionnaire, she found that Mexican-American children expressed a greater tendency to want to achieve for others than did Anglo children. The research reported below studied achievement motivation in children of three ethnic groups in Houston, Texas: Mexican-American, Black, and Anglo. It was predicted that Mexican-American and Black children would score higher in family achievement – oriented toward achievement goals

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which would benefit the family or achievement for recognition from family members. It was also predicted that the Anglo children in the study would score higher on n Achievement.

Method The subjects were 180 fourth grade children (mean age, 10.4) from Catholic parochial schools in Houston, Texas.1, 2 Sixty children were MexicanAmerican, 60 Black, and 60 Anglo. Half of the subjects in each group were male, half were female. There were also equal numbers of children of the lower and middle socioeconomic classes in each sex and ethnic group. Father’s occupation was used as an indicator of SES (Moore and Holtzman, 1965). A research team administered a short questionnaire in English to all fourthgrade children at the schools from which subjects were drawn. The questionnaire contained items concerning the language(s) spoken by the child and the parents, family activities, number of persons residing in the child’s home and their relationship to the child, and the size of the home. The children were also asked to draw a human figure. Those who had difficulty answering the questions or drawing the human figure were eliminated; the others were placed in a pool from which the subjects for the study were selected on the basis of ethnicity, sex, and SES.

Mexican-Americans The majority of Mexican Americans selected for this study are bilingual. These people are well identified with the traditional Mexican-American system of values, that is, they have close ties to members of their extended families, they are familiar with both Mexican and Mexican-American history, and their interpersonal relationships are characterized by warmth and a commitment to mutual help. Child-rearing practices emphasize respect for adults, family, and religious authority, and there is strong identification with Mexican Catholic ideology. The majority of the children selected to participate in this study were second- and third-generation Americans.

Blacks The Black residents of the areas of Houston from which our subjects were selected differ in many respects from Black populations in most urban settings in the United States. Most of these people are bilingual (French/ English) and most of the adults were reared in rural areas of Louisiana. Observations of these subjects indicated an emphasis on strong ties to the

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extended family, respect for adults, respect for family and religious authority, and identification with the teachings of the Catholic Church.

Anglo-Americans The majority of Anglos from which we chose our subjects were Caucasians who made no indication that they identified with their original ethnic groups. None of the children were bilingual. Observations of the Anglo families indicated that there was a strong emphasis on encouraging children to develop identities separate from those of the family group. Children were also encouraged to be individually competitive.

Procedure The subjects were asked to tell a story to each of seven line drawings depicting a person(s) in a setting related to education. The tester asked each child to tell the most interesting story he could think of. In composing the story, each child was asked to answer three questions: (1) What is happening? (2) What happened before? (3) How will the story end? The content of each of the seven cards in the set is as follows: (1) student and teacher, (2) student and mother, (3) student and father, (4) two students of the same ethnic group, (5) two students, one of darker complexion than the other, (6) student, parents, and principal, and (7) student studying alone. Different male and female sets of cards were constructed for each of the ethnic groups. The subjects were tested individually in two separate sessions. Three cards were administered during the first sessions and four during the second. The subjects were tested by a member of their ethnic group. To score for n Achievement, a version of the McClelland scoring system devised by Riccuiti and Clark (1957) was abbreviated. A maximum of four points could be given for each story. One point was given for each of the following categories:3 (1) imagery – reference made to achievement or to a goal related to achievement (competition with a standard of excellence); (2) instrumental activity – any activity independent of the original statement indicating that the character in the story is doing something to attain an achievement goal; (3) positive outcome of instrumental activity – activity leads to attainment of the achievement goal; and (4) thema – the plot of the story revolves around achievement. The scoring categories for family achievement 4 are as follows: (1) imagery – reference made to achievement or attainment of an achievement goal (competition with a standard of excellence) from which the family would benefit or that would gain recognition from family members; (2) instrumental activity – any activity independent of the original statement that helps the character achieve for his family; (3) positive outcome of instrumental

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activity – activity leads to attainment of the achievement goal; and (4) thema – achievement is the central plot or theme of the story. Those who scored the stories were trained with the manual by McClelland, Atkinson, Clark, and Lowell (Atkinson, 1958). All stories were scored blind, without knowledge of the sex or ethnic group membership of the subject.

Results The findings listed in Table 1 show that Mexican-American and Black subjects scored higher on family achievement than did Anglo children, while Anglo children scored higher than Mexican Americans and Blacks on n Achievement. A 3 × 2 × 2 ANOVA revealed significant ethnic effects for both the family achievement (F = 5.79, p < .01) and the n Achievement data (F = 5.73, p < .01). Sex and SES effects were insignificant for both the n Achievement and family achievement data. A separate ANOVA on the n Achievement data from the three parent cards of the SSPST yielded a significant ethnic effect (F = 6.87, p < .01). Mean scores for each subgroup on these three cards are contained in Table 2. A close examination of the data revealed that the highest scores on n Achievement were those of Mexican-American males; Mexican Americans scored lower than Anglos as a group because of the lower scores of MexicanAmerican females. Black and Anglo females also scored lower on n Achievement than the males of their respective groups. Females in all three ethnic groups scored higher than males on family achievement. Results of post hoc comparisons (Tukey) showed that Mexican Americans scored significantly higher than Black Americans on n Achievement (XM A− XB A = 2.39, p < .05) and significantly higher than Anglos on family achievement (XM A− XA A = 1.81, p < .05). There was no significant difference between Anglos and Mexican Americans on n Achievement.

Table 1: Ethnic group means and standard deviations of scores on family achievement and need achievement Ethnic group Black-American Family achievement

Need achievement

Family achievement

Anglo-American

Need achievement

Family achievement

Need achievement

X

S.D.

X

S.D.

X

S.D.

X

S.D.

X

S.D.

X

S.D.

3.11 3.19

3.75 2.54

3.13 2.50

4.22 3.16

3.51 3.54

3.29 2.97

5.77 4.66

4.62 4.25

1.65 1.77

2.27 3.14

5.65 5.39

6.72 5.01

Sex Male Female

Mexican-American

N = 30 for each group.

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Table 2: Ethnic group means and standard deviations on need achievement scores on the three parent cards of the SSPST Ethnic group Black-American

Mexican-American

Anglo-American

Sex

N

X

S.D.

N

X

S.D.

N

X

S.D.

Male Female

30 30

2.80 2.23

1.16 1.17

30 30

2.80 3.23

1.63 1.81

30 30

2.17 1.83

1.44 1.41

Also, Anglo subjects scored significantly higher than Blacks on n Achievement (XA A− XB A = 2.68, p < .01), but Blacks scored significantly higher than Anglos on family achievement (XB A− XA A = 1.44, p < .05).

Discussion The results obtained here support the contention by Maehr (1974) that contextual conditions are important in expressions of achievement motivation and that the particular form in which achievement is expressed is determined by the definition which culture gives to it. The importance of contextual conditions for eliciting achievement responses is most evident in our finding that Mexican-American and Black children tended to score higher on n Achievement than Anglo children on those cards with parental figures, but scored lower than Anglos overall.5 These findings seem to be in line with those obtained by Schwartz (1969) with Mexican-American children in Los Angeles. Schwartz found that Mexican Americans, in contrast to Anglo-Americans, were more concerned about adult than about peer approval of their actions. Since the Mexican-American and Black subjects in the current study expressed achievement motivation in the form of family achievement, then, it seems likely that if more cards in the test set had contained scenes with parent figures the overall n Achievement scores of these subjects would have been higher. The most important cultural determinant of achievement motivation, at least for the members of the three groups studied here, may be the degree to which identification with the family is encouraged in socialization. The Mexican-American and Black groups seemed to encourage children to identify with the family early in life and to remain so identified, while the Anglo group seemed to encourage children to consider themselves as separate individuals early in life. The finding that females in all three cultural groups scored higher than males on family achievement and lower on n Achievement may indicate that the females were socialized to identify with the family more than were the males.

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The discovery that Mexican-American males scored higher on n Achievement than the other subgroups and were exceeded on family achievement only by Mexican-American females may indicate that Mexican-American males have been socialized both to achieve for the self and for the family. This may be a result of the separation of the sex roles in Mexican-American culture (Madsen, 1964; Ramirez and Castaneda, 1974; also Tuddenham, Brooks, and Melkovich, 1974) and Mexican culture (Diaz-Guerrero, 1955). Researchers have indicated that Mexican and Mexican-American males are not subjected to as much pressure as females to adhere to convention and that as they get older they interact less with family members and more with persons outside the extended family. Tuddenham et al. (1974) found that Mexican-American mothers reported more sex differences in behaviors of their ten-year-old children than Black, Anglo, or Oriental mothers. The results of this study apply only to motivation attributed to like-sexed pictures. Future research should counterbalance sex of the main character in the pictures to ensure that data are not affected by the fact that achievement in most cultures is frequently associated with the male role. In the past, it has been all too readily concluded that Mexican Americans and Blacks have little motivation to achieve, and it has been assumed that somehow their cultures interfere with the development of this motivation. The results of the current study, however, show that the aforementioned conclusions are unjustified. That is, members of certain cultural groups may have appeared to exhibit little achievement motivation because the particular methodology used did not tap achievement motivation as interpreted by that cultural group and/or because the achievement motivation expressed was not recognized as such due to the narrow definition of achievement used.

Authors’ Note The work described in this paper was supported by a grant from the center for Research in Social Change and Economic Development, Rice University, Houston, Texas, financed under ARPA order 738. Subsequent analysis of results was supported in part by Research Grant HD 04612, NICHD, Mental Retardation Research Center, UCLA; by the California Department of Mental Hygiene; and by the University of California.

Notes 1. The authors would like to thank the Diocese of Houston for making subjects available for this study. 2. All of the schools were in neighborhoods which were ethnically homogenous and most of the instructional and administrative personnel in the schools were of the same ethnic group as the community and children. 3. The four scoring categories given above are those found by Riccuiti and Clark (1957) to have the greatest validity in scoring for n Achievement.

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4. Family achievement should not be confused with s Power, as defined by McClelland et al. (1972). 5. The card that elicited most stories with family achievement themes from MexicanAmerican and Black children showed a child, parent, and a school principal in the principal’s office. A common story to this card was the following: The child is experiencing difficulty in school or has no interest in his(her) studies; the parents are asked to go to school to confer with the principal; after the conference, the parents take an interest in the child’s progress in school and ask the child to study more; this motivates the child to work hard and he(she) succeeds in school making his(her) parents proud of him(her).

References Atkinson, J. W. [ed.] (1958) Motives in Fantasy, Action and Society. Princeton, NJ.: Van Nostrand. Caudill, W. and G. A. De Vos (1956) “Achievement culture and personality: the case of the Japanese Americans.” Amer. Anthropologist 58: 1102–1126. De Vos, G. A. (1968) “Achievement and innovation in culture and personality,” in E. Norbeck, D. Price-Williams, and W. M. McCord (eds.) Personality: An Interdisciplinary Approach. New York: Holt, Rinehart & Winston. Diaz-Guerrero, R. (1955) “Neurosis and the Mexican family structure.” Amer. J. of Psychiatry 112: 411–417. Gallimore, R., J. W. Boggs, and C. Jordan (1974) Culture, Behavior, and Education: A Study of Hawaiian-Americans. Beverly Hills: Sage. Gallimore, R., L. B. Weiss, and R. Finney (1974) “Cultural differences in delay of gratification: a problem of behavior classification.” J. of Personality and Social Psychology 30, 1: 72–80. Gray, T. (1975) “A bicultural approach to the issue of achievement motivation.” Ph.D. Dissertation: Stanford University, School of Education. Madsen, W. (1964) Mexican Americans of South Texas. New York: Holt, Rinehart & Winston. Maehr, M. L. (1974) “Culture and achievement motivation.” Amer. Psychologist 29: 887–895. McClelland, D. C. (1961) The Achieving Society. New York: Free Press. ———, W. N. Davis, R. Kalin, and E. Wanner (1972) The Drinking Man. New York: Free Press. Moore, B. M. and W. Holtzman (1965) Tomorrow’s Parents: A Study of Youth and Their Families. Austin: Univ. of Texas Press. Ramirez, M. and A. Castaneda (1974) Cultural Democracy, Bicognitive Development and Education. New York: Academic Press. Riccuiti, H. N. and R. A. Clark (1957) A Comparison of Need-Achievement Stories Written by Experimentally “Relaxed” and “Achievement Oriented” Subjects: Effects Obtained with New Pictures and Revised Scoring Categories. Princeton, N.J.: Educational Testing Service. Schwartz, A. J. (1969) “Comparative values and achievement of Mexican-American and Anglo pupils.” Center for the Study of Evaluation, UCLA Graduate School of Education, Report No. 37. Tuddenham, R. D., J. Brooks, and L. Melkovich (1974) “Mothers’ reports of behavior of ten-year-olds: relationship with sex, ethnicity and mother’s education.” Developmental Psychology 10, 6: 959–995.

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56 Motivation and Learning Environment Differences between Resilient and Nonresilient Latino Middle School Students Hersholt C. Waxman, Shwu-yong L. Huang and Yolanda N. Padrón

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lthough many programs and school-based interventions have been found to be effective for some types of students at risk of failure, these programs and interventions have not necessarily been effective for Latino students because programs need to specifically address many of the concerns of these students. Furthermore, even within the general Latino population, it cannot be assumed that all Latino students have similar backgrounds, motivation, and perceptions toward school (Reyes & Valencia, 1993). Some Latino students, for example, have been very successful academically in school, whereas other Latino students have experienced failure and despair in school. Consequently, it may be necessary to first look at Latino students who have done well in school and then see how they differ from less successful Latino students. One area of research that has important implications for the educational improvement of Latino students is that of examining resilient students, or students who succeed in school despite the presence of adverse conditions (Gordon & Song, 1994; Matsen, 1994; McMillan & Reed, 1994; Wang & Gordon, 1994; Winfield, 1991). Although the resilience construct has been widely used in areas like developmental psychopathology (Garmezy, 1991; Matsen, 1994; Matsen, Best, & Garmezy, 1990;

Source: Hispanic Journal of Behavioral Sciences, 19(2) (1997): 137–155.

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Rutter, 1987, 1990), its application to educational phenomena has been fairly recent. Wang, Haertel, and Walberg (1994) defined educational resilience as “the heightened likelihood of success in school and other life accomplishments despite environmental adversities brought about by early traits, conditions, and experiences” (p. 46). Alva (1991) used the term academic invulnerability to describe students who “sustain high levels of achievement motivation and performance, despite the presence of stressful events and conditions that place them at risk of doing poorly in school and ultimately dropping out of school” (p. 19). Some Latino students do well in school despite coming from at-risk environments, and it is important to know why these resilient students succeed, whereas other Latino students (i.e., nonresilient students) from equally stressful environments do not. This approach is important because it focuses on the predictors of academic success rather than on academic failure. This focus may also help us design more effective educational interventions because it enables us to specifically identify those alterable factors that distinguish resilient and nonresilient students. The research thrust in this area is to extend previous studies that merely identified and categorized students at risk and to shift to studies that focus on identifying potential individual and school processes that lead to and foster success (Winfield, 1991). In other words, the construct of educational resilience is not viewed as a fixed attribute of some students but, rather, as alterable processes or mechanisms that can be developed and fostered for all students. Fixed attributes of individuals such as students’ ability have not been found to be characteristic of resilient students (Bernard, 1993; Gordon & Song, 1994; Matsen et al., 1990). On the other hand, there are several alterable processes or characteristics that have been found to be associated with resilient children. Bernard (1993), for example, maintained that there are four attributes or personal characteristics that resilient children have: (a) social competence like responsiveness, (b) problem-solving skills, (c) autonomy, and (d) a sense of purpose. McMillan and Reed (1994) described four factors that appear to be related to resiliency: (a) individual attributes, (b) positive use of time, (c) family, and (d) school. There have been very few studies, however, that have actually compared resilient and nonresilient Latino students on these characteristics. Furthermore, the research in this area has not typically used the resilience construct. Instead, it has generally focused on characteristics that have differentiated more successful and less successful students. In one such study, Alva (1991) examined the characteristics of a cohort of l0th-grade Mexican American students and found that successful or invulnerable students reported higher levels of educational support from their teachers and friends and were more likely to “(a) feel encouraged and prepared to attend college, (b) enjoy coming to school and being involved in high school activities, (c) experience fewer conflicts and difficulties in their intergroup relations with other students, and (d) experience fewer family

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conflicts and difficulties” (p. 31). She also supported the view that research on students at risk needs to focus on aspects of school success rather than school failure. She also maintained that educational policies need to focus on expanding both the protective resources and students’ subjective appraisals (e.g., perceptions or attitudes toward their classroom environment). In a study designed to understand successful high school students, Reyes and Jason (1993) examined factors that distinguished the success and failure of Latino students from an inner-city high school. Based on their 9th-grade attendance rate and academic achievement, they identified 24 l0th-grade students as being at high risk for dropping out of school and 24 students as at low risk. They individually interviewed each participant on a number of topics that covered four areas: (a) family background, (b) family support, (c) overall school satisfaction, and (d) gang pressures. They found that there were no differences between the two groups on (a) socioeconomic status, (b) parent-student involvement, and (c) parental supervision. Low-risk students, however, reported significantly more satisfaction with their school than did high-risk students. On the other hand, high-risk students were more likely to respond that they had (a) been invited to join a gang and (b) brought a weapon to school. The two studies previously described are examples of the growing body of research trying to address the issue of why some Latino students do well in school and succeed in school, whereas others have not been successful. One concern with these studies, however, is that they typically use only one indicator of success (e.g., grades or achievement data for 1 year) rather than measures that more accurately reflect the construct of educational resilience or being successful over time despite attending at-risk school environments. Furthermore, these studies do not examine important psychosocial behaviors that have been found to significantly influence students’ cognitive and affective outcomes and several key motivational variables, like achievement motivation and academic self-concept.

Purpose of the Present Study Although basic skills deficiencies are often cited as the most critical educational problem for Latino students and other students at risk of failure (Slavin, 1989), fostering or maintaining an effective classroom learning environment has been suggested as a means of enabling them to be successful in school (Chavez, 1988; Padrón, 1992; Pierce, 1994). There have been a few studies that have looked at the classroom learning environment of students at risk of academic failure (Duncan & Newby, 1993; Pierce, 1994; Waxman 1989; Waxman, Huang, Knight, & Owens, 1992), but those studies have not specifically compared resilient and nonresilient students’ perceptions of their classroom learning environment and instructional learning environment in inner-city schools. Similarly, there have been very few studies that have examined the

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classroom learning environment of Latino students (Padrón, 1992). It is especially important to examine the learning environment of Latino students because there is some preliminary evidence that they perceive their learning environments very differently from English-monolingual students (Padrón, 1989) and African American students (Waxman, 1989). Furthermore, several studies have found that students perceive that there are differences in the ways high and low achievers are treated in the classroom (Babad, 1990; Weinstein, 1983, 1989; Weinstein & Middlestadt, 1979). Another concern with the prior research in the field is that most of the studies on learning environments have not included measures of students’ motivation and aspirations. It is important to include students’ motivation and aspirations as important aspects of the learning environment because they have been found to be highly related to both students’ academic achievement and the classroom learning environment (Cheng, 1994; Knight & Waxman, 1990, 1991; Uguroglu & Walberg, 1986). Furthermore, the variables of student motivation and classroom learning environments have often been researched and discussed separately, but they are so closely related conceptually that they need to be empirically examined together (Knight & Waxman, 1990). Only a limited number of studies, however, have investigated both the classroom learning environment and students’ motivation. The purpose of the present study is to compare resilient and nonresilient Latino students’ motivation and classroom learning environment in mathematics. In addition, other important background characteristics such as academic aspirations, attendance record, and student’ personal time allocation are examined between the two student groups because they have previously been found to be important variables that are related to students’ academic achievement (Dossey, Mullis, Lindquist, & Chambers, 1988). Furthermore, grade-and sex-related differences are examined in the present study because they have been previously found to affect at-risk students’ attitudes of their classroom environment (Duncan & Newby, 1993; Waxman & Eash, 1983). This study specifically addresses the following research questions: 1. Are there significant differences between resilient and nonresilient Latino students on background characteristics, academic aspirations, attendance records, and time allocation? 2. Are there significant differences between resilient and nonresilient Latino students on the dimensions of academic self-concept, achievement motivation, involvement, affiliation, satisfaction, and parent involvement? 3. Are there significant differences in the dimensions of academic selfconcept, achievement motivation, involvement, affiliation, satisfaction, and parent involvement by students’ sex and grade level? 4. To what extent do students’ background characteristics, classroom and instructional learning environment, and motivation discriminate resilient from nonresilient students?

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Methods Participants The present study was conducted in the five middle schools of a multicultural school district located in a major metropolitan city in the south central region of the United States. The school district was selected because it had relatively equal representations of Latino, African American, Asian Americans, and White students in each school and classroom. About 25% of the students enrolled in the district were Latinos, 30% were Whites (i.e., White, nonLatino), 25% were African Americans, and 20% were Asian Americans. In addition, this district was selected because Latino students represented an unsuccessful minority group. Latino middle school students in this district scored significantly lower than all other ethnic groups on statewide standardized achievement tests in mathematics and on the district-administered Four-Step Problem Solving Test (Hofmann, 1986). Furthermore, Latino students in this district had a significantly higher drop-out rate than all the other ethnic groups. Finally, we selected this school district because there is no tracking and students are heterogeneously grouped for mathematics. In other words, each mathematics class would generally include both resilient and nonresilient students. The majority of these middle school Latino students are foreign born, and the second largest number of these students were born in the United States but entered elementary school speaking a primary language other than English. Both groups of students typically received limited primary language instruction and were generally placed in submersion classroom environments with little special assistance. Most of the Latino students in the district came from working-class families. Most of these Latino parents do not have high school degrees, but they do have stable jobs in this urban community that has a large number of thriving businesses located within it. Despite the fact that school district is classified by the state as below average in property wealth, only 15% of the students come from low-income families. There is a very strong academic orientation in this district, as evidenced by the facts that nearly two thirds of the students in the district attend college, and only 6% of the students drop out of school. Furthermore, the composite standardized achievement test scores for middle school students in the district show students scoring at around the 70th percentile.

Instruments The following three standardized instruments were adapted and incorporated for use in the present study: (a) the Multidimensional Motivational Instrument (MMI; Uguroglu, Schiller, & Walberg, 1981; Uguroglu & Walberg, 1986),

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(b) the Classroom Environment Scale (CES; Fraser, 1982, 1986), and (c) the Instructional Learning Environment Questionnaire (ILEQ; Knight & Waxman, 1989, 1990). All of the items on these instruments were modified to a personal form in the present study, which elicits an individual student’s responses to his or her role in their mathematics class, rather than a student’s perception of the class as a whole (Fraser, 1991). The Achievement Motivation and Academic Self-Concept scales from the MMI were used in the present study. The instrument has been found to have test-retest reliability and construct and predictive validity (Uguroglu et al., 1981; Uguroglu & Walberg, 1986). The Achievement Motivation scale measures the extent to which students feel the intrinsic desire to succeed and earn good grades in mathematics, and the Academic Self-Concept scale measures the extent to which students exhibit pride in their classwork and expect to do well in mathematics. The CES is a questionnaire that has been widely used in a variety of different educational settings to measure students’ perceptions of their relationships with students and teachers as well as the organizational structure of the classroom. The content and concurrent validities of the CES have been established through correlational studies and classroom observation (Fisher & Fraser, 1983; Fraser, 1982, 1986; Moos, 1979). Adequate internal consistency reliability coefficients were also obtained in previous studies (Fisher & Fraser, 1983; Fraser, 1982, 1986; Moos, 1979). For the present study, the two scales that were used were (a) the Involvement scale, which measures the extent to which students participate actively and attentively in their mathematics class, and (b) the Affiliation scale, which measures the extent to which students know, help, and are friendly toward each other in their mathematics class. The ILEQ measures students’ perceptions of several aspects of their instructional learning environment. It has been found to have adequate internal consistency reliability coefficients and test-retest reliability coefficients (Knight & Waxman, 1989, 1990; Waxman et al., 1992). For the present study, the two scales that were used were (a) the Satisfaction scale, which measures the extent of students’ enjoyment of their mathematics class and school work in mathematics, and (b) the Parent Involvement scale, which measures the extent to which parents are interested and involved in what their children are doing in mathematics. Each scale from the three instruments includes four items, and all of the items were measured on a 4-point, Likert-type scale ranging from 1 (not at all true) to 2 (not very true) to 3 (sort of true) to 4 (very true). Student responses to each item within the same scale were added and averaged. Consequently, a mean value of 4 indicates that the student responded favorably with the scale, whereas a mean value of 1 indicates that the student responded unfavorably to the scale. Several background items selected from the National Educational Longitudinal Study of 1988 (NELS:88) were also included in the final study

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survey (Hafner, Ingels, Schneider, & Stevenson, 1990). These items included questions about students’ (a) background characteristics (e.g., mathematics grades), (b) academic aspirations (e.g., how far they will go in school), (c) attendance (e.g., number of days missed), and (e) time allocation (e.g., time spent on homework). Students’ mathematics achievement was measured using the Four-Step Problem Solving Test (Hofmann, 1986). This test consists of 10 nonroutine mathematics problems, each with four related questions: (a) reading to understand the problem, (b) selecting a strategy, (c) solving the problem, and (d) reviewing and extending the problem. It is a multiple-choice, paper-andpencil test designed to measure problem-solving mathematics skills of middle school students. The range for the total test is 0 to 40. The school district in the present study annually administers the Four-Step Problem Solving Test to all middle school students to assess their problem-solving achievement in mathematics, which is the district’s top priority in mathematics.

Procedures The scales from the three instruments and the background items from the NELS:88 survey were combined into one survey and were administered concurrently by trained researchers near the end of the school year during students’ regular mathematics class. We selected two scales from each of the three instruments because the school district only allowed us about 45 minutes to administer the combined survey instrument. Students were informed by the researchers that they were not tests and that completed questionnaires would not be seen by their teachers or other school personnel. All middle school students in the district were asked to complete the questionnaire as part of an ongoing evaluation of the mathematics curriculum. The response rate for the student questionnaire was about 97%, and it took students approximately 40 minutes to complete. From the entire population of Latino students in the district who completed the questionnaire, a stratified sample of 60 resilient and 60 nonresilient Latino students were randomly selected to be included in the present study. Students identified as gifted or talented, special education, or developmental were excluded from the population to avoid potential effects related to ability differences. Students were classified as resilient if they (a) scored on or above the 75th percentile on the district-administered, standardized FourStep Problem Solving Test over a 2-year period and (b) reported receiving A’s or B’s in mathematics over a 2-year period. Students were classified as nonresilient if they (a) scored on or below the 25th percentile on the Four-Step Problem Solving Test for a 2-year period, (b) reported receiving C’s, D’s, or F’s for mathematics this year, and (c) reported receiving B’s, C’s, D’s, or F’s in mathematics the previous year. A stratified sampling technique was used to

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obtain an equal number of students by sex and grade within each student group (i.e., resilient or nonresilient). Chi-square tests were used to compare the frequencies of responses between resilient and nonresilient students on the background items from the NELS:88 survey. A three-way multivariate analysis of variance (MANOVA) was used to determine (a) whether there are motivational and perceptional differences by students’ sex, grade, and student classification (resilient or nonresilient) and (b) whether there are any interaction effects by sex, student classification, and/or grade level. As a follow-up procedure, univariate analysis of variance (ANOVA) and post hoc multiple comparison tests were also performed to determine where the significant differences were. Finally, descriptive discriminant analysis was used to determine the extent to which the two groups differ with respect to their classroom learning environment, instructional learning environment, motivation, and background characteristics. To ensure adequate reliability and validity of the six scales used in this study, internal consistency (Cronbach alpha) reliability and discriminant validity (correlations between scales) were conducted. These coefficients were calculated using the individual student as unit of statistical analysis. The results indicated that the mean alpha coefficient of these scales was .60, and the individual coefficients ranged from .42 to .73, indicating that the survey instrument has adequate reliability given the few number of items per scale. The mean correlation between the scales was .29, and the individual correlations between scales ranged from .11 to .59, indicating that the survey instrument has adequate discriminant validity. We also examined the reliability and validity coefficients separately for resilient and nonresilient students but did not find any substantial differences between the two groups of students.

Results The descriptive and chi-square results for the two student groups revealed that there were no significant differences between the two groups on whether they spoke a non-English language before they started school, c 2(1) = 1.35, p = .256. About 76% of the resilient students indicated that they spoke a language other than English before they started school, whereas about 67% of the nonresilient students responded that they also spoke a language other than English before starting school. There were, however, statistically significant differences between the two groups on the extent to which students were held back a grade in school, c 2(1) = 23.48, p = .000. About 53% of the nonresilient students indicated that they were held back a grade in school, compared with only 13% of resilient students. There were significant differences between the two student groups on their academic aspirations. Resilient students were significantly more likely to indicate that they were sure that they would graduate from high school,

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c 2(2) = 17.01, p = .000, and they were significantly more likely to respond that they would graduate college and attend graduate schools, c 2(4) = 29.00, p = .000. About 78% of the resilient students indicated that they would graduate from high school, compared with only 43% of the nonresilient students. Similarly, over 90% of the resilient students indicated that they would graduate college or attend graduate school, compared with only about 46% of the nonresilient students. There were also significant differences between the two groups on attendance records. Resilient students were less likely to report cutting or skipping classes, c 2(3) = 10.53, p = .015, and being late for school than nonresilient students, c 2(4) = 21.87, p = .000. There were statistically significant differences between the two groups on two of the time allocation items. Resilient students reported that they spent significantly more time doing mathematics homework each week than nonresilient students, c 2(4) = 11.71, p = .020. Resilient students also indicated that they spent more time on additional reading than nonresilient students, c 2(4) = 21.81, p = .000. There were no significant differences between the two groups on the amount of time they spent watching television on weekends, c2(4) = 4.03, p = .402, or during the weekdays, c 2(4) = 4.89, p = .298, and on the amount of time spent listening to CDs, tapes, or the radio, c 2(4) = 7.54, p = .110. The three-way MANOVA results indicated that there are significant main effects of group and grade on middle school students’ motivation and perceptions of their learning environment. Resilient students’ overall motivation and perceptions of their mathematics classroom learning environment were significantly different from those of less resilient students, F(6, 103) = 7.36, p = .0001. Students’ overall motivation and perceptions of their learning environment also differed by grade, F(12, 206) = 1.97, p = .0280. There were, however, no significant main effects for sex, or interaction effects of (a) group by sex, (b) group by grade, (c) sex by grade, or (d) group by sex and grade. The descriptive and univariate ANOVA results for students’ motivation and perceptions by group and grade revealed that resilient students had significantly higher perceptions of involvement, F(1, 108) = 33.52, p = .000, satisfaction, F(1, 108) = 15.48, p = .000, academic self-concept, F(1, 108) = 28.10, p = .000, and achievement motivation, F(1, 108) = 13.15, p = .000, than nonresilient students. There were no significant differences between the two groups of students on the Affiliation, F(1, 108) = 3.18, p = .077, and Parent Involvement, F(1, 108) = 0.13, p = .718, scales. With the exception of parent involvement, the mean values for the resilient students were over 3.0, which indicates a highly positive attitude and motivation. With the exception of affiliation, the mean values on the scales for the nonresilient students ranged from 2.6 to 2.9. These values are slightly higher than 2.5 median value, which indicates that nonresilient students had slightly higher than average perceptions and motivation. The standard deviations were similar for the two groups, with the exception of achievement motivation for which nonresilient students had greater variation among their responses.

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In regard to the grade-related differences, sixth-grade students reported significantly higher involvement than seventh-grade students, F(2, 108) = 5.20, p = .007. Eighth-grade students also reported significantly higher achievement motivation than seventh-grade students, F(2, 108) = 3.40, p = .037. There were no significant differences on the Affiliation, F(2, 108) = 2.48, p = .088, Satisfaction, F(2, 108) = 2.35, p = .100, Parent Involvement, F(2, 108) = 0.91, p = .405, and Academic Self-Concept scales, F(2, 108) = 0.77, p = .464, among the three grade levels. The standard deviations were generally similar across the three grade levels. A discriminant function analysis was performed to determine the extent to which the two groups differ with respect to their classroom learning environment, instructional learning environment, motivation, and background characteristics. To reduce the large number of variables examined in this study to a more parsimonious model, only those variables that were previously found to differ significantly between the two groups were entered directly into a discriminant model to see how well they were able to discriminate between the two groups of students. Descriptive discriminant analysis was used instead of predictive discriminant analysis because the purpose of the analysis was to describe the MANOVA results (Huberty & Barton, 1989). The direct entry model examines the independent contribution of each of the variables in determining group membership. The model produced a Wilks’s lambda of .501, F(12,107) = 8.87, which was statistically significant at the p < .0001 level. The discriminant function had a canonical correlation of .71, indicating a moderately strong relationship between the groups and the discriminant function. The squared canonical correlation coefficient for the model was .50, indicating that about 50% of the variance between the two groups can be explained by the 12 variables in this model. A classification matrix revealed that overall, 86% of the cases were correctly classified, with 90% of the resilient student cases correctly classified and 83% of the nonresilient student cases correctly classified. The standardized discriminant function coefficients describe the impact or independent contribution of a given variable on the grouping variable, holding constant the impact of all the other discriminating variables. The results indicated that the variables of not held back in school, academic aspirations, and expectations for high school graduation were found to have the greatest impact, after adjusting for all the other variables in the analysis. The variables time spent on homework and academic self-concept were found to have the least impact on the grouping variable. The canonical structure coefficients for each variable provide an indication of the relative contribution of each variable to the overall discriminant function. It describes how closely a variable and the discriminant function are related. The results indicated that 10 of the 12 independent variables included in the discriminant analysis were found to have structure coefficient values of .40 or greater and have the greatest practical significance for distinguishing

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between resilient and nonresilient students. These variables are academic aspirations, involvement, academic self-concept, expectations for high school graduation, not held back in school, satisfaction, late for school, time spent reading additional material, achievement motivation, and time spent on homework. Only the variables of days missed in school and cut or skipped class do not appear to be highly related to the discriminant function.

Discussion In the present study, we specifically focused on Latino middle school students from a multicultural, metropolitan school district and found that their motivation and psychosocial processes significantly differed between resilient and nonresilient students. Despite coming from the same school environment and similar home environments, some Latino students have done exceptionally well in their mathematics classes, whereas others have done quite poorly. As expected, we also found that resilient students are much more motivated than their nonresilient classmates and that they are much more satisfied and involved with their mathematics classes. These findings are similar to other studies that have found that student satisfaction differentiates resilient and nonresilient students (Alva, 1991; Reyes & Jason, 1993). Another interesting finding of the present study was that there was not a statistically significant difference between resilient and nonresilient students on the extent to which they spoke a language other than English before arriving at school. About 76% of the resilient students and 67% of the nonresilient students spoke a non-English language before they started going to school. This finding lends supports to other studies that have similarly found that language factors are not significant predictors and do not hinder Latino secondary students’ academic achievement (Adams, Astone, Nunez-Wormack, & Smodlaka, 1994; Buriel & Cardoza, 1988). Unlike previous studies, the findings from the present investigation did not reveal any sex-related differences. The grade-related differences found in this study revealed that sixth-grade students were more involved than seventh-grade students and that eighth-grade students had higher achievement motivation than seventh-grade students. Although not statistically significant, seventh-grade students were also found to be less satisfied with their mathematics class than sixth- and eighth-grade students. Additional studies may want to specifically investigate why seventh-grade Latino students have lower perceptions than other middle school students. Curriculum factors and/or instructional processes may need to be explored. Another important finding from this study related to the high academic aspirations held by resilient Latino students. Although the findings for the nonresilient Latino students are very similar to the overall national results for Hispanic students from the NELS:88 (Peng, Wright, & Hill, 1995), the

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results for resilient students are much higher. Although there were no significant differences found between resilient and nonresilient students on their perceptions of parental involvement, which measures aspects of (a) parental interest (e.g., “My parents are often interested in what I do in mathematics”) and (b) parent expectations (e.g., “My parents expect me to do well in mathematics”), it is still possible that there might be differences in parents’ aspirations for their children. Another possible explanation for resilient Latino students’ high aspirations stems from the overall high academic press and expectations for students in the district. After all the quantitative data were collected, we informally asked two of the middle school mathematics teachers to help us explain why resilient students do significantly better in mathematics and have higher academic aspirations. They cited several personality traits like persistence and positive work habits that they thought distinguished resilient from nonresilient students. We specifically asked them about family characteristics that might distinguish the two groups of students, but they could not identify any family demographics that they thought were different. Further studies, however, may need to explicitly focus on students’ home and family characteristics. Students can be exposed to inappropriate educational experiences in either the family, school, or community (Pallas, Natriello, & McDill, 1989). Community demographics and family conditions, however, cannot be greatly changed by educators, whereas educational policy and practice can be modified to improve the education of students at risk (Comer, 1987; Waxman, 1992). Policymakers, administrators, teachers, and parents need to know why some students are successful and do well in school, whereas other students (a) from identical socioeconomic backgrounds, (b) from similar home environments, (c) with similar ability, and (d) from the same schools and classrooms do not do well academically. Examining these factors will allow us to investigate the circumstances that place these students at risk as well as those processes or factors that foster success. One of the major advantages of the approach of studying educational resilience is that it shifts us away from the educational research and policy perspective that has primarily focused on school failure and predictors of school failure to one that now focuses on the academic success of students who come from disadvantaged circumstances. In the present study, we examined indicators of at least four important factors that McMillan and Reed (1994) identified as being related to resiliency: (a) individual attributes, such as students’ motivation; (b) school and classroom factors, like satisfaction, involvement, and affiliation; (c) family factors, like parent involvement; and (d) positive use of time, like doing homework. Future studies should investigate other indicators of these four factors as well as examine other variables or factors that differentiate resilient and nonresilient Latino students. Nelson-LeGall and Jones (1991), for example, argue that classroom help-seeking behavior is a strategy or skill that allows learners to cope with academic difficulties and thus become

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a protective mechanism in the classroom learning context. Clark (1991) similarly suggests that social identity and support networks are resilient behaviors that need to be fostered and developed by students, and Barbarin (1993) maintains that we need to focus on the coping processes students use to mediate risk factors. These variables and others, like peer-group support, problem-solving skills, and students’ cognitive learning strategies, need to be explored in future studies. Although the present study specifically focused on examining motivational and psychosocial differences between resilient and nonresilient Latino students, other theoretical and conceptual work in the area has focused on the processes and mechanisms that can be developed and altered to facilitate students’ resilient behaviors. Rutter (1987, 1990), for example, has identified four processes that can be developed to facilitate resiliency: (a) reducing the risk impact and changing students’ exposure to the risk, (b) reducing the negative chain reactions that often follow exposure to risks, (c) improving students’ self-efficacy or self-esteem, and (d) opening up or creating new opportunities for students. Matsen (1994) has similarly described four strategies for fostering resiliency: (a) reducing vulnerability and risk, (b) reducing stressors, (c) increasing available resources, and (d) mobilizing protective processes. Swanson and Spencer (1991) provide some specific suggestions for enhancing most of these resiliency processes. They maintain that to reduce the risk impact, we should (a) increase access to academically challenging programs for disadvantaged students, (b) forge alliances between schools, churches, organizations, and businesses, and (c) increase funding for early childhood programs. To reduce negative chain reactions, Swanson and Spencer argue that teacher training, teacher recruitment, and teacher retention need to be addressed and altered, and parent involvement in schools also needs to be increased. To improve students’ self-efficacy, they argue that schools should recognize and demand academic performance and also redesign classrooms into heterogeneous ability groups rather than track by ability level. Finally, to open up opportunities, they maintain that there should be increased funding for compensatory education, student financial aid, pilot programs, and updated technological equipment. They also call for integrating resources from schools, businesses, and communities to help students make a smooth transition from the school to work environment. Although the results of the present causal-comparative study do not allow us to lend support to Rutter’s (1987, 1990), Matsen’s (1994), and Swanson and Spencer’s (1991) research, the findings from this study suggest that future experimental studies examining areas such as improving students’ motivation and self-efficacy may be warranted. An important methodological consideration that needs to be examined in other studies is the criteria chosen to define educationally resilient and nonresilient Latino students. Several specific criteria were chosen for the present study. First, standardized achievement test scores for a 2-year period were

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used. Because the construct of educational resilience suggests sustained success or success over time, it is important that at least two measures of achievement over time are used. Standardized test scores are admittedly a narrow measure of students’ achievement, but they do represent one of the primary outcomes that school districts use to assess their educational accomplishments. The addition of student grades as a criterion helps support the success criteria. Again, we used grades from a 2-year period to examine the resilient criteria. Resilient students received A’s or B’s for a 2-year period, whereas nonresilient students typically received C’s or less for the 2-year period. Finally, the selection of only mathematics test scores suggests that a student may be educationally resilient in one content or subject area but not resilient in another. Given the large body of research that has found that there are content-specific attitudinal, instructional, curricular, and achievement differences for students (Needels & Gage, 1991; Stodolsky, 1988; Stodolsky & Grossman, 1995), it may be important to conduct content-specific research on resilience before we determine whether or not educational resilience is a content-specific or generic phenomenon. In other words, additional studies should examine if educational resilience is content specific (i.e., different according to the content area examined) or generic (i.e., similar across all content areas). Although the findings from the present study have some important educational implications, further descriptive, correlational, and especially experimental research is needed to verify these results. Longitudinal studies are also essential to adequately study the educational resilience phenomena. It is important to investigate at what point resilience develops, and it is also necessary to look at the long-term stability of the construct. Further studies also need to specifically examine how aspects of the classroom learning environment and instructional learning environment can be changed so that they can serve as protective mechanisms for students in at-risk school environments (Waxman, 1992). In addition, affective or motivational training programs may need to be developed and implemented to see if they improve Latino students’ affective and cognitive outcomes. These and similar issues should be examined so that we can continue to understand why some Latino students are resilient and how we can help other students develop resiliency and become more successful.

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Matsen, A. S., Best, K. M., & Garmezy, N. (1990). Resilience and development: Contributions from the study of children who overcome adversity. Development and Psychopathology, 2, 425–444. McMillan, J. H., & Reed, D. F. (1994). At-risk students and resiliency: Factors contributing to academic success. The Clearing House, 67, 137–140. Moos, R. H. (1979). Evaluating educational environments: Procedures, measures, findings, and policy implications. San Francisco: Jossey-Bass. Needels, M., & Gage, N. L. (1991). Essence and accident in process-product research on teaching. In H. C. Waxman & H. J. Walberg (Eds.), Effective teaching: Current research (pp. 3–31). Berkeley, CA: McCutchan. Nelson-LeGall, S., & Jones, E. (1991). Classroom help-seeking behavior of AfricanAmerican children. Education and Urban Society, 24, 27–40. Padrón, Y. N. (1989, April). A comparison of bilingual and English-monolingual students’ perceptions of their classroom learning environment in reading. Paper presented at the annual meeting of the American Educational Research Association, San Francisco. Padrón, Y. N. (1992). Comparing bilingual and monolingual students’ perceptions of their classroom learning environment. In H. C. Waxman & C. D. Ellett (Eds.), The study of learning environments (Vol. 5, pp. 108–113). Houston: University of Houston. Pallas, A. M., Natriello, G., & McDill, E. L. (1989). The changing nature of the disadvantaged: Current dimensions and future trends. Educational Researcher, 18(5), 16–22. Peng, S. S., Wright, D., & Hill, S. T. (1995). Understanding racial-ethnic differences in secondary school science and mathematics. Washington, DC: National Center for Education Statistics. Pierce, C. (1994). Importance of classroom climate for at-risk learners. Journal of Educational Research, 88, 37–42. Reyes, O., & Jason, L. A. (1993). Pilot study examining factors associated with academic success for Hispanic high school students. Journal of Youth and Adolescence, 22, 57–71. Reyes, P., & Valencia, R. R. (1993). Educational policy and the growing Latino student population: Problems and prospects. Hispanic Journal of Behavioral Sciences, 15, 258–283. Rutter, M. (1987). Psychosocial resilience and protective mechanisms. American Journal of Orthopsychiatry, 37, 317–331. Rutter, M. (1990). Psychosocial resilience and protective mechanisms. In J. Rolf, A. Masten, D. Cichetti, K. Nuechterlein, & S. Weintraub (Eds.), Risk and protective factors in the development of psychopathology (pp. 181–214). New York: Cambridge University Press. Slavin, R. E. (1989). Students at risk of school failure: The problem and its dimensions. In R. E. Slavin, N. L. Karweit, & N. A. Madden (Eds.), Effective programs for students at risk (pp. 3–19). Boston: Allyn & Bacon. Stodolsky, S. S. (1988). The subject matters: Classroom activity in math and social studies. Chicago: University of Chicago. Stodolsky, S. S., & Grossman, P. L. (1995). The impact of subject matter on curricular activity: An analysis of five academic subjects. American Educational Research Journal, 32, 227–249. Swanson, D. P., & Spencer, M. B. (1991). Youth policy, poverty, and African-Americans: Implications for resilience. Education and Urban Society, 24, 148–161. Uguroglu, M. E., Schiller, D. P., & Walberg, H. J. (1981). A multidimensional motivational instrument. Psychology in the Schools, 18, 279–285. Uguroglu, M. E., & Walberg, H. J. (1986). Predicting achievement and motivation. Journal of Research and Development in Education, 19, 1–12. Wang, M. C., & Gordon, E. W. (Eds.). (1994). Educational resilience in inner-city America: Challenges and prospects. Hillsdale, NJ: Lawrence Erlbaum.

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Wang, M. C., Haertel, G. D., & Walberg, H. J. (1994). Educational resilience in inner cities. In M. C. Wang & E. W. Gordon (Eds.), Educational resilience in inner-city America: Challenges and prospects (pp. 45–72). Hillsdale, NJ: Lawrence Erlbaum. Waxman, H. C. (1989). Urban Black and Hispanic elementary school students’ perceptions of classroom instruction. Journal of Research and Development in Education, 22, 57–61. Waxman, H. C. (1992). Reversing the cycle of educational failure for students in at-risk school environments. In H. C. Waxman, J. Walker de Felix, J. Anderson, & H. P. Baptiste (Eds.), Students at risk in at-risk schools: Improving environments for learning (pp. 1–9). Newbury Park, CA: Corwin. Waxman, H. C., & Eash, M. J. (1983). Utilizing students’ perception and context variables to analyze effective teaching: A process-product investigation. Journal of Educational Research, 76, 322–325. Waxman, H. C., Huang, S. L., Knight, S. L., & Owens, E. W. (1992). Investigating the effects of the classroom learning environment on the academic achievement of at-risk students. In H. C. Waxman & C. D. Ellett (Eds.), The study of learning environment (Vol. 5, pp. 92–100). Houston: University of Houston. Weinstein, R. S. (1983). Student perceptions of schooling. Elementary School Journal, 83, 287–312. Weinstein, R. S. (1989). Perceptions of classroom processes and student motivation: Children’s views of self-fulfilling prophecies. In C. Ames & R. Ames (Eds.), Research on motivation in education: Goals and cognitions (Vol. 3, pp. 187–221). San Diego, CA: Academic Press. Weinstein, R. S., & Middlestadt, S. E. (1979). Students’ perceptions of teacher interactions with male high and low achievers. Journal of Educational Psychology, 71, 421–431. Winfield, L. F. (1991). Resilience, schooling, and development in African-American youth: A conceptual framework. Education and Urban Society, 24, 5–14.

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57 Attracting and Retaining Teachers: A Question of Motivation Karin Müller, Roberta Alliata and Fabienne Benninghoff

Introduction

M

atching vacant teaching posts with qualified candidates is a key issue for the organization and running of schools. Given the cyclical patterns of teacher supply and demand, this matching operation is not an easy one. In a bid to overcome its short-term, annual recruitment horizon and to take early political action in order to avoid a shortage or surplus of teachers, the Canton of Geneva’s Education Department put in place a human resources planning system (Gestion prévisionnelle des enseignants [GPE]) which allows the Department to forecast demand for teachers up to five years in advance. All in all, the Education Department employs 7300 teachers at primary and secondary school level, teaching students from age 4 to age 19. However, forecasting the number of teachers needed is not enough for purposes of directing policy responses. What are the most significant measures for attracting and retaining competent teachers within the profession? The Canton of Geneva’s human resources planning system for teachers consists of complementary tools (i.e. a database, a dashboard of indicators, a prospective system and also surveys) to deliver information that will enable decision makers to identify areas where action might be particularly effective. The two surveys are aimed at a better understanding of the key stages in teaching careers: the motivation for entering teaching and the reasons for leaving the profession and taking early retirement.

Source: Educational Management Administration & Leadership, 37(5) (2009): 574 –598.

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The objective of our present article is to define to what extent an understanding of these different types of teacher motivation can provide a decision framework for defining teacher policies that will make it possible to attract, retain and develop effective teachers.

Education System in Switzerland Swiss Institutional Background Switzerland has a federalist system where responsibility for education is divided between the Confederation (e.g. vocational training and tertiary education) and the cantons (e.g. compulsory schooling). However, responsibilities are not distributed in a simple, dichotomic way between the Confederation and the cantons. The Confederation and the cantons cooperate and provide mutual support for each other, in a spirit of ‘co-operative federalism’. The new Federal Constitution of 18 April 1999 confirms the historical sovereignty of the 26 cantons: ‘the cantons are sovereign insofar as their sovereignty is not limited by the Federal Constitution; they shall exercise all rights which are not transferred to the Confederation’ (Article 3). In concrete terms, this means that the cantons have the right to legislate in certain domains. This is also the case for the education sector, and, according to the Federal Constitution: 1) Education is a cantonal matter. 2) The cantons are to ensure sufficient primary education, open to all children. This education shall be compulsory, and shall be placed under state direction or supervision. It shall be free in all public schools. The school year shall begin between mid-August and mid-September. (Article 62)

Since the Swiss system of education is essentially the responsibility of the cantons, it is not correct to talk about a Swiss education system, since Switzerland does not have a single ‘Ministry of Education’ but rather 26 independent and distinct systems. Within the cantons, educational responsibilities are administered by the cantonal departments of education.

Teacher Policy on the Current Political Agenda The Teacher’s Key Role in a Changing Environment Interest in teacher policy research has intensified over the last few years for a number of reasons. First of all, key correlations exist between teacher quality and working conditions, on the one hand, and student learning, on the other. These correlations offer extensive political leverage for

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improving school performance (e.g. Rivkin et al., 1998; Gustafsson, 2003; SECTQ, 2004). Second, given the size of the teacher workforce, policies that address issues like working conditions or curriculum reforms have a major impact on the organization and coordination of schools. Recent research projects have thus focused particularly on understanding the teacher’s role in respect of changes in society, the economy and schools in order to define effective teacher policies. The high level of international involvement (a total of 25 countries) in a recent study conducted by the Organisation for Economic Co-operation and Development (OECD), focusing on ‘attracting, developing and retaining effective teachers’, illustrates the scale of global interest (OECD, 2005). In Switzerland, worries about attracting, recruiting and retaining teachers have also been addressed by the Swiss Conference of Cantonal Ministers of Education (CDIP) that has drawn up guidelines for a recruiting strategy for teachers (Müller et al., 2003).

Geneva’s Human Resources Planning System for Teachers Education being primarily the responsibility of cantons, Geneva’s Department of Education is also in charge of the planning and management of teaching personnel. In 2001, the Education Department decided to set up GPE – making it possible to anticipate recruitment needs and define policy options over a mid-term horizon of four to five years. The planning system sets out to capture the most relevant factors influencing the supply and demand of teachers and to provide valuable assistance to policymakers for the recruitment of competent teachers. The GPE management tool is made up of four instruments: (1) the database, which constitutes the central database for teaching personnel; (2) the dashboard, with indicators that make it possible to track the evolution of the education system; (3) the prospective system, which is used as a tool to estimate quantitative needs for teaching personnel; and (4) the surveys, which permit the identification of key factors that are likely to influence the movements of teaching personnel (motivation for entering the profession and motivation for taking early retirement).

Theoretical Framework and Analysis of the Literature Work Motivation Theories When it comes to work motivation, many theoretical strands have been put forward to explain the relationship between individual motivation, job satisfaction and performance at work. The underlying hypothesis is that, with given individual capacities (intellectual, physical, know-how) and the organization put in place by a firm or administration (technical, human resources,

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administrative), motivation can directly influence the individual performance of each employee – and ultimately influence the success of an organization. Although there are multiple definitions of motivation, a certain consensus has evolved on the main dimension that characterizes motivation. In fact, since motivation is difficult to observe directly, it has been defined by the behaviour that individuals are supposed to develop (Roussel, 2000). Vallerand and Thill (1993: 18) summarize the concept of motivation as a ‘hypothetical construct that is used to describe internal and/or external forces that generate the kickoff, the direction, the intensity, and the persistence of behaviour’. As a result, motivation can be defined as ‘a process that activates, orients, reinforces and maintains the behaviour of individuals towards the achievement of intended objectives’ (Roussel, 2000: 5). Ryan and Deci’s (2000a: 54) definition of motivation underlines this process-oriented concept: ‘to be motivated means to be moved to do something. A person who feels no impetus or inspiration to act is thus characterized as unmotivated, whereas someone who is energized or activated toward an end is considered motivated’. Based on Kanfer’s (1990) taxonomy of theories of motivation, there are three main paradigms that regroup current theoretical approaches: the first paradigm regroups need-motive-value approaches: according to these motivation theories, what leads an individual to start a type of behaviour, to direct it towards specific objectives and to support it both intensely and persistently is explained by needs, values and motives that have to be satisfied (e.g. Maslow’s need hierarchy theory, Alderfer’s ERG theory, Herzberg’s dual-factor theory, McClelland’s achievement motivation theory, Adams’ equity theory). The second paradigm regroups cognitive-choice theories: this paradigm rests on the guiding principle that ‘behaviour is determined by the subjective value of the objectives towards which the individual is working, but also by their expectancy to see their behaviour producing the required results’ (Oubraye-Rossel and Roussel, 2001) (e.g. Vroom’s expectancy theory, Weiner’s attribution theory). The third paradigm regroups self-regulation/metacognition theories: these theories try to explain how goals can have an effect on individual work motivation and to understand the processes that determine the objectives chosen by the worker. These theories include Carver and Scheier’s control theory, Locke’s goal-setting theory and the social learning theory of Bandura). Self-regulation is a fairly new construct of motivation, and recent research on strategies for enhancing motivation, have focussed on its promotion. The term self-regulated can be used to describe performance guided by three key processes: self-observation (monitoring one’s activities), self-judgement (self-evaluation of one’s performance) and self-reaction (reactions to performance outcomes) (Zimmermann and Schunk, 2001). We place our study within the theoretical framework of the first paradigm, which aims to identify the internal and external forces that have an impact on an individual’s motivations. More specifically, we make reference to the cognitive evaluation theory (Deci, 1971; Deci, 1975; Amabile et al., 1976;

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Zuckermann et al., 1978) that has been extended into the self-determination theory (Ryan et al., 1985; Gagné and Deci, 2005). These theories draw a distinction between two fundamental types of motivation. According to Ryan and Deci (2000a: 55) ‘the most basic distinction is between intrinsic motivation, which refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, which refers to doing something because it leads to a separable outcome’. Intrinsic motivation is also described as an ‘inherent tendency to seek out novelty and challenges, to extend and exercise one’s capacities, to explore, and to learn’ (Ryan and Deci, 2000b: 70), while extrinsic motivation regulates behaviour ‘in order to attain a separable outcome’ (Ryan and Deci, 2000b: 71). Self-determination theory considers extrinsic motivation from the angle of autonomy and control. It states that extrinsic motivation varies greatly with regard to its degree of autonomy: from external regulation (controlled motivation) right through to integrated regulation (autonomous motivation). The latter results from external values and behavioural regulations that tend to be internalized through socialization, thus leading to self-regulated behaviour (goal internalization). This means that ‘a behavioural regulation and the value associated with it have been internalized. Internalization is defined as people taking in values, attitudes or regulatory structures, such that the external regulation of a behaviour is transformed into an internal regulation and thus no longer requires the presence of an external contingency’ (Gagné and Deci, 2005: 334). To sum up, research findings on work motivation generally identify three sources of work motivation: intrinsic motivation, extrinsic motivation, and goal internalization as a subgroup of extrinsic motivation. Studies (e.g. Deci, 1971; Lepper et al., 1973; Deci, 1975) that analysed the relationship between intrinsic and extrinsic motivations showed that they are not necessarily independent of each other and that they can interact positively or negatively. These studies revealed, for example, that extrinsic rewards, such as pay, can have a detrimental effect on intrinsic interest and task persistence. However, these undermining effects of extrinsic rewards do not occur automatically. According to Kanfer (1990: 88): ‘Fisher (1978), for example, showed that financial rewards did not affect intrinsic motivation in situations consistent with societal norms about the role of pay for time and effort in real jobs’.

Motivation in Organization Theory and Human Resources Management Employee motivation is regarded as a critical factor by organization and human resource management theories, since organizations that can create work environments that attract, motivate and retain effective individuals will be better positioned to succeed in a competitive environment. As a consequence, these theories set out to define organizational designs and human resource strategies that ensure high employee motivation.

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Motivation-based organization theories that adopt a behavioural view emphasize the difference between intrinsic and extrinsic motivations (Argyris, 1964; McGregor, 1960; Osterloh et al., 2001). Drawing on the findings of psychological approaches, such as the cognitive evaluation theory (Deci, 1975) and the observed relationship between extrinsic and intrinsic motivations, organization theories aim to develop strategies to manage the potential trade-off between the two types of motivation. Osterloh and Frey (2000) state that there are three aspects that should be taken into account when considering the integration into an organization of market elements, such as profit centres or variable pay for performance: increased control, reduced personal relationships and also performance-based rewards have potentially negative effects on intrinsic motivations. Qualified and motivated employees are considered to be a key factor for organizational success, according to resource-based human resource management theory (Wright and McMahan, 1992). Human resource management strategies are used to develop policies to select, develop, motivate and retain employees. Among these workforce management approaches, motivational inducement systems are applied in order to energize, direct, or sustain behaviour within organizations. Leonard et al. (1999) distinguish four commonly employed inducement systems applied in organizations: reward systems, managerial systems, task systems and social systems.

Findings Regarding Teacher Motivation In line with the theoretical framework of work motivation cited above, both Kyriacou and Coulthard’s (2000) and Obin’s (2002) findings on the motivational choices that prompt people to enter teaching lead to three distinct categories: (1) intrinsic reasons related to the teaching activity itself, such as the transmission of subject knowledge and expertise; (2) extrinsic reasons, such as working conditions, autonomy, pay level, job security and status; and (3) altruistic reasons, such as the desire to help children to succeed and the consideration of teaching as a socially valuable profession. Within the self-determination theory, this latter category may be considered as internalized extrinsic motivation, since it represents values associated with the teaching profession. Surveys carried out in the UK (Sturman, 2004), Australia (MCEETYA, 2003) and France (Esquieu, 2003; Esquieu, 2005) reveal a remarkable stability of motivational hierarchy: extrinsic aspects of a teacher’s job play an important role in respect of job security, flexibility to organize work and autonomy in pedagogic choices. Salary and financial benefits, though, are less important for those considering teaching. Some research findings suggest that pay incentives are unsuccessful in increasing teacher motivation, since teachers are mainly motivated by gratification derived from higher-order needs, such as social relations and esteem (Sylvia and Hutchinson, 1985).

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Barmby and Coe (2004) conclude from their literature survey that working conditions are nevertheless important considerations for teachers: stress, long hours and relatively low remuneration are decisive factors that discourage potential candidates from choosing teaching as a career. Moreover, research into teacher motivation has revealed that key correlations exist between a student’s motivation and the teacher’s motivation. Pelletier et al. (2002: 193) found that ‘by the same way students could become less self-determined when exposed to controlling teachers, our results indicate that, when teachers are pressured by the school’s administration or by colleagues to behave in a specific manner, they also indicate that they are less self-determined toward their work’. Furthermore, the less teachers are selfdetermined towards teaching, the more controlling they become with students, which has a negative effect on the student’s intrinsic motivation and selfdetermination (Reeve et al., 1999). However, existing research does not establish a clear consensus regarding the benefits of teacher motivation for increased levels of student achievement (Bishay, 1996). In addition, a recent study conducted in the UK (Day et al., 2006) performed a quantitative analysis of the variations in a teacher’s lifecycle on their motivation. The authors identified six professional life phases related to a teacher’s experience and their relationship with specific motivational or demotivational factors. The first phase (0–3 years of experience) was thus associated with a crucial motivational factor, namely the support of the school and department leaders. Conversely, declining pupil behaviour had a negative impact on the motivation of this population of ‘novice’ teachers. As far as the second phase was concerned (4 –7 years), the study identified the management of heavy workloads as being the most demotivating factor. In phase 3 (8–15 years), holding positions of responsibility, with the possibility of progression in their career, had a positive impact on the motivation of this teacher group. In phase 4 (16–23 years), further career advancement and good results had a positive impact on teacher motivation. Phase 4 was also associated with a large number of negative motivational factors, however, such as managing heavy workloads, facing additional responsibilities in school or demands outside of school, achieving a work-life balance, a feeling of career stagnation, lack of support in school and poor pupil behaviour. As for phase 5 (24 –30 years), the most important reasons for teacher demotivation were a lack of support in school and bad pupil behaviour. Finally, in phase 6 (31 years and above), teachers generally considered they were having positive teacher-pupil relations and appreciated pupils’ progress. In contrast, however, health issues were beginning to surface, and teachers were demotivated by government policies and pupil behaviour. Research into teacher motivation is also often related to research into job satisfaction. According to Scholl (2002a: 2) these are ‘related but distinct behavioural forces with different determinants and different outcomes’. While motivation is generally ‘future directed’ and has previously been defined as a

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process that activates, orients, and maintains the behaviour of individuals towards the achievement of intended objectives, job satisfaction is defined as the ‘extent to which expectations are met resulting in positive feelings’ (Scholl, 2002b: 3) and is therefore more ‘present directed’. Scholl (2001: 1) states that ‘dissatisfaction generally manifests itself in low membership motivation (absenteeism, turnover), and may result in the reduction of Extra Role Behaviour originally motivated by one of the inducement systems’. Research focusing on teachers and retention shows that teachers are more satisfied with their job, (1) if they feel supported by the school administration and by parents, (2) if they benefit from a certain autonomy in carrying out their job, and (3) if student behaviour and the school atmosphere are pleasant (NCES, 1997; Forneck et al., 2000; Gonik et al., 2000). Conversely, the physical and psychological fatigue of teachers increases, (1) if they face difficult relations with students and parents, (2) if they are subject to numerous reforms (pedagogic, organizational, technological, etc.), (3) if administrative tasks are increased, and (4) if they believe that teaching has lost its positive image (Spear et al., 2000; Basaglia and D’Oria, 2003; Cros and Obin, 2003; Papart, 2003). Studies that investigated specific reasons given by teachers for leaving their job mention the following factors as being particularly decisive: too heavy a workload, numerous government initiatives and reforms, the desire to take up a new challenge, a discouraging school situation (student behaviour, school management, etc.), stress, and personal circumstances (Smithers and Robinson, 2003; Luekens et al., 2004).

Towards a Decision Framework for an Effective Teacher Policy Our present study is setting out to develop a decision framework for an effective teacher policy based on teacher motivation. First of all, taking work motivation theory as a basis, we single out those motivations that are particularly significant for explaining decisions to enter or leave the teaching profession. Having identified these main sources of teacher motivation, we then focus on those that are potentially accessible to human resource policy measures, in a bid to identify a teacher workforce policy that will make it possible to attract, develop and retain effective teachers.

Method and Data Sources Method The GPE has been conducting an annual survey since 2002, in a bid to better understand teachers’ motivation for entering and also for leaving the teaching profession. In this article, we present the results of the most recent

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surveys (candidates: 2004/5 academic year; teachers taking early retirement: 2003/4 academic year). We saw that the results were homogenous over the years, indicating the potential transferability of the findings. Anonymous questionnaires were sent by post to all the candidates who fulfilled all the recruitment requirements (population 1) and to all the teachers taking early retirement (population 2).

Participants and Instruments Survey of Motivations for Entering Teaching The most recent survey among potential future teachers was distributed to 590 candidates who fulfilled all the recruitment requirements. The participation rate was 52% (306 questionnaires returned).2 Women constitute the majority in the candidate survey (66%). They apply more for jobs as primary teachers (82%) than as secondary-level teachers (53%). Even though most candidates are aged between 20 and 29 (48%), a large proportion are between 30 and 39 years of age (32%) or even aged 40 or more (20%). In general, women candidates are younger than male candidates. This tendency is more marked for those applying for posts as primary teachers. Candidates took the decision to enter teaching at very different times: 36% decided to take up teaching 5 or more years ago, 42% between 1 and 5 years ago and 24% less than a year ago. Recent decisions are more common among candidates applying to be secondary teachers. It should also be noted that a quarter of candidates decided to enter the teaching profession after initial professional experience in another field. The questionnaire drawn up for carrying out the candidates’ survey included 43 questions on the motivation for entering teaching and 12 questions for measuring the teachers’ socio-demographic characteristics. In order to structure the analysis, 35 items were grouped in seven motivation categories: (1) humanistic values; (2) professional vocation; (3) working conditions; (4) personal experience; (5) social status; (6) mobility; and (7) choice by default.

Survey of Motivations for Leaving Teaching The questionnaire for teachers taking early retirement was sent to 204 teachers, 121 of whom (59%) replied. Somewhat more than 50 percent of the teachers decided to take early retirement less than one year prior to reaching retirement age, and slightly more than one third took this choice less than three years prior to retirement age.3 The average age of teachers taking advantage of the early retirement plan (Plan d’encouragement au départ anticipé, Plend4) is 59 years. Teachers in primary education – the

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majority of whom are women – are generally younger when they leave the profession – at an average age of 57 years. In terms of the geographical location of the last school in which they worked, we saw that two-thirds of the teachers came from urban areas, one teacher in five from rural areas and 11% from suburban areas. For the purpose of this second survey, the questionnaire was structured in three sections. In the first section, the teacher was invited to draw up an assessment of their career and to reflect on the positive features and weaknesses of the teaching profession. The second section was the longest in the questionnaire, since it included a question made up of 38 items, each of which constituted a reason for leaving teaching. These items were grouped in eight categories related to: (1) work conditions; (2) workload; (3) quality of relationships with principals; (4) fatigue and health; (5) private life (a wish to spend more time with the family); (6) school policy; (7) Plend characteristics; and (8) private life (a wish to spend more time on leisure activities). This last section measured five socio-demographic characteristics of participants.

Data Analysis Data analysis was carried out in four stages: (1) a descriptive data analysis (frequencies) was used to draw up profiles of teachers according to their socio-demographic characteristics together with their motivation for entering or leaving the teaching profession; (2) a bivariate analysis (chi-squared tests) was applied, taking into account motivation for entering or leaving teaching together with socio-demographic variables, such as gender and the educational level being taught; (3) a multivariate analysis (factor analysis, cluster analysis) made it possible to identify different groups on the basis of the teacher’s motivational profile with regard to their decision to enter or leave teaching. The objective of the factor analysis is to reduce the large number of variables to fewer dimensions and to achieve a twodimensional representation of the essential information. This reduction is possible on account of the correlations that exist between the variables and is achieved by constructing synthetic variables, through a linear combination of the initial variables (Benzécri, 1973; Lebart et al., 1995). As far as cluster analysis is concerned, this consists in grouping the closest elements together in order to produce homogenous classes of individuals (Gordon, 1981; Lebart et al., 1995). Then (4), in order to map the motivations in more detail, we depicted the reasons for entering or leaving teaching on two matrixes. The x-axis represents the respective percentage of teachers who agreed with the entry or exit motivations suggested in the questionnaire (scale 1 to 10). On the y-axis, we classified each motivation on a scale according to its accessibility and responsiveness to policy measures in order to identify potential leverage (scale 1 to 10). The upper right quadrant of

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the two resulting matrixes thus sets out the critical motivations that are highly responsive to political actions taken by educational decision makers. Finally, by comparing these two matrixes, we set out to identify transversal teacher policy priorities.

Results In the following, we highlight four groups of results that are organized on the basis of the data analysis stages set out above. The first three analyses are grouped according to survey.

Analysis of Motivations for Entering Teaching Descriptive Analysis of Entry/Exit Motivations The main motivations for teaching are grouped into three categories. Humanistic values are those which motivate candidates the most – for example, the wish to work in contact with children and young people (91 %), to help them succeed (95%), or the desire to transmit knowledge to them (88%), and the desire to give all students an equal chance (86%). Motivations associated with professional vocation – for example, identification with the teaching profession (76%), the possibility of exercising a profession they feel passionately about (93%) – and work conditions linked to the characteristics of the profession – for example, the possibility to work in a spirit of cooperation (91%) and to carry out an evolutionary and demanding job (91%) – also constitute key motivation categories for entering the profession (Table 1).

Differences in Motivational Orientation with Respect to Education Level and Gender The global results presented above obviously mask certain disparities. It is clear, for example, that significant differences (chi-squared tests, p < 0.05) exist among the motivations as a function of education level and gender. As far as education level is concerned, candidates applying to be primary teachers have a tendency to place more importance on the humanistic values and psychological aspects of teaching, as well as on the social role and the evolutionary and demanding aspect of the job, whereas candidates applying to be secondary teachers are more attracted by work conditions, and mainly by the flexibility of the schedule and the holidays. With regard to gender, we find that female candidates are more motivated by the relational and psychological aspects of the teaching profession. Furthermore, their job applications have more frequently been stimulated by previous professional experience in teaching.

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Table 1: Entry motivations by education level (classified by categories) (in %) Entry motivations Category 1: Humanistic values Wish to help children and young people to succeed Opportunity to put key values into practice Wish to work in contact with children and young people Interest in work where human relations are important Wish to transmit knowledge to children and young people Interest in didactic and pedagogical aspects Opportunity to give all students an equal chance Interest in psychological aspects Wish to contribute to improving society Category 2: Professional vocation Profession that can be exercised with passion Identification with the teaching profession Teaching is still a useful profession An opportunity to avoid routine A vocation A profession for life A profession to be exercised for a few years only Category 3: Work conditions Category 3a: Characteristics of the profession Wish to work in a spirit of cooperation and sharing experience Motivated by an evolutionary and demanding job Appreciation of the autonomy and independence of teaching Interest in the possibilities for continuous training/professional development Motivated by on-the-job teacher training An opportunity to take on interesting responsibilities Category 3b: Extrinsic conditions Opportunity to reconcile private and professional life Appreciation of flexibility in schedule and activity rate Appreciation of a stable and secure job An opportunity to grant importance to family life Wish for sufficient holidays and leisure time Motivation of social security benefits and salary Constitutes an interesting complementary activity Category 4: Personal experience Currently the most appropriate choice Motivated by previous teaching experience Education pursued confirms this choice

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Total (n = 306)

Primary level (n = 129)

Secondary level (n = 165)

95

98

92

91 91

95 98

86 85

89

95

84

88

85

90

86 86

92 85

81 87

83 82

91 86

76 78

93 76 76 72 68 56 15

94 78 74 80 67 56 15

92 73 78 66 69 56 15

91

95

86

91

96

86

80

76

82

78

85

74

71 47

71 50

70 45

71

70

72

68

59

72

62 61

54 62

67 59

48

41

52

42

33

49

26

24

27

72 70 61

71 64 68

73 72 56

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Entry motivations Category 5: Social status Finds it meaningful to exercise a profession of general interest Wishes to practice a profession that has an important social role Teaching is a profession valued by society Aspires to attain the social status associated with the profession Category 6: Mobility Interest in working in different sectors Opportunity to work in different schools and locations within the canton Permits a professional change Permits work in different cantons and countries Category 7: Choice by default Main objective of studies Gave up an academic or research career Difficulty in finding another job

Attracting and Retaining Teachers

399

Total (n = 306)

Primary level (n = 129)

Secondary level (n = 165)

65

64

66

58

68

50

22 19

24 13

21 23

57 37

56 44

57 33

34 29

31 32

37 27

38 23 14

45 19 9

33 26 18

Note: The percentages refer to the respondents who stated that their choice to enter teaching was influenced ‘quite a lot’ or ‘very much’ by each motivation.

Motivational Typologies of Teachers A multiple correspondence analysis (Benzécri, 1973; Lebart et al., 1995) summarized the various response categories for the entry motivation variables in factors whose values were estimated for each individual. A hierarchical cluster analysis was performed on the resulting factor values, using Ward’s (1963) algorithm, in order to establish groups of teachers who were as homogeneous and as distinct from other groups as possible. This analysis of the motivational profiles of teacher candidates provided four groups. The ‘passionate’ group takes in 37% of all candidates. They identify strongly with the motivations related to the social dimension of teaching and the evolutionary nature of the job (e.g. an interest in professional development). They are also strongly motivated by the prospect of transferring their subject knowledge to students. The ‘engaged’ group accounts for another 37% of all candidates. Their motivational profile corresponds largely to that of the previous group. Their degree of agreement is less strong, however. Finally, candidates with ‘mitigated’ (9%) and ‘disillusioned’ (17%) motivations represent the last two groups. Even though they are somewhat motivated by the working conditions and humanistic values, they acknowledge that teaching is not their preferred professional choice.

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Analysis of Motivations for Leaving Teaching Descriptive Analysis of Entry/Exit Motivations The eight types of motivation defined above can be grouped into two categories of factors: (1) motivational factors internal to the profession, or negative private motivations, which influence leaving decisions ( pushing factors); and (2) motivational factors external to the profession, or positive private motivations, which attract teachers towards the choice of departure ( pulling factors). Five types of motivation correspond to the pushing factors category: these motivations are related to changes in work conditions, workload, fatigue and health, relationships with principals and school policies. Three types of motivations are pulling factors: these motivations are associated with private life – for family or leisure related reasons – or with the Plend characteristics. According to the results of our study, two pushing factors have a key influence on early retirement decision: changes in work conditions (e.g. the manner of implementing institutional changes (50%), the effort put into disciplining rather than into teaching students [44%]), and workload, such as the evolution of work contents (55%) and an increasing workload (50%). Two pulling factors also made a considerable contribution to the choice of those opting for early retirement: the Plend characteristics – especially with regard to the advantageous conditions involved (e.g. the attractiveness of the retirement package [63%]) – and the desire to spend more time on leisure activities (to profit from their remaining energy (84%) and to devote time to their hobbies [69%]) (Table 2).

Differences in Motivational Orientation with Respect to Education Level and Gender As with the survey on entry motivations, the global results for the motivational factors behind early retirement also mask a number of disparities. For example, there are significant differences (chi-squared tests, p < 0.05) between education levels. Aspects related to changes in workload content were mentioned much more frequently by teachers in primary education than by secondary teachers. In the same way, work conditions – and more particularly the feeling of lack of freedom or autonomy – also pushed primary education teachers to leave the profession prematurely more than secondary teachers. Other factors, such as advantageous Plend conditions, also motivated primary education teachers more than secondary teachers. As far as the gender variable is concerned, we found that women were over-represented among the teachers who were motivated by the wish to spend time on non-professional activities. Men, however, were overrepresented among teachers motivated by social changes and, more particularly, by the perception of a decline in pupils’ competencies.

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Table 2: Motivation for leaving teaching by education level (classified by categories) (in %) Total (n = 121)

Primary level (n = 40)

Secondary level (n = 81)

50

58

46

44

50

40

Deterioration of profession’s image Student behaviour Students’ competence level Feeling out of touch with students

44 33 25 13

43 33 15 8

44 33 30 15

Category 2: Workload Evolution of work contents Increasing work load Contents of institutional changes Feeling of lack of freedom, autonomy

55 50 44 19

70 58 45 35

47 46 44 10

22

18

24

19

18

19

26 16

26 29

26 25

34 28

46 40

27 22

26

41

18

15 15

8 23

20 10

63 47

82 51

53 44

84 69 41

90 79 39

81 65 42

13 11

8 21

1 6

Exit motivations Category 1: Work conditions Manner of implementing institutional changes Too much effort going into disciplining rather than into teaching students

Category 3: Relationships with principals Relations with education department (administration) Lacking support of school management Category 4: Fatigue and health Lacking the energy required to teach Health reasons Category 5: Private life (family) To devote time to family To take up non-professional activities (volunteering, etc.) Spouse is already retired Category 6: School policy Relations with school leaders Unsatisfactory professional development Category 7: Characteristics of early retirement conditions (Plend) Attractiveness of retirement package Likely disappearance of retirement package Category 8: Private life (leisure) To profit from remaining energy To devote time to hobbies To travel Others Tired of teaching a specific school subject Feeling unable to keep up with teaching content

Note: The percentages refer to the respondents who stated that their early retirement choice was influenced ‘quite a lot’ or ‘very much’ by each motivation.

Motivational Typologies of Teachers In this second survey, the combined method of multiple correspondence analysis and hierarchical cluster analysis set out above was similarly employed.

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When the profiles of teachers taking early retirement are analysed below, this shows that a teacher’s overall assessment of his or her career correlates with his or her attitude towards institutional, pedagogic and social changes. The largest portion of teachers (49%) taking early retirement have a ‘positive assessment of their career’ in overall terms. They do not mention ‘changes’ as being a decisive factor in their decision to leave. They succeeded in adapting their professional commitment to an evolving environment. Thirty-two per cent of teachers finish their career with a fairly ‘mixed assessment’. Their decision to leave has been influenced by pedagogical and institutional changes and an increasing workload. Also, they feel that the image of the teaching profession has lost a lot of its appeal. There are 19% of teachers with an overall ‘negative assessment of their career’. Their decision to leave has been largely influenced by institutional and pedagogic changes and increasing workload. They also mention insufficient support from their professional environment (school leaders and administration).

Evaluation by Matrix Analysis The matrix for candidates (Figure 1) shows that altruistic motivations and intrinsic motivations rank high on the scale of motivation for becoming a teacher (x-axis) but low in respect of their accessibility and responsiveness to political action (y-axis) (lower right quadrant). Certain extrinsic motivations are highly ranked by teachers and are also susceptible to potential policy measures: i.e. possibilities for professional development, image of the profession, the evolving nature of the job and autonomy. However, there are a number of extrinsic factors, such as salary and job mobility, that score high with regard to their accessibility to political action but are of relatively low importance on the motivational scale (upper-left quadrant). This might be explained by the fact that Swiss teachers have a high salary level compared with other countries in Europe (OECD, 2005). Looking at the reasons for taking early retirement from teaching (Figure 2), private motivations, such as spending more time with the family, and on hobbies and travelling, rank high on the motivational scale. However, these private motivations offer little scope for potential policy intervention (lower-right quadrant). Factors that are highly responsive to political measures and have a key influence on a teacher’s decision to take early retirement are the way that institutional changes are carried out, the content of reforms, an increasing workload and advantageous pre-retirement benefits.

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Salkind_Chapter 57.indd 403 Motivated by social security benefits and salary

0.00

2.00

Difficulties finding another job

Job for a few years only

4.00

6.00

Wish to help children to succeed

8.00

10.00

Profession to exercice with passion

Allows to put important values into practice

Wish to improve society

Allows to give the same chance to all pupils

Transmitting knowledge to the children

Interest for the didactic and teaching aspects

Previous experience in teaching motivates application

Desire to work in spirit of collaboration

Social contacts and human relations

Appreciate flexibility of schedule and activity rate

Importance for becoming a teacher

Renouncement of academic career

Attain social status associated profession

Allows to work in different cantons, countries

Interest for professional development

Motivated by an evolutionary and demanding job

Allows to avoid routine

Stable employment and working conditions

Appreciate autonomy and independence of teaching

Identification with teaching profession

Allows to reconcile private and professional life

Allows to take interesting responsibilities

Allows to work in different schools

Sufficient holidays and time for leisure

Attracting and Retaining Teachers

Degree of responsiveness to policy measures

Figure 1: Motives for entering teaching and responsiveness to policy measures

0.00

2.00

4.00

6.00

8.00

10.00

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Degree of responsiveness to policy measures

2.00

Student’s behaviour

4.00

Importance for leaving teaching

6.00

Too much effort on disciplining than on teaching students

Increasing work load

To take up non-professional activities (volunteer, etc.) To devote to family To travel

Spouse is already retired

Tired of teaching a specific school subject

Health reasons

Feeling out of touch with students

Degredation of profession’s image

Lacking required energy to teach

Feeling not able to keep up with teaching content

Student’s competence level

Relations with school leaders

Relations with school leaders

Feeling of lack of freedom,autonomy

To devote to hobbies

Attractiveness of retirement package

Way of implementing institutional changes

Evolution of work contents

Lacking support of school management Contents of institutional changes

Unsatisfactory professional development

Relations with education department (administration)

Figure 2: Motives for leaving teaching and responsiveness to policy measures

0.00

2.00

4.00

6.00

8.00

10.00

8.00

To profit of energy that remains

404 Motivation

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405

A Decision Framework for Defining Teacher Policies How can the results of our previous analysis of teachers’ motivation for entering and leaving the profession be of assistance in defining teacher policies aimed at attracting and retaining teachers in their profession? We have identified three issues that have been shortlisted from our previous matrix analysis that might be of particular interest, since they also have an impact on attracting new candidates as well as on retaining experienced teachers. Their transversal character holds scope for promising leverages for anchoring teacher policies over the full length of teachers’ careers. More specifically, these three transversal issues relate to (1) job characteristics (e.g. activities), (2) working conditions, and (3) the image of the teaching profession (see Table 3). They all show similar patterns in Table 3: Transversal issues to attract, develop and retain teachers Transversal issues Job characteristics

Working conditions

Professional image

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Motivations for entering teaching

Motivations for leaving teaching

Motivational inducement systems involved

Little job routine Working in a social network providing various human contacts (students, colleagues, parents)

Increasing work load (e.g. increasing diversity of tasks, more administrative work) Increasing number of meetings

Task system (e.g. job definition, job description)

An evolving and demanding job

Dissatisfaction with content and the way that institutional reforms have been implemented

Leadership system (e.g. change implementation) Professional development system (e.g. enhancement of teacher’s competencies)

Transmission of knowledge to young people

Too much effort going into disciplining rather than into teaching students Student behaviour

Task system (e.g. evolution of teacher’s responsibilities and professional activities) Social system (e.g. perception of teacher’s role in society)

Autonomy in pedagogical choices and activities

Lack of autonomy and flexibility

Task system (e.g. structures and processes to carry out professional activities) Professional development system (e.g. opportunities to acquire skills and knowledge)

Autonomy in performing teaching activities

Lack of hierarchical support Lack of flexibility

Leadership system (e.g. guidance and support to carry out professional activities) Social system (e.g. teamwork and feedback procedures) Reward system (e.g. pay and working conditions)

Identification with teaching profession

Degradation of teaching profession’s image

Task system (e.g. vision creation and mission development) Social system (e.g. shared vision and set of norms)

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respect of teacher motivation: initially they have a positive impact but, over the years, they develop into the main reasons for leaving teaching. With regard to job characteristics, one key factor is the way teachers face change in the course of their career. Table 3 shows that the fact that there is very little job routine is something that attracts teacher candidates to the profession. However, frequent changes in the activities involved in their job and their professional environment, due to school reforms for example, can become a key argument for losing one’s motivation to teach. Furthermore, Table 3 shows that there is a similar pattern for working conditions, especially with regard to autonomy. It is important for teachers at the start of their career to have sufficient autonomy to implement their pedagogical choices and their professional activities. However, the reality for an experienced teacher is somewhat different. They regret having too little autonomy and flexibility with regard to pedagogical choices and feel there is a lack of hierarchical support for specific measures – leading to major frustration and teacher losses. Finally, strong identification with the teaching profession fades over time. It seems that the initial enthusiasm for teaching cannot, unfortunately, be maintained over the years. More experienced teachers regret, on a systematic basis, that the professional image of teaching has deteriorated over the course of their career and that they do not identify themselves with the current profession any more. These changes highlight a key question for school principals and other practitioners: how can the initial motivational factors be maintained as teachers progress in their career? Our research is able to offer a number of answers to this fundamental question. Teaching-policy levers ought, in fact, to prevent the development of the gaps between entry and leaving motivations. Taking the motivational inducement system of Leonard et al. (1999), we can identify five determinants for leveraging the motivation of teachers: (1) task system; (2) leadership system; (3) reward system; (4) social system; and (5) professional development system. The last columns of Table 3 indicates what leverage could be used on a general basis to address issues related to job characteristics, work conditions and professional image. More specifically: what kind of measures can be put in place by school authorities to prevent a loss in teacher motivation from coming about? Table 4 summarizes potential measures for keeping the initial motivational factors alive over a teacher’s career. It is evident that several inducement systems are required in parallel in order to tackle motivational issues. Teacher policies can only be successful if they address motivational determinants in a complementary manner. Research on educational leadership shows that effective education leadership has a positive impact on teaching and learning. Leithwood et al. (2004) identify three sets of practices that make up the basic core of successful leadership: setting directions, developing people, and redesigning the organization. Developing people by providing teachers with necessary support and training to succeed is therefore a key task for those in leadership roles.

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Task system

Determine teacher’s job definitions and job descriptions Communicate expected competencies, organizational goals and the role which teachers are expected to play: e.g. subject matter and pedagogical knowledge, organizational and communication skills

Provide organizational structures and processes that allow flexibility and autonomy in carrying out teaching activities Offer possibilities to diversify teacher’s activities (e.g. project involvement, professional experience in other domains)

Create visions of current and future evolution of the teaching profession Evaluate missions with regard to social, institutional changes

Transversal issues

Characteristics of job activities

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Working conditions

Professional image Integrate teachers as responsible actors of change and job evolution

Offer possibilities for taking on new responsibilities (e.g. school leadership, mentoring, training)

Build adequate leadership to guide and support teachers in order to achieve common objectives (e.g. supervision, feedback)

Provide a professional network (e.g. school leaders, mentors) to provide a framework for and support teachers’ activities at their workplace Build strong leadership systems in order to lead und support institutional and pedagogic changes

Leadership system

Provide attractive working conditions to improve image

Competitive salary Emphasize flexible working conditions and job autonomy, e.g. flexible working hours, possibility of working part time, job sharing, etc. Possibility of reducing workload towards the end of the career

Provide a professional network and feedback (e.g. school leaders, mentors) to provide a framework for and support teachers’ activities at their workplace Let teachers express their preferences in respect of their activities (choice of degree, of school, of branch)

Reward system

Table 4: Policy measures derived from motivational inducement systems

Maintain high standards for professional development in order to retain highly qualified staff

Provide a work environment and resources that allow teachers to carry out their professional activities Provide opportunities for self-development and self-realization at the workplace

Conceive of teacher’s professional development as a continuing activity over the teacher’s career Identify individual training needs and adapt specific professional development possibilities Complement teaching reforms with training opportunities that facilitate implementation of changes in a positive way

Professional development system

Share and develop visions of the teaching profession in cooperation with key stakeholders Develop strategies to maintain and promote the image of teaching

Encourage team building and peer recognition among teachers

Clarify expectations with regard to the teacher’s function, role and profile with key stakeholders (e.g. policymakers, parents, teachers) Prepare (future) teachers adequately for the reality of the job: confront expectations and reality early on in initial teacher education through practical experience and field training

Social system

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First of all, change management emerges as one of the key elements with regard to evolving job characteristics. A clear understanding of the job definition and its evolution as a result of changing roles and modified expectations is crucial (task system). Furthermore, a strong leadership system is required in order to implement change and reforms. Professional development systems should provide additional support all along a teacher’s career and, finally, interactions with key stakeholders, such as parents, administrative authorities, political organizations and business associations, are necessary in order to clarify and share the teacher’s function, roles and profiles (social system). Working conditions provide an important additional lever of teacher policy, since they touch on all five motivational inducement systems. In general, working conditions should be conducive to a teacher’s motivation to carry out their professional work in a flexible and autonomous manner by providing the opportunity to work in a professional network and offering hierarchical support. Moreover, working conditions should provide an opportunity for teachers to keep up with evolving teaching contents and materials. Finally, it is important to develop and enhance the professional image of teachers both inside and outside the school system. The task system, for example, allows visions regarding the current and future evolution of the teaching profession to be updated. It would appear, in fact, that some of the perceived loss of a teacher’s image can be explained by the evolution of the job, which is perceived in a negative way by older teachers who fail to see their initial role confirmed. Furthermore, attractive working conditions and stringent requirements on the continuing education of teachers are measures that help maintain a positive professional image outside of the school system too. From our previous analyses based on theories of work motivation and organizational behaviour, we see that employee motivation is a critical element in terms of its influence on individual performance and on the capacity of organizations to attain their objectives. Set in the context of schools, teacher motivation plays an essential role with regard to student learning as well as to a school’s capacity to achieve its objectives as an organization. As a result, teacher motivation plays a key role in defining policies to attract, maintain and develop teachers, as has been illustrated by the measures identified above. Our suggested policy measures have been prioritized in respect of their potential impact on teacher motivation. Additional criteria, however, such as their political and economic feasibility, are to be considered for deciding on their final implementation.

Notes 1. In Switzerland, a distinction is drawn between pre-schools (Kindergarten, école enfantine or scuola dell’infanzia) and childcare outside the family (day nurseries, day-care mothers, play groups). Children of all cantons are entitled to have access to pre-school education before they enter compulsory education. Cantons and /or communes are responsible for organising and funding pre-school education.

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2. Currently we do not have any information on non-respondents but we intend to collect data on the whole populations in forthcoming surveys so that we can compare the basic characteristics of respondents and non-respondents. 3. In Switzerland, the official retirement age for men is 65 years and, for women, 63 years. 4. The Plend early retirement plan (Plan d’encouragement au départ anticipé) was introduced in 1994 as a permanent measure, forming part of the Canton of Geneva’s human resources policy for its public administration. A certain number of conditions must be fulfilled in order to benefit from this retirement plan; these relate to age, for instance (women: a minimum of 57 years old; men: a minimum of 58 years old) and seniority (a minimum 10 years’ service as an employee with the canton of Geneva).

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Vallerand, R.J. and Thill, E.E. (1993) ‘Introduction au Concept de Motivation’, in J. Vallernad and Thill, E.E. (eds) Introduction À La Psychologie De La Motivation, pp. 201–38. Laval (Quebec): Editions etudes vivantes. Ward, J.H. (1963) ‘Hierarchical Grouping to Optimize an Objective Function’, Journal of American Statistical Association 58: 236– 44. Wright, P.M. and McMahan, G.C. (1992) ‘Theoretical Perspectives for Strategic Human Resource Management’, Journal of Management 18: 295–320. Zimmermann, B.J. and Schunk, D.H. (2001) Self-Regulated Learning and Academic Achievement: Theory, Research and Practice. Hillsdale, NJ: Erlbaum. Zuckermann, M., Porac, J., Lathin, D., Smith, R. and Deci, E.L. (1978) ‘On the Importance of Self-Determination for Intrinsically Motivated Behavior’, Personality and Social Psychology Bulletin 4: 443–46.

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SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY

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SAGE LIBRARY OF EDUCATIONAL THOUGHT AND PRACTICE

SAGE DIRECTIONS IN EDUCATIONAL PSYCHOLOGY VOLUME IV

Edited by

Neil J. Salkind

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Introduction and editorial arrangement © Neil J. Salkind 2011 First published 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act, 1988, this publication may be reproduced, stored or transmitted in any form, or by any means, only with the prior permission in writing of the publishers, or in the case of reprographic reproduction, in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. Every effort has been made to trace and acknowledge all the copyright owners of the material reprinted herein. However, if any copyright owners have not been located and contacted at the time of publication, the publishers will be pleased to make the necessary arrangements at the first opportunity. SAGE Publications Ltd 1 Oliver’s Yard 55 City Road London EC1Y 1SP SAGE Publications Inc. 2455 Teller Road Thousand Oaks, California 91320 SAGE Publications India Pvt Ltd B 1/I 1, Mohan Cooperative Industrial Area Mathura Road New Delhi 110 044 SAGE Publications Asia-Pacific Pte Ltd 33 Pekin Street #02-01 Far East Square Singapore 048763 British Library Cataloguing in Publication data A catalogue record for this book is available from the British Library ISBN: 978-0-85702-178-6 (set of five volumes) Library of Congress Control Number: 2010923776 Typeset by Mukesh Technologies Pvt. Ltd., Pondicherry, India. Printed on paper from sustainable resources Printed by MPG Books Group, Bodmin Cornwall

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Contents Volume IV Section III: Motivation (Continued) 58.

59. 60. 61.

62. 63. 64. 65. 66.

Interpersonal Relationships, Motivation, Engagement, and Achievement: Yields for Theory, Current Issues, and Educational Practice Andrew J. Martin and Martin Dowson Classroom and Individual Differences in Early Adolescents’ Motivation and Self-Regulated Learning Paul R. Pintrich, Robert W. Roeser and Elisabeth A.M. De Groot Atkinson’s Theory of Achievement Motivation: First Step toward a Theory of Academic Motivation? Martin L. Maehr and Douglas D. Sjogren Motivation and Engagement across the Academic Life Span: A Developmental Construct Validity Study of Elementary School, High School, and University/College Students Andrew J. Martin Motivation and Achievement: A Quantitative Synthesis Margaret E. Uguroglu and Herbert J. Walberg Academic Motivation and Achievement among Urban Adolescents Joyce F. Long, Shinichi Monoi, Brian Harper, Dee Knoblauch and P. Karen Murphy Intrinsic Motivation and School Misbehavior: Some Intervention Implications Howard S. Adelman and Linda Taylor Reinforcement, Reward, and Intrinsic Motivation: A Meta-Analysis Judy Cameron and W. David Pierce Motivation in Transition Barbara Stauber

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Section IV: Research Design, Measurement and Statistics and Evaluation Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing Raymond Hubbard and R. Murray Lindsay 68. Alphabet Soup: Blurring the Distinctions between p’s and a ’s in Psychological Research Raymond Hubbard 69. Research Methods: Experimental Design Julian C. Stanley

67.

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What Can We Learn from International Assessments? Robert J. Mislevy Power, Control, and Validity in Research Randall M. Parker Testing Reasoning and Reasoning about Testing Walt Haney

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58 Interpersonal Relationships, Motivation, Engagement, and Achievement: Yields for Theory, Current Issues, and Educational Practice Andrew J. Martin and Martin Dowson

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ew would dispute the importance of high-quality interpersonal relationships in young people’s capacity to function effectively, including in their academic lives. The literature consistently notes the substantial role that relationships play in students’ success at school (e.g., Creasey et al., 1997; Culp, Hubbs-Tait, Culp, & Starost, 2000; Field, Diego, & Sanders, 2002; Marjoribanks, 1996; Martin, Marsh, McInerney, Green, & Dowson, 2007; Pianta, Nimetz, & Bennett, 1997; Robinson, 1995). Guided by a core definition of relationship as “a state of connectedness between people, especially an emotional connection” (Webster’s Online Dictionary, 2007), we suggest that the concept of relationships provides an organizing framework for considering theories, issues, and practices relevant to achievement motivation. We also seek to demonstrate that the greater the connectedness on personal and emotional levels (also referred to as relatedness and relational processes) in the academic context, the greater the scope for academic motivation, engagement, and achievement. The purposes of this article are multifold. It elucidates the ways in which relationships affect achievement motivation and the benefits accrued from considering a relational perspective on achievement motivation. It describes a number of important motivation- and achievement-related theories and Source: Review of Educational Research, 79(1) (2009): 327–365.

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demonstrates the central role of interpersonal relationships in each of these theories. It explores practical implications of a relational understanding of both theory and current issues in terms of practices relating to student-, teacher/classroom-, and school-level actions. Finally, it concludes with an integrative framework that summarizes theory, constructs, mechanisms, and practices relevant to the relational dynamics underpinning motivation, engagement, and achievement in the academic context. Figure 1 presents an organizing framework for this review.

Part I: The Importance and Process of Relatedness The role of relatedness in academic, social, emotional, and cognitive development How motivation affects achievement motivation Foreseen yields of positive relationships for achievement motivation

Part II: Relatedness and Theories of Achievement Motivation The role of relatedness in: • Attribution theory • Expectancy-value theory • Goal theory • Self-determination theory • Self-efficacy theory • Self-worth motivation theory

Part III: Trilevel Approach to Action from a Relational Perspective Student-level action • Universal student programs and intervention • Targeted student programs for at-risk populations • Extracurricular activity • Cooperative learning • Mentoring Teacher/classroom-level action • Connective instruction • Professional development • Teacher retention and training • Classroom composition School-level action • School as community • Effective leadership

Part IV: Integrative Model of Theory and Practice Connecting: • Theory to Constructs to Mechanisms to Practice

Figure 1: Organizing framework for review

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Part I: The Importance and Process of Relatedness Why Positive Interpersonal Relationships Are Important for Young People A substantial body of research demonstrates the importance of positive interpersonal relationships for healthy human functioning (e.g., see Berkowitz, 1996; Bronfenbrenner, 1986; De Leon, 2000; Fyson, 1999; Glover, Burns, Butler, & Patten, 1998; Hill, 1996; Moos, 2002; Royal & Rossi, 1996; Sarason, 1993; Weisenfeld, 1996). Relationships are a major source of happiness and a buffer against stress (Argyle, 1999; Glover et al., 1998; McCarthy, Pretty, & Catano, 1990). Through relationships, individuals receive instrumental help for tasks and challenges, emotional support in their daily lives, and companionship in shared activities (Argyle & Furnham, 1983; Gutman, Sameroff, & Eccles, 2002; Irwin, 1996). Conversely, the loss of relationship is a source of unhappiness and distress (Bronfenbrenner, 1974; Cowen, 1988; Gaede, 1985). Interpersonal relationships are also important for social and emotional development (Abbott & Ryan, 2001; Kelly & Hansen, 1987; McCarthy et al., 1990). For example, during childhood and adolescence, key aspects of development involve, and rely on, positive relationships (Damon, 1983; Hartup, 1982). Relationships are also a critical factor in young people’s engagement and motivation at school (Ainley, 1995; Battistich & Hom, 1997; Hargreaves, Earl, & Ryan, 1996; Pianta, 1998). This latter issue is the focus of our review.

Relationships and Achievement Motivation: Causal Effects and Value-Added Explanations Motivation is defined as a set of interrelated beliefs and emotions that influence and direct behavior (Wentzel, 1999; see also Green, Martin, & Marsh, 2007; Martin, 2007, 2008a, 2008b, in press). We propose that relationships affect achievement motivation by directly influencing motivation’s constituent beliefs and emotions. Ongoing social interactions teach individuals about themselves and about what is needed to fit in with a particular group. Accordingly, individuals develop beliefs, orientations, and values that are consistent with their relational environment. Hence, relatedness in the academic domain teaches students the beliefs, orientations, and values needed to function effectively in academic environments. In turn, these beliefs (if positive and adaptive) direct behavior in the form of enhanced persistence, goal striving, and self-regulation. In high-quality relationships, individuals not only learn that particular beliefs are useful for functioning in particular environments, but they actually internalize the beliefs valued by significant others (Wentzel, 1999).

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In this way, beliefs held by others become a part of the individual’s own belief system. In the academic context, for example, good relationships with a particular teacher are likely to lead students to internalize at least some of that teacher’s beliefs and values about school and schoolwork. These internalized beliefs and values then have the potential to be transferred to other academic settings. Thus, students learn not only how to behave in a particular academic setting but also how to be a student in academic situations more generally (Ryan & Deci, 2000). Relatedness is an important self-system process in itself. As such, it has an energizing function on the self, working through the activation of positive affect and mood (Furrer & Skinner, 2003). This intrapersonal energy, gained from interpersonal relationships, provides a primary pathway toward motivated engagement in life activities. A complementary perspective on these processes is provided by the need to belong hypothesis. This hypothesis suggests that “human beings have a pervasive drive to form and maintain at least a minimum quantity of lasting, positive, and significant interpersonal relationships” (Baumeister & Leary, 1995, p. 497). When the need for belongingness is fulfilled, this fulfillment produces positive emotional responses. In the academic domain, these emotional responses are said to drive students’ achievement behaviors, including their responses to challenge, self-regulation, participation, and strategy use (Meyer & Turner, 2002). Relatedness affects individuals’ motivation and behavior by way of positive influences on other self-processes relevant to achievement motivation. For example, in the context of a student’s life, positive emotional attachments to peers, teachers, and parents promote not only healthy social, emotional, and intellectual functioning but also positive feelings of self-worth and self-esteem (Connell & Wellborn, 1991). This is important because self-worth and self-esteem are both related to sustained achievement motivation (Covington, 2002; Thompson, 1994). Finally, relatedness is linked to key psychological needs in a way that fosters achievement motivation. Work on autonomy in previous decades is a good example. Autonomy and relatedness have been linked (under various terminologies) in work on (a) agency (i.e., existence of an organism as an individual, giving rise to self-expansion and self-protection) and communion (i.e., participation of the individual in a larger organism, giving rise to cooperation) by Bakan (1966); (b) the importance of both individuational and relational needs along the lines proposed by Angyal (1941,1965), who identified orientations toward self-determination and self-surrender as complementary needs, and by Maslow (1968), who recognized the need for love and belongingness in the path to self-actualization; and (c) individualism and interdependence (Waterman, 1981) under a framework that provides support for the scope of individualistic values to facilitate helping, cooperation, and other prosocial behaviors. Indeed, these early integrations of autonomy and relatedness have been influential in later theorizing

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on motivation specifically (e.g., see Deci & Ryan, 2000) and personality more generally (e.g., see McAdams, Hoffman, Mansfield, & Day, 1996).

Benefits Accrued through Positive Interpersonal Relationships There are a number of benefits accrued through taking relatedness into account when examining achievement motivation theories and processes. First, relatedness serves as an explanatory construct through which diverse theories of achievement motivation can be integrated. In fact, relatedness may even transcend broader divisions of psychology beyond motivation psychology. For example, the belongingness hypothesis has wide application in educational, personality, and social psychology (Baumeister & Leary, 1995). Second, relatedness provides a useful diagnostic tool with which to view and understand adaptive behavior in the classroom and to treat achievement motivation problems in the classroom that are other related. For example, adjustment and adaptation problems in school have been linked to the failure of learning environments to meet students’ need to belong (Baumeister & Leary, 1995; Wentzel, McNamara Barry, & Caldwell, 2004). Third, relatedness recognizes and actively accommodates the interconnectedness of the social, academic, and affective dimensions of the self and the need for educational programs to recognize this interconnectedness (Weissberg, Kumpfer, & Seligman, 2003). Thus, the concept of relatedness can act as an impetus and explanation for educational programs that accommodate the whole self. Fourth, positive relationships are valued outcomes in their own right. The present review deals with relatedness as a means to greater theoretical and practical clarity with respect to achievement motivation. However, positive relationships can also be recognized as important end states in themselves. Thus, whatever their value for clarifying human motivation and achievement, relationships and relatedness are critical for understanding human functioning more widely. In addition to these more direct benefits derived through a closer understanding of relatedness in the classroom, there may also be indirect yields from a closer consideration of relatedness. Relatedness may help explain why the effect of adaptive beliefs on achievement motivation varies across contexts. For example, there is variation across studies with respect to the effects of various beliefs and goals on achievement motivation. Performance goals have been shown to be both adaptive and maladaptive for achievement motivation. Clearly, these results are inconsistent (for examples of the ongoing debate over the adaptiveness of performance orientation, see Brophy, 2005; Harackiewicz, Barron, Pintrich, Elliott, & Thrash, 2002; Kaplan & Middleton, 2002; Martin, 2006c), and it may be that relatedness can explain some of this inconsistency. Specifically, relatedness may act as a mediating variable with respect to the interface of goals and achievement motivation. In performanceoriented environments where students experience positive relationships, these environments may be perceived by students as being supportive in the

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path to achievement. When this is the case, achievement motivation may be facilitated and sustained in the context of a performance orientation. On the other hand, a performance-oriented environment in the context of poor relationships may be perceived as a “dog-eat-dog” context rather than a supportive one. Hence, relatedness could be a mediating process that can inform current theoretical debates and empirical inconsistencies.

Part II: Relatedness and Theories of Achievement Motivation The Role of Interpersonal Relationships and the Other in Achievement Motivation Theory Our analysis of motivation-related theory falls largely within the socialcognitive domain and primarily utilizes social-cognitive perspectives (e.g., Dweck & Leggett, 1988; Schunk, 1991). This social-cognitive analysis brings into consideration six theoretical viewpoints. Each of these viewpoints, while maintaining the relevance of relationships to their conceptualizations, differs in the way in which interpersonal relationships are invoked. These viewpoints are attribution theory, expectancy-value theory, goal theory, selfdetermination theory, self-efficacy theory, and self-worth motivation theory. It is important that not all theories are historically social-cognitive theories per se. Rather, we invoke their social-cognitive elements for the purposes of our synthesis. We also recognize that other theories (not addressed here) include social-cognitive elements as a source of influence.

Rationale for the Choice of Theories Theories in this study represent major frameworks in achievement motivation have been developed over the past 40 years that drive current research (McInerney & Van Etten, 2004). At the time of writing we conducted a somewhat expeditious search of the Education Resources Information Center (ERIC) data base limited to publications that are: (a) journal articles, (b) peer reviewed, (c) dealing with motivation and/or achievement as keywords from the six theoretical positions outlined, (d) written in English, and (e) published since 2000 (inclusive). Through searches of keyword and/or mapping onto subject headings, this identified close to 1,500 articles dealing with “self-efficacy ” “self-worth/self-esteem”, “achievement goals”, “goal orientation”, “attribution/s”, “expectancy/ies”, and “self-determination”. Whilst we recognize that this is an ever changing and fluid tally that does not denote these constructs’ relative importance or substance, we present the tallies to demonstrate the current and recent relevance of these constructs and the theories to which they relate in published educational research.

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These theories also share a common social-cognitive heritage. Social-cognitive theories examine, inter alia, cognition and behavior (e.g., attributions, expectancies, purposes, perceived needs, capacities, and vulnerabilities) that are contextually located and influenced. This is not to imply that the place of relationships is explicit and central in each theory; however, when it comes to operationalizing the theories in achievement motivation research, there is often a clear relevance for interpersonal relationships. Indeed, this relevance is the focus of the present review. Although we propose that relationships are important to achievement motivation, this does not mean that the role of self-generated cognitions and emotions should be ignored. We recognize – as do the theories we examine – that the self has powerful generative capacities of its own. Similarly, we recognize that in addition to relatedness and its impact on motivation, engagement, and achievement, there is the key issue of students’ academic proficiency. This proficiency encompasses general skills such as critical thinking, self-regulation, and metacognition, as well as more-specific skills, such as decoding texts, comprehension, and mathematical reasoning. Hence, we suggest that relatedness is a necessary but not sufficient condition for explaining variation in educational outcomes.

Review of Theories Attribution theory. According to attribution theory, the causes individuals attribute to events have an impact on the way they cognitively, affectively, and behaviorally respond on future occasions (Schell, Bruning, & Colvin, 1995; Weiner, 1986, 1994). Four attributions are typically identified in the literature: attributions to luck, task difficulty, ability, and effort. For example, failure on an exam may be attributed to bad luck, difficult questions, low ability, or insufficient effort. These causal attributions can also be mapped according to their locus, stability, and controllability (Weiner, 1994). Thus, the causes of an event may be located within the person or external to the person, may be stable or unstable, or may be controllable or uncontrollable. The control dimension is of particular interest in this review because it tends to be a significant determinant of students’ responses to setback, pressure, and fear of failure (Borkowski, Carr, Rellinger, & Pressley, 1990; Groteluschen, Borkowski, & Hales, 1990; Martin, Marsh, & Debus, 2001b). One means by which students gain a sense of control is through the feedback they receive from significant others such as their parents and teachers (Fabricius & Hagen, 1984; Weiner, 1986). The significance of this other person an important mechanism for a sense of control, and this significance is established, at least in part, through the nature and strength of the relationship. It has been suggested that control (or helplessness) is learned by observing powerful models, such as parents (Peterson, Maier, & Seligman, 1993). Furthermore, parents and teachers

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who provide reinforcement and feedback that are commensurate with students’ performance enhance students’ perceived control over educational outcomes (Perry & Tunna, 1988; Thompson, 1994). Hence, a defining aspect of students’ attributional profiles is in part relationally determined. Put simply, students can learn control from these significant others and the way these significant others relate to them. It has also been suggested that attributions in the interpersonal context give rise to socially based emotions (Hareli & Weiner, 2002). Recent work has proposed that socially based emotions are the result of attributional inferences focusing on the perceived causes of a particular outcome (Hareli & Weiner, 2002). This can have two impacts. First, it affects the observer’s emotions directly. In an adaptive scenario, a student attributing another student’s success to effort can experience positive affect and feelings of admiration for that student. On the other hand, a student attributing another student’s poor performance to a lack of ability may experience negative affect (Hareli & Weiner, 2000). In both cases, emotion is evoked in the academic context through the attributions students make about others’ academic outcomes. There is a second way socially based emotions emerge as a result of attributional inferences. Here, observers’ inferences about the cause of an event can shape the student’s emotions and behavior. For example, observers (e.g., teachers, parents) view a student’s performance and make inferences about the causes of the outcome, and these then influence the student’s reactions to the outcome and subsequent behavior. In the adaptive scenario described above, a teacher explicitly attributing a student’s success to effort can evoke positive affect and feelings of pride in the student. On the other hand, a teacher explicitly attributing poor performance to a lack of ability may evoke negative affect and shame in that student. Again, academically related emotion is evoked through the attributions for success and failure in a relational context, and this emotion has achievement motivation relevance. Taken together, on the matter of relatedness and attributions, these findings underscore “the interconnection of the self and others in achievement settings, and the necessity of a transactional analysis to understand the social dynamics that accompany achievement performance” (Hareli & Weiner, 2002, p. 191). Expectancy-value theory. Atkinson (1957) viewed the motivation to achieve success as a product of the individual’s perceived probability of success and the incentive value of that success. Similarly, the motivation to avoid failure was seen as a product of perceived probability of failure and the negative incentive value of failure. More recent formulations of expectancy-value theory (e.g., Eccles, 1983; Wigfield, 1994; Wigfield & Tonks, 2002) have refined and extended Atkinson’s original formulation by suggesting that (a) the expectancy-value framework can be applied to the whole range of behavior, not just risk-taking behaviors; (b) the strength of an individual’s motivation is based on the valuing of proximal and distal outcomes associated with a behavior or pattern of behaviors; and (c) motivation is dependent on the perception of the

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likelihood of a desired outcome occurring, contingent on a behavior or pattern of behaviors (see also Nicholls, Cheung, Lauer, & Patashnick, 1989; Wigfield & Tonks, 2002). In an educational context, students who believe they are capable of mastering their schoolwork typically have positive expectations for success and, hence, high motivation and achievement (Nicholls et al., 1989). What further contributes to students’ motivation and achievement is their valuing of an academic task, as well as the interface of their expectancies and task values (Arbreton & Blumenfield, 1997; Eccles, 1983). In a recent model representing the development of students’ expectancies for success and task values, Wigfield and Tonks (2002) identified the role of significant socializers’ attitudes, beliefs, and behaviors in the development of students’ expectancies and values. In particular, expectancies and values are influenced by the socializers with whom students have significant relationships. Thus, expectancy-value theory implicates relationships as an important component of its theoretical framework, and expectancies and values may be conceptualized as being, in part, relationally determined. Goal theory. Goal theory focuses on the meaning students attach to achievement situations and the purpose for their actions (Ames, 1992; Barker, Dowson, & McInerney, 2002; Dweck, 1992; Pintrich, Marx, & Boyle, 1993). Goals proposed in early theorizing were the desire to affirm competence (mastery goal) and the desire to demonstrate superiority (performance goal). More-recent developments in goal theory have added social goals. Social goals focus on social reasons for achievement, such as affiliating with others, gaining approval from others (e.g., parents and peers), and complying with group norms (Dowson & McInerney, 2001, 2003; Elliot, 1997, 1999; McInerney, Roche, McInerney, & Marsh, 1997; Middleton & Midgley, 1997; urdan & Maehr, 1995). Goal theorizing has now also introduced an approach and avoidance distinction (e.g., Barker et al., 2002; Elliot, 1997). Goals may be conceptualized as being directed toward approach or toward avoidance. Approach goals are those that draw participation in an activity. Avoidance goals drive withdrawal from activities or avoidance of negative implications and consequences. Mastery, performance, and social goals can be located on approach–avoidance axes. A mastery avoidance goal, for example, represents the desire not to fail at developing mastery, a performance avoidance goal as the desire not to demonstrate lack of ability, and a social avoidance goal as, for example, working mainly to avoid disapproval from parents and teachers (Barker et al., 2002; Dowson & McInerney, 2003; Elliot, 1997; Martin, 2001, 2002b, 2006a). Whether directed toward approach or avoidance, the goals students adopt, their relative importance, and their effects on motivation and achievement are related to the influence of others (e.g., McInerney, Hinkley, Dowson, & Van Etten, 1998; Wentzel, 1994). For example, Martin et al. (2007) demonstrated a significant link between the quality of teacher-student relationships

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and students’ mastery orientation and avoidance goals (see also Anderman & Maehr, 1994; Meece, 1991, for other aspects of teacher behavior and students’ goals). They also demonstrated a significant association between (a) students’ relationships with peers and their mastery and avoidance goals and (b) students’ relationship with parents or caregivers and these goals (see also Creasey et al., 1997 for the influence of relational contexts with peers and parents). Indeed, there may be different impacts of teachers, parents, and peers on different goals. For example, Martin et al. (2007) found relationships with teachers had the most impact on students’ mastery and avoidance goals, and Dowson and McInerney (2003) found that parents may have the most impact on students’ social goals. All this suggests that the goals students adopt, and the way these goals are expressed, are not independent of the influence of the relationships students have with teachers, peers, and parents. For this reason, students’ goals can be conceptualized as both arising from and being fulfilled in relational contexts (see also Lemos, 1996; Stipek, Giwin, Salmon, & MacGyvers, 1998; Taylor, 1995). Self-determination theory. Of the theories reviewed here, self-determination theory is among the most explicit in its recognition of relatedness as a fundamental ingredient of motivation. It proposes that for one to be motivated and to function at optimal level, a set of psychological needs must be supported (Deci & Ryan, 2000; La Guardia & Ryan, 2002; Reeve, Deci, & Ryan, 2004). These needs are relatedness, competence, and autonomy. Relatedness refers to the connection and sense of belonging with others. This connectedness and belonging provides the required emotional security that individuals need to actively explore and effectively deal with their worlds. From a learning perspective, a strong sense of relatedness better positions students to take on challenge, set positive goals, and establish high expectations that extend and motivate them. Moreover, relatedness needs constitute a motivating force for internalizing social regulations and adapting to interpersonal circumstances (La Guardia & Ryan, 2002). In turn, meeting these relatedness needs is likely to enable students to negotiate the affective and social world of the classroom and school, and this enhanced affective and social integration interfaces with enhanced motivational processes (Furrer & Skinner, 2003; Weissberg et al., 2003; Wentzel et al., 2004). For example, to the extent that home and school expectations and goals are aligned, children who are more warmly involved with their parents experience better academic functioning in class, and children with a heightened sense of relatedness with parents are more engaged at school and display higher self-esteem while at school (Avery & Ryan, 1987; Ryan, Stiller, & Lynch, 1994). Quality relatedness with parents also predicts quality relatedness with teachers (Ryan et al., 1994). Self-efficacy theory. Self-efficacy theory is centrally relevant to individuals’ belief in their capacity to successfully carry out given tasks and the consequent impact this self-belief has on motivation and achievement

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(Bandura, 1986, 1997; Schell et al., 1995; Schunk & Miller, 2002). Self-efficacy is hypothesized to support a generative capacity such that individuals high in self-efficacy generate and test alternative courses of action when they do not meet with initial success (Schunk, 1991; Schunk & Miller, 2002). High self-efficacy can also enhance one’s functioning through elevated levels of effort and persistence and can also enhance one’s ability to deal with problematic situations by influencing cognitive and emotional processes related to the situation (Bandura, 1986,1997; Zimmerman, Bandura, & Martinez-Ponz, 1992). Students can gain a sense of self-efficacy through the problem-solving modeling and supportive communication of significant others (Bandura, 1997). Moreover, those with whom students identify and to whom they are closely connected are more-powerful channels of this modeling and positive communication (Bandura, 1997; Meece, 1997; Schunk & Miller, 2002). In this sense, relatedness is a mechanism through which modeling takes place. Furthermore, a key interpersonal influence on self-efficacy is the vicarious influence from others through social models (Bandura, 1997). For these reasons, efficacious self-beliefs, and the extent to which these are held by self, can be conceptualized as a relationally influenced process. And although self-efficacy is often discussed in individualistic terms, both the extent to which self-efficacy beliefs change over time and the ways these beliefs affect motivation and achievement are determined in the social domain (e.g., Bandura, 1986; Parker & Martin, in press). Hence, self-efficacy may be conceptualized in relational terms rather than in solely individual terms (Schunk, 1991; Schunk & Miller, 2002). Perhaps a focus for future research is whether relationships are a moderator of these processes such that relatedness (e.g., high, low) and modeling (e.g., yes, no) interact to affect achievement motivation or whether relatedness is a mediator of these processes such that modeling predicts achievement motivation by way of relational factors. Self-worth motivation theory. Self-worth motivation theory describes the bases of, and the processes involved in, protecting or enhancing one’s self-worth (Covington, 1992, 1998, 2002). According to this theory, students’ self-worth is largely derived through their ability to perform academically and competitively (Covington, 2002; Robinson, 1995). One reason students come to equate their worth with ability is that their worth, in part communicated to them by significant others, is made conditional on achievement. These conditional relationships, then, have a significant impact on students’ propensity to self-protect (Covington, 1992; Martin, 2002c, 2007; Martin & Marsh, 2003). In turn, such self-protection can have a negative impact on students’ engagement and achievement (Covington, 1992; Martin, Marsh, & Debus, 2001a, 2001b, 2003; Thompson, 1994). This suggests that students’ relationships, especially the conditionality of those relationships, affects their self-worth and then their motivation and achievement. Thus, self-worth theory may also be conceptualized in relational terms.

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From an empirical perspective, Martin, Marsh, Williamson, and Debus (2003) have shown that students’ motive to protect self-worth and the specific strategies in which they engage to do this are influenced by significant others. In particular, they found that students’ parents were a factor in their fear of failure. They also found that the characteristic way in which that fear was responded to (e.g., through self-handicapping or defensive pessimism) was often linked to the characteristic way in which their parents dealt with their own fear. This impact of the family and relatedness is supported by other research demonstrating the intergenerational transmission of fear of failure and the impact of approval withdrawal on students’ fear of failure (Elliot & Thrash, 2004).

Summary of Key Relational Ideas Emanating from Theory The discussion above identifies key motivation- and achievement-related concepts, ideas, and processes underpinned or directed by relatedness, connectedness, and belonging. A summary of these linkages is presented in Table 1. Attribution theory focuses on the causes ascribed to outcomes and events in one’s life and the impact of these causal attributions on behavior, affect, and cognition. Personal attributions may be learned from, or modeled on, the attributional “styles” or patterns of others. Specific consequences of attributions (such as a sense of personal control) can also be developed

Table 1: Summary of key theories and key concepts relevant to relatedness Theory

Key concepts

Link to relatedness or the other

Attribution theory

Perceived causes of an event or outcome shape behavior, affect, and cognition; key causal ascriptions – control, locus, stability Positive expectations and high value placed on task or outcome enhances motivation Reasons for engaging in a particular behavior or pursuing a particular goal Relatedness a psychological need Belief in capacity to achieve in a specific domain or task

Perceived causes learned or inferred from significant others; dimensions such as control shaped by feedback from others

Expectancy-value theory

Goal theory

Self-determination theory Self-efficacy

Self-worth motivation theory

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Link between worth and achievement; fear of failure

Socializers’ beliefs, attitudes, and behaviors communicate level of expectation and nature of value Communicated through others’ values, expectations, and group norms Relatedness need met through warmth, support, and nurturance Modeled and communicated by significant others; vicarious influence from others Relationships (approval, affirmation) conditional on level of achievement; specific response to fear of failure linked to how significant others respond

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through feedback from and observation of significant others. Self-efficacy refers to a belief in one’s capacity and agency to achieve a desired outcome. This sense of capacity and agency can be instilled through direct or vicarious influence, modeling, and open communication from others. Related to this, expectancies and values have also been substantively linked to socializers’ beliefs, attitudes, and behaviors. Goal theory focuses on the why of behavior, which can be communicated through the values and expectations of significant others (working at individual, group, and organizational levels). Self-determination theory focuses on the psychological need for relatedness, which is satisfied through the warmth, support, and nurturance of significant others. Self-worth motivation theory focuses on the link between worth and achievement. It demonstrates that this link is in part determined by relationships in the child’s life in which worth, affirmation, and approval are communicated in either conditional or unconditional ways.

Part III: A Trilevel Approach to Action from a Relational Perspective To the extent that relatedness is central to achievement motivation theory, then educational practice relevant to motivation can also be framed in relational terms. A useful heuristic by which to organize and consider educational practice rests on the multiple tiers at which educational outcomes unfold and at which intervention and practice can be directed. Tiered approaches to intervention and practice are not uncommon and have recently been advocated as best practice in addressing diverse education- and health-based problems and challenges (e.g., see National Institutes of Health, 2008, and National Institute of Child Health and Human Development, 2008, for links to research along these lines). Such tiered approaches are now identified as particularly effective in reaching diverse populations with varying degrees and types of need. The tiered approach is also a useful way of organizing the discussion of relational action. Accordingly, we consider relatedness at the three levels that typically characterize the natural structure of students’ educational environs, namely, (a) practice at the level of the student, (b) practice at the level of the teacher or classroom, and (c) practice at the level of the school. We argue that analyzing action in this trilevel fashion represents an integrative means by which to address relational practice in the context of theory. To support this argument, we point to the fact that previous research has focused on one or more of these three levels to enhance the quality of pedagogy (Hill & Rowe, 1996; Kontos & Wilcox-Herzog, 1997b; Marzano, 2003), improve middle schooling (Eccles, 1999), enhance the educational outcomes of boys (Martin, 2003a, 2003b, 2004; Weaver-Hightower, 2003), assist Indigenous Australian students (Munns, 1998), address the educational needs of disadvantaged students (Battistich & Hom, 1997; Becker & Luthar, 2002),

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smooth educational transition (Barratt, 1998; Maehr & Midgley, 1996; Martin, 2008a), and build resilience and buoyancy (Cunningham, Brandon, & Frydenberg, 1999; Howard & Johnson, 2000; Martin & Marsh, 2006, 2008, in press). The key principles derived from theory outlined in Part II are also useful in identifying key elements to consider at each of the three levels of intervention. Thus, we should be looking to practice at each level that involves or encompasses key constructs and mechanisms detailed in the key theories discussed in Part II. Along these lines, Pintrich (2003) recently identified substantive questions for the development of a motivational science. Taken together, these questions underscore the importance of considering, conceptualizing, and articulating a model of motivational practice from salient and seminal theorizing related to self-efficacy, attributions, expectancy and valuing, goal orientation, self-determination, and self-worth perspectives. As we discuss each level of practice, it is important to recognize that no one practice is a sufficient condition for an encompassing approach to relational intervention. Moreover, in the context of a tiered model, approaches are most effective if integrated. For example, a school implementing cooperative learning, mentoring, or an expanded approach to extracurricular activity as its only targeted effort to meet the relational needs of its students is unlikely to achieve the interpersonal yields of schools doing more than this. Likewise, the benefits to be derived from practice will be limited if there is not sufficient depth such that the fullness of any one practice is not amply addressed. We propose, then, that a powerful implementation of the various practices described below will rest on breadth, depth, quality, and integration.

Practice at the Student Level At the student level, we emphasize universal student programs and intervention, targeted student programs assisting at-risk populations, extracurricular activity, cooperative learning, and mentoring. Although there are many other practices at the student level that facilitate relatedness, we emphasize these practices because they are underpinned by elements of theory described above, represent opportunities to enhance connectedness between students, and are grounded in individual, student-to-student, or studentto-adult approaches to enhancing educational outcomes.

Universal Student Programs and Intervention In terms of the theoretical foundations described earlier, there are many in-school and out-of-school programs in which students engage that not only enhance academic outcomes and prevent maladaptive outcomes but also offer scope for personal growth and development (indeed, a recent issue

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of American Psychologist, 38 (6–7), 2003, focused on such programs and interventions for young people). Even broadly based relational programs offer scope to build bridges to students’ academic lives. Such programs typically range in specific purpose but are often aimed at enhancing or intervening in students’ emotional, social, physical, behavioral, and academic development. These programs comprise positive interpersonal relationships and support, helping students feel valued, developing supportive relationships, establishing a meaningful place for the individual in a group, and fostering individuals’ usefulness to others (Dryfoos, 1990; Martin, 2008a; Nation et al., 2003; Weissberg et al., 2003). Martin (2005, 2008a) also identified elements that contribute to effective motivation and engagement interventions based on the seminal theory described above. The first element comprised optimistic expectations held by adults for the students, directly invoking self-efficacy principles through the modeling of efficacious behavior by adults and expectancy–value principles through communicating efficacy-related expectations to students (e.g., see Bandura, 1997; Wigfield & Tonks, 2002). A focus on mastery was a second element, invoking principles of goal theory that identify the importance of significant adults in shaping students’ goals (e.g., see Anderman & Maehr, 1994; Creasey et al., 1997; Meece, 1991). These adults are also influential in shaping the climate, the third element identified by Martin. Specifically, a climate of cooperation, consistent with goal theory and relevant climate research (Ames, 1992; Dweck, 1992; Elliot, 1997; Qin, Johnson, & Johnson, 1995; Roeser, Midgley, & urdan, 1996; urdan, Midgley, & Anderman, 1998), evokes a sense of belonging that fulfills relatedness needs, consistent with self-determination theory (Deci & Ryan, 2000; La Guardia & Ryan, 2002). This climate of cooperation also serves to diminish evaluative concerns and a consequent fear of failure, in keeping with tenets of self-worth motivation theory (Covington, 1992, 1998, 2002; Martin & Marsh, 2003).

Targeted Student Programs for At-Risk Populations: Special Focus on Indigenous Students As discussed, universal intervention programs typically involve practices directed at all students, whether they be high or low achievers, motivated, or unmotivated. However, there has been some concern that such programs may increase the gap between the strong and the struggling students such that the strugglers gain but the strong gain more (e.g., Ceci & Papierno, 2005). We propose that a relational perspective on educational practice may hold specific and differentiated benefits for groups that are at risk, even under a universal intervention paradigm. To illustrate, we focus on students from disadvantaged groups. Although these groups are by no means exhaustive of student groups at risk, they are an informative means of examining the potential for a relational approach in addressing their educational needs.

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In many countries, Indigenous students represent a distinct group of disadvantaged student. In Australia, for example, across reading, mathematical literacy, and scientific literacy, Indigenous students achieve at a much lower standard than their non-Indigenous counterparts, and the dropout rate in high school is markedly higher for Indigenous groups (Groome & Hamilton, 1995; Martin, 2003c; Munns, 1998). Research conducted among Indigenous students has found that the impact of positive relationships on a number of educational outcomes can be substantial (see, e.g., Collins, 1993; Groome & Hamilton, 1995; Richer, Godfrey, Partington, Harslett, & Harrison, 1998). Given the fact that many Indigenous students experience difficulties with their teacher, interpersonal relationships are a critical concern when schools are seeking to enhance Indigenous students’ educational outcomes (Richer et al., 1998). Reviews point to three levels of relationships relevant to the educational needs of Indigenous students (Martin, 2006a, 2006b; Munns, 1998; see also Fanshawe, 1989). The first involves an active daily connection with the school. This relationship is underpinned by ongoing connections with the Indigenous community, Indigenous Studies as part of the general curriculum, and a focus on the interests of Indigenous students as a policy priority. Together, these aspects of relationship with school enhance students’ academic and nonacademic morale (Fanshawe, 1989; Martin, 2006a, 2006b; Munns, 1998). The second, interpersonal relationships, involves teachers’ getting to know students, developing trust within the class and school, and developing Indigenous cultural knowledge and understanding. The third, pedagogical relationships, involves connecting with students by means of challenging and interesting work, effective instructional strategies, and positive expectations held by teachers for students. In the context of Indigenous education, predictors of this relationship include teacher satisfaction, appropriate and respectful views of students’ Indigenous status, collaborative lesson planning, and effective early intervention policies and programming (Munns, 1998). Taken together, school, interpersonal, and pedagogical relatedness can be an organizing concept for improving educational outcomes of Indigenous students – and potentially the educational outcomes of other disadvantaged minorities and groups. In line with this, lessons learned through Indigenous education are echoed in those learned in other cultural settings. Graham (1994), for example, developed a taxonomy for considering motivation among African Americans. Notwithstanding the important historical and social factors that distinguish them from other racial groups, Martin (2003c) suggested that this framework provided a useful means by which to think about Indigenous students’ educational status and outcomes. According to Graham, a central element of such a motivational psychology must address socialization antecedents of achievement strivings. Similarly, pedagogical principles

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have been drawn from the work of Ladson-Billings with exemplary teachers of African American students (Ladson-Billings, 1995). According to LadsonBillings, culturally responsive teachers create social interactions through maintaining fluid teacher-student relationships, demonstrating connectedness with all students, developing a community of learners, and encouraging students to learn collaboratively. As can be readily surmised, these are principles of effective teaching that should be effective with any group. However, they have particular scope for classrooms characterized by diversity, and in particular with students who are academically disadvantaged, such as Indigenous minorities (e.g., Indigenous Australians, Native Americans) and educationally disadvantaged ethnic minorities and groups (e.g., African Americans and Mexican Americans), where they are most needed.

Extracurricular Activity Extracurricular involvements traverse in-school and out-of-school programs. Extracurricular involvement encompasses, among other things, activities such as sport, music, dance, clubs, and church. The weight of evidence suggests that most extracurricular activities are a positive influence in young people’s lives, including in their educational, social, and emotional lives (Barber, Eccles, & Stone, 2001; Cooper, Valentine, Nye, & Lindsay, 1999; Eccles & Barber, 1999; Marsh, 1992; Marsh & Kleitman, 2002; Valentine, Cooper, Bettencourt, & DuBois, 2002). Significantly, relatedness and belonging are important reasons such activities are thought to yield positive effects. Extracurricular activity provides young people with safe and caring environments (McLaughlin, Irby, & Langman, 1994) in which prosocial adults (Mahoney, Schweder, & Stattin, 2001; Roth & Brooks-Gunn, 2000) are able to promote self-efficacy and model effective behaviors, consistent with selfefficacy theory (Bandura, 1997; Schunk & Miller, 2002). Extracurricular activity helps develop social skills and social capital (Broh, 2002), thereby building a student’s sense of control, as articulated by attribution theory (Weiner, 1986, 1994; see also Perry & Tunna, 1988; Thompson, 1994), and autonomy, consistent with a self-determination perspective (Deci & Ryan, 2000; La Guardia & Ryan, 2002; Reeve et al., 2004). Moreover, extracurricular activity provides an adolescent with a sense of belonging to a personally valued group (Brown & Evans, 2002), harnessing principles from expectancy-value and self-determination frameworks (Deci & Ryan, 2000; Wigfield & Tonks, 2002). To the extent that these connections and modeling are aligned with academic goals, they have the potential to promote achievement motivation. Hence, through a relational framework underpinned by principles salient in theorizing, extracurricular activity can facilitate educational and other outcomes.

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Cooperative Learning Also relevant at the student level and related in part to goal theory is the relative emphasis on cooperative (relational) and competitive (anti- or at least a relational) activities among students. Cooperation can be operationally defined as the presence of joint goals, mutual rewards, shared resources, and complementary roles (Qin, Johnson, & Johnson, 1995). In cooperative situations, students strive to reach their goals through the support and joint focus of others in their group or class. In competitive situations, students strive to reach their goals individually, or against (rather than with) others (Anderman & Maehr, 1994; Barker et al., 2002). Thus, whereas cooperation is focused on the notion of relatedness and mutual action with the other, the notion of competition tends to be antithetical to it. Evidence suggests that cooperative efforts are more effective than competitive efforts for many learning-related tasks, such as those involving decoding and recall of information (Barker et al., 2002; Johnson, Maruyama, Johnson, Nelson, & Skon, 1981), and more conducive to higher level thinking and problem solving (Johnson et al., 1981; Qin et al., 1995; Slavin, 1983). Cooperative learning theorists might explain such findings by arguing that the pursuit of joint goals and mutual rewards and the sharing of intellectual and physical resources (all factors relying on relatedness and inter-connectedness) contribute to the advancement of achievement and motivation underpinning these outcomes.

Mentoring Within the school environment, mentoring harnesses relatedness between younger students and older students (or adults) who provide support and guidance in particular domains. Mentoring is implemented in numerous ways, including high school students “adopting” elementary school students, elementary school activity days (e.g., high school students teaching younger students skills for better schoolwork), former students visiting the school (e.g., to encourage reading or to identify postschool pathways relying on academic engagement), underachievers choosing a teacher–mentor to work with, or pairings in partnership with local industry (see Noble & Bradford, 2000). It has been suggested that the enhanced interpersonal connectedness that is part of these programs contributes directly to engagement and achievement gains (Karcher, Davis, & Powell, 2002). In a recent model representing the development of students’ expectancies for success and task values, Wigfield and Tonks (2002) emphasized the role of significant socializers’ (e.g., mentors) beliefs and behaviors on the academic development of students. From a self-efficacy perspective, students gain a sense of efficacy, at least in part, through the problem-solving modeling and supportive communication of others (Bandura, 1997). Mentors are likely to be powerful channels of modeling and positive communication, and so quality relatedness in the mentor process is an important part of this.

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Practice at the Teacher and Classroom Level A pervading theme underpinning the theoretical traditions in Part II is the role of teachers (and classroom factors) in shaping students’ achievement motivation. Attribution theory proposes that students gain a sense of control and locus through feedback from teachers or by observing models demonstrating a sense of control (Fabricius & Hagen, 1984; Perry & 1988; Peterson et al., 1993; Thompson, 1994; Weiner, 1986). Expectancy-value theory identifies the role of significant socializers’ attitudes, beliefs, and behaviors in the development of students’ expectancies and values (Wigfield & Tonks, 2002). From a goal theory perspective, teacher-set tasks, assessment, and grouping strategies influence the goals students adopt (Anderman & Maehr, 1994; Meece, 1991). Belongingness in the classroom, central to self-determination theory, is cultivated by the teacher and the students collected in the classroom (Deci & Ryan, 2000; La Guardia & Ryan, 2002; Reeve et al., 2004). Students gain a sense of self-efficacy through the modeling and supportive communication of teachers (Bandura, 1997). From a self-worth motivation perspective, Martin, Marsh, Williamson, et al. (2003; see also Covington, 1992, 1998; Thompson, 1994) have shown that students’ motive to protect self-worth is influenced by teachers while other research has demonstrated the impact of approval withdrawal on students’ fear of failure (Elliot & Thrash, 2004). Indeed, teacher and classroom practice can be a vehicle for providing students with a sense of being at one with the group along the lines of communion posited by Bakan some four decades ago and yet let students retain the complementary but nonover-lapping sense of personal agency that is a hallmark of student motivation, engagement, and achievement (Bakan, 1966; see also, for early work, Angyal, 1941, 1965; Maslow, 1968; Waterman, 1981; for later work, see Deci & Ryan, 2000; McAdams et al., 1996). All this being the case, it is clear that the means by which teachers and classroom practice affect achievement motivation are directly and indirectly shaped by relational factors and processes. At the teacher and classroom level, we suggest that instructional, professional development, teacher retention and training, and organizational practices can be conceptualized in terms of these relational factors and processes. In particular, the emerging concept of connective instruction may have implications for teachers’ ongoing professional development, the importance of teacher retetion and attracting prosocial and positive (young) adults to teacher training, and the nature of classroom composition in affecting the motivation and engagement of students and classroom climate. Although not the only teacher and classroom practices that affect achievement motivation, they are a useful and informative means by which to frame practice in relational terms.

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Connective Instruction To the extent that relationships are a vital underpinning of student motivation, engagement, and achievement, teachers who frame practice in relational terms are more likely to foster motivated, engaged, and achieving students. Many studies support this contention (e.g., Abbott & Ryan, 2001; Battistich & Hom, 1997; Elicker & Fortner-Wood, 1995; Fyson, 1999; Kontos & WilcoxHerzog, 1997a, 1997b; Martin, 2006d). Specifically, research supports the following points: a. Students’ sense of support (e.g., being liked, respected, and valued by the teacher) predicts their expectancies for success and valuing of subject matter. Indeed, support from teacher is a consistently influential factor in motivation and achievement (Goodenow, 1993a). b. Students who believe that their teacher is caring also believe they learn more (Teven & McCroskey, 1997). c. Students’ feelings of acceptance by teachers are associated with emotional, cognitive, and behavioral engagement in class (Connell & Wellborn, 1991). d. Teachers who support a student’s autonomy tend to facilitate greater motivation, curiosity, and desire for challenge (Flink, Boggiano, & Barrett, 1990). e. Teachers higher in warmth tend to develop greater confidence in students (Ryan & Grolnick, 1986). Conversely, research also supports the following conclusions: f. When teachers are more controlling, students tend to show less mastery motivation and lower confidence (Deci, Schwartz, Sheinman, & Ryan, 1981). g. Teachers who are not perceived as warm typically evince lower motivation and achievement among students (Kontos & Wilcox-Herzog, 1997b). Relationships, therefore, are central to the issue of teaching and instruction. The concept of connective instruction, built on the previously proposed pastoral pedagogy (Cavanagh, 2001; Hunter, 1994; Martin, 2006a, 2006b), relational pedagogy (Bergum, 2003; Boyd, MacNeil, & Sullivan, 2006; Gadow, 1999), and connective pedagogy (Corbett, 2001a, 2001b; Corbett & Norwich, 1999), is relevant here. Pastoral pedagogy, introduced by Hunter (1994), described how modern teachers harness principles of the Christian pastorate to shape the ethical development of students (see also Cavanagh, 2001). Relational pedagogy refers to pedagogy that has as its foundation the need for good relationships between student and teacher that must also be accompanied by enhanced student learning (Boyd et al., 2006). Extending

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Gadow’s (1999) work, connective pedagogy deals with the delivery of teaching that interpersonally connects with learners, seeks to make the learning material meaningful (i.e., another form of connection), connects with external sectors to maximize student development, and looks to connect with significant others, such as parents, in students’ lives (Corbett, 2001a, 2001b; Corbett & Norwich, 1999). Martin (2006a, 2006b; see also Martino & Pallotta-Chiarolli, 2003; Munns, 1998, for cognate perspectives) offered an adaptation of these notions to more centrally position relatedness and connectedness between teacher and student in the context of instruction itself. Martin proposed such instruction – connective instruction – as that which connects the student and teacher on three levels: the level of substance and subject matter, the interpersonal level, and the instructional level (see also Martino & Pallotta-Chiarolli, 2003; Munns, 1998). Hence, connective instruction comprises three relationships: the substantive relationship (the connection between the student and the subject matter and substance of what is taught – i.e., connecting to the what), the interpersonal relationship (the connection between the student and the teacher himself or herself – i.e., connecting to the who), and the instructional relationship (the connection between the student and the instruction or teaching – i.e., connecting to the how). Although connective instruction emphasizes the impact of teacher on student, there is also an impact of student(s) on teacher such that the teacher is able to refine or adjust subject matter, interpersonal relatedness, and instruction on the basis of students’ responses to the teacher’s connective instruction. Connective instruction, then, may be viewed as a bidirectional process that is mutually beneficial and enhancing to both teacher and student. Substantive connectiveness (connecting to the what ). The first relationship in connective instruction is that between the student and the actual subject matter and nature of tasks conducted in the teaching and learning context. Core elements of subject matter that facilitate students’ connection to the teaching and learning context include setting tasks that are appropriately challenging, assigning work that is important and meaningful, building variety into content and assessment tasks, and utilizing material that arouses curiosity and is interesting to young people (e.g., Covington, 1998; Martin, 2002a, 2003a, 2003b; McInerney, 2000). These elements reflect content, subject matter, and learning tasks to which a student can meaningfully connect. These are a means by which the student engages with the what of teaching and learning. A good deal of this component of relational pedagogy rests on the valuing dimension of expectancy-value theory and the mastery dimension of goal theory, which emphasize relevance, contextual dimensions of subject matter, utility, interest, and satisfaction in learning (see Eccles, 1983; Elliot, 1997, 1999; McInerney, 2000; Wigfield, 1994; Wigfield & Tonks, 2002).

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Interpersonal connectiveness (connecting to the who). The second relationship in the connective instruction framework is that between the student and the teacher. Previously identified characteristics of quality interpersonal relationships in the teaching and learning context include actively listening to students’ views, allowing students to have input into decisions that affect them, getting to know students, showing no favoritism but affirming all students, accepting students’ individuality, and having positive but attainable expectations for students (Martin, 2002a, 2003a, 2003b; Slade, 2001; see also Flink et al., 1990; Goodenow, 1993a; Teven & McCroskey, 1997, for research confirming the yields of such relational characteristics). These elements are a means by which the student engages with the who in the teaching and learning context. This component explicitly invokes interpersonal relationships as central to learning and instruction – and by implication is perhaps most closely aligned with self-determination theory and its relatedness construct (Ryan & Deci, 2000). Whereas other theories might rely on interpersonal relatedness more as a conduit for their constructs and processes (e.g., for enhancing self-efficacy, control, self-worth, expectations, valuing) – self-determination theory quite centrally comprises the need for interpersonal relatedness as an important end in itself. Instructional connectiveness (connecting to the how ). The third relationship in connective instruction is that between the student and the teaching or instruction itself. Elements of effective instruction include maximizing opportunities for students to develop competence, providing clear feedback to students, explaining things clearly and carefully, injecting variety into teaching methods, encouraging students to learn from their mistakes, clearly demonstrating to students how schoolwork is relevant or meaningful, ensuring all students keep up with the work, and allowing for opportunities to catch up (e.g., Baird, 1999; Bandura, 1997; Covington, 1997; Craven, Marsh, & Debus, 1991; Martin, 2002a, 2003a, 2003b). These elements characterize high-quality instructional practice and are a means by which the student engages with the how of teaching and learning. They bring into consideration teacher-based behaviors that emphasize effective feedback and reward (attribution theory), nurturing of students’ expectancies and valuing of subject matter (expectancy-value theory), development of a mastery and improvement focus (goal theory), use of modeling (self-efficacy theory), and reduction of achievement stress and fear of failure (self-worth motivation theory). The role of the student in connective instruction. Connective instruction also recognizes that teaching is not a unidirectional process. Rather, at each of the three levels (substantive, interpersonal, and instructional) there is the opportunity for the teacher to refine or adjust the relevant level. For example, in response to a lack of student interest in a particular lesson, the teacher might adjust subject matter, how he or she is relating interpersonally to students, the instructional techniques themselves, or a combination of these.

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Hence, in the true spirit of relatedness, there exists a bidirectional process potentially mutually beneficial to all parties. In sum, connective instruction explicitly recognizes that relatedness is an instructional need and that students are likely to be more engaged and motivated when this need is met (Battistich & Hom, 1997; Burroughs & Eby, 1998; Chavis & Newbrough, 1986; N. Fry, 1994; Fyson, 1999; McCarthy et al., 1990). Through meeting this relatedness need, connective instruction facilitates students’ identification with the school and provides a connection with instruction on a more meaningful basis (see Munns, 1998). Jointly, identification with school and connection with instruction are proposed to promote adaptive academic engagement and motivation.

Professional Development Seminal motivation theory and conceptualizing around instruction itself (e.g., connective instruction) can also be a basis for teacher education and professional development (Bergum, 2003; Boyd et al., 2006; Cavanagh, 2001; Corbett, 2001a; Hunter, 1994; Martin, 2006a, 2006b). Teacher training and preservice education have been a focus of much prior research, with a number of journals specifically devoted to it. However, relatively less attention has been given to the professional development of teachers in the workforce. Teacher professional development (or in-servicing) has the potential for enhancing the educational outcomes of students and assisting teachers to operate more effectively in the classroom (Rowe & Rowe, 1999). Cherubini, Zambelli, and Boscolo (2002) examined the effects of professional development on teachers’ success in facilitating student motivation. Teachers participated in professional development related to theoretical and methodological aspects of motivation research and strategies to modify and sustain student motivation. Their findings showed that participants increased their practical knowledge about student motivation, were better able to identify and consider motivational problems, and planned new instructional programs to sustain their students’ motivation (see also Schorr, 2000). Similarly, Stipek et al. (1998) found that teachers participating in professional development focusing on student motivation were more likely to emphasize mastery and understanding in their teaching, to encourage student autonomy, and to create psychologically safer classroom environments. Participating teachers also made more-accurate assessments of students’ motivation – an important precursor to effective and targeted intervention (Martin, 2008a). Recent reviews have pointed to the need for teacher professional development in assisting disengaged and disadvantaged students. It is noteworthy that one of the key areas targeted for such professional development is improving teacher-student relationships (Becker & Luthar, 2002). Integrating theory and research detailed in Parts II and III suggests that professional development along these lines should focus on (a) developing a sense of community among

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students through relationally supportive school structures (Battistich & Hom, 1997; Cumming, 1996); (b) cultivating cooperative and mastery-oriented climates as articulated in goal theory (Qin et al., 1995); (c) integrating students within their peer groups (Bolger, Patterson, & Kupersmidt, 1998) to develop a sense of belonging consistent with self-determination theory; (d) developing competence and personal control in the context of interpersonal relatedness (Connell & Wellborn, 1991) along the lines of that articulated under self-efficacy and attribution principles, respectively; (e) reducing emphases on teacheras-authority (Flink et al., 1990), consistent with connective instructional principles introduced above (see also Bergum, 2003; Boyd et al., 2006; Cavanagh, 2001; Corbett, 2001a, 2001b; Hunter, 1994; Martin, 2006a, 2006b); and (f ) providing positive role modeling (Hernandez, 1995), consistent with self-efficacy theory. These are all a means of intentionally directing professional development toward relational understandings of teaching and learning. This accords with our overall relational conceptualization of motivationand achievement-related theory, key issues, and practices described above.

Teacher Retention and Training In almost every organizational setting, the workplace is changing, and at a seemingly increasing pace (Schabaracq & Cooper, 2000). Most employees work long hours, often not sufficiently remunerated (Dollard, 2006). Reports of an increasing lack of control, less input into decision making, and less involvement in the scheduling of work tasks and methods of work are consistently associated with poorer well-being (Karasek & Theorell, 1990). Indeed, stressrelated workers’ compensation claims continue to rise at an alarming rate. For example, in Australia (the context for the present authors), stress-related claims increased by more than 60% between 1996–1997 and 2002–2003 (Office of the Australian Safety and Compensation Council, 2006), and in the united States, more than half of working adults say they are concerned about the amount of stress in their lives (Stambor, 2006). Of particular relevance to this review, some researchers place school teachers among the group of employees facing many or all of the above pressures (Martin & Marsh,in press). Such research has identified stress, disengagement, heavy workloads, little support, and high turnover in this challenging setting (Fry & Martin, 1994; Mayer, 2006; McCormack, Gore, & Thomas, 2006; Richardson & Watt, 2006; Smithers & Robinson, 2003) – factors that significantly hamper individual career and employment development. It is important to note that such factors also lead to high rates of teacher attrition, high mobility, and even difficulties attracting sufficient numbers of teachers into teacher training (G. Fry & Martin, 1994; Organisation for Economic Co-operation and Development, 2005; Smithers & Robinson, 2003; Vinson, 2002). One of the effects of teacher attrition and mobility is that there are fewer opportunities for consistent and stable relationships between student and

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teacher and, by implication, fewer consistent prosocial and positive adults in students’ lives. Similarly, failure to attract potentially good teachers to teaching means a more limited pool of available such people for children and young people and the consequent cost of this in terms of children’s and young people’s potentially supportive interpersonal relationships. The present review, then, echoes calls in other research for support needed by teachers and schools to more effectively deal with the stressors that lead to attrition, mobility, and alternative career choices (G. Fry & Martin, 1994; Martin & Marsh, in press; Mayer, 2006; McCormack et al., 2006; Organisation for Economic Co-operation and Development, 2005; Richardson & Watt, 2006; Smithers & Robinson, 2003; Vinson, 2002).

Classroom Composition From a relational perspective, it is also important to consider the nature and number of students in the classroom. If, as key theories (e.g., goal theory, self-efficacy theory, attribution theory) propose, motivation and achievement are affected by goal climates, peers, and models with whom one identifies (e.g., other students), then it follows that research and practice must look more closely at the composition of students in the classroom. To date, most multilevel research examining variance in achievement and motivation at the classroom level attributes such variance to the teachers themselves (e.g., see Hill & Rowe, 1996; Papaioannou, Marsh, & Theodorakis, 2004; Rowe & Rowe, 1999). Relatively little research, however, has attempted to disentangle the effects of the teacher from those of the class. If, for example, there is an effect of class composition on motivation and engagement, then there are implications from a relational perspective. Some immediate questions from an achievement motivation perspective would be: What students are collected together? How many are there? Where are they seated? Whom do they work with or alongside? How do they interact? How do they get on? Disentangling the relative role of teacher from that of class composition is most appropriately handled by multilevel cross-classification analyses in which there are multiple teachers, each of whom teaches multiple classes. Marsh, Martin, and Cheng (2008) conducted such analyses and showed that there were some differences between classes but that these differences did not always generalize over different classes taught by the same teacher. Hence, over and above teacher effects are the effects of class composition. The researchers concluded that both the quality of the teaching and the classroom composition are factors in motivation (see also Martin & Marsh, 2005). This achievement has implications for classroom climate research, which suggests that the motivational climate may also be a function of the particular collection of students in that class. Whereas in recent years there has been substantial focus on teacher effectiveness and characteristics of effective teachers, it might now be timely to revisit the issue of class composition and

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perhaps from a relational perspective. More specifically, in the context of achievement motivation, research might investigate the characteristics of effective classrooms, the students collected together in the classroom, the bases on which they are collected together, and how they interact. Moving beyond the students themselves are other factors relevant to the classroom and its environment that affect relatedness among students and between students and teachers. These include such factors as the classroom’s physical space (encompassing size, organization of furniture and equipment, lighting, temperature, etc.), its location in the school itself (e.g., in terms of noise, proximity to other classrooms for ease of movement, etc.), and even the time of day at which classroom activities are conducted. Prior work has been conducted into cognate issues such as seating arrangement (Hastings & Schwieso, 1995; Marx, Fuhrer, & Hartig, 1999), streaming (Marsh, 1987; Marsh & Hau, 2003), single-sex class composition (Marsh, 1989; Marsh & Rowe, 1996; Martin, 2004; Martin & Marsh, 2005), and the physicality of the learning environment (O’Hare, 1998; Stone, 2001). Hence, class composition and other class environment factors from a relational and achievement motivation perspective are an avenue for further research. Moreover, from a relational perspective, such research would also need to establish how much variance in achievement motivation at the class level is a function of teacherstudent interactions (i.e., class-level variance due to teacher–student relatedness) and how much is unique to student–student interactions (i.e., class-level variance due to student-student relatedness).

Practice at the School Level The theories informing this discussion deal primarily with intrapsychic, individualistic constructs that are directed at individuals or relatively small groups and activated by individuals such as teachers, counselors, psychologists, and the like. Although the issue of relatedness may be more aligned with research and practice at the individual and interpersonal level, it is important to consider what application of theory can be directed at the school level. A thoroughgoing treatment of relatedness would encompass integrated recommendations at all levels: student, teacher or classroom, and school. For example, hypothesized under goal theory are mastery and performance classroom climates that also have implications for whole-school climates (e.g., see Duda, 2001; Middleton & Midgley, 1997; Papaioannou et al., 2004; Roeser et al., 1996; urdan et al., 1998). The notion of fear of failure and disengagement at the school level is not inconsistent with predictions under need achievement and self-worth motivation theories (Atkinson, 1957; Covington, 1992, 1998; McClelleand, 1965). Work in the areas of attributions and learned helplessness shows that through observing potent models, even relatively large groups can acquire helpless behaviors

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and dispositions (Peterson et al., 1993). Indeed, recent multilevel modeling research has examined school-level variance in constructs central to self-efficacy, expectancy-value, goal, self-worth motivation, and self-determination theories (Marsh et al., 2008; Martin & Marsh, 2005). Hence, there are extensions of achievement motivation theory and research to school-level considerations that are logical and defensible. Given this, we address two issues relevant to such considerations: school as community and effective leadership. Again, they are not the only school-level practices that are relevant to relationships, but they are a useful means by which to consider relatedness at a school level as relevant to achievement motivation.

School as Community Cooperative climates develop a sense of community and belonging, consistent with predictions under goal and self-determination theories (Ames, 1992; Dweck, 1992; Elliot, 1997; Qin et al., 1995; Ryan & Deci, 2000). A sense of community affects young people’s sense of self and efficacy. It can also affect their engagement. In the educational context, Becker and Luthar (2002) suggest that an important means of enhancing motivation is through promoting a sense of belonging in school. In fact, it has been suggested that there can be tension between the emphasis on social cohesion (e.g., school as community) and a strong academic mission – with schools often pursuing one more than the other. Indeed, research under the goal theory framework has attempted to resolve similar dissonance through the articulation of multiple goals (e.g., see Heyman & Dweck, 1992; urdan & Maehr, 1995; Wentzel, 1992). Encouragingly, it has been found that achievement can result from an integrated emphasis on social cohesion and academic mission (Shouse, 1996) and that psychological school membership (students’ perceived belonging) is significantly linked to academic motivation and achievement (Goodenow, 1993b). Conversely, alienation may be conceptualized, not just in relational terms (i.e., not feeling at home in a particular institution), but also in academic terms (i.e., not being able to relate to particular content or the presentation of that content). For these reasons, relational perspectives would support greater school-level action to enhance a sense of community, belonging, and connectedness at school (following others, e.g., Cumming, 1996; Hernandez, 1995; Mann, 1989).

Effective Leadership In our discussion of teacher- and classroom-level practice, we described how feedback, modeling of efficacy and control, effective reward contingencies, expectations, set tasks, assessment and grouping strategies, supportive communication, and the transfer of fear and approval are means

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by which teachers relationally influence students’ achievement motivation. It is not inconceivable that similar dynamics are relevant at upper levels, such as at the school executive or leadership level. Research into school effectiveness consistently emphasizes the importance of effective leadership (Edmonds, 1979; Levine & Lezotte, 1990; Marzano, 2003; Sammons, 1999). There are many features of effective leadership that have parallels with motivation and achievement theories, including visibility and energy that serve as modeling behavior (see self-efficacy theory), high expectations for staff and students (see expectancy-value theory), openness to feedback and input that can enhance teachers’ sense of control and autonomy (see attribution and self-determination theory), and advocacy for the school that demonstrates valuing (see expectancy-value theory). Other relational features include emotional and professional support of staff, mutual respect between staff and the executive, connectedness to the student body, interest in and involvement with parents, and links to the community and industry (Blum, Butler, & Olson, 1987; Hallinger & Murphy, 1987; Levine & Lezotte, 1990; Sammons, Hillman, & Mortimore, 1995). In implementing schoollevel action along these lines, however, it is important not to underestimate the yields of intervention at the student and classroom levels. For example, in the context of the multiple and sharp developmental trajectories occurring through childhood and adolescence, the impact of relational intervention may be greater when directed to students and classrooms than when directed to school executives.

Part IV: Integrative Model of Theory and Practice In finalizing our review, we synthesize its key elements into an integrative model of theory and relational practice. Table 2 presents this model and summarizes the relevant theories, their component constructs, recommended educational practice, and the mechanisms and conduits within the theories that inform or implement such practice. Also evident in the table are some of the congruencies between central constructs in the model, including competence-based constructs such as self-efficacy, expectancies, and worth, and control-based constructs such as control and autonomy. The table also shows that there are commonalities in terms of the mechanisms that are the means by which these theories and component constructs are relationally translated to educational practice. These include the roles of modeling, communication of expectations, task assignment, skill development, reward contingencies, and feedback to students – all central to motivation- and achievement-related theories detailed in Part II. It is also evident in Table 2 that interpersonal relationships are directly or indirectly present in the way theory is manifested in students’ academic lives.

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Table 2: Summary of constructs, mechanisms, and practice relevant to relatedness Theory

Key constructs relevant to review

Attribution theory

• Perceived control • Perceived locus • Helplessness

Expectancy-value theory

• Expectancy for success • Valuing of school, subjects, etc.

Goal theory

• • • • •

Mastery goals Performance goals Social goals Motivational climate (Approach and avoidance extensions)

Mechanisms or conduits • Feedback to students • Reward contingencies • Observation of and identification with relevant others • Communication of expectancies • Communication of valuing • Modeling of valuing • Responses to or treatment of students in class • Tasks set • Assessment and grading practices • Development of climate • Reasons for learning valued by relevant others • Warmth, support, and nurturance • Promoting independence • Self-responsibility

Self-determination theory

• Relatedness or belonging • Autonomy • Competence

Self-efficacy

• Self-efficacy • Control

• Modeling • Positive communication from relevant others • Vicarious influence

Self-worth motivation theory

• Self-worth • Fear of failure • Disengagement

• Approval, affirmation • Conditions of love, approval • Intergenerational transfer of love • Reward contingencies • Grading practices

Trilevel educational practice Practice at student level: • Universal student programs and intervention • Targeted student programs and intervention • Extracurricular activity • Cooperative learning • Mentoring

Practice at teacher and classroom level: • Connective instruction • Professional development • Teacher retention and training • Classroom composition

Practice at the school level: • School as community • Effective leadership

Moving beyond theory, Table 2 suggests that interpersonal relationships play a pivotal part in resolving complex or critical concerns with respect to current and prospective educational practice. For these reasons, we argue that motivation- and achievement-based theory, key issues, and practice may be conceptualized from a relational perspective. Hence, the interplay of theory and practice from a relational perspective provides direction for educators seeking to enhance students’ achievement motivation.

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Conclusion This review has elucidated the multiple ways in which interpersonal relationships affect motivation and achievement, the benefits derived from relational perspectives on motivation and engagement, achievement motivation theories relevant to relationships, and relational practices underpinning student-, teacher- or classroom-, and school-level actions. Theory and research support the proposition that positive relationships with significant others are cornerstones of young people’s capacity to function effectively in social, affective, and academic domains. With a focus on the latter, we conclude that high-quality interpersonal relationships in students’ lives contribute to their academic motivation, engagement, and achievement. Further, relational elements of educational theory provide guidance for educational practice directed at student motivation and achievement. Taken together, this integration of relationally based theory and practice holds implications for researchers studying issues relevant to motivation and achievement and is also relevant to educators seeking to enhance educational outcomes that rely in large part on the extent to which their students are interpersonally connected to the significant others in their academic lives.

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59 Classroom and Individual Differences in Early Adolescents’ Motivation and Self-Regulated Learning Paul R. Pintrich, Robert W. Roeser and Elisabeth A.M. De Groot

C

urrent research on early adolescence and schooling has examined the relations between biological changes and schooling (e.g., Simmons & Blyth, 1987), cognitive development and schooling (e.g., Entwisle, 1990; Keating,1990), and social and motivational development and schooling (e.g., Eccles et al., 1993). Although this research has begun to provide us with excellent descriptions of how different characteristics of junior high and senior high schools influence the course of adolescent development, there has not been much integration across the different domains of development. In fact, Keating (1990) suggests the need for models that “open the gate of our typically closed-system models of thinking, learning, and instruction” ( p. 76) to include motivational, cognitive, and contextual dimensions. The purpose of this article is to explicitly integrate the motivational and cognitive domains of adolescent development and examine their interrelations in the classroom context of middle schools. One hallmark of adolescent thinking is the ability to monitor and regulate one’s thinking and learning (Keating, 1990). Although there has been research on the development of adolescents’ ability to regulate their learning (Sternberg & Powell, 1983), there has been very little research on how this ability is related to students’ motivational beliefs, goals, and values. In fact, Keating (1990) noted that “tracking the development of these motivational aspects that relate to cognitive performance remains a key topic for future Source: The Journal of Early Adolescence, 14(2) (1994): 139–161.

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research” (p. 76). In the classroom context, the use of cognitive and self-regulatory strategies has been shown to be an important component of student performance and achievement in the classroom (Pintrich & De Groot, 1990; Zimmerman & Martinez-Pons, 1986). This research has shown that students who use cognitive strategies such as elaboration (e.g., summarizing, paraphrasing) and organization (making outlines, drawing up tables or charts) engage the content at a deeper level of processing and are more likely to recall the information and be able to use it at a later date. In contrast, students who do not use any strategies to help them encode the information or rely only on rote rehearsal strategies seem to process the information at a more superficial or surface level and do not perform as well on memory and transfer tasks (Weinstein & Mayer, 1986). Besides these basic cognitive strategies, research has shown that metacognitive control and self-regulatory strategies also are important for learning (Brown, Bransford, Campione, & Ferrara, 1983; Keating, 1990). Metacognitive control strategies include planning (e.g., setting goals), monitoring (e.g., tracking attention and comprehension, self-testing for understanding), and regulating (e.g., rereading, adjusting reading speed) strategies that help guide and direct students’ cognition. Besides these metacognitive control strategies, there are a variety of other self-regulatory strategies that are important for performance (Corno, 1986, 1993). In the classroom context, students’ ability to manage and regulate their effort (i.e., persist with difficult tasks, maintain attention with uninteresting tasks) seems to be an important component of self-regulated learning (Pintrich & De Groot, 1990; Zimmerman & Martinez-Pons, 1986). For the most part, however, cognitive development research has not focused on how students are motivated to use these cognitive and selfregulatory strategies. Nevertheless, it does appear that certain motivational beliefs and affective reactions are related to how adolescents approach and become cognitively involved in different classroom academic tasks (Graham & Golan, 1991; Nolen, 1988; Pintrich, 1989; Pintrich & De Groot, 1990; Pintrich & Schrauben, 1992). In our research, we have focused on three motivational components – value, expectancy, and affect. As in all motivational theories, these motivational beliefs and reactions are assumed to lead to three general types of motivated behavior including choice (i.e., choosing to do some tasks and not others), level of activity or engagement (i.e., engaging the task at a sustained and deep level), and persistence (i.e., continued effort in the face of difficulty). However, motivational research does not usually examine the use of different strategies or resources through which the learner would enact these motivational beliefs. Given that the use of different cognitive and self-regulatory strategies represents motivated behavior in terms of a sustained and deeper level of cognitive engagement, we have focused on the relations between the motivational and cognitive components thereby describing a motivated and self-regulating learner.

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In our model, we have defined value components in terms of two constructs – goal orientation and task value beliefs (Pintrich, 1989; Pintrich & Garcia, 1991; Pintrich & Schrauben, 1992; Pintrich, Smith, Garcia, & McKeachie, 1993). Goal orientation refers to two general approaches to academic tasks in line with a more qualitative view of motivation (Ames, 1992) whereby the different goal orientations can lead students in qualitatively different directions as they perform an academic task. The two dimensions are an intrinsic goal orientation where the student focuses on mastery and learning and an extrinsic goal orientation where the student approaches the task with a concern about grades, pleasing others, or besting others (cf. Dweck & Leggett, 1988; Harter, 1981). In contrast, task value beliefs reflect a more quantitative approach to motivation where higher levels of task value should result in more motivated behavior. Following Eccles (1983), we have proposed that there are three general aspects of task value beliefs – interest, utility, and importance. Interest refers to students’ personal interest and liking of the course material. Utility is students’ perceptions of how useful the course material is to them. Importance concerns students’ beliefs about how significant the course content is for them and their future goals (Pintrich, 1989). Conceptually, we propose that goal orientation and task value are separate motivational components. However, although we have found that intrinsic goal orientation, extrinsic goal orientation, and task value beliefs are separate factors with college age adolescents (Pintrich & Garcia, 1991; Pintrich et al., 1993), in our work with middle school adolescents (Pintrich & De Groot, 1990), we have found that these beliefs are less differentiated and form a general value factor that reflects intrinsic goal orientation and high levels of interest, utility, and importance beliefs. In this article, we focus on this general factor that we have labeled intrinsic value in line with our earlier work (Pintrich & De Groot, 1990). The second component in our research on motivational beliefs includes the expectancy component, self-efficacy. Self-efficacy beliefs are defined as students’ judgments of their capability to accomplish a task in a specific situation and have been linked to a number of positive performance and achievement outcomes (Bandura, 1986; Schunk, 1985). In our work, we have operationalized self-efficacy beliefs at a slightly more global level to include adolescents’ judgments of their capabilities to learn and succeed in a specific course (Pintrich & De Groot, 1990). The third motivational component in our model is the affective construct of test anxiety. There seems to be two aspects of test anxiety – a worry component and an emotionality component (Liebert & Morris, 1967). The worry component is more cognitive in nature and refers to negative thoughts and self-talk that interfere with effective performance. The emotionality component includes the experience of negative emotions and physiological arousal. There are a large number of studies that show that high levels of test anxiety disrupt performance and have detrimental effects on student achievement (Hill & Wigfield, 1984).

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Following the Keating (1990) call for more integrative research on motivation and self-regulation, the first research question in this study concerns the relations between these three motivational components (intrinsic value, self-efficacy, and test anxiety) and adolescents’ use of cognitive and self-regulatory strategies. In a previous study (Pintrich & De Groot, 1990), we found that higher levels of intrinsic value and self-efficacy were positively related to the use of cognitive and self-regulatory strategies for middle school adolescents. These findings parallel other studies (e.g., Ames & Archer, 1988; Graham & Golan, 1991; Nolen, 1988) that have shown that adopting an intrinsic, mastery, and task-involved orientation to a learning task results in deeper levels of cognitive processing and that higher levels of self-efficacy lead to more strategy use and self-regulated learning (Pintrich & Schrauben, 1992; Schunk, 1989). In addition, we found in the previous study that test anxiety was not related to cognitive strategy use but was negatively related to adolescents’ self-regulation (Pintrich & De Groot, 1990) as has been found in other studies (e.g., Benjamin, McKeachie, & Lin, 1987). In our earlier study, however, we only had measures of students’ motivation and cognition at one point in time that limited our ability to examine the development of the relations over time. In the present study, we have measures of both motivation and self-regulated learning at two points in time. One purpose of the present study is to provide a partial replication of the results from our earlier study. In addition, we go beyond those results by describing the relations between motivation and self-regulation over the course of a school year in middle school classrooms. Many of the studies that have directly investigated the relations between motivation and cognition have not examined classroom context effects. Accordingly, our second general research question concerns how adolescents’ classroom experiences are related to their motivation and self-regulated learning. As Eccles et al. (1993) have suggested, there are a number of dimensions of classrooms that can have a positive or negative influence on the course of adolescent development, especially adolescents’ motivation. In this article, we focus on three general aspects of middle school adolescents’ classroom experience – the nature of academic work, the teacher’s instructional style, and cooperative goal structure. In research on the influence of classroom characteristics on adolescent development, there is an important conceptual and methodological issue concerning the nature and measurement of the classroom characteristics. Ames (1992) and others (e.g., Ryan & Grolnick, 1986; Weinstein, 1989; Winne & Marx, 1982) have argued strongly for the inclusion of students’ perceptions of classroom experience as an important mediator of actual classroom experience. Research on classroom climate in general (e.g., Moos, 1979) and specific research on how individuals construct meaning regarding the “functional significance” (Ryan & Grolnick, 1986) of context or how they create a “psychological climate” (Maehr, 1984) of a particular environment

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suggests the importance of student perceptions of the classroom. We follow in this tradition and use student perceptions of the classroom as our measure of the classroom characteristics. At the same time, following the analyses used by Ryan and Grolnick (1986), we examine the relative effects of the classroom as a whole by including an overall classroom mean as one measure of the environment as well as a measure of the disparity in an individual’s perceptions of the classroom by including a deviation measure that subtracts the individual’s scale score from the classroom mean. By including both these measures, we can examine questions regarding overall classroom influences as well as the individual-within-a-context questions thereby shedding some light on the relative importance of classroom and individual differences effects. In addition, many of these studies of students’ perceptions of the classroom environment (e.g., Ames & Archer, 1988; Ryan & Grolnick, 1986) do not examine or factor out students’ entering individual characteristics before measuring their classroom perceptions. For example, both Ames and Archer (1988) and Ryan and Grolnick (1986) used the more common design of giving a classroom perception measure followed at some later point in time by measures of outcomes (e.g., individual differences in perceived competence, goal orientation, self-efficacy, intrinsic motivation, strategy use). This type of design does not control for preexisting individual differences. In this study, we have a pretest measure of students’ motivation and cognition followed by a measure of classroom perceptions and a posttest on student motivation and cognition. This design allows us to examine the relative influence of entering both individual differences and classroom perceptions on students’ motivation and cognition. In terms of the classroom dimensions, academic work has been shown to influence learners by focusing their attention on particular aspects of the content, specifying ways to process information, and promoting interest (Doyle, 1983). Blumenfeld, Mergendoller, and Swarthout (1987) also have argued that features of academic classroom work can influence students’ motivation and cognition. In addition, motivation researchers have suggested that providing students with some choice and control over their learning will result in higher levels of motivation and interest (Ames, 1992; Deci & Ryan, 1985). Eccles et al. (1993) have found that there is usually a decline in the amount of autonomy, choice, and control over academic work as adolescents enter middle school with concomitant declines in their motivation. Accordingly, our measure of academic work included students’ perceptions of how much interest is generated by the work, the amount of choice they have, and whether the work helps them learn the course content. It was expected that perceptions of the classroom work as productive would be related to higher levels of student motivation, especially more positive intrinsic value beliefs, as well as more frequent use of cognitive and self-regulatory strategies.

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The second dimension of classroom experience includes students’ perceptions of the teacher’s instructional behavior. Students’ perceptions of their teachers and their instructional behavior have been shown to be related to motivational beliefs (Eccles, 1983) as well as actual academic performance (Brophy & Good, 1986). The research from the process-product paradigm suggests that both management behavior (e.g., maintaining control and order) as well as instructional behavior (e.g., providing clear explanations) are important characteristics of good teachers (Brophy & Good, 1986). More recently, Blumenfeld, Puro, and Mergendoller (1992) have suggested that teachers need to combine instructional strategies to facilitate student motivation (i.e., enhancing student interest and value for the course material; cf. Brophy, 1983) with instructional strategies that enhance cognitive engagement (i.e., clear explanations, pressing for understanding through questioning and feedback). We included students’ perceptions of these instructional behaviors and expected that students who perceived more frequent use of these behaviors would report more positive motivational beliefs and higher levels of cognitive and self-regulatory strategy use. Finally, cooperative goal structures in the classroom have been shown to enhance student motivation including attributions, interest and value beliefs, and self-efficacy and perceptions of competence (Slavin, 1980). In addition, students’ perceptions of classroom mastery and cooperative goal structures are related to their use of cognitive strategies (Ames & Archer, 1988) as well as their overall academic achievement (Slavin, 1980). Although there are many studies of cooperative goal structures in classrooms, very few classroom studies have examined the relative contributions of the opportunity to work together with other aspects of the classroom. It may be that the nature of the academic work or the teacher’s instructional behavior may have an influence on students’ motivation and cognition over and above any effect from cooperative learning opportunities. We expected that students’ perceptions of the opportunity to work with other students would be positively correlated with students’ motivational beliefs and their use of cognitive and self-regulatory strategies. However, we also were interested in examining the relative strength of the three classroom variables as predictors of students’ motivation and self-regulated learning. The third general research question concerns the relations between the individual student characteristics of motivation and self-regulated learning and the three classroom characteristics. Many motivational models tend to emphasize the strength of individual differences as predictors of students’ achievement (Corno & Snow, 1986), whereas classroom research on tasks (Blumenfeld et al., 1991; Doyle, 1983) and more recent work on situated cognition (Brown, Collins, & Duguid, 1989) suggest that the situational features of the classroom have a powerful influence on students’ motivation and cognition. Given the design of the study, which includes measures of students’ motivation and self-regulated learning at the beginning of the school year and

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at the end, we can examine the relative strength of individual differences versus classroom characteristics as predictors of end-of-the-year student motivation and self-regulated learning. In summary, three main research questions were investigated: (a) What is the relation between adolescents’ motivational beliefs (intrinsic value, selfefficacy, and test anxiety) and self-regulated learning (use of cognitive strategies, use of metacognitive control strategies) over the course of a school year? (b) What is the relation between characteristics of the classroom environment and adolescents’ motivation and self-regulated learning? and (c) What is the relative strength of adolescent personal characteristics and perceptions of classroom experience as predictors of motivation and self-regulated learning at the end of the school year?

Method Subjects The sample included 100 seventh-grade students from 14 classrooms in two middle schools. The subjects were predominantly White, middle-class adolescents from a small city in southeastern Michigan. There were 55 girls and 45 boys in the sample. The mean age was 12 years, 3 months.

Measures The students responded twice to a self-report questionnaire, the Motivated Strategies for Learning Questionnaire (MSLQ) (see Pintrich & De Groot, 1990; Pintrich et al., 1993) that included 56 items on student motivation, cognitive strategy use, and self-regulation that formed the scales for the motivational and self-regulatory variables. Administration of the questionnaire took place during the fall semester (October, Time 1) and again in the spring of the following year (May, Time 2). During the spring administration, students answered a version of the MSLQ that included, in addition to the same 56 items on the first questionnaire, 12 items that asked students about the class work, their teacher, and the opportunities to work with other students in that class. These 12 items were adapted from classroom climate scales Moos (1979), and formed the scales for the classroom experience variables. Scale construction for the motivational and self-regulation variables was guided by previous work done with the MSLQ with this age group (Pintrich & De Groot, 1990). Three motivation scales were formed. The Intrinsic Value scale (Time 1 alpha = .87, Time 2 alpha = .90) was constructed by taking the average of the students’ responses to the nine items concerning intrinsic interest (“I think what we are learning in this Science class is interesting.”)

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and perceived importance of course work (“It is important for me to learn what is being taught in this Social Studies class.”), as well as preference for challenge and mastery goals (“I prefer class work that is challenging so I can learn new things.”). It should be noted that the items were keyed in terms of the class in which the students took the questionnaire; that is, if they were in a science class, the items referred to science. The Self-Efficacy scale (alphas = .91, .92) consisted of nine items regarding perceived competence and confidence in performance of class work (e.g., “I expect to do very well in this class.” “I am sure that I can do an excellent job on the problems and tasks assigned for this class.”). Finally, four items constituted the Test Anxiety scale (alphas = .75, .84), including items concerning worry and cognitive interference on tests (“I am so nervous during a test I cannot remember facts I have learned.” “When I take a test I think about how poorly I am doing.”). In addition to the three motivation scales, two cognitive scales, Cognitive Strategy Use and Self-Regulation, were constructed. The Cognitive Strategy Use scale (alphas = .83, .88) consisted of 13 items averaged to form the scale score. Items pertained to the use of rehearsal strategies (e.g., “When I read material for science class, I say the words over and over to myself to help me remember.”), elaboration strategies such as summarizing and paraphrasing (e.g., “When I study for this English class, I put important ideas into my own words.”), and organizational strategies (e.g., “I outline the chapters in my book to help me study.”). The Self-Regulation scale (alphas = .63, .71) was constructed from nine items that asked about metacognitive strategies such as planning, skimming, and comprehension monitoring (e.g., “I ask myself questions to make sure I know the material I have been studying,” “I find that when the teacher is talking I think of other things and don’t really listen to what is being said,” and “I often find that I have been reading for class but don’t know what it is all about,” with the latter two items reflected before scale construction). In addition, students’ strategies for managing their effort such as persistence at difficult or boring tasks and working diligently were included in the self-regulation scale (e.g., “Even when study materials are dull and uninteresting, I keep working until I finish,” and “When work is hard, I either give up or study only the easy parts,” with the latter reflected before scale construction). Factor analysis was used as a guide to create three classroom perception scales. A varimax rotated solution generated three interpretable factors that accounted for 67% of the variance. The first scale, the Productive Classroom Work scale (alpha = .83) consisted of five items concerned with how the student perceived the class assignments in terms of utility and interest, choice of work, and subject matter in general (e.g., “Students have some choice over the topics for class reports.”). The Teacher Effectiveness scale (alpha = .85) was constructed by taking the mean of five items regarding the teacher’s treatment of the subject matter in a clear and interesting manner, good classroom management, and fair grading procedures (e.g., “The teacher explains

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the material well.” “The teacher has good control of this class.”). Finally, the Cooperative Work scale comprised two items (alpha = .79). Items asked whether or not the teacher encouraged students to work together on assignments or provided opportunities to do so (e.g., “The teacher encourages us to work on assignments together,” and “I had the opportunity to work with other students in this class.”). Three different versions of these three scales were created, following Ryan and Grolnick (1986). First, an individual difference perception score was created that represented the individual’s mean score on the scale regardless of classroom. Second, these scales were then aggregated to the classroom level to create social consensus scores for perceptions of work, teacher, and opportunities to work cooperatively with every student in the same classroom assigned the classroom mean. Finally, an individual student deviation score for these three class perceptions was computed by subtracting the classroom mean from his or her individual score on the same scale.

Results Gender differences were examined in preliminary analyses on all of the motivational and cognitive variables and revealed only one significant difference for boys and girls on the self-efficacy measure. Accordingly, in all analyses except those including self-efficacy, gender was not included in the analyses. The first question of the study concerned the relations between the motivational and self-regulated learning components, and the results generally replicated our previous findings. Table 1 displays the zero-order correlations and summary statistics for the motivational and self-regulated learning variables at Time 1 and Time 2. The autocorrelations of the motivational and cognitive scales at Times 1 and 2 were moderately large, ranging from .47 to .61. As predicted and paralleling our earlier results (Pintrich & De Groot, 1990), at both Time 1 and Time 2, higher levels of intrinsic value (r = .66, r = .76) and self-efficacy (r = .41, r = .61) were correlated with high levels of cognitive strategy use. Test anxiety was not significantly related to cognitive strategy use. Similar to the cognitive strategy use results, higher levels of intrinsic value (r = .69, r = .73) and self-efficacy (r = .50, r = .67) were related to higher levels of self-regulation. Test anxiety was negatively related to self-regulation at both Time 1 and Time 2 (r = −.25, r = −.29). In terms of the relations between the motivational beliefs and self-regulated learning variables over time, five separate regressions were run. Each of the Time 2 variables was predicted by the five Time 1 variables, and the results are presented in Table 2. In general, the dependent measure was predicted most strongly by the parallel measure at Time 1. For self-efficacy, test anxiety, and cognitive strategy use at Time 2, the strongest and only significant predictors were self-efficacy at Time 1 (beta = .47), test anxiety at Time 1

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Table 1: Summary statistics and zero-order correlations for motivation and self-regulated learning variables at Time 1 and Time 2 Variable 1. Intrinsic value 2. Intrinsic value 3. Self-efficacy 4. Self-efficacy 5. Test anxiety 6. Test anxiety 7. Strategy use 8. Strategy use 9. Self-regulation 10. Self-regulation Mean Standard deviation

Time 1 2 1 2 1 2 1 2 1 2

1

2

3

4

5

6

7

– .47*** – .53*** .41*** – .35*** .72*** .57*** –.14 –.17* –.40*** –.01 –.25** –.32*** .66*** .51*** .41*** .48*** .76*** .26** .69*** .38*** .50*** .38*** .73*** .29***

– –.30*** –.41*** .43*** .61*** .47*** .67***

– .60*** – –.08 .01 –.05 –.09 –.25** –.11 –.14 –.29**

5.41 1.03

5.56 1.10

3.42 1.49

5.41 1.10

5.45 1.01

3.23 1.63

8

9

– .61*** – .73*** .49*** – .50*** .73*** .49*** 4.99 0.92

5.08 0.96

4.84 1.00

10

– 4.88 1.07

Note: N = 100. *p < .05; **p < .01; ***p < .001.

Table 2: Standardized regression effects of Time 1 motivation and self-regulated learning variables on Time 2 motivation and self-regulated learning variables Predictors a Intrinsic value Self-efficacy c Test anxiety Strategy use Self-regulation Total adjusted R 2

Intrinsic value b .20 .19 –.07 .42** –.17 .30***

Self-efficacy b –.17 .47*** .08 .21 .19 .36***

Test anxiety b .16 –.20 .54*** .05 –.03 .36***

Strategy use b .11 .07 .14 .48*** .09 .37***

Self-regulation b –.02 .03 –.05 .31* .26 .25***

Note: N = 100. a Time 1 variables. b Time 2 variables. c For self-efficacy, there was a significant gender difference, so gender was added as the first term in the regression equation. The standardized regression coefficient for gender on self-efficacy at Time 2 was β = –.01 ( p = .94). *p < .05; **p < .01; ***p < .001.

(beta = .54) and cognitive strategy use at Time 1 (beta = .48), respectively. These variables accounted for about 36% of the variance in self-efficacy, test anxiety, and cognitive strategy use at the end of the year. A different pattern of results was found for intrinsic value and self-regulation at Time 2. The strongest predictor of intrinsic value at the end of the year was cognitive strategy use at Time 1 (beta = .42). Time 1 level of intrinsic value had a positive, but nonsignificant effect on intrinsic value at the end of the year (beta = .20, p = .13). For self-regulation at the end of the year, students’ reported use of cognitive strategies was the best predictor (beta = .31), with prior levels of self-regulation having a positive effect that was not significant (beta = .26, p = .08). The cognitive and motivation variables at Time 1 accounted for 30% of the variance in students’ intrinsic value and 25% of their self-regulation at the end of the year.

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The second research question addressed the relations of these motivational and cognitive variables to children’s perceptions of their classrooms. The productive classroom work and teacher effectiveness perception scales shared modest correlations with the cooperative work scale (r = .38, r = .37, respectively), whereas perceptions of productive work and teacher effectiveness had a fairly high intercorrelation (r = .69). From these original scales, we constructed two additional measures of the classroom environment. First, we aggregated student perceptions to the class level yielding a social consensus measure for each of the three scales. Next, an individual deviation score was calculated for each of the three class perceptions by subtracting the classroom mean from individual student perceptions. T tests were run on the classroom mean and deviations perceptions, and one significant gender difference was found. Boys viewed their teachers’ instructional and management styles more favorably than did girls. This difference was manifest in the deviation teacher perception scores, with boys being above the classroom mean and girls below the classroom mean on the average across all classrooms. Because of this difference, gender was used as a control in the regression analyses in the final section. Table 3 presents the zero-order correlations and summary statistics for the classroom perception variables and the motivational and self-regulated learning variables at Time 1 and Time 2. Table 3 includes the original scales, the social consensus measures, and the deviation scores for each of the classroom perceptions. In general, the results for the deviation scores mirrored the results for the original scales, so only the results for the deviation and classroom-level perceptions will be discussed here. Recall that correlations involving the deviation scores represent the relationship of the motivation and cognition variables with a student’s perception of the classroom as more or less than the class average on a given construct. Table 3 shows significant relationships between intrinsic value and the deviation perceptions of productive class work (r = .44, r = .70), teacher effectiveness (r = .26, r = .50), and cooperative work (r = .24, r = .32), with the larger magnitude relations occurring at Time 2. The same pattern emerged for self-efficacy and cognitive strategy use. To the extent that students perceived their teacher as more effective, their work as interesting and productive, and opportunities to work together more than did their classmates, they also reported higher levels of self-efficacy and strategy use, with stronger effects at Time 2. Students who showed lower levels of test anxiety at both time 1 and 2 (r = .20, r = .21) percieved their teachers as more effective than did their classmates. Finally, higher levels of self-regulation were related to favorable perceptions of productive work at Time 1 and Time 2, and of the teacher’s effectiveness at the end of the year. Table 3 also displays the correlations among the motivation, cognitive strategy variables and the social consensus measures of the classroom environment. No significant correlations between the social consensus measures

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1 1 1 1 1 2 2 2 2 2

Time

4.79 1.33

.38*** .26** –.11 .40*** .33*** .82*** .56*** –.21* .69*** .60***

Productive work

5.52 1.19

.19* .30** –.12 .18* .10 .60*** .40*** –.18* .47*** .41***

Teacher

5.55 1.25

.15 .17 .01 .21* .05 .33*** .32*** –.06 .32*** .26**

Cooperative work

4.79 0.70

.01 .13 –.01 .06 .13 .43*** .39*** –.11 .40*** .37***

Productive work

5.52 0.76

–.02 .07 .07 .02 .06 .34*** .22* –.03 .32*** .29**

Teacher

5.55 0.54

–.15 .11 –.06 –.14 –.05 .11 .24** –.28** .19* .25**

Cooperative work

Classroom-level aggregate perceptions of

0.00 1.13

.44*** .23** –.12 .43*** .30** .70*** .42*** –.18 .56*** .48***

Productive work

0.00 0.91

.26** .33*** –.21* .22* .09 .50*** .34*** –.20* .35*** .29**

Teacher

0.00 1.13

.24* .13 .04 .30** .08 .32*** .24* .06 .27** .16

Cooperative work

Student deviation scores for classroom perceptions of b

Note: N = 89. a Classroom perceptions variables are presented in three forms. In the first column are individual difference classroom perceptions, the second column presents within-classroom averages of student perceptions, and the third column presents individual difference scores minus the classroom average. b Because these are deviation scores from classroom means, a positive correlation indicates the extent to which a student’s individual perception score being higher than the class average is related to increased levels of a motivation or strategy variable. *p < .05; **p < .01; ***p < .001.

Mean Standard deviation

Intrinsic value Self-efficacy Test anxiety Strategy use Self-regulation Intrinsic value Self-efficacy Test anxiety Strategy use Self-regulation

Student motivation

Individual difference class perceptions of

Table 3: Summary statistics and zero-order correlations for classroom perception variablesa with motivation and self-regulated learning at Time 1 and Time 2

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and students’ Time 1 motivation or cognition emerged. At Time 2, however, higher classroom level perceptions of a teacher’s effectiveness, as well as productive work were related to increased levels of intrinsic value, self-efficacy, strategy use, and self-regulation. In addition, higher classroom perceptions of cooperative work were related to increased levels of students’ self-efficacy, strategy use, and self-regulation and decreased levels of students’ test anxiety at the end of the year. The third research question was concerned with what predicts a student’s motivation and self-regulated learning at the end of the school year. In order to assess the relative impact of the student’s entry-level characteristics (Time 1 measures of intrinsic value, self-efficacy, strategy use, etc.) and their classroom experiences as measured by their perceptions (productive work, teacher effectiveness, cooperative work) on these motivational and cognitive components, regression analyses were used. Five separate regression analyses were run, each examining how an initial level of a motivational belief or self-regulatory learning strategy and the student’s classroom experience predicted the end-of-the-year measure of the same motivational or cognitive variable. Both the social consensus and deviation class perception variables were included in these analyses. The results are summarized in Table 4. These regressions also were done with multiplicative interaction terms included, and no significant interactions emerged between the entry characteristics and classroom perceptions on Time 2 variables. Table 4: Standardized regression effects of Time 1 motivation, self-regulated learning, and class perception variables on Time 2 motivation and self-regulated learning variables Intrinsic value a

Predictors Intrinsic valueb Self-efficacyb Test anxietyb Strategy useb Self-regulationb Gender Class perception of productive work Class perception of teacher Class perception of cooperative work Deviation perception of productive work Deviation perception of teacher Deviation perception of cooperative work Adjusted R

2

Self-efficacy a

Test anxiety a

.17* __ — — — –.01

.46*** — — — –.08

.52*** — — .17

.52*** –.08

.53*** –.27*

–.07 .54***

Strategy use a Self-regulation a

.45*** — –.03

.37*** .04

.02 –.01

.30* .03

.22 .04

.05

–.27**

.13

.16

.31***

–.13

.36***

.33**

.11

–.05

–.02

.03

.07

.04

.07

.13

–.01

.00

.69***

.54***

.39***

.61***

.44***

Note: N = 89. a Time 2 variables. b Time 1 variables. *p < .05; **p < .01; ***p < .001.

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In general, Table 4 shows, as one would expect, that the Time 1 estimates of the student’s motivational and self-regulatory characteristics had large, positive effects on the Time 2 measures of the same construct. The one exception to this is for intrinsic value, where the initial level had only a small effect on value at the end of the year (beta = .17). In addition, these results suggest that both class-level environmental variables, as well as individual difference perceptions of the classroom affect students’ year-end motivation and selfregulatory learning. Specifically, classroom-level assessments of productive work were related positively to Time 2 measures of intrinsic value (beta = .52), self-efficacy (beta = .53), and cognitive strategy use (beta = .30). In addition, the extent to which a student perceived the work as more productive than did his or her classmates also had positive effects on value, self-efficacy, and strategy use and self-regulation above and beyond those effects due to the general consensus of what tasks were like in a class. Test anxiety at Time 2 was the only variable for which perceptions of productive work was not significantly predictive. Two other significant effects emerged. First, to the extent a student perceived fewer opportunities to work cooperatively, high levels of test anxiety were reported. Finally, higher levels of self-efficacy were related to lower classroom-level perceptions of a teacher’s effectiveness. With the exceptions of test anxiety and self-regulation, initial personal levels of these motivational and cognitive constructs, as well as perceptions of productive work, accounted for large proportions in Time 2 levels of intrinsic value (r 2adj = .69), self-efficacy (r 2adj = .54), and strategy use (r 2adj = .61). Favorable perceptions of productive classroom work and Time 1 measures of self-regulation have equal effects on end-of-the-year selfregulation, accounting for a fair amount of the variance (r 2adj = .44). Finally, modest levels of the variance in year-end test anxiety are accounted for by opportunities for students to work cooperatively and initial levels of test anxiety (r 2adj = .39).

Discussion In terms of our first research question on the relations between motivation and cognition, the results replicated our earlier results (Pintrich & De Groot, 1990) with a different sample of early adolescents. In fact, the direction and magnitude of the correlations were very similar over the two studies. Students who had positive motivational beliefs, which included a general intrinsic orientation focused on learning and mastery, positive perceptions of interest and value regarding course material, and high self-efficacy beliefs, were more likely to report using cognitive and self-regulated learning strategies that will result in deeper processing of the material and better understanding. At the same time, students who reported higher levels of test anxiety were less likely to be self-regulating. This finding is in line with a general

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information processing and social cognitive view of anxiety (Bandura, 1986; Benjamin et al., 1987) that highlights the interfering effects of anxiety on cognitive processing. This overall pattern of results for the three motivational components and self-regulated learning components has been found in a number of other experimental and correlational studies (e.g., Graham & Golan, 1991; Nolen, 1988; see review by Pintrich & Schrauben, 1992) and seems to represent a fairly reliable and valid set of findings. The fact that motivational beliefs and self-regulated learning variables were linked to each other usually begs the question of causality. That is, do positive motivational beliefs drive or power cognitive engagement and selfregulation, or does being self-regulating and cognitively skilled result in more positive motivational beliefs? The regression results predicting Time 2 motivation and self-regulated learning from Time 1 variables without the classroom perceptions included showed, for the most part, that the best predictor of later motivation or cognition was earlier motivation or cognition. The main exception to this general finding was that use of cognitive strategies earlier in the year was the best predictor of later intrinsic orientation, suggesting that students who are more cognitively engaged report more mastery goals and higher levels of interest and value later in the year. This finding of deeper processing leading to qualitatively better motivation is the reverse of the usual suggested path of mastery goals leading to better cognitive engagement (cf. Ames, 1992; Graham & Golan, 1991; Nolen, 1988). However, considered with the zero-order correlations, this finding suggests that the relations between motivation and cognition are reciprocal, especially in the classroom setting, as suggested by social cognitive theory (Bandura, 1986). Accordingly, although goal theory usually assumes a unidirectional influence from motivational goals to cognitive engagement, the results here begin to specify the nature of the reciprocal relations between motivation and cognitive self-regulation as suggested by Keating (1990). It may be more useful for future research to focus on describing in more detail the reciprocal nature of the relations between motivation and self-regulated learning rather than trying to define the one correct causal sequence in a deterministic fashion. More important, it appears that the functional significance of the classroom context influenced the motivational and self-regulated learning variables over time. In terms of the second general question, students’ motivational beliefs were positively related to positive features of the classroom as would be predicted from Eccles et al. (1993). Students reported that they were more likely to be focused on learning and mastery and have higher levels of interest and value for the course material when the classes they were in provided them with some choice of tasks, the teacher made the work interesting, provided good explanations, and allowed them to work with others. In addition, these same features of the classroom were related to higher levels of self-efficacy and lower levels of test anxiety. Students also

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reported that they were more likely to use cognitive strategies for learning and to regulate their own thinking and effort in classrooms that had these positive features. These findings are interesting, but given that the classroom features were measured by students’ perceptions, it was important to control for initial levels of students’ motivation and self-regulated learning as well as examine both general classroom effects and individual perceptions of the classroom. By examining students’ entry characteristics and later classroom perceptions together (as in Table 4), we attempted to determine the relative contribution of entry characteristics and classroom features to students’ end-of-the-year motivation and self-regulated learning. In general, the results showed that both entry characteristics and classroom features contributed very significantly to student outcomes with high levels of variance accounted for by the predictors, but the relative strength varied depending on the outcome. First, in terms of the relative strength of the different classroom variables, both between-classroom and within-classroom variables were significantly related to motivation and cognition. In terms of the motivational beliefs, intrinsic value was strongly influenced by both between- and within-classroom features of classroom academic work. In fact, perceptions of productive classroom work was more than three times more important for end-of-the-year intrinsic value than was students’ entry level of intrinsic value. Students who were in classrooms that allowed task choice and had interesting tasks showed higher levels of intrinsic motivation in general; students who perceived more within-classroom choice and interest were more intrinsically motivated later in the year, regardless of their initial levels of intrinsic value. This suggests that intrinsic value may be more context dependent and that teachers can influence students’ general orientation to the academic work and facilitate students’ interest, value, and focus on mastery and learning. In contrast, entry level of test anxiety was a much stronger predictor of later test anxiety than any of the classroom perception variables, suggesting that test anxiety is a more traitlike characteristic of students, this is brought with them to the classroom situation, at least in terms of these three dimensions of the classroom. In fact, test anxiety had the lowest overall variance accounted for by the predictors. However, in classrooms, where there was more opportunity to work collaboratively, test anxiety was lower, regardless of initial levels of anxiety or within-classroom perceptions, suggesting one general classroom strategy that all teachers can use to help students become less anxious (cf. Hill & Wigfield, 1984). Self-efficacy, in contrast to the more situational intrinsic value and more traitlike anxiety, was predicted by both initial self-efficacy levels and between-classroom work equally. In addition, students that perceived their classroom work as more productive than their classmates felt more efficacious. Accordingly, classroom work that provided more choice and was more interesting was related to higher self-efficacy levels, regardless of initial

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levels of efficacy. Interestingly and unexpectedly, in classrooms where the teachers were perceived as more effective overall, students had lower self-efficacy. This may be due to the perception that these teachers are so effective at management and instruction that students are less willing to attribute their success at learning to themselves and hence have somewhat lower self-efficacy for learning. At the same time, it is important to note that the perceptions of efficacy were not so low as to have any detrimental effect on cognitive engagement. Students who perceived their teacher as effective were still cognitively engaged (see Table 3). Future research will have to examine this question more carefully by including attributional and control belief scales about responsibility for learning. The same basic pattern that was shown in the self-efficacy results held for cognitive strategy use and self-regulation; entry levels on these cognitive variables accounted for approximately equal proportions of the variance in outcome levels as classroom work perceptions. In terms of cognitive strategy use, there was both an overall between-classroom effect as well as a within-classroom effect. Students were more likely to use cognitive strategies if they were in classrooms that had task choice and interesting tasks overall as well as if they perceived more choice and interest within their class. For self-regulation, only the within-classroom perception was significantly related to use of regulatory strategies. Accordingly, it appears that both student entry characteristics and between- and within-classroom characteristics have an influence on adolescents’ motivational beliefs and their self-regulated learning. Adolescents bring with them to the classroom certain motivational beliefs and levels of strategy use and self-regulation that influence their later motivation and self-regulated learning. These individual differences are important and do relate to future achievement (cf. Corno & Snow, 1986). At the same time, classroom features, particularly the nature of the classroom work, can influence these student outcomes as well (Eccles et al., 1993). In earlier studies (e.g., Ryan & Grolnick, 1986), the within-classroom differences seemed to be more important than the social consensus between classroom differences as predictors of student motivation. Our results show both may be important, even after initial individual differences are taken into consideration. Accordingly, there may be two levels of the functional significance of the classroom: an overall between-classroom level that represents a social consensus regarding adolescents’ perceptions and a within-classroom level that reflects adolescents’ differing perceptions of the classroom. Both levels of these classroom features can provide the context in which individual differences in student motivation and self-regulation operate, demonstrating the interplay between not only adolescent motivation and cognition but also the classroom context in middle schools. Finally, in terms of the educational implications of this research, there are several important suggestions for teachers in middle schools. First,

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motivational beliefs in adolescents, particularly their interest, value, and intrinsic goals for class work, are not stable traits that imply that middle school students are either motivated or not. It appears that the nature of the classroom work can influence these motivational beliefs. If students are given work that is interesting, allows some choice, and provides opportunities to work cooperatively with one another, then they will be more likely to be motivated and cognitively engaged. Second, it may not be that difficult to implement some of these changes in classroom work. Although Lepper and his colleagues (Lepper & Malone, 1987; Malone & Lepper, 1987) have provided suggestions to improve intrinsic motivation through the use of choice and control options in fantasy and simulation situations, the simple provision of choice for the timing of when tasks are completed offers students some control over their learning but does not usurp the teacher’s responsibility for curriculum management nor does it require the use of nontraditional tasks. Others have shown that teachers can use somewhat traditional tasks and still increase motivation and cognition. For example, Blumenfeld (1992) has shown that instructional activities that are based on children’s experience and real-life events and that ask students to apply their knowledge can foster both motivation and cognitive engagement. This highlights a third important implication of our work: the need for teachers to consider both motivation and cognition simultaneously, and not simply focus on motivating the students without considering the cognitive consequences of motivational enhancement. For example, whereas cooperative groups may be more motivating, they can lead to less cognitive engagement due to group distractions. Blumenfeld (1992) has suggested that not only do teachers need to “bring the task to the students” by making the tasks more motivating, interesting, and relevant, but they also need to “bring the student to the task” (p. 110) by making the students accountable for deeper levels of cognitive engagement through evaluation and assessment procedures. Not only should tasks be interesting, but there should be a press for deeper cognitive engagement through the use of higher level questioning during class instruction and requests for written work that requires this type of thinking. By considering both student motivation and cognition and how they are influenced by the nature of classroom instruction and tasks, teachers will be able to create classrooms that are both motivating and thoughtful, a context that can only benefit the development of young adolescents in middle schools.

References Ames, C. (1992). Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology, 84, 261–271. Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Student learning strategies and motivation processes. Journal of Educational Psychology, 80, 260–267. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice-Hall.

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Benjamin, M., McKeachie, W., & Lin, Y. (1987). Two types of test anxious students: Support for an information processing model. Journal of Educational Psychology, 73, 816–824. Blumenfeld, P. (1992). The task and the teacher: Enhancing student thoughtfulness in science. In J. Brophy (Ed.), Advances in research on teaching ( Vol. 3, pp. 81–114). Greenwich, CT: JAI. Blumenfeld, P., Mergendoller, J., & Swarthout, D. (1987). Task as a heuristic for understanding student learning and motivation. Journal of Curriculum Studies, 19, 135–148. Blumenfeld, P., Puro, P., & Mergendoller, J. (1992). Translating motivation into thoughtfulness. In H. Marshall (Ed.), Redefining student learning: Roots of educational change (pp. 207–239). Norwood, NJ: Ablex. Blumenfeld, P., Soloway, E., Marx, R., Krajcik, J., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educational Psychologist, 26, 369–398. Brophy, J. (1983). Conceptualizing student motivation. Educational Psychologist, 18, 200–215. Brophy, J., & Good, T. (1986). Teacher behavior and student achievement. In M. Wittrock (Ed.), Handbook of research on teaching (pp. 328–375). New York: Macmillan. Brown, A. L., Bransford, J. K., Campione, J. C., & Ferrara, R. A. (1983). Learning, remembering and understanding. In J. Flavell & E. Markman (Eds.), Handbook of child psychology: Vol. 3. Cognitive development (pp. 77–166). New York: Wiley. Brown, J., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational Researcher, 18, 32–42. Corno, L. (1986). The metacognitive control components of self-regulated learning. Contemporary Educational Psychology, 11, 333–346. Corno, L. (1993). The best-laid plans: Modern conceptions of volition and educational research. Educational Researcher, 22, 14 –22. Corno, L., & Snow, R. (1986). Adapting teaching to individual differences among learners. In M. Wittrock (Ed.), Handbook of research on teaching (pp. 605–629). New York: Macmillan. Deci, E., & Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum. Doyle, W. (1983). Academic work. Review of Educational Research, 53, 159–200. Dweck, C., & Leggett, E. L. (1988). A social cognitive approach to motivation and personality. Psychological Review, 95, 256–273. Eccles, J. (1983). Expectancies, values and academic behaviors. In J. T. Spence (Ed.), Achievement and achievement motives (pp. 75–146). San Francisco: Freeman. Eccles, J., Midgley, G, Wigfield, A., Buchanan, C. M., Reuman, D., Flanagan, C., & Mac Iver, D. (1993). Development during adolescence: The impact of stage-environment fit on young adolescents’ experiences in schools and in families. American Psychologist, 48, 90–101. Entwisle, D. (1990). Schools and the adolescent. In S. Feldman & G. Elliott (Eds.), At the threshold: The developing adolescent (pp. 197–224). Cambridge, MA: Harvard University Press. Graham, S., & Golan, S. (1991). Motivational influences on cognition: Task involvement, ego involvement, and depth of information processing. Journal of Educational Psychology, 83, 187–194. Harter, S. (1981). A new self-report scale of intrinsic versus extrinsic orientation in the classroom: Motivational and informational components. Developmental Psychology, 17, 302–312. Hill, K., & Wigfield, A. (1984). Test anxiety: A major educational problem and what can be done about it. Elementary School Journal, 85, 105 –126.

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Keating, D. (1990). Adolescent thinking. In S. Feldman & G. Elliott (Eds.), At the threshold: The developing adolescent (pp. 54 – 89). Cambridge, MA: Harvard University Press. Lepper, M., & Malone, T. (1987). Intrinsic motivation and instructional effectiveness in computer-based education. In R. Snow & M. Farr (Ed.), Aptitude, learning, and instruction: Vol. 3. Conative and affective process analyses (pp. 255–286). Hillsdale, NJ: Lawrence Erlbaum. Liebert, R. M., & Morris, L. W. (1967). Cognitive and emotional components of test anxiety: A distinction and some initial data. Psychological Reports, 20, 975–978. Maehr, M. (1984). Meaning and motivation: Toward a theory of personal investment In R. Ames & C. Ames (Eds.), Research on motivation in education ( Vol. 1, pp. 39–73). San Diego, CA: Academic Press. Malone, T., & Lepper, M. (1987). Making learning fun: A taxonomy of intrinsic motivations for learning. In R. Snow & M. Farr (Eds.), Aptitude, learning, and instruction: Vol. 3. Conative and affective process analyses (pp. 223–253). Hillsdale, NJ: Lawrence Erlbaum. Moos, R. (1979). Evaluating educational environments. San Francisco: Jossey-Bass. Nolen, S. (1988). Reasons for studying: Motivational orientations and study strategies. Cognition and Instruction, 5, 269–287. Pintrich, P. R. (1989). The dynamic interplay of student motivation and cognition in the college classroom. In C. Ames & M. Maehr (Eds.), Advances in motivation and achievement: Vol. 6. Motivation and enhancing environments (pp. 117–160). Greenwich, CT: JAI. Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82, 33– 40. Pintrich, P. R., & Garcia, T. (1991). Student goal orientation and self-regulation in the college classroom. In M. Maehr & P. R. Pintrich (Eds.), Advances in motivation and achievement: Vol. 7. Goals and self-regulatory processes (pp. 371– 402). Greenwich, CT: JAI. Pintrich, P. R., & Schrauben, B. (1992). Students’ motivational beliefs and their cognitive engagement in academic tasks. In D. Schunk & J. Meece (Eds.), Students perceptions in the classroom: Causes and consequences (pp. 149–183). Hillsdale, NJ: Lawrence Erlbaum. Pintrich, P. R., Smith, D., Garcia, T, & McKeachie, W. J. (1993). Reliability and predictive validity of the Motivated Strategies for Learning Questionnaire (MSLQ). Educational and Psychological Measurement, 53, 810–813. Ryan, R., & Grolnick, W. (1986). Origins and pawns in the classroom: Self-report and projective assessments of individual differences in children’s perceptions. Journal of Personality and Social Psychology, 50, 550–558. Schunk, D. (1985). Self-efficacy and school learning. Psychology in the Schools, 22, 208–223. Schunk, D. (1989). Social cognitive theory and self-regulated learning. In B. Zimmerman & D. Schunk (Eds.), Self-regulated learning and academic achievement: Theory, research, and practice (pp. 83–110). New York: Springer-Verlag. Simmons, R. G., & Blyth, D. A. (1987). Moving into adolescence: The impact of pubertal change and school context. Hawthorne, NY: Aldine. Slavin, R. E. (1980). Cooperative learning. Review of Educational Research, 50, 315–342. Stemberg, R., & Powell, J. (1983). The development of intelligence. In J. Flavell & E. Markman (Eds.), Handbook of child psychology (Vol. 3, pp. 341– 419). New York: Wiley. Weinstein, C. E., & Mayer, R. E. (1986). The teaching of learning strategies. In M. Wittrock (Ed.), Handbook of research on teaching (pp. 315–327). New York: Macmillan.

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Weinstein, R. (1989). Perceptions of classroom processes and student motivation: Children’s views of self-fulfilling prophecies. In C. Ames & R. Ames (Eds.), Research on motivation in education: Vol. 3. Goals and cognitions (pp. 187–221). New York: Academic Press. Winne, P., & Marx, R. (1982). Students’ and teachers’ views of thinking processes for classroom learning. Elementary School Journal, 82, 459–518. Zimmerman, B., & Martinez-Pons, M. (1986). Development of a structured interview for assessing student use of self-regulated learning strategies. American Educational Research Journal, 23, 614–628.

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60 Atkinson’s Theory of Achievement Motivation: First Step toward a Theory of Academic Motivation? Martin L. Maehr and Douglas D. Sjogren

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ducators generally agree that a major variable affecting classroom performance is motivation. However, important as motivational variables may be in understanding, predicting, and controlling classroom behavior, there is a paucity of information and theory associated with them. There are many theories of human motivation; but little attempt has been made to extend these theories in a systematic way to educational situations. Furthermore, the occasional application of psychological theory to education has not typically eventuated in a theory of academic motivation nor a unified and coherent body of information. As a result, there is very little in the way of motivation theory which is clearly of help to the classroom teacher or to education in general. The situation is not without hope, however. The work of several theorists has shown promise of evolving postulates and hypotheses relevant to the teaching-learning process. Prominent among these is the theory of achievement motivation, particularly as formulated by Atkinson (1957, 1964, 1965; Atkinson & Feather, 1966).1 This theory has provided a productive approach to a variety of behavioral phenomena; thus it has been suggested (e.g., Atkinson, 1966; Weiner, 1967) that it may also give direction to educationally relevant research. Can it indeed serve as a first step toward a theory of academic motivation? It is the goal of this paper to consider that question. Source: Review of Educational Research, 41(2) (1971): 143–161.

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Atkinson’s Theory of Achievement Motivation Essentially, Atkinson’s theory of achievement motivation can be summarized in the following equation: Ta = Ts + T−f + Text , where Ta = an active impulse to undertake a particular achievement-oriented activity; Ts = Ms (Ps) (Is); Ms = tendency to approach success, usually assessed with the aid of the Thematic Aperception Test (TAT); Ps = subjective probability of success, ranging on a scale from 0.00 to 1.00; Is = incentive value of success; it is assumed that Is = (1 – Ps); T–f = Maf(Pf ) (If ); Maf = tendency to avoid failure, usually assessed with the aid of the Test Anxiety Questionnaire (TAQ); Pf = subjective probability of failure; Pf = (1 – Ps); If = incentive value of failure; If = (1 – Pf); in computing the values in the equation, the sign is assumed to be negative (–If); Text = positive extrinsic tendency to perform the activity; these are tendencies which are not associated with pride in achievement per se; included, e.g., would be motives to comply or seek for approval which may eventuate in achievement behavior in a given context; the inclusion of Text in the formula represents a recent recognition of the fact that social contexts typically also bring non-nAch motives to bear on the achieving situation. Although its essentials are contained in the above equation, certain assumptions and characteristics of the theory might profitably be emphasized. First, it should be noted that the theory is assumed to be appropriate in “achievement situations,” i.e., situations in which a person not only sees himself as responsible for a somewhat uncertain outcome but knows that the outcome for which he is responsible will be evaluated against a standard of excellence. In other words, achievement situations are situations which require skill and competence. Second, it is assumed that in such achievement situations two conflicting predispositions will be energized: a motive to approach success and a motive to avoid failure. Furthermore, it should be noted that these conflicting motives are part of the person’s enduring personal orientation and that the relative strength of these two motives will vary from person to person. Thus, unlike most expectancy x value theories, a personality or individual difference factor is considered along with situational factors.

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Finally, it should be emphasized that the theory, again in contrast to most expectancy x value theories, assumes that the value factor (Is, If) is directly dependent on the expectancy factor (Ps, Pf); the incentive values of success and failure are inverse linear functions of the probability of success and failure. It is noteworthy that this simplifying assumption has some empirical justification, at least in the case of Ps and Is (Litwin, 1958). Given this general theoretical framework, a variety of specific predictions can be made. The major, over-riding hypothesis is that, in achievement situations, Ss for whom Ms > Maf (achievement-oriented Ss) will exhibit lowest motivation where Ps = 0.00 or 1.00 and highest motivation where Ps = 0.50. Conversely, Ss for whom Maf > Ms (failure-threatened Ss) will exhibit lowest motivation when Ps = 0.50.

Supporting Studies That the Atkinson theory represents more than intriguing speculation is clear from a perusal of the literature. There is a substantial body of data which indicates that the formulation is viable at least for the moment. Perhaps the major prediction of the theory is that achievement-oriented Ss will be more motivated toward moderately difficult tasks than failurethreatened Ss; in educational terms, achievement-oriented Ss will be more inclined toward challenge. Using choice-preference, persistence, or level of performance as the index of motivation, support for this general hypothesis was obtained in a variety of situations. Choice-preference. In a number of studies involving competitive, gamelike situations (McClelland, 1958; Atkinson, Earl, & Litwin, 1960; Litwin, 1958, 1966; Atkinson & Litwin, 1960), achievement-oriented Ss consistently showed a greater tendency to choose alternatives or perform tasks which had an intermediate probability of success. Kogan and Wallach (1967), among others, suggested this differential tendency to select or choose tasks of intermediate difficulty is limited to the narrow confines of competitive games. However, it is clear from other studies utilizing non-game-like experimental contexts that this is not true. For example, in a study concerned with aspiration level on an intellectual task and without the presence of a game-like atmosphere, Moulton (1965) found the predicated differential tendencies. This differential tendency of achievement-oriented and failure-threatened Ss to opt for challenge was also exhibited in curricular (Isaacson, 1964) and job (Mahone, 1960) choices. In short, the theory correctly predicts a differential preference for intermediate success levels; moreover, this prediction is not to be limited to the playing of competitive games. While there is strong support for the hypothesis that achievement-oriented Ss are more likely than failure-threatened Ss to prefer tasks of moderate difficulty, it should not be concluded that the predicted patterns of motivational increase-decrease across all Ps levels has also received confirmation. One may,

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as Heckhausen (1968) did, question whether the predicted symmetrical curves about Ps = .50 have validity, since achievement-oriented Ss have often been found to prefer levels slightly less than Ps = .50. Perhaps of greater interest is the fact that a U-shape function about Ps = .50 has not typified the choices of failure-threatened Ss. Thus it may be misleading to conclude that failure-threatened Ss actually avoid the Ps = .50 level, as the theory asserts. For the most part, the data only indicate that failure-threatened Ss prefer it less than achievement-oriented Ss. Thus, when the preference patterns of achievement-oriented and failure-threatened Ss are compared, achievement-oriented Ss do indeed exhibit a higher level of preference for moderately difficult tasks. However, considering the preference pattern of failure-threatened Ss separately, it is clear that their preference for moderately difficult tasks is not consistently less than their preferences for either difficult or easy tasks. If anything, they too exhibit a preference for moderate difficulty levels, though not perhaps to the same degree as achievement-oriented Ss. That achievementoriented Ss have a greater preference for moderately difficult tasks than failurethreatened Ss seems clear. What remains to be demonstrated is that failure-threatened Ss exhibit maximum avoidance in the moderate difficulty range, as the theory predicts. Persistence. The Atkinson model has also been effectively applied to the prediction of task persistence. In a series of papers, Feather (1961, 1962, 1963) not only developed a rationale for the application of the theory to the study of persistence but provided evidence confirming the validity of this application. In theory, achievement-oriented Ss should be more motivated, or approach-oriented, to the task (and hence, persist longer) when Ps = .50; failurethreatened Ss should be less motivated, in fact avoidance-oriented, toward the task (and hence, persist for a shorter period) under the same Ps = .50 condition. In confirmation of this prediction, Feather found that when Ss were told that a task was easy (presumably, Ps > .50) and then experienced failure (presumably lowering Ps to or in the direction of .50), achievement-oriented Ss exhibited greater persistence than failure-threatened Ss. Similarly, when Ss were told that a task was hard and then succeeded, achievement-oriented Ss likewise showed greater persistence. Indirect confirmation of the Atkinson model in the case of persistence behavior was also obtained by Maehr and Videbeck (1968). In a study on the effects of general risk-taking tendency (defined by a behavioral measure) on preference for reinforcements levels, these researchers found that regardless of general risk orientation, Ss were most persistent at moderate (50%) reinforcement-success schedules and least persistent at minimal (15%) and maximal (85%) schedules. Since the task was of an achievement nature and since the Ss (undergraduate male volunteers) were presumably high in success motivation, the data were interpreted as supportive of the hypothesis that achievement-oriented Ss are more motivated and, hence, more persistent when Ps = .50 than at lower or higher Ps levels. However, it is important to emphasize that while these findings are in accord with Feather’s results

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and in a sense enhance them, the study did not clearly define a difference between achievement-oriented and failure-threatened Ss. All in all, there seems to be strong support for the Atkinson model in the case of persistence behavior. Performance. Attempts to apply the Atkinson model to performance have eventuated in conflicting results, at Atkinson and others (Atkinson, 1967; Klinger, 1966) noted. Achievement-oriented Ss have not necessarily shown their best performance under a Ps = .50 condition (e.g., Smith, 1964; O’Connor, Atkinson, & Horner, 1966) and only occasionally have failure-threatened Ss shown deteriorated performance at this level (e.g., Karabenick & Youssef, 1968). Atkinson (Atkinson & Feather, 1966, p. 335; Atkinson, 1967) suggested that other conditions or tendencies (Text) may summate with the tendency for success and the tendency to avoid failure, resulting in extreme and counterproductive drive in the case of achievement-oriented Ss and in moderate and productive resultant achievement motivation in the case of failurethreatened Ss. The validity of this explanation is yet to be determined. It is, however, worth noting that the lack of success in predicting performance is not altogether surprising in view of the complexity of the factors typically involved. Furthermore, one might argue that preferences and tendencies to persist should have a long-term or eventual effect on outcomes in educational situations, even though short-term studies of performance do not reveal the predicted patterns. But such arguments do not settle the matter; they only suggest a direction for future research. Conclusion. It seems clear that Atkinson’s theory accurately predicts a differential orientation toward moderate success levels on the part of achievementoriented and failure-threatened Ss. That the results have been less clear in the case of performance data may be at least partially explainable by the more complicated nature of performance. Questions of whether failurethreatened Ss actually avoid the moderate levels and equally prefer either minimal or maximal success levels still remain. Finally, although it would be amiss to conclude that the model has validity only in the socially competitive game-like situation, it must be pointed out that most of the studies have at least implied some form of social competition in eliciting achievement. Moreover, in one study (de Charms & Davé, 1965) specifically designed to be self- rather than socially-competitive, the predicted effects were not found.

Unresolved Issues Granted that there is support for the basic conceptions of the model, are these conceptions really applicable to educational situations? Is the theory so structured that it can serve not only as a guide for educationally relevant research but also as a frame of reference for interpreting educational behavior and structuring instructional procedure? Without doubt, some issues need to be resolved before experimentation and application can proceed very far.

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Measurement A very critical area is the need for a better instrument for measuring Ms . The most commonly used procedure at the present time is to score responses to certain pictures from the TAT according to the achievement theme of the responses (McClelland, Atkinson, Clark, & Lowell, 1953; Atkinson, 1958). The index obtained through such a procedure is not notably reliable, perhaps primarily because subtle environmental cues present during the administration of the test can have strong effects on Ss’ responses (Klinger, 1967; Weinstein, 1969). Aside from such technical reservations, it may also be noted that the administration and scoring of such an instrument is so difficult that even if the instrument is reliable, it would not be practical for an applied situation. Attempts to alleviate this situation have been made. In particular, it may be noted that a number of objective measures of Ms have been tried, such as the Edwards Personal Preference Schedule (Edwards, 1954) and the Iowa Picture Interpretation Test (Hurley, 1955, 1957; Johnston, 1957). These have not had notable success and are questionable substitutes for a projective-type measure (Atkinson & Litwin, 1960; Heekhausen, 1967). Similarly, Atkinson and O’Connor (1966) tried unsuccessfully to develop an objectively scoreable questionnaire which would assess Ms on the basis of behavioral preferences. Some hope in this regard may be found in an instrument developed by Mehrabian (1968, 1969) and given preliminary testing by Weiner and Kukla (1970). A recently developed test by Herman (1969, 1970) likewise holds promise. However, both the Mehrabian and the Herman instruments have had only limited testing and their utility in an applied setting is yet to be determined. In view of such lack of success with nonprojective measures, one might conclude that this confirms the initial assumption of McClelland et al. (1953), based on Freud, that motives are best expressed in fantasy; what you obtain on objective measures is subject to strong social restraints which preclude the expression of actual inclinations. However, since the projective devices have not provided ultimate success in this regard either, it is difficult to accept this assumption. Perhaps test development has simply proceeded along inappropriate lines. For example, designers of objective instruments have typically developed items on an a priori or theoretical basis and then determined the validity of the instrument on the basis of correlations with standard projective measures or designated achievement behavior. A more appropriate tactic might be to build a scale empirically by selecting items that differentiate between people who perform a task with high achievement motivation and those who perform with low achievement motivation.

n-Achievement or n-Competition? It was previously concluded that Atkinson’s theory is not limited to the confines of competitive, game-like situations. But the extent to which external (social) as

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opposed to internal standards are effective remains an intriguing question. To put it differently, was Atkinson describing the behavior of people who like to win (over others), or simply, as he implied, the behavior of people who like to do well in terms of either external or internal standards? Perhaps there may be different types of achievement motivated Ss: those who are self-competitive and those who are socially competitive. In their early work, McClelland et al. (1953, p. 107 ff.) speculated on this possibility. More recently, Veroff (1969) suggested that nAch may take different forms at different age levels. Presumably, in the earlier stages of development nAch is reflected in a drive for competence (cf. White, 1959, 1960) which is relatively independent of the social comparison processes described by Festinger (1954). Later, during middle childhood when social comparison processes become both possible and important, nAch may be exhibited in an inclination toward socially competitive patterns. Finally, during adolescence, the individual presumably arrives at some sort of integrated nAch patterns which is a product of his experiences during both the early and late stages. Thus he might ultimately be more or less autonomous in his achievement orientation. Conceivably then, at least two types of achievement pattern might eventuate: a self-competitive and an other-competitive pattern. It may well be that the Atkinson model, while not limited to competitive games, is primarily appropriate to the other-competitive type. The preponderance of research has employed social norms of one kind or another in communicating the standard of excellence. What further enhances the importance of this question is that its answer may solve yet another perplexing problem. Throughout the literature one finds repeated references to the fact that women often do not respond to achievement cues in the same manner as men nor reflect the achievement behavior predicted by nAch theory (McClelland et al., 1953; Veroff, Wilcox, & Atkinson, 1953; Skolnick, 1966; McClelland, 1966). Quite possibly such disconcerting sex differences can be appropriately understood as differences in competitive orientation rather than as differences in achieving tendency per se. In any case, however one wishes to approach the sex difference problem (see, e.g., French & Lesser, 1964; Horner, 1968; Houts & Entwistle, 1968; Weston & Mednick, 1970), the competitive forms that achievement may take emerge as a crucial question.

nAch and Long-Term Achievement Yet another important question is what effect changes in Ps during performance may have. The results of Feather’s research (1961, 1962, 1963) seem to clearly indicate that both achievement-oriented and failure-threatened Ss persist under the expectancy conditions as the theory would predict. However, it must be kept in mind that in these studies Ps was a presumed function of E’s initial characterization of a task as easy or hard followed by S’s success or failure. Certainly an educator would be interested in persistence

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(and motivation) under those circumstances. However, he is quite likely to be more interested in persistence under slightly different circumstances, viz., under circumstances in which varying degrees (or ratios) of success are achieved in the course of performance. Such a situation, in which Ps must be self-determined by S based on the reinforcement he has thus far received, is more analogous to the typical situation. The question is, is nAch theory really applicable to a situation in which the actual ratio of success to persistence is of concern? One may infer from Atkinson and Cartwright (1964) that it should be. However, empirical data in this regard are limited. The notion that persistence (and motivation) will be greatest for achievement-oriented Ss when there is a challenge (Ps approaching .50) is intuitively plausible. One might, however, wonder whether continued effort at a task in which Ps remains at or around .50 might not become somewhat discouraging. An even more serious reservation in this regard concerns the issue of whether the failure-threatened S would be most persistent both when success is achieved 0.00% and 100% of the time in an ongoing achievement process.

Cross-cultural Generalizability Problems in motivating students are doubtless no more salient than in the case of the confrontation between the middle-class school and the “other” class and culture child. The viability of Atkinson’s theory within educational psychology depends to a considerable extent on its cross-cultural applicability. Is the theory applicable outside of white middle-class culture? Most of the confirming studies reported in the literature were conducted with middle-class and predominantly white Ss. Furthermore, if the theory is found to be relevant only in socially competitive situations (a moot point raised previously), it hardly needs to be added that the typical classroom is not the situation in which the non-middle-class child could be expected to exhibit whatever competitive predispositions he might have. Equally germane is Katz’s (1967) criticism that nAch indexes typically do not tap those areas in which disadvantaged children have a desire or hope for achievement. Supporting this is the finding that lower-class Black Ss (Rosen, 1959), and in some cases (Littig, 1968) upper- and middle-class Black Ss, exhibit lower achievement imagery on the conventional projective measures. There is, however, some evidence that the theory is not entirely devoid of cross-cultural applicability. First of all, Mingione’s work (1965, 1968) indicates the direction that cross-cultural assessment might take. Reasoning that the usual TAT procedure does not effectively elicit achievement imagery in Black Ss, Mingione (1965) developed pictorial stimuli which were designed to be more “culture fair.” Nevertheless, her results still showed differences between Black and white Ss with white Ss exhibiting more achievement imagery. Subjects in this study were grade school children living in

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North Carolina. In a subsequent study using “disadvantaged” Ss residing in Connecticut and employing verbal stimuli in eliciting achievement imagery, Mingione (1968) found no differences between Black and white Ss. Second, de Charms and Carpenter (1969) provided strong support for the valitdy of Atkinson’s model in the case of Black disadvantaged fifth- and seventh-grade children. In this study high nAch Ss (TAQ was not employed) exhibited greater preferences for moderate success levels on spelling and arithmetic tasks than low nAch Ss. In the case of the spelling task, low nAch Ss actually exhibited elevated preferences at low and high Ps levels and lowered preferences at moderate levels, a prediction that has seldom been found to occur regardless of subject population. What is most interesting, however, is that the experimental situation was not socially competitive in nature and was consistent with the realities of school performance. It will be remembered that when a similar non-competitive condition prevailed in the case of a game-like experiment using middle-class Ss (de Charms & Davé, 1965), Atkinson’s predictions were not confirmed. It is difficult to fully explain the incongruity existing in the case of these two studies. One might hypothesize that whereas a competitive atmosphere may be necessary to elicit achievement behavior in middle-class Ss, it is not in the case of Black lower-class Ss. It does not seem logical to argue that the type of task made a difference since game-like tasks have been effective in most other studies, but a possible Situation × Task × Subject interaction should not be ruled out and is worth exploring. Although there is at least some evidence of the cross-cultural applicability of the theory in the case of preference data involving minimal time and personal commitment, one might wonder whether the predicted effects would occur in a continuing task. For example, would the failure-threatened culturally disadvantaged child really continue to persist and perform well with little or no success, as he should according to the Atkinson equation? At least on an intuitive basis, it is possible to accept the hypothesis that failurethreatened white middle-class Ss may at times exhibit a tendency to work hard although they achieve little success; perhaps this is simply because persistence under all circumstances is considered a virtue by the middle class. On the same basis, however, it may be questioned whether the culturally disadvantaged S will persist when achieving little success. In fact, implicit in the theory itself is an argument against the validity of the usual predictions in the case of culturally disadvantaged Ss. Presumably, the nAch motives are energized only when the individual sees himself as responsible for achieving or not achieving the standard of excellence. In other words, perception of responsibility for and control over behavior is a requisite for the obtainment of the predicted curvilinear function. It may be noted, however, that not only nAch but also perception of responsibility may vary from situation to situation and from individual to individual. The work of Rotter and his associates (Rotter, 1966; Lefcourt, 1966) on locus of control is interesting in this regard.2 Their work strongly suggests that the culturally disadvantaged S may see the typical achievement

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situation as externally based and controlled rather than as a function of his own behavior. For this S, then, what was meant to be an achievement situation may well be perceived as a gambling situation, in the control of chance or the fates. In a gambling situation, a linear relationship between Ps and motivation is predicted for both achievement-oriented and failure-threatened Ss (Atkinson, 1964, p. 251). In any case, it is clear that in applying the Atkinson Model to other than white middle-class groups, other critical factors must be taken into account. In discussing the cross-cultural generalizability of the theory it is well to refer to two additional factors that need further study: mode of success and variations in task. There is a growing amount of evidence which indicates that children from different socioeconomic groups show different responses to different modes of feedback (Zigler & Kanzer, 1962; Zigler & Child, 1969; Stuempfig & Maehr, 1970). Within the wider context of nAch theory research, one finds clear interest in and guidelines for studying the effects of various modes of success on varying personality types. For example, French (1958) reported that S’s personal orientation (in terms of nAff and nAch) moderated responsiveness to different modes of feedback communication. And Atkinson (1966) emphasized that any consideration of academic motivation would have to take account of motives other than nAch.3 The recent inclusion of the Text term in the formal statement of the theory is indicative of the recognition of the importance of non-nAch motives in determining achievement in social settings. Yet all of this is only a bare beginning on a problem which is perhaps primary to the educator. Regarding the relationship of achievement motivation and task, theoretical concerns have tended to preclude research in this area. However, if the theory is applied to education, the question takes on major importance. All of this would seem to suggest that the development of Atkinson’s theory toward a general theory of academic motivation might well proceed by focusing on a major motivational problem: the achievement of disadvantaged children. Not only would this serve an important social need; it could conceivably force the kind of theoretical adjustments necessary to the development of a productive theory of academic motivation. For example, it is clear from the discussion thus far that the theory cannot now be applied to widely diverse cultural groups without taking factors other than nAch into account. Possibly all of these additional factors can be appropriately subsumed under the Text term. Possibly this strains the theory to the breaking point and a new conception is demanded. Thus Klinger and McNelly (1969), in considering differential achieving orientations of different social groups, suggested a role theory interpretation which, they argued, provides a more parsimonious and productive conception of achieving behavior as it occurs within given social contexts. Doubtless there are other possibilities, but the point to be made here is that application of the theory within a cross-cultural context is a potentially profitable way of determining if and how Atkinson’s model can evolve a theory of academic motivation.

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A Practical Problem There is one final question that must be raised: how might one implement the theory if it is to serve as a guide to practice? The theory is primarily concerned with how specialized environmental events differentially affect certain kinds of persons. Although educators are concerned with individual differences in response to environmental events, the exigencies of education make it incumbent that they be more concerned with how environmental variables have a greater or lesser effect on all students regardless of individual differences. It is difficult, if not impossible at present, to create a specialized environment for each student. A theory of academic motivation must tell the educator how to manipulate environments in order to obtain the greatest over-all effects. Thus the question of concern to the educator is: how do I manipulate the school environment so that I can maximize motivation in all students regardless of personality differences? That one can effectively approach achievement motivation in this way can be seen in the work of Alschuler (1968) who, by organizing the learning task as a self-competitive game in which the individual was responsible for setting his own goals and was graded on his own terms, significantly increased students’ achievement. Along a similar line, de Charms (1968) developed a theory of motivation which, while not ignoring the personality dimension, gave full due to the possibility that the manipulation of environment alone may determine (effectively if not completely) achievement motivation. Thus, de Charms suggested that when environmental events reduce S’s freedom or make him feel like a “pawn” he will be less achievement oriented than when he performs freely or as an “origin.” Such social-psychological approaches as are implicit in the work of Alschuler and de Charms, among others, probably ought to receive increased consideration. Furthermore, there is no reason why the body of literature associated with Atkinson’s theory cannot at least serve as a heuristic device in work along these lines.

Implications for the Study of Academic Motivation Although one can level many criticisms against Atkinson’s theory, the fact remains that it does suggest new interpretations and potentially fruitful hypotheses related to a number of educational practices. Some of these deserve to be outlined here (see also Atkinson, 1966; Weiner, 1967).

Ability Grouping One example of the applicability of the theory to education involves the practice of ability grouping. Ability grouping is based on the assumption that

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intelligence is the most important variable affecting performance in school: students will achieve more because the teaching can be geared to the ability level of the group. This assumption is probably not without validity, but consider how achievement motivation might affect performance in ability groups. First, it must be assumed that achievement motivation is not correlated with ability; there is evidence to support this assumption (Mahone, 1960; O’Connor et al., 1966.) If, then, students are placed into groups homogeneous according to ability but heterogeneous with respect to achievement motivation, what behaviors are predicted? Assuming also that there is more of a challenge (Ps → .50) in a homogeneous ability group, the theory would predict that those students with high achievement motivation would show high preference for and strong persistence at tasks; those students with low achievement motivation would have low preference for and low persistence at tasks. Ultimately, these variations in persistence and preference would be expected to affect performance. These predictions were substantiated to some extent in a study by O’Connor et al. (1966). They found that achievement-oriented students showed greater growth in scholastic achievement and more interest in school work when placed in an ability-grouped class. Failure-threatened students showed no difference in scholastic achievement but had less interest in school work when placed in such a class. The investigators speculated whether their finding might explain such phenomena as the differential performance of college freshmen who often come from high ability groups but who vary greatly in their performance and persistence in college. The evidence is perhaps not sufficient to justify changes in ability-grouping practices; but as Smith (1969, p. 243) suggested, it is sufficient to justify experimentation using variation in both achievement motivation and ability as a basis for forming homogeneous groups.

Programmed Instruction Another example is to be found in the case of programmed instruction. This educational innovation was to have solved the “motivational problem”; that it has not is obvious to any teacher (see Maehr, 1968). The limitations of programmed instruction in this regard may be partially attributable to the fact that programmers have been content with certain over-simplified assumptions concerning motivation. A typical assumption is that a high success ratio will elicit maximum motivation in all Ss. In light of the discussion thus far, this is obviously a misleading assumption. Atkinson’s theory directly repudiates the notion that all Ss will be equally motivated by the same success ratios. While achievement-oriented Ss will in all probability be more motivated across all success ratios than failure-threatened Ss, the major prediction of the theory relates to the point that maximal motivation in the case of each will occur at quite different success levels. Achievement-oriented Ss will be maximally motivated under moderate success ratios; conversely, failurethreatened Ss will be maximally motivated under either high or low success

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levels but minimally motivated at intermediate levels. In other words, the typical error rate will, according to the Atkinson theory, maximize motivation of failure-threatened but not achievement-oriented Ss. Whether or not this application of the theory to programmed tasks is valid is yet to be determined. Preliminary studies by Kight and Sassenrath (1966) and Shrable and Sassenrath (1969) possibly yield some support in this regard. However, since the measure of achievement orientation employed in these studies was the Iowa Picture Interpretation Test, a questionable alternative to the TAT procedure (see Heckhausen, 1967), their findings must be viewed as suggestive rather than definitive. In any case, the point to be stressed is that Atkinson’s theory provides a potentially fruitful perspective for analyzing behavior on programmed tasks.

Independent Study Independent study is another educational practice that might be more effective if the results of research on achievement motivation were considered. (See Alexander & Hines, 1967, for a discussion of independent study.) According to the original conception of achievement motivation (McClelland et al., 1953), the person high in nAch is presumably highly inclined to take responsibility for his achievement behavior. Subsequently, Bartman (as quoted in Heckhausen, 1967) found evidence that high nAch Ss were indeed more able to handle an independent program of study than low nAch Ss. Moreover, McKeachie (1961) and Morris (1967), while exploring personality-environment interactions in the classroom, uncovered an interesting fact. Both studies found evidence to suggest that high nAch Ss outperform low nAch Ss in classrooms which allow for or are more dependent on self-motivation. More specifically, on the basis of Atkinson’s model it migh be expected that achievement-oriented and failure-threatened students would differ in the goals and tasks they select for themselves. The success-motivated student would seek out situations that were challenging. The behavior of the person with low achievement motivation would be less certain. He may seek out very easy tasks and learn little or he may seek out tasks so difficult that he is not able to learn them. Either result would be undesirable. Clearly, experimentation in independent study with achievement motivation as an independent variable is called for.

Personnel Practices A final example of the kinds of question that the theory prompts concerns personnel practices. McClelland (1961) suggested that high productivity in a society is associated with the society’s capacity to develop and effectively employ achievement-motivated persons. Such people seek out, perform well, and persist in challenging situations or in situations that present a substantial degree of risk to the individual.

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Are the personnel practices in education such that individuals with high achievement motivation are attracted to and remain in education? Quite possibly not. The techniques generally used to attract and hold teachers, e.g., tenure, salary schedules, pay, vacations, fringe benefits, certification, etc., are techniques most likely to appeal to the person with low achievement motivation. If teachers do tend to be low in achievement motivation, what effect might this have? One may wonder, e.g., about the kind of environment created by the failure-threatened teacher. What kind of rewards would he employ? To what extent would he encourage independence and present a challenge? What kind of materials would he select and recommend? One may also wonder how students varying in nAch respond to such environments. Along a quite different line, one might question whether the lack of innovation in education is directly attributable to the lack of nAch extant in in the educational establishment. Might it be that the supposedly static condition of education is a direct result of personnel practices which tend to discourage the recruitment and retention of the risk taker and innovator? At the least it can be said that Atkinson’s model stimulates a host of questions in this area, questions that ought to be confronted and dealt with by educational researchers.

Toward a Theory of Academic Motivation It has been amply illustrated that Atkinson’s theory suggests a variety of studies relevant to a broad range of questions of relevance to education. The heuristic value of the theory can hardly be denied. Just as evident are the limitations of the theory in its present form. It is not now, nor does it presume to be, a theory of academic motivation. Although it suggests a variety of insights into the educational process, it can provide only limited advice for the practitioner. It is our view that the limitations can be overcome. Ways of doing just that have been suggested. In any case, we feel it is obvious that the theory can serve as a first step toward a theory of academic motivation.

Notes 1. The work of Crandall and her associates (e.g., Crandall, Katkovsky, & Crandall, 1965; Crandall, 1969; McGhee & Crandall, 1968: see also Battle, 1965, 1966), which in some sense derives from the work of Rotter (1954, 1966), is particularly interesting as is also the work of Cattell (e g., Cattell, Seakey, & Sweeney, 1966; Cattell & Butcher, 1968; Hundleby & Cattell, 1968) and Sarason, Davidson, Lightall, Waite, & Ruebush (1960). However, the educationally relevant findings proceeding from these and other approaches to motivation will be only incidentally treated in this paper. It may also be noted that much of the earlier work on achievement motivation was reviewed by Crandall (1963). 2. Rotter (1966) suggested that nAch and locus of control are indeed separable factors. Therewith, a number of researchers (Feather, 1967, 1969; Weiner & Kukla, 1970) have begun to explore the interaction of these variables and/or the relationship of

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the attribution of causality to achievement motivation. De Charms (1968) actually reformulated achievement motivation theory in locus of causality terms. 3. Parenthetically, it may be noted that a number of studies (e.g., McKeachie, 1961; McKeachie, Lin, Milholland, & Isaacson, 1966; McKeachie, Isaacson, Milholland, & Lin, 1968; Morris, 1967) have explored the interaction of classroom environment (e.g., achievement oriented, affiliation oriented) and personal orientation (e.g., low-high nAch). In general, results have provided some but not consistent confirmation of the basic notion that high and low nAch Ss should be most motivated in presumably matching high-low nAch environments. One of the obvious difficulties with these studies is the definition and assessment of environments. What is an achievement or an affiliation environment? Be that as it may and complex as the problem is, it deserves further consideration on the part of educational researchers.

References Alexander, W. M., & Hines, V. A. Independent study in secondary schools. New York: Holt, 1967. Alschuler, A. S. How to increase motivation through climate and structure. Achievement Motivation Development Project Working Paper No. 8., Harvard University, Graduate School of Education, 1968. Atkinson, J. W. Motivational determinants of risk-taking behavior. Psychlogical Review, 1957, 64, 359–373. Atkinson, J. W. (Ed.) Motives in fantasy, action, and society. Princeton, N. J.: D. Van Nostrand, 1958. Atkinson, J. W. An introduction to motivation. Princeton, N. J.: D. Van Nostrand, 1964. Atkinson, J. W. Some general implications of conceptual developments in the study of achievement-oriented behavior. In M. R. Jones (Ed.), Human motivation: A symposium. Lincoln: University of Nebraska Press, 1965. Atkinson, J. W. Mainsprings of achievement oriented activity. In J. D. Krumboltz (Ed.), Learning and the educational process. Chicago: Rand McNally, 1966. Atkinson, J. W. Implications of curvilinearity in the relationship of efficiency of performance to strength of motivation for studies of individual differences in achievementrelated motives. Paper presented at National Academy of Sciences meeting, University of Michigan, October 24, 1967. Atkinson, J. W., Bastian, J. R., Earl, R. W., & Litwin, G. H. The achievement motive, goal setting, and probability preferences. Journal of Abnormal and Social Psychology, 1960, 60, 27–36. Atkinson, J. W., & Cartwright, D. Some neglected variables in contemporary conceptions of decision and performance. Psychological Reports, 1964, 14, 575–590. Atkinson, J. W., & Feather, N. T. (Eds.) A theory of achievement motivation. New York: Wiley, 1966. Atkinson, J. W., & Litwin, G. H. Achievement motive and test anxiety conceived as motive to approach success and motive to avoid failure. Journal of Abnormal and Social Psychology, 1960, 60, 52–63. Atkinson, J. W., & O’Connor, P. Neglected factors in studies of achievement-oriented performance: Social approval as incentive and performance decrement. In J. W. Atkinson & N. T. Feather (Eds.), A theory of achievement motivation. New York: Wiley, 1966. Battle, E. S. Motivational determinants of academic task persistence. Journal of Personality and Social Psychology, 1965, 2, 209–218. Cattell, R. B., & Butcher, H. J. The prediction of achievement and creativity. Indianapolis: Bobbs-Merrill, 1968.

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Cattell, R. B., Sealey, A. P., & Sweeney, A. B. What can personality and motivation source trait measurements add to the practice of school achievement? British Journal of Educational Psychology, 1966, 36, 280–295. Crandall, V. C, Katkovsky, W., & Crandall, V. J. Children’s beliefs in their own control of reinforcements in intellectual-academic achievement situations. Child Development, 1965, 36, 91–109. Crandall, V. J. Achievement. In The sixty-second yearbook of the National Society for the Study of Education, Part I, Child Psychology, 1963. Crandall, V. J. Sex differences in expectancy of intellectual and academic reinforcement. In C. P. Smith (Ed.), Achievement-related motives in children. New York: The Russell Sage Foundation, 1969. de Charms, R. Personal causation. New York: Academic Press, 1968. de Charms, R., & Carpenter, V. Measuring motivation in culturally disadvantaged school children. In H. J. Klausmeier & G. T. O’Hearn (Eds.), Research and development toward the improvement of education. Madison, Wis.: Dembar Educational Services, 1969. de Charms, R., & Davé, P. Hope of success, fear of failure, subjective probability, and risk-taking behavior. Journal of Personality and Social Psychology, 1965, 1, 558–568. Edwards, A. L. Edwards Personal Preference Schedule. New York: Psychological Corporation, 1954. Feather, N. T. The relationship of persistence at a task to expectation of success and achievementrelated motive. Journal of Abnormal and Social Psychology, 1961, 63, 552–561. Feather, N. T. The study of persistence. Psychological Bulletin, 1962, 59, 94 –115. Feather, N. T. Persistence at a difficult task with alternative task of intermediate difficulty. Journal of Abnormal and Social Psychology, 1963, 66, 604–609. Feather, N. T. Valence of outcome and expectation of success in relation to task difficulty and perceived locus of control. Journal of Personality and Social Psychology, 1967, 7, 372–376. Feather, N. T. Attribution of responsibility and valence of success and failure in relation to initial confidence and task performance. Journal of Personality and Social Psychology, 1969, 13, 129–144. Festinger, L. A theory of social comparison processes. Human Relations, 1954, 7, 117–140. French, E. G., Effects of interaction of motivation and feedback of performance. In J. W. Atkinson (Ed.), Motives in fantasy, action, and society. Princeton, N. J.: D. Van Nostrand, 1958. French, E. G., & Lesser, G. S. Some characteristics of the achievement motive in women. Journal of Abnormal and Social Psychology, 1964, 68, 119–128. Heckhausen, H. The anatomy of achievement motivation. New York: Academic Press, 1967. Heckhausen, H. Achievement motive research: Current problems and some contributions towards a general theory of motivation. In W. J. Arnold (Ed.), Nebraska symposium on motivation, 1968. Lincoln: University of Nebraska Press, 1968. Hermans, H. J. M. The validity of different strategies of scale construction in predicting academic achievment. Educational and Psychological Measurement, 1960, 29, 877–883. Hermans, H. J. M. A questionnaire measure of achievement motivation. Journal of Applied Psychology, 1970, 54, 353–363. Horner, M. Sex differences in achievement motivation and performance in competitive and non-competitive situations. (Doctoral dissertation, University of Michigan) Ann Arbor, Mich.: University Microfilms, 1968. No. 69–12, 135. Houts, P. S., & Entwistle, D. R. Academic achievement effort among females: Academic attitudes and sex-role orientation. Journal of Counseling Psychology, 1968, 15, 284 –286.

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McKeachie, W. J. Motivation, teaching methods, and college learning. In M. R. Jones (Ed.), Nebraska symposium on motivation, 1961. Lincoln: University of Nebraska Press, 1961. McKeachie, W. J., Isaacson, R. L., Milholland, J. E., & Lin, Y. G. Student achievement cues and academic achievement. Journal of Consulting and Clinical Psychology, 1968, 32, 26–29. McKeachie, W. J., Lin, Y. G., Milholland, J., & Isaacson, R. Student affiliation motives, teacher warmth and academic achievement. Journal of Personality and Social Psychology, 1966, 4, 457–461. Mehrabian, A. Male and female scales of the tendency to achieve. Educational and Psychological Measurement, 1968, 28, 493–502. Mehrabian, A. Measures of achieving tendency. Educational and Psychological Measurement, 1969, 29, 445–451. Mingione, A. D. Need for achievement in Negro and white children. Journal of Consulting Psychology, 1965, 29, 108–111. Mingione, A. D. Need for achievement in Negro, white, and Puerto Rican children. Journal of Consulting and Clinical Psychology, 1968, 32, 94 –95. Morris, J. L. Teacher-student interaction as a determinant of academic grades in the secondary school. The Australian Journal of Education, 1967, 11, 13–23. Moulton, R. W. Effects of success and failure on level of aspiration as related to achievement motives. Journal of Personality and Social Psychology, 1965, 1, 399– 406. O’Connor, P., Atkinson, J. W., & Horner, M. Motivational implications of ability grouping in schools. In J. W. Atkinson & N. T. Feather (Eds.), A theory of achievement motivation. New York: Wiley, 1966. Rosen, B. C. Race, ethnicity, and the achievement syndrome. American Sociological Review, 1959, 24, 47–60. Rotter, J. B. Social learning and clinical psychology. Englewood Cliffs, N. J.: Prentice-Hall, 1954. Rotter, J. B. Generalized expectancies for internal versus external control of reinforcement. Psychological Monographs, 1966, 80 (1, Whole No. 609). Sarason, S. B., Davidson, K. S., Lightall, F. F., Waite, R. R., & Ruebush B. K. Anxiety in elementary school children. New York: Wiley, 1960. Shrable, K., & Sassenrath, J. M. Effects of achievement motivation and test anxiety on performance in programmed instruction. Paper presented at American Educational Research Association convention, Los Angeles, February 1969. Skolnick, A. Motivational imagery and behavior over twenty years. Journal of Consulting Psychology, 1966, 30, 463– 478. Smith, C. P. Relationships between achievement-related motives and intelligence, performance level, and persistence, Journal of Abnormal and Social Psychology, 1964, 68, 523–532. Smith, C. P. (Ed.) Achievement-related motives in children. New York: The Russell Sage Foundation, 1969. Stuempfig, D. W., & Maehr, M. L. Persistence as a function of conceptual structure and quality of feedback. Child Development, 1970, 41(4), in press. Veroff, J. Social comparison and the development of achievement motivation. In C. P. Smith (Ed.), Achievement-related motives in children. New York: The Russell Sage Foundation, 1969. Veroff, J., Wilcox, S., & Atkinson, J. W. The achievement motive in high school and college age women. Journal of Abnormal and Social Psychology, 1953, 48, 108–119. Weiner, B. Implications of the current theory of achievement motivation for research and performance in the classroom. Psychology in the Schools, 1967, 4, 164 –171.

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Weiner, B., & Kukla, A. An attributional analysis of achievement motivation. Journal of Personality and Social Psychology, 1970, 15, 1–20. Weinstein, M. S. Achievement motivation and risk preference. Journal of Personality and Social Psychology, 1969, 13, 153–172. Weston, P. J., & Mednick, M. T. Race, social class and the motive to avoid success in women. Journal of Cross-Cultural Psychology, 1970, 1, 284–291. White, R. W. Motivation reconsidered: The concept of competence. Psychological Review, 66, 297–333. White, R. W. Competence and the psychosexual stages of development. In M. R. Jones (Ed.), Nebraska symposium on motivation. Lincoln: University of Nebraska Press, 1960. Zigler, E., & Child, I. L. Socialization. In G. Lindzey & E. Aronson (Eds.). The handbook of social psychology, Vol. III. (2nd ed.) Reading, Mass.: Addison-Wesley, 1969. Zigler, E., & Kanzer, P. The effectiveness of two classes of verbal reinforcers on the performance of middle- and lower-class children. Journal of Personality, 1962, 30, 157–163.

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61 Motivation and Engagement across the Academic Life Span: A Developmental Construct Validity Study of Elementary School, High School, and University/ College Students Andrew J. Martin

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tudents in elementary school, high school, and university/college share a great deal in common. In each context, students are required to apply themselves over a sustained period of time to develop their academic skills, engage with key performance demands, negotiate the rigors of competition, deal with setback and adversity, cope with possible self-doubt and uncertainty, and develop psychological and behavioral skills to effectively manage the ups and downs of the ordinary course of academic life. Given these congruencies across distinct educational stages, it is feasible to propose that there will be core and common constructs relevant and meaningful across the academic life span. This study seeks to assess this issue in the context of academic motivation and engagement; more specifically, it seeks to assess the validity of recently developed academic motivation and engagement instrumentation in the context of students from elementary school, high school, and university/college. Analyses conducted in this investigation across these three distinct educational stages are proposed as a developmental construct validity study of academic motivation and engagement.

Source: Educational and Psychological Measurement, 69(5) (2009): 794 – 824.

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Substantive Background: An Integrative Framework for Motivation and Engagement and Implications for Measurement The substantive background to the study centers on academic motivation and engagement and the need for more pragmatic and integrative approaches to their measurement and theorizing. In critical reviews of motivation and engagement research, it has been suggested that such research oftentimes yields limited practical implications and applications and that there is a need to devise research that advances scientific understanding but that also has applied utility. Hence, there have been calls to give greater attention to useinspired basic research in education and psychology contexts (Stokes, 1997; see also Greeno, 1998; Pintrich, 2000, 2003). Critical reviews of motivation and engagement research also point to the fact that such research is diverse and fragmented. As a result, there have also been calls for more integrative approaches to its research and theorizing (Bong, 1996; Murphy & Alexander, 2000; Pintrich, 2003). It is in this context that the Motivation and Engagement Wheel (Martin, 2001, 2002, 2007a) was developed. The wheel is presented in Figure 1. As Figure 1 shows, there are two levels at which the wheel has been conceptualized: the integrative higher order level, comprising 4 factors, and the lower (or first-order) level, comprising 11 factors. As discussed fully by Martin (2007a, 2008a, 2008b), higher order factors (and corresponding first-order factors) are adaptive cognitions (self-efficacy, valuing, mastery orientation), adaptive behaviors (planning, task management, persistence), impeding/maladaptive cognitions (anxiety, failure avoidance, uncertain control), and maladaptive behaviors (self-handicapping, disengagement). Initially this wheel was developed to better understand motivation and engagement among high school students; however, in the present study its application to elementary school and university students is assessed from a developmental construct validity perspective (described below).

Higher Order Dimensions of Motivation and Engagement Martin (2007a, 2008a, 2008b) proposed that over the past four decades a number of psychological theories and models have been developed that explain the nature of human cognition and behavior. He demonstrated that there are significant commonalities across these theories and models, which provide direction as to fundamental (higher order) dimensions of motivation and engagement. These commonalities operate at three levels. The first level delineates cognitive and behavioral elements, including work encompassing cognitive and behavioral orientations in learning strategies (Pintrich & DeGroot, 1990; Pintrich & Garcia, 1991), cognitive antecedents of

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ADAPTIVE BEHAVIOR Valuing

Persistence

Mastery orientation

Planning

Selfefficacy

Task management

Anxiety Disengagement

Failure avoidance Selfhandicapping MALADAPTIVE BEHAVIOR

Uncertain control IMPEDING/MALADAPTIVE COGNITION

Source: Adapted from Martin (2003a).

Figure 1: Motivation and engagement wheel

behavioral strategies used to negotiate environmental demands (Buss & Cantor, 1989), cognitive-behavioral approaches to engagement and behavior change (Beck, 1995), and cognitive-affective and behavioral dimensions to academic engagement (Miller, Greene, Montalvo, Ravindran, & Nichols, 1996; Miserandino, 1996). The second level demonstrates the differential empirical strength of distinct aspects of motivation and engagement – for example, self-efficacy reflects highly adaptive motivation (Bandura, 1997; Pajares, 1996), anxiety impedes individuals’ engagement (Sarason & Sarason, 1990; Spielberger, 1985), and behaviors such as self-handicapping reflect quite maladaptive engagement (Martin, Marsh, & Debus, 2001a, 2001b, 2003; Martin, Marsh, Williamson, & Debus, 2003). The third level informs the structure of motivation and engagement frameworks, such as those hypothesizing and empirically demonstrating hierarchical models of human cognition and behavior that encompass specific factors under more global characterizations (e.g., Elliot & Church, 1997; Marsh & Shavelson, 1985; Shavelson, Hubner, & Stanton, 1976).

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Taken together and in consideration of the joint issues of motivational and behavioral orientations; cognitive-behavioral frameworks; differing empirical levels of adaptive, impeding, and maladaptive dimensions in applied settings; and hierarchical models of cognition and behavior, Martin (2007a, 2008a, 2008b) proposed that motivation can be characterized in terms of four higher order dimensions: (a) adaptive cognition, (b) adaptive behavior, (c) impeding/ maladaptive cognition, and (d) maladaptive behavior. These dimensions and their component first-order factors have been synthesized under the Motivation and Engagement Wheel (Martin, 2001, 2003a, 2003c, 2007a, 2008b) presented in Figure 1.

First-Order Dimensions of Motivation and Engagement Pintrich (2003) identified core substantive questions for the development of a motivational science. Taken together, these questions underscore the importance of considering, conceptualizing, and articulating a model of motivation from salient and seminal theorizing related to self-efficacy, control, valuing, goal orientation, need achievement, self-worth, and self-regulation. These, it is suggested, provide a useful heuristic for the identification of first-order constructs for operationalizing the Motivation and Engagement Wheel. As discussed fully by Martin (2001, 2002, 2003c, 2007a), (a) self-efficacy theory (e.g., Bandura, 1997) is reflected in the self-efficacy dimension of the wheel, (b) attributions and control are reflected in the uncertain control dimension (tapping the controllability element of attributions; see Connell, 1985; Weiner, 1994), (c) valuing (e.g., Eccles, 1983; Wigfield & Tonks, 2002) is reflected in the valuing dimension, (d) self-determination (in terms of intrinsic motivation; see Ryan & Deci, 2000) and motivation orientation (see Dweck, 1986; Martin & Debus, 1998; Nicholls, 1989) are reflected in the mastery orientation dimension, (e) self-regulation (e.g., Martin, 2001, 2002, 2003c, 2007a; Martin, Marsh, & Debus, 2001a, 2001b, 2003; Zimmerman, 2002) is reflected in the planning, task management, and persistence dimensions, and (f ) need achievement and self-worth (e.g., Atkinson 1957; Covington, 1992; Martin & Marsh, 2003; McClelland, 1965) are reflected in the failure avoidance, anxiety, self-handicapping, and disengagement dimensions. Hence, the wheel comprises 11 lower, or first-order, dimensions (see Figure 1).

Measurement and the Motivation and Engagement Scale Alongside the Motivation and Engagement Wheel is its accompanying instrumentation, the Motivation and Engagement Scale (MES). Typically administered to high school students, the Motivation and Engagement Scale–High School (MES-HS; Martin, 2001, 2003c, 2007a, 2007b, 2008a)

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demonstrates a strong factor structure that is invariant across gender and age (but there are mean-level differences such that females generally report higher levels of motivation than do males, and middle high school students report lower motivation than do junior and senior high school students) and is reliable and normally distributed. It has also been found to predict a variety of educational outcomes such as enjoyment of school, classroom participation, educational aspirations, and achievement-related outcomes such as school grades. To extend this line of research, the present investigation assesses parallel forms of the MES: for elementary school students, the Motivation and Engagement Scale-Junior School (MES-JS), and for college or university students, the Motivation and Engagement Scale–University/College (MES-UC). Over the past few years, there has been growing research around the Motivation and Engagement Wheel and its accompanying instrumentation, the MES. The MES is robust in the high school (Martin, 2007a), workplace (Martin, in press b; see also Martin 2005b, 2005c), music (Martin, 2008b), sport (Martin, 2008b), and physical activity domains (Martin, Tipler, Marsh, Richards, & Williams, 2006). The wheel and MES are useful as bases for educational intervention (Martin, 2005a, 2008b). The wheel and MES are helpful foundations for assessing group-level (climate) effects (Martin & Marsh, 2005). Finally, the wheel and MES are useful in addressing more specific educational issues such as domain specificity (Green, Martin, & Marsh, 2007), teacher effects (Martin & Marsh, 2005), and the role of parents and teachers in the motivation and engagement process (Martin, 2003b, 2006). However, to date, there has been no thoroughgoing and detailed scoping of the wheel and MES across the span of education – that is, across elementary school, high school, and university samples (but see Martin, in press-b, for brief research in the context of sport, music, work, and daily life motivation and engagement). The present study does so from a proposed developmental construct validity perspective.

Methodological Background: A Developmental Construct Validity Perspective Researchers in psychology and education have increasingly emphasized the need to develop and evaluate instruments within a construct validation framework (e.g., see Marsh, 2002; Marsh & Hau, 2007). Investigations that adopt a construct validation approach can be classified as within-network or between-network studies. Moreover, it is proposed here that when construct validity is assessed across distinct educational stages it constitutes something of a developmental construct validity perspective. Specifically, it is proposed that a dual within- and between-network approach across elementary school, high school, and university represents a developmental construct validity approach to assessing the generality of motivation and engagement across the academic life span.

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Within-Network Validity Beginning with a logical analysis of internal consistency of the construct definition, measurement instruments, and generation of predictions, withinnetwork studies typically employ empirical techniques such as exploratory factor analysis, confirmatory factor analysis (CFA), and reliability analysis. The present study conducts within-network analyses across the three samples using CFA to test the multidimensional motivation and engagement framework and reliability analysis to test the internal consistency of scores. Consistent with previous studies of high school students (e.g., Green et al., 2007; Martin, 2001, 2003c, 2007a) and across diverse performance settings such as music and sport (Martin, 2008b), it is hypothesized that at each educational stage (elementary school, high school, and university), the motivation and framework instrumentation (MES) will evince a sound first- and higher order factor structure and comprise reliable scores.

Between-Network Validity Between-network research explores relationships between a target central framework and a set of factors external to the framework. It typically does so through statistical procedures such as correlation, regression, or structural equation modeling (SEM) analyses to examine relationships between measures and instruments. The present study conducts between-network analyses across the three samples by assessing (a) the invariance of factor structure across gender, age groups, and educational stages (elementary school, high school, university/college); (b) mean-level differences across educational stages; and (c) the empirical links between the hypothesized first- and higher order factors and a set of cognate between-network measures (enjoyment of school or university, class participation, positive intentions, academic buoyancy, homework/assignment completion). Each of these between-network techniques is described in turn. Factorial invariance in the structure of motivation and engagement. As described by Martin (2007a, 2008b), insufficient attention is given to analyses of the factor structure of motivation and engagement and the extent to which a given motivation and engagement instrument and its components are invariant across different groups. Such concerns about factor structure invariance are most appropriately evaluated using CFA to determine whether – and how – the structure of motivation and engagement varies according to key subpopulations (see Hattie, 1992; Marsh, 1993). Martin (2004, 2007a) has previously shown the MES factor structure (factor loadings, uniquenesses, correlations/variances) to be invariant across early-, mid-, and late-adolescent samples and also across gender. The present study is an opportunity to assess invariance across gender and age within elementary school and university.

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It is also an opportunity to assess invariance across elementary school, high school, and university samples. Consistent with previous studies of high school students (e.g., Green et al., 2007; Martin, 2001, 2003c, 2007a) and across diverse performance settings such as music and sport (Martin, 2008b), it is hypothesized that factor structure (including loadings, correlations/variances, and uniquenesses) across gender, age, and educational stage will evince relative invariance. Mean-level educational stage effects. Very little research has assessed mean levels of motivation and engagement across the academic life span: elementary school, high school, and university. The transition from elementary to middle school has been found to pose difficulties and challenges unique to that time (Anderman & Midgley, 1997; Roeser, Eccles, & Sameroff, 2000), and a decline in student motivation and engagement is typically found to emerge after this transition (see Martin, 2001, 2003c, 2004, 2007a; Wigfield & Tonks, 2002), including changes in subjective task value (Wigfield, Eccles, Mac Iver, Reuman, & Midgley, 1991). As students move on to university/ college, some research has found them to be more confident in the quantity and quality of their abilities, whereas other research finds it a difficult transition with less support and structure and a major challenge in asserting one’s identity among highly capable peers (Martin, Marsh, Williamson, et al., 2003). Increasingly, universities and colleges are recognizing the stresses and strains of undergraduate life and the difficulties in making a successful transition from high school (see Martin, Milne-Home, Barrett, & Spalding, 1997; Martin, Milne-Home, Barrett, Spalding, & Jones, 2000). Indeed, Martin and colleagues (Martin, Marsh, Williamson, et al., 2003) found university to present distinct challenges that instill doubts and uncertainties that in some cases lead to self-handicapping, poorer academic performance, and eventual dropout. Taken together, then, it is hypothesized that elementary school students will evince relatively higher mean levels of motivation and engagement than high school and university students do; however, no predictions are made regarding the relative mean levels of the latter two groups. Motivation, engagement, and cognate correlates. Consistent with the construct validity approach, it is proposed that five between-network constructs provide a theoretically relevant basis for examining the external validity of the MES across the academic life span: positive intentions, class participation, enjoyment of school, academic buoyancy, and homework/assignment completion. In terms of positive intentions, several researchers have shown that students higher in motivation and engagement are more likely to take advanced or optional courses and also more likely to report future course enrolment intentions (Meece, Wigfield, & Eccles, 1990). In addition to positive intentions, class participation is deemed a feasible between-network construct. Learning environments that foster student participation are found to enhance students’ commitment to learning (Richter & Tjosvold, 1980), whereas a lack of participation is found to lead to unsuccessful educational

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outcomes such as emotional withdrawal and poor identification with the school (Finn, 1989). Enjoyment of school is another feasible between-network construct. Elliot and Sheldon (1997), for example, included enjoyment as one of the five key variables in their study of goal pursuit. Even research in higher education finds that enjoyment is a key factor in students’ engagement at university (Lee, Sheldon & Turban, 2003). Martin and Marsh (2006, 2008a, 2008b) have shown academic buoyancy to be a factor relevant to students’ ability to deal with academic setback in the ordinary course of academic life and also have shown a variety of motivation and engagement factors to be significantly associated with such buoyancy. It is also proposed that in addition to these four intrapsychic measures, there is a need for more behavioral measures (Green et al., 2007) that in the present study take the form of homework/assignment completion. Consistent with previous studies of high school students (e.g., Green et al., 2007; Martin, 2001, 2003c, 2007a) and across diverse performance settings such as music and sport (Martin, 2008b, in press-a, in press-b), it is hypothesized that the adaptive dimensions will be positively (to a modest or strong degree) associated with these correlates, the impeding/maladaptive dimensions will be associated at near-zero or negatively (to a weak or modest degree), whereas maladaptive dimensions will be more markedly negatively (to a modest or strong degree) associated with these correlates.

Aims of the Study The overarching aim of this study is to examine the developmental construct validity of motivation and engagement across elementary school, high school, and university samples. More specifically, this study assesses a recently developed integrative motivation and engagement instrumentation across the academic life span with a view to assessing (a) within-network validity in terms of firstand higher order factor structure and reliability and (b) between-network validity in terms of invariance of factor structure across groups (gender, age, educational stage), mean-level differences across educational stage, and associations with cognate correlates.

Method Elementary School Sample and Procedure The elementary school sample comprised 624 upper-age elementary students in five schools. All schools were located in urban areas drawing from two capital cities in Australia. Students were age 9 to 11.5 years (n = 114, 56%

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female and 44% male) and 11.5 years to 13 years (n = 510, 38% female and 62% male). The mean age of students was 11.13 (SD = 0.69) years. Teachers read the MES-JS (Martin, 2007b) items aloud to students during class or pastoral care/tutorial groups. The rating scale was first explained, and sample items were presented. Students were then asked to complete the instrument as the teacher read out each item in turn and to return the completed form to the teacher at the end of class or pastoral care/tutorial group. Previous work has been conducted in a smaller urban and rural elementary school sample (Martin, Craven, & Munns, 2006); however, this work only comprised a factor analysis of the MES-JS with no invariance testing, mean-level analyses, analyses in the context of the academic life span, and external validity checks. The present study, then, is a significant progression on previous work.

High School Archive Sample and Procedure The high school sample comprised data collected from 21,579 high school students from 58 Australian schools. Thirty-six schools were government, and 22 schools were independent, and they were from urban and regional areas across most states in Australia. Students were age 12 to 13 years (n = 6,640, 49% female and 51% male), 14 to 15 years (n = 7,894, 43% female and 57% male), and 16 to 18 years (n = 7,045, 44% female and 56% male). The mean age of students was 14.52 (SD = 1.57) years. The high school sample is something of an archive sample that has been compiled over recent years across numerous research projects. Portions of the data have been reported elsewhere with a more substantial construct validity study by Martin (2007a) assessing the MES-HS among 12,237 high school students, all of whom are included as part of the present archive sample of 21,579 students. The reader is urged to consult Martin (2007a; see also Martin, 2008b, in press-a, in press-b) for these academic motivation and engagement data in the context of other performance domains such as sport, music, and work as the first substantial large-sample investigation into the MES-HS. The archive dataset represents the integration of data collected over the previous 5 years and so can be considered to be relatively current. Teachers administered the MES-HS (Martin, 2001, 2003c, 2007a, 2007b) to students during class or pastoral care/ tutorial groups. The rating scale was first explained, and sample items were presented. Students were then asked to complete the instrument on their own and to return the completed form to the teacher at the end of class or pastoral care.

University Sample and Procedure University (college) respondents were 420 undergraduate students from two Australian universities. One university is well established and one of the oldest in the country (68% of sample). The other is a more recently

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established institution (32%). Most respondents were women (80%), and 20% were men. Most students were enrolled in education (66%), with other students enrolled in arts (18%), psychology/social science (8%), social work (3%), science (3%), and communications (2%). Most were full-time students (96%), with 4% part-time. Most were in their first year of study (65%), with 25% in second year, 7% in third year, and 3% in fourth or fifth year. The mean age of students was 21.47 (SD = 6.62) years, with 60% under 20 years of age and 40% 20 years and over. Students completed the instrument in lecture or tutorial time. Students were asked to complete the MES-UC (Martin, 2007b) on their own and return the completed instrument at the end of the lecture or tutorial they were attending at the time.

Materials Motivation and Engagement Scale General overview. The MES-JS (Martin, 2007b), MES-HS (Martin, 2001, 2003c, 2007a, 2007b), and MES-UC (Martin, 2007b) are instruments that measure elementary, high school, and university students’ motivation and engagement, respectively. Adapted from the MES-HS, the MES-JS and MES-UC assess motivation and engagement through three adaptive cognitive dimensions (self-efficacy, valuing, mastery orientation), three adaptive behavioral dimensions (persistence, planning, task management), three impeding/maladaptive cognitive dimensions (anxiety, failure avoidance, uncertain control), and two maladaptive behavioral dimensions (self-handicapping, disengagement). Each of the 11 factors comprises four items – hence, the MES is a 44-item instrument. The MES-JS and MES-UC comprise the same number of items (44) and the same number of first-order (11) and higher order (4) factors as the original high school instrument (MES-HS). As much as possible, item adaptation aimed to make simple and transparent word and terminology changes in order to remain very parallel to the high school form. In the appendix, a sample item from the MES-HS is presented along with its MES-JS and MES-UC adaptations (see Martin, 2007a, for a full account of the origins of and rationale for the scale and item development). To simplify the survey for younger students, the MES-JS asks students to rate themselves on a shorter scale of 1 (strongly disagree) to 5 (strongly agree), whereas for the MES-HS and MES-UC, students rate themselves on a scale of 1 (strongly disagree) to 7 (strongly agree). In most studies using the MES (e.g., Martin, 2007a, 2008a, 2008b, in press-a), the 7-point rating scale is typically used. However, the elementary school sample posed a distinct challenge in that a simpler survey form was desirable: Pilot work indicated that students had difficulty teasing apart the finer-grained rating points on the 7-point scale.

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Adaptive cognitive and behavioral dimensions. Each adaptive dimension falls into one of two groups: cognitions and behaviors. Adaptive cognitions include self-efficacy, mastery orientation, and valuing. Adaptive behaviors include persistence, planning, and task management. Self-efficacy is students’ belief and confidence in their ability to understand or to do well in their school or university work, to meet challenges they face, and to perform to the best of their ability. Valuing of school or university is how much students believe what they do and learn at school or university is useful, important, and relevant to them. Mastery orientation entails being focused on understanding, learning, solving problems, and developing skills. Planning is how much students plan their work and how much they keep track of their progress as they are doing it. Task management refers to the way students use their time, organize their timetables, and choose and arrange where they prepare for school or university and school or university tasks. Persistence reflects students’ capacity to persist in situations that are challenging and at times when they find it difficult to do what is required. Impeding and maladaptive cognitive and behavioral dimensions. Impeding/ maladaptive cognitive dimensions are anxiety, failure avoidance, and uncertain control. Anxiety has two parts: feeling nervous and worrying. Feeling nervous is the uneasy or sick feeling students get when they think about their school or university work or tasks. Worrying is their fear of not doing very well in their school or university work. Failure avoidance occurs when the main reason students try at school or university is to avoid doing poorly or to avoid being seen to do poorly. Uncertain control assesses students’ uncertainty about how to do well or how to avoid doing poorly. Maladaptive behavioral dimensions are self-handicapping and disengagement. Self-handicapping occurs when students reduce their chances of success at school or university. Examples are engaging in other activities when they are meant to be doing their school or university work or preparing for upcoming school or university tasks. Disengagement occurs when students give up or are at risk of giving up at school or university or in particular school or university activities.

Between-Network Correlates Students were also administered items that explored their enjoyment of school or university (4 items; e.g., elementary school item: “I like school,” Cronbach’s α = .94; high school item: “I like school,” α = .91; university item: “I like university,” α = .91), class participation (4 items; e.g., elementary school item: “I get involved in things we do in class,” α = .90; high school item: “I get involved in things we do in class,” α = .90; university item: “I get involved in things we do in class,” α = .93), positive intentions (4 items; e.g., high school item: “I intend to complete school,” α = .82; university item: “I intend to complete university,” α = .72), and academic buoyancy (4 items; e.g., elementary

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school item: “I think I’m good at dealing with schoolwork pressures,” α = .78; high school item: “I think I’m good at dealing with schoolwork pressures,” α = .80; university item: “I think I’m good at dealing with university pressures,” α = .84). These measures were rated on a scale of 1 (strongly disagree) to 7 (strongly agree) and were adapted directly from Martin (2007a, 2008b; see also Martin & Marsh, 2006, 2008a, 2008b), who has shown them to be reliable, a good fit to the data in CFA, and significantly associated with motivation and engagement in other performance domains such as sport and music. Homework /assignment completion (“How often do you do and complete your assignments?”) was a single item assessed on a rating scale of 1 (never) to 5 (always).

Confirmatory Factor Analysis and Structural Equation Modeling CFA and SEM, performed with LISREL 8.80 (Jöreskog & Sörbom, 2006), were used to test the hypothesized models. In CFA and SEM, the researcher posits an a priori structure and tests the ability of a solution based on this structure to fit the data by demonstrating that (a) the solution is well defined, (b) parameter estimates are consistent with theory and a priori predictions, and (c) the subjective indices of fit are reasonable (McDonald & Marsh, 1990). Maximum likelihood was the method of estimation used for the models. In evaluating goodness of fit of alternative models, the root mean square error of approximation (RMSEA) is emphasized, as are the comparative fit index (CFI), the non-normed fit index (NNFI), and an evaluation of parameter estimates. For RMSEAs, values at or less than .05 and .08 are taken to reflect a close and reasonable fit, respectively (see Jöreskog & Sörbom, 1993). The CFI and NNFI vary along a 0 to 1 continuum in which values at or greater than .90 and .95 are typically taken to reflect acceptable and excellent fits to the data, respectively (McDonald & Marsh, 1990). The CFI contains no penalty for a lack of parsimony, whereas the RMSEA contains penalties for a lack of parsimony.

Missing Data For large-scale studies, the inevitable missing data are a potentially important problem, particularly when the amount of missing data exceeds 5% (e.g., Graham & Hoffer, 2000). A growing body of research has emphasized potential problems with traditional pairwise, listwise, and mean substitution approaches to missing data (e.g., Graham & Hoffer, 2000), leading to the implementation of the expectation maximization (EM) algorithm, the most widely recommended approach to imputation for data that are missing at random, as operationalized using missing value analysis in LISREL. In fact, less than 5% of the MES data were missing in each of the elementary

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school, high school, and university samples, and so the EM algorithm was implemented for all samples. Also explored were alternative approaches to this problem, which showed that results based on the EM algorithm used here were very similar to those based on the traditional pairwise deletion methods for missing data – as would be expected to be the case when there were so few missing data.

Multigroup Confirmatory Factor Analysis and Tests of Invariance Two broad sets of invariance tests were conducted. The first assessed invariance within samples. The second assessed invariance between samples. For the within sample invariance tests, for each of elementary school, high school, and university, multigroup CFAs were conducted to assess invariance across gender and age. For the between-sample invariance tests, three invariance analyses were conducted – between high school and university on the original 7-point rating scale; between elementary school, high school, and university using a common 5-point rating scale (reliabilities for the transformed 5-point variables: high school α range = .75 to .81; university α range = .66 to .86); and between elementary school and university on a common 5-point rating scale (the common 5-point rating scale was derived by aggregating the first and last 2 points of the 7-point rating scale). Although the chi-square difference test is the most straightforward means of assessing differences between nested models, problems associated with such tests exist (e.g., see McDonald & Marsh, 1990; Tabachnick & Fidell, 2007). Hence, in formally assessing differences in models, emphasis is given to differences in fit indices (Cheung & Rensvold, 2002).

Multiple-Indicator, Multiple-Cause Models Notwithstanding the importance of testing for invariance in factor structure, there is also reason to investigate the mean-level developmental effects on the 11 facets of the MES-JS, MES-HS, and MES-UC. Kaplan (2000) suggested the multiple-indicator, multiple-cause (MIMIC) approach, which is similar to a regression model in which latent variables (e.g., multiple dimensions of motivation and engagement) are “caused” by discrete grouping variables (e.g., educational stage) that are represented by single indicators. This MIMIC model assessed the role of educational stage (elementary school, high school, university) as a predictor of motivation and engagement. Being a multinomial predictor and using high school as the reference point, educational stage was represented by two dummy variables: high school (0) versus elementary school (1) and high school (0) versus university (1); hence, positive beta weights for both dummy variables indicate

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higher scores for elementary school and university students compared with high school students, and negative beta weights for both dummy variables indicate lower scores for elementary school and university students compared with high school students.

Results First- and Higher Order Confirmatory Factor Analysis In the first instance, an 11-factor model was examined using CFA. The CFA yielded a very good fit to the data for elementary school ( c2 = 1,881.10, df = 847, p < .001, CFI = .98, NNFI = .97, RMSEA = .04), high school ( c2 = 28,217.75, df = 847, p < .001, CFI = .98, NNFI = .98, RMSEA = .04), and university ( c2 = 1,697.75, df = 847, p < .001, CFI = .96, NNFI = .95, RMSEA = .05). Factor loading ranges and means are presented in Table 1. Taken together, for all three samples the loadings are acceptable. This is supported by the acceptable reliability coefficients (e.g., see Henson, 2001) also presented in Table 1. Correlations for the sample are presented in Table 2. Predictably, for the three samples all adaptive dimensions were strongly (significantly) positively correlated and correlated strongly (significantly) negatively with maladaptive dimensions and slightly (but significantly) negatively or at near-zero with impeding/maladaptive dimensions. Maladaptive dimensions were markedly (significantly) positively correlated, as were impeding/maladaptive dimensions. For the three samples, all correlations indicate lower levels of shared variance between factor groupings than within factor groupings. In addition to the first-order dimensions constituting the 11 facets of the Motivation and Engagement Wheel, there is also hypothesized a higher order structure delineated by adaptive cognitive dimensions, adaptive behavioral dimensions, impeding/maladaptive cognitive dimensions, and maladaptive behavioral dimensions. In higher order models, correlations between first-order dimensions are constrained to be zero, and relations among these first-order dimensions are explained in terms of higher order dimensions. For each of elementary school, high school, and university samples, the higher order CFAs comprised the 44 items, the 11 first-order dimensions, and the 4 higher order dimensions. The higher order elementary school structure fit the data very well ( c2 = 2,155.87, df = 886, p < .001, CFI = .97, NNFI = .97, RMSEA = .05), as did the higher order model for high school students ( c2 = 36,732.07, df = 886, p < .001, CFI = .98, NNFI = .98, RMSEA = .04) and university students ( c2 = 1,968.82, df = 886, p < .001, CFI = .95, NNFI = .94, RMSEA = .05). Table 2 presents higher order correlations, which broadly confirm cluster correlations in the first-order model.

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.87 / .77 / .73 .86 / .82 / .82 .79 / .81 / .75

Adaptive behavior Planning Task management Persistence Higher order

.82 / .81 / .87 .70 / .81 / .72

.68−.77 (.73) / .61−.78 (.72) / .72−.84 (.79) .33−.85 (.63) / .65−.84 (.74) / .50−.79 (.65) ES: Range = .72−.89, Mean = .81 HS: Range = .70-.87, Mean = .79 UNI: Range = .64−. 80, Mean = .72

.52−.74 (.65) / .61−.74 (.68) / .55−.82 (.69) .61−.85 (.76) / .65−.84 (.70) / .71−.83 (.77) .65−.73 (.69) / .62−.75 (.69) / .62−.82 (.72) ES: Range = .51−.87, Mean = .69 HS: Range = .56-.83, Mean = .69 UNI: Range = .51−.74, Mean = .65

.73−.89 (.80) / .57-.79 (.70) / .33−.91 (.66) .61−.88 (.78) / .71-.85 (.76) / .62−.87 (.74) .63−.79 (.70) / .60−.79 (.71) / .59−.75 (.66) ES: Range = .72−.80, Mean = .76 HS: Range = .84-.88, Mean = .86 UNI: Range = .59−.90, Mean = .74

.60−.72 (.67) / .63-.75 (.69) / .54−.71 (.62) .69−.79 (.73) / .65-.78 (.72) / .63−.82 (.73) .49−.77 (.65) / .55-.76 (.68) / .49−.70 (.61) ES: Range = .84−.90, Mean = .87 HS: Range = .84-.92, Mean = .87 UNI: Range = .75−. 89, Mean = .80

CFA loadings range (mean) ES/ HS/UNI

−.26*** (HS > U) −.13*** (HS > U) −.24*** (HS > U)

−.49*** (HS > ES)

−.24*** (HS > U)

−.47*** (HS > ES)

−.47*** (HS > ES) −.31*** (HS > ES)

.22*** (U > HS) −.14*** (HS > U) −.28*** (HS > U)

.30*** (U > HS)

.35*** (ES > HS)

.04*** (ES > HS) −.18*** (HS > ES) −.50*** (HS > ES)

.26*** (U > HS) .24*** (U > HS) .24*** (U > HS)

.36*** ( U > HS)

.45*** (ES > HS)

.33*** (ES > HS) .26*** (ES > HS) .25*** (ES > HS)

.15*** ( U > HS) .31*** ( U > HS) .38*** ( U > HS)

HS (0) vs. UNI (1)

.24*** (ES > HS) .30*** (ES > HS) .50*** (ES > HS)

HS(0) vs. ES (1)

Motivation and Engagement

***p < 0.001

Note: ES = elementary school; HS = high school; UNI = university. Means, standard deviations, skewness, and kurtosis are available from the author on request. High school results are bolded to assist readability.

Maladaptive behavior Self-handicapping Disengagement Higher order

Impeding/maladaptive cognition Anxiety .75 / .77 / .78 Failure avoidance .84 / .79 / .85 Uncertain control .78 / .79 / .80 Higher order

.76 / .77 / .71 .82 /.81 / .82 .74 / .77 / .70

Adaptive cognition Self-efficacy Mastery orientation Valuing Higher order

Cronbach’s a ES/ HS/UNI

Table 1: Cronbach’s alphas, confirmatory factor analysis (CFA) loadings, and multiple-indicator, multiple-cause modeling standardized betas

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−.24 / −.16 / −.24

−.54 / –.34 / −.50

−.47 / –.37 / −.30

−.59 / –.62 / −.47

Failure avoid

Uncertain control

Self-handicapping

Disengagement

.71 / .68 / .64

Persistence

−.08 / .03 / −.08

.57 / .58 / .25

Task management

Anxiety

.60 / .55 / .41

.75 / .76 / .61

– .78 / .73 / .60

Planning

First-order correlations Self-efficacy Mastery orientation Valuing

Self-efficacy

.72 / .78 / .71 .56 / .54 / .42 .50 / .56 / .42 .58 / .59 / .48 .03 / .21 / .17 −.15 / −.05 / −.11 −.38 / –.10 / −.12 −.37 / –.26 / −.26 −.59 / –.56 / −.36

–

Mastery orientation

.51 / .57 / .43 .52 / .58 / .39 .52 / .65 / .64 .04 / .14 / .08 −.22 / –.11 / −.28 −.42/–.17 / −.13 −.49/−.32 / −.32 −.75 / −.71 / −.63

–

Valuing

.63 / .79 / .57 .59 / .74 / .65 −.19 / .11 / .13 −.23 / −.02 /−.15 −.39 / −.17 / −.21 −.36 / –.33 / −.30 −.45 / −.51 / −.26

–

Planning

−.48 / −.51 / −.26

−.36 / −.32 / −.24

−.35 / −.15 / −.10

−.20 / −.02 / −.10

−.11 / .15 / .09

.63 / .66 / .46

–

Task management

−.19 / .07 / .08 −.29 / −.09 / −.31 −.52 / −.27 / −.38 . −.45/−.40 / −.45 −.59/−.60 / −.54

–

Persistence

Elementary school / High school / University

Table 2: Interscale correlations in confirmatory factor analysis: first- and higher order solutions

.50 / .43 / .39 .40 / .49 / .47 .26 / .19 / .17 .11 / .06 / .10

–

Anxiety

.57 / .53 / .45 .50 / .45 / .53 .36 / .32 / .40

–

Failure avoid

.62 / .53 / .36 .51 / .43 / .39

–

.65 / .59 / .51

–

Uncertain Selfcontrol handicapping

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Higher order correlations Adaptive cognitions Adaptive behaviors Impeding/ maladaptive cognitions Maladaptive behaviors

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−.56 / −.14 / −.33

−.74 / −.68 / −.66

−.79 / −.75 / −.69

–

Adaptive behaviors

−.46 / −.16/−.29

.86 / .78 / .77

−

Adaptive cognitions

.70 / .61 / .73

–

–

Impeding/ maladaptive Maladaptive cognitions behaviors

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Multigroup Confirmatory Factor Analysis and Invariance Tests Eight models were tested in each of the multigroup CFAs assessing invariance of factor structure across gender, age, and educational stage. The initial five models related to the first-order factor structure. The first model allowed all factor loadings, uniquenesses, and correlations to be freely estimated; the second held first-order factor loadings invariant across groups; the third held first-order factor loadings and correlations/variances invariant; the fourth held first-order factor loadings and uniquenesses invariant; and the fifth held first-order factor loadings, uniquenesses, and correlations/ variances invariant. The final three models focused on invariance of higher order loadings and correlations/variances: The sixth freely estimated the higher order loadings and correlations/variances, the seventh held higher order loadings invariant, and the eighth held higher order loadings and correlations/variances invariant. Within-sample invariance tests. For elementary school, results in Table 3 indicate that when successive elements of the first- and higher order factor structure are held invariant across groups, the fit indices are predominantly comparable across (Table 3 also indicates c2, df, and p values) (a) males and females (ranges: CFIs = .97 for first-order and .96 for higher order solutions; NNFIs = .98 for first-order and .97 for higher order solutions; RMSEAs = .05 for first-order and higher order solutions) and (b) younger (9–11.5 years) and older (11.5–13 years) students (ranges: CFIs = .97 for first-order and .96 for higher order solutions; NNFIs = .96 for first-order and higher order solutions; RMSEAs = .05 for first-order and higher order solutions). For high school, the fit indices are predominantly comparable across (a) males and females (ranges: CFIs = .98 for first-order and .97 for higher order solutions; NNFIs = .98 for first-order and .97 for higher order solutions; RMSEAs = .04 for first-order and higher order solutions) and (b) early (12–13 years), middle (14–15 years), and late (16–18 years) adolescence (ranges: CFIs = .98 for first-order and .97 for higher order solutions; NNFIs = .98 for first-order and .97 for higher order solutions; RMSEAs = .04 for first-order and higher order solutions). For university, the fit indices are predominantly comparable across (a) males and females (ranges: CFI = .93 to .94 for first-order and .92 to .93 for higher order solutions; NNFIs = .93 for first-order and .92 for higher order solutions; RMSEAs = .06 for first-order and higher order solutions) and (b) younger (17–19 years) and older (20 or more years) students (ranges: CFIs = .94 for first-order and .92 for higher order solutions; NNFIs = .93 for first-order and .92 for higher order solutions; RMSEAs = .05 to .06 for firstorder and .06 for higher order solutions). For all three samples, the application of recommended criteria for evidence of lack of invariance (i.e., a change of .01 in fit indices; see Cheung & Rensvold, 2002) indicates that there is invariance across groups.

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3,320 / 41,931 / 3,208 3,325 / 42,050 / 3,217 3,364 / 42,582 / 3,261

1,849 / 2,812 / 1,849 1,855 / 2,824 / 1,855 1,876 / 2,866 / 1,876

/ 1,727 / 1,793 / 1,771 / 1,837

1,727 / 2,607 1,793 / 2,739 1,771 / 2,695 1,837 / 2,827

3,036 / 31,021 3,156 / 32,005 2,993 / 32,800 3,091 / 33,857

/ 2,792 / 2,924 / 2,875 / 3,004

1,694 / 2,541 / 1,694

1,849 / 1,849 / 1,849 1,855 / 1,855 / 1,855 1,876 / 1,876 / 1,876

3,011 / 30,639 / 2,728

3,409 / 39,563 / 3,285 3,413 / 39,595 / 3,320 3,558 / 40,077 / 3,455

/ 1,727 / 1,793 / 1,771 / 1,837

1,727 / 1,727 1,793 / 1,793 1,771 / 1,771 1,837 / 1,837

3,084 / 28,859 3,165 / 29,343 3,108 / 31,109 3,269 / 31,759

/ 2,761 / 2,923 / 2,983 / 3,162

1,694 / 1,694 / 1,694

df

2,947 / 28,707 / 2,720

c 2

.96 / .97 / .93 .96 / .97 / .93 .96 / .97 / .92

.97 / .98 / .94 .97 / .98 / .94 .97 / .98 / .94 .96 / .98 / .94

.97 / .98 / .94

.96 / .97 / .93 .96 / .97 / .93 .96 / .97 / .92

.97 / .98 / .94 .97 / .98 / .94 .97 / .98 / .94 .97 / .98 / .93

.97 / .98 / .94

Comparative fit index

Elementary school / High school / University

/ .93 / .93 / .93 / .93

/ .93 / .93 / .93 / .93 .96 / .97 / .92 .96 / .97 / .93 .96 / .97 / .92

.96 / .98 .96 / .98 .96 / .98 .96 / .98

.96 / .98 / .93

.96 / .97 / .92 .96 / .97 / .92 .96 / .97 / .92

.97 / .98 .97 / .98 .97 / .98 .96 / .98

.97 / .98 / .93

Nonnormed fit index

.05 / .04 / .06 .05 / .04 / .06 .05 / .04 / .06

.05 / .04 / .05 .05 / .04 / .06 .05 / .04 / .06 .05 / .04 / .06

.05 / .04 / .05

.05 / .04 / .06 .05 / .04 / .06 .05 / .04 / .06

.05 / .04 / .05 .05 / .04 / .06 .05 / .04 / .06 .05 / .04 / .06

.05 / .04 / .05

Root mean square error of approximation

Note: High school results are bolded to assist readability. All chi-square values significant at p < .001. Maximum 90% confidence interval range for all first-order root mean square errors of approximation (RMSEAs) = .04 to .06. Maximum 90% confidence interval range for all higher order RMSEAs = .04 to .07.

Invariance across age groups First-order parameters are free (Model 1: no invariance) First-order factor loadings invariant (Model 2) Model 2 + correlations/variances invariant Model 2 + uniquenesses invariant Model 2 + correlations/variances, uniquenesses invariant Higher order parameters free Higher order factor loadings invariant (Model 3) Model 3 + correlations/variances invariant

Invariance across males and females First-order parameters are free (Model 1: no invariance) First-order factor loadings invariant (Model 2) Model 2 + correlations/variances invariant Model 2 + uniquenesses invariant Model 2 + correlations/variances, uniquenesses invariant Higher order parameters free Higher order factor loadings invariant (Model 3) Model 3 + correlations/variances invariant

Table 3: Invariance tests across gender and age group

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Between-sample invariance tests. The final set of invariance tests assessed first- and higher order factor structure across elementary school, high school, and university samples. This is a direct assessment of the generalizability of the framework and measurement across diverse settings. Fit indices in Table 4 (Table 4 also indicates c2, df, and p values) show that when successive elements of the factor structure are held invariant across high school and university samples on the original 7-point rating scale (ranges: CFIs and NNFIs = .98 for first-order and higher order solutions; RMSEAs = .04 for firstorder and higher order solutions), there is invariance across all first-order and higher order parameters. In terms of elementary school, high school, and university samples on a common 5-point scale (the common 5-point rating scale was derived by aggregating the first and last 2 points of the 7-point rating scale), there is also invariance across the three samples (ranges: CFIs and NNFIs = .98 for first-order and higher order solutions; RMSEAs = .04 for first-order and higher order solutions). Finally, when assessing invariance between elementary school and university samples (thereby omitting the extremely large high school sample that could bias invariance findings), there is also evidence of invariance when aspects of factor structure (loadings, correlations/variances, uniquenesses) are systematically constrained to be equal (ranges: CFI = .96 to .97 for first-order and .96 for higher order solutions; NNFI= .96 to .97 for first-order and .96 for higher order solutions; RMSEAs = .05 for first-order and higher order solutions). For each of these three sets of between-sample invariance tests, the application of recommended criteria for evidence of lack of invariance (i.e., a change of .01 in fit indices) indicates that there is invariance across elementary school, high school, and university domains.

Multiple-Indicator, Multiple-Cause Modeling The previous analyses explored possible differences in factor structure as a function of educational stage. It was also of interest to explore possible mean-level differences in motivation and engagement as a function of educational stage (elementary school, high school, university). MIMIC modeling was the analytical method used to examine this and involved structural equation models in which educational stage was used as a predictor of the first- and higher order factors of the wheel. The first-order model yielded a good fit to the data (c2 = 39,347.85, df = 914, p < .001, CFI = .95, NNFI = .94, RMSEA = .04), as did the higher order model (c2 = 45,508.66, df = 966, p < .001, p < .001, CFI = .95, NNFI = .94, RMSEA = .05). Beta coefficients are presented in Table 1 along with the main effects for educational stage. Results show that there are significant stage differences on all motivation and engagement factors. Compared with high school students, elementary school and university students are significantly higher on all

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1,694 1,727 1,793 1,771 1,837 1,849 1,855 1,876

2,541 2,607 2,739 2,695 2,827 2,812 2,824 2,866

1,694 1,727 1,793 1,771 1,837 1,849 1,855 1,876

df

.97 .97 .97 .97 .96 .96 .96 .96

.98 .98 .98 .98 .98 .98 .98 .98

.98 .98 .98 .98 .98 .98 .98 .98

Comparative fit index

.97 .97 .96 .96 .96 .96 .96 .96

.98 .98 .98 .98 .98 .98 .98 .98

.98 .98 .98 .98 .98 .98 .98 .98

Nonnormed fit index

.05 .05 .05 .05 .05 .05 .05 .05

.04 .04 .04 .04 .04 .04 .04 .04

.04 .04 .04 .04 .04 .04 .04 .04

Root mean square error of approximation

Note: All chi-square values significant at p < .001. Maximum 90% confidence interval range for all first-order root mean square errors of approximation (RMSEAs) = .03 to .05. Maximum 90% confidence interval range for all higher order RMSEAs = .04 to .05.

3,472 3,657 3,931 3,895 4,181 4,403 4,419 4,561

26,203 26,645 27,480 26,878 27,550 34,745 34,823 35,171

Invariance elementary, high school, university (5-point scale) First-order parameters are free (Model 1: no invariance) First-order factor loadings invariant (Model 2) Model 2 + correlations/variances invariant Model 2 + uniquenesses invariant Model 2 + correlations/variances, unique invariant Higher order parameters free Higher order factor loadings invariant (Model 3) Model 3 + correlations/variances invariant

Invariance elementary and university (5-point scale) First-order parameters are free (Model 1: no invariance) First-order factor loadings invariant (Model 2) Model 2 + correlations/variances invariant Model 2 + uniquenesses invariant Model 2 + correlations/variances, unique invariant Higher order parameters free Higher order factor loadings invariant (Model 3) Model 3 + correlations/variances invariant

28,875 29,002 29,249 29,110 29,291 37,548 37,563 37,609

c2

Invariance high school and university (7-point scale) First-order parameters are free (Model 1: no invariance) First-order factor loadings invariant (Model 2) Model 2 + correlations/variances invariant Model 2 + uniquenesses invariant Model 2 + correlations/variances, unique invariant Higher order parameters free Higher order factor loadings invariant (Model 3) Model 3 + correlations/variances invariant

Table 4: Invariance tests across samples

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adaptive dimensions. Also, compared with high school students, elementary school and university students are significantly lower in uncertain control, self-handicapping, and disengagement. However, compared to high school students, elementary school and university students are significantly higher on anxiety and failure avoidance. As a general finding, there is a greater difference between elementary and high school students than between high school and university students. Again, however, note that the high school and university 1-to-7 rating continuum was transformed to a 1-to-5 rating continuum to place high school and university on the same scale of measurement as elementary school; hence, caution is advised when interpreting these findings. Due to the large high school sample, caution is also advised when interpreting the significance of the MIMIC results, and this being the case, greater emphasis is given to findings in relation to selfefficacy, mastery orientation, valuing of school, planning, task management, persistence, uncertain control, and self-handicapping that yielded standardized beta values greater than .30.

Motivation, Engagement, and Between-Network Cognate Correlates As indicated earlier, consistent with the between-network construct validity approach, it was of interest to explore the nature of relationships between each facet of motivation and a set of key between-network correlates across the three educational stages. To this end, the three samples were also administered items that explored enjoyment of school or university (elementary school, high school, university), class participation (elementary school, high school, university), positive academic intentions (high school, university), academic buoyancy (elementary school, high school, university), and homework completion (high school, university). For each of the three samples, first- and higher order CFAs were conducted. The first-order elementary school CFA yielded a very good fit to the data (c2 = 2,915.33, df = 1,393, p < .001, CFI = .98, NNFI = .98, RMSEA = .04) and showed that (a) adaptive dimensions are significantly positively associated with these between-network constructs and (b) impeding/maladaptive and maladaptive dimensions (particularly uncertain control, self-handicapping, and disengagement) are negatively correlated with these constructs. Table 5 presents findings. These first-order findings were broadly supported in the high school sample (c2 = 52,112, df = 1,650, p < .001, CFI = .98, NNFI = .98, RMSEA = .04) and the university sample (c2 = 3,251.39, df = 1,650, p < .001, CFI = .96, NNFI= .96, RMSEA = .05). Interestingly – and consistent with Martin (2007a; see also Martin & Marsh, 2006, 2008a, 2008b) – academic buoyancy is a notable exception in being more markedly correlated with impeding/maladaptive cognitions than with maladaptive behaviors,

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.40 / .46 / .34 .33 / .41 / .26 .51 / .48 / .41 −.16 / −.08 / −.15 −.24 / −.15 / −.19 −.40 / −.25 / −.24 −.40 / −.30 / −.30 −.49 / −.46 / −.33

.40 / .49 / .21 .40 / .48 / .11 .46 / .54 / .35

−.11 / −.04 / −.23 −.16 / −.17 / −.33 −.28 / −.26 / −.29

−.32 / −.34 / −.28 −.67 / −.68 / −.57

.59 / .67 / .55 .55 / .59 / .32 −.28 / −.20 / −.38 −.66 / −.71 / −.62

Higher order correlations Adaptive cognitions Adaptive behaviors Impeding/maladaptive cognitions Maladaptive behaviors

.41 / .31 / .34 .61 / .39 / .28 −.66 / −.74 / −.87 −.33 / −.33 / −.30

−.29 / −.25 /−.25 −.29 / −.29 / −.23

−.62 / −.69 / −.74 −.34 / −.31 / −.39 −.52 / −.47 / −.54

.47 / .35 / .19 .39 / .27 / .13 .53 / .37 / .27

.42 / .38 / .41 .35 / .20 / .16 .31 / .25 / .26

Buoyancy

− / .73 / .81 − / .62 / .50 − / −.19 / −.29 − / −.72 / −.73

− / −.40 / −.34 − / −.68 / −.67

− / .02 / −.10 − / −.18 / −.35 − / −.32 / −.31

− / .49 / .30 − / .50 / .23 − / .60 / .53

− / .67 / .68 − / .56 / .56 − / .68 / .72

Positive intent

− / .42 / .10 − / .50 / .16 − / −.12 / −.06 − / −.53 / −.11

− / −.37 / −.19 − / −.47 / −.19

− / .04 / −.06 − / −.14 / −.11 − / −.24 / .05

− / .42 / .12 − / .40 / .11 − / .48 / .13

− / .35 / .05 − / .34 / .01 − / .39 / .15

Homework completion

Note: Elementary school r > +/−.07, significant at p < .05; high school r > +/−.02 significant at p < .05 (but note large sample); university r > +/−.12 significant at p < .05. High school results are bolded to assist readability.

.52 / .54 / .52 .55 / .53 / .46 −.41 / −.21 / −.26 −.54 / −.50 / −.41

.44 / .51 / .45 .48 / .45 / .36 .42 / .46 / .44

.43 / .57 / .45 .57 / .55 / .37 .55 / .63 / .51

Participation

First-order correlations Adaptive cognitions Self-efficacy Mastery orientation Valuing Adaptive behaviors Planning Task management Persistence Impeding/maladaptive cognitions Anxiety Failure avoidance Uncertain control Maladaptive behaviors Self-handicapping Disengagement

Enjoyment

Elementary school / High school / University

Table 5: First- and higher order correlations with between-network constructs

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largely a function of its very high correlation with anxiety (discussed fully in Martin & Marsh, 2006, 2008a, 2008b). Again, however, due to the large high school sample, caution is advised when interpreting the correlations – emphasis is given to the size and direction of the correlation coefficients themselves rather than to their significance levels. The higher order factor analysis for elementary school ( c2 = 3,361.64, df = 1,453, p < .001, CFI = .97, NNFI = .97, RMSEA = .05) provides general support for the first-order findings. Higher order correlations are also presented in Table 5 (again, due to the large samples involved, emphasis is given to the size and direction of the correlation coefficients themselves rather than to their significance levels). Consistent with the elementary school findings, the higher order factor analysis for high school ( c2 = 67,868.55, df = 1,724, p < .001, CFI = .98, NNFI = .98, RMSEA = .04) provides support for the first-order findings, as did the higher order model for the university sample ( c2 = 3,683.58, df = 1,724, p < .001, CFI = .95, NNFI = .95, RMSEA = .05).

Discussion Through the integration of multivariate measurement and the hypothesized motivation and engagement framework, this study supports the developmental construct validity of motivation and engagement at the elementary school, high school, and university/college levels. From this developmental construct validity perspective, perhaps the most significant yield of this study is the predominantly comparable findings across three very distinct educational stages. The data confirm the hypothesized generality of the wheel and its accompanying instrumentation among very young students in elementary school through to mature-age students in university. In some ways, the most revealing tests are the multigroup invariance analyses across the elementary school, high school, and university samples. These analyses directly address the question posed at the outset of the study regarding the generality of the proposed motivation and engagement framework in diverse educational settings. The invariance data suggest that there is generality – and developmental validity – of the framework across the academic life span. Notwithstanding the important consistencies across the three educational stages, findings also suggest issues distinct to each academic setting. For example, the data show that elementary school students reflect higher levels of motivation and engagement, and this is consistent with prior work showing declines between elementary and middle or high school (e.g., Anderman & Midgley, 1997; Roeser et al., 2000; Wigfield et al., 1991; Wigfield & Tonks, 2002). In terms of university students, there is some question as to their level of motivation relative to school students, with some research recognizing

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the challenges they face in higher education and other research reporting on their confidence in their abilities (e.g., see Martin, Marsh, Williamson, et al., 2003; Pitts, 2005). The present data shed light on these competing views by showing that, notwithstanding equivalence in factor structure, university students reflect higher mean levels of motivation and engagement than do their high school counterparts. In the case of all MIMIC analyses, however, due to the large samples involved, emphasis is given to the size and direction of the standardized beta coefficients rather than to the attained significance levels. Because the constructs within the wheel have a theoretical basis, researchers are able to draw on theory to provide direction for intervention aimed at addressing facets within the wheel. Research shows that targeted intervention is more effective than intervention that does not focus on specific target behaviors (O’Mara, Marsh, Craven, & Debus, 2006), and so it is proposed that intervention programs seeking to build specific academic skills and competencies need to provide targeted support that can do this. The wheel provides a basis for doing so. Martin (2007a; see also Martin, 2008b, in press-a, for strategy in sport and music settings) has proposed specific classroom strategy that targets each of these dimensions, and this strategy incorporated into intervention work has demonstrated significant yields for students (Martin, 2005a, 2008a). In addition to what Martin (2007a) suggests in terms of specific classroom strategy, there are other approaches to intervention that have more of a measurement basis to them. One such approach that Martin (2008b) has previously proposed in relation to motivation and engagement involves performance profiling. Performance profiling (Butler & Hardy, 1992) has very direct synergies with the wheel both in form and substance – indeed, Martin (2008b) has demonstrated how performance profiling can be conducted with the wheel in the domains of sport and music. Performance profiling provides a means by which to effectively and parsimoniously contextualize individuals’ profiles in reference to a set of psychological and behavioral criteria. Although there are various ways and levels to profile under a performance profiling schedule, the example in the present study is the mean-level profile (rounded) for the high school sample as a whole (n = 21,579). In Figure 2, the traditional performance profiling format (see Butler & Hardy, 1992; see also Martin, 2008b; Weinberg & Gould, 1999) has been adapted to interface with the Motivation and Engagement Wheel. Obviously, at the individual level it would reflect the student’s mean scores on each dimension. Or, it could be readily employed at a class or school level (and bringing into focus the issue of multilevel models of motivation and engagement; for multilevel research along these lines, see Marsh, Martin, & Cheng, 2008; Martin & Marsh, 2005).

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ADAPTIVE COGNITION

Mastery orientation

7

Planning

6 Valuing

ADAPTIVE BEHAVIOR Task management

5 4

Selfefficacy

3

Persistence

2 1

Anxiety Disengagement Failure avoidance MALADAPTIVE BEHAVIOR

Self handicapping

Uncertain control

IMPEDING/ MALADAPTIVE COGNITION

Source: Adapted from Butler and Hardy (1992), Martin (in press-b), and Weinberg and Gould (1999).

Figure 2: Performance profile for motivation and engagement, reflecting mean level/7 (rounded to nearest 0.5) profile for high school sample (n = 21,579)

Limitations, Future Directions, and Conclusion This study provides an enhanced understanding of the validity of motivation and engagement in the context of three educational stages: elementary school, high school, and university. There are, however, a number of potential limitations important to consider when interpreting findings. First, although the large sample involved in the study is a distinct strength of the research, it posed some challenges when interpreting data, with the need to emphasize the practical significance of findings as much as or more than the statistical significance of findings. It is also important to recognize that the data presented in this study are all self-reported. Although this is a logical and defensible methodology in its own right given the substantive focus, it is important to conduct research that examines the same constructs using data derived from additional sources such as, for example, achievement and that from teachers and parents. Just as important as the self-report nature of findings is the fact that the data presented in the study are cross-sectional. Tracking the same students over time and assessing factor structure and interrelationships from a longitudinal perspective would shed further light on the developmental processes

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relevant to motivation and engagement. In addition, examining reliability and stability of the scores over time and the causal ordering of motivation and engagement in relation to the cognate constructs assessed here are other issues of interest in longitudinal work. The nature of quantitative survey–based methods also warrants some further comment. Although Martin, Marsh, Williamson, and Debus (2003) conducted qualitative work among university samples, future research might encompass qualitative work that can more fully scope the detailed nature and extent of motivation and engagement across the academic life span. Alongside this qualitative work, there may also be yields in multilevel approaches to developmental construct validity in motivation and engagement. Advances in statistical software enable researchers to more accurately assess the relative influence of individual-, class-, and school-level factors using multilevel modeling (see Goldstein, 2003), and so future research can readily explore the influence of class- and school-level motivation climates relative to individuallevel variation in motivation and engagement as relevant to developmental construct validity. To conclude, the research presented here supports the developmental construct validity of the Motivation and Engagement Wheel and its accompanying instrumentation, the MES, across the academic life span. The findings of this investigation hold implications for researchers studying issues relevant to motivation and engagement across the academic life span. The findings also present new insights and opportunities for educators seeking to enhance the educational outcomes of their students – outcomes that are affected by motivation and engagement and the extent to which educators can effectively measure and enhance them.

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Covington, M. V. (1992). Making the grade: A self-worth perspective on motivation and school reform. Cambridge, UK: Cambridge University Press. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048. Eccles, J. (1983). Expectancies, values, and academic behaviors. In J. Spence (Ed.), Achievement and achievement motivation. Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation, Journal of Personality and Social Psychology, 72, 218–232. Elliot, A. J., & Sheldon, K. M. (1997). Avoidance achievement motivation: A personal goals analysis. Journal of Personality and Social Psychology, 73, 171–185. Finn, J. D. (1989). Withdrawing from school. Review of Educational Research, 59, 117–142. Goldstein, H. (2003). Multilevel statistical models. London: Edward Arnold. Graham, J. W., & Hoffer, S. M. (2000). Multiple imputation in multivariate research. In T. D. Little, K. U. Schnable, & J. Baumert (Eds.), Modeling longitudinal and multilevel data: Practical issues, applied approaches, and specific examples (pp. 201–218). Mahwah, NJ: Lawrence Erlbaum. Green, J., Martin, A. J., & Marsh, H. W. (2007). Motivation and engagement in English, mathematics and science high school subjects: Towards an understanding of multidimensional domain specificity. Learning and Individual Differences, 17, 269–279. Greeno, J. (1998). The situativity of knowing, learning, and research. American Psychologist, 53, 5–26. Hattie, J. (1992). Self-concept. Hillsdale, NJ: Lawrence Erlbaum. Henson, R. K. (2001). Understanding internal consistency reliability estimates: A conceptual primer on coefficient alpha. Measurement and Evaluation in Counseling and Development, 34, 177–189. Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Chicago: Scientific Software International. Jöreskog, K. G., & Sörbom, D. (2006). LISREL 8.80. Chicago: Scientific Software International. Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Thousand Oaks, CA: Sage. Lee, F. K., Sheldon, K. M., & Turban, D. B. (2003). Personality and goal-striving process: The influence of achievement goal patterns, goal level, and mental focus on performance and enjoyment. Journal of Applied Psychology, 88, 256 –265. Marsh, H. W. (1993). The multidimensional structure of academic self-concept: Invariance over gender and age. American Educational Research Journal, 30, 841–860. Marsh, H. W. (2002). A multidimensional physical self-concept: A construct validity approach to theory, measurement, and research. Psychology: The Journal of the Hellenic Psychological Society, 9, 459–493. Marsh, H. W., & Hau, K.-T. (2007). Applications of latent-variable models in educational psychology: The need for methodological-substantive synergies. Contemporary Educational Psychology, 32, 151–171. Marsh, H. W., Martin, A. J., & Cheng, J. (2008). A multilevel perspective on gender in classroom motivation and climate: Potential benefits of male teachers for boys? Journal of Educational Psychology, 100, 78–95. Marsh, H. W., & Shavelson, R. J. (1985). Self-concept: Its multifaceted, hierarchical structure. Educational Psychologist, 20, 107–125. Martin, A. J. (2001). The Student Motivation Scale: A tool for measuring and enhancing motivation. Australian Journal of Guidance and Counselling, 11, 1–20.

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Martin, A. J. (2002). Motivation and academic resilience: Developing a model of student enhancement. Australian Journal of Education, 14, 34 – 49. Martin, A. J. (2003a). How to motivate your child for school and beyond. Sydney, Australia: Bantam. Martin, A. J. (2003b). The relationship between parents’ enjoyment of parenting and children’s school motivation. Australian Journal of Guidance and Counselling, 13, 115–132. Martin, A. J. (2003c). The Student Motivation Scale: Further testing of an instrument that measures school students’ motivation. Australian Journal of Education, 47, 88–106. Martin, A. J. (2004). School motivation of boys and girls: Differences of degree, differences of kind, or both? Australian Journal of Psychology, 56, 133–146. Martin, A. J. (2005a). Exploring the effects of a youth enrichment program on academic motivation and engagement. Social Psychology of Education, 8, 179–206. Martin, A. J. (2005b). Perplexity and passion: Further consideration of the role of positive psychology in the workplace. Journal of Organizational Behavior Management, 24, 203–205. Martin, A. J. (2005c). The role of positive psychology in enhancing satisfaction, motivation, and productivity in the workplace. Journal of Organizational Behavior Management, 24, 113–133. Martin, A. J. (2006). The relationship between teachers’ perceptions of student motivation and engagement and teachers’ enjoyment of and confidence in teaching. Asia-Pacific Journal of Teacher Education, 34, 73–93. Martin, A. J. (2007a). Examining a multidimensional model of student motivation and engagement using a construct validation approach. British Journal of Educational Psychology, 77, 413–440. Martin, A. J. (2007b). The Motivation and Engagement Scale. Sydney, Australia: Lifelong Achievement Group. www.lifelongachievement.com. Martin, A. J. (2008a). Enhancing student motivation and engagement: The effects of a multidimensional intervention. Contemporary Educational Psychology, 33, 239–269. Martin, A. J. (2008b). Motivation and engagement in music and sport: Testing a multidimensional framework in diverse performance settings. Journal of Personality, 76, 135–170. Martin, A. J. (in press-a). How domain specific are motivation and engagement across school, sport, and music? A substantive-methodological synergy assessing young sportspeople and musicians. Contemporary Educational Psychology. Martin, A. J. (in press-b). Motivation and engagement in diverse performance domains: Testing their generality across school, university/college, work, sport, music, and daily life. Journal of Research in Personality. Martin, A. J., Craven, R. G., & Munns, G. (2006). Motivation and engagement in young children: How well does a high school conceptualization generalize to junior school? In R. G. Craven., J. S. Eccles, & T. M. Ha (Eds.), Self-concept, motivation, social and personal identity for the 21st century. Proceedings of the Fourth International Biennial SELF Research Conference, Ann Arbor, University of Michigan. Martin, A. J., & Debus, R. L. (1998). Self-reports of mathematics self-concept and educational outcomes: The roles of ego-dimensions and self-consciousness. British Journal of Educational Psychology, 68, 517–535. Martin, A. J. & Marsh, H. W. (2003). Fear of failure: Friend or foe? Australian Psychologist, 38, 31–38.

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Martin, A. J., & Marsh, H. W. (2005). Motivating boys and motivating girls: Does teacher gender really make a difference? Australian Journal of Education, 49, 320–334. Martin, A. J., & Marsh, H. W. (2006). Academic resilience and its psychological and educational correlates: A construct validity approach. Psychology in the Schools, 43, 267–282. Martin, A. J., & Marsh, H. W. (2008a). Academic buoyancy: Towards an understanding of students’ everyday academic resilience. Journal of School Psychology, 46, 53–83. Martin, A. J., & Marsh, H. W. (2008b). Workplace and academic buoyancy: Psychometric assessment and construct validity amongst school personnel and students. Journal of Psychoeducational Assessment, 26, 168–184. Martin, A. J., Marsh, H. W., & Debus, R. L. (2001a). A quadripolar need achievement representation of self-handicapping and defensive pessimism. American Educational Research Journal, 38, 583–610. Martin, A. J., Marsh, H. W., & Debus, R. L. (2001b). Self-handicapping and defensive pessimism: Exploring a model of predictors and outcomes from a self-protection perspective. Journal of Educational Psychology, 93, 87–102. Martin, A. J., Marsh, H. W., & Debus, R. L. (2003). Self-handicapping and defensive pessimism: A model of self-protection from a longitudinal perspective. Contemporary Educational Psychology, 28, 1–36. Martin, A. J., Marsh, H. W., Williamson, A., & Debus, R. L. (2003). Self-handicapping, defensive pessimism, and goal orientation: A qualitative study of university students. Journal of Educational Psychology, 95, 617–628. Martin, A. J., Milne-Home, J., Barrett, J., & Spalding, E. (1997). Stakeholder perceptions of the institution: To agree or not to agree. Journal of Institutional Research in Australasia, 6, 53–67. Martin, A. J., Milne-Home, J., Barrett, J., Spalding, E., & Jones, G. (2000). Graduate satisfaction with university and perceived employment preparation. Journal of Education and Work, 13, 199–214. Martin, A. J., Tipler, D. V., Marsh, H. W., Richards, G. E., & Williams, M. R. (2006). Assessing multidimensional physical activity motivation: A construct validity study of highschool students. Journal of Sport and Exercise Psychology, 28, 171–192. McClelland, D. C. (1965). Toward a theory of motive acquisition. American Psychologist, 20, 321–333. McDonald, R. P., & Marsh, H. W. (1990). Choosing a multivariate model: Noncentrality and goodness-of-fit. Psychological Bulletin, 107, 247–255. Meece, J. L., Wigfield, A., & Eccles, J. S. (1990). Predictors of mathematics anxiety and its influence on young adolescents’ course enrolment intentions and performance in mathematics. Journal of Educational Psychology, 82, 60–70. Miller, R. B., Greene, B. A., Montalvo, G. P., Ravindran, B., & Nichols, J. D. (1996). Engagement in academic work: The role of learning goals, future consequences, pleasing others, and perceived ability. Contemporary Educational Psychology, 21, 388 – 422. Miserandino, M. (1996). Children who do well in school: Individual differences in perceived competence and autonomy in above-average children. Journal of Educational Psychology, 88, 203–214. Murphy, P. K., & Alexander, P. A. (2000). A motivated exploration of motivation terminology. Contemporary Educational Psychology, 25, 3–53. Nicholls, J. G. (1989). The competitive ethos and democratic education. Cambridge, MA: Harvard University Press.

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“If I try hard, I believe I can do my schoolwork well”

“Learning at school is important”

“I feel very pleased with myself when I really understand what I’m taught at school”

“Before I start a project, I plan out how I am going to do it”

“I usually do my homework in places where I can concentrate”

“If I can’t understand my schoolwork, I keep going over it until I do”

“When I have a project to do, I worry about it a lot”

Self-efficacy

Valuing

Mastery orientation

Planning

Task management

Persistence

Anxiety

Motivation and engagement scale–junior school

Sample Motivation and Engagement Scale Items

“When exams and assignments are coming up, I worry a lot”

“If I can’t understand my schoolwork at first, I keep going over it until I do”

“When I study, I usually study in places where I can concentrate”

“Before I start an assignment, I plan out how I am going to do it”

“I feel very pleased with myself when I really understand what I’m taught at school”

“Learning at school is important”

“If I try hard, I believe I can do my schoolwork well”

Motivation and engagement scale–high school

Appendix

“When exams and assignments are coming up, I worry a lot”

“If I can’t understand my university work at first, I keep going over it until I do”

“When I study, I usually study in places where I can concentrate”

“Before I start an assignment, I plan out how I am going to do it”

“I feel very pleased with myself when I really understand what I’m taught at university”

“Learning at university is important”

“If I try hard, I believe I can do my university work well”

Motivation and engagement scale–university/college

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“The main reason I try at school is because I don’t want to disappoint my parents”

“When I get a bad mark I don’t know how to stop that happening again”

“I sometimes don’t work very hard at school so I can have a reason if I don’t do well”

“I’ve given up being interested in school”

Failure avoidance

Uncertain control

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Self-handicapping

Disengagement

“I’ve pretty much given up being involved in things at school”

“I sometimes don’t study very hard before exams so I have an excuse if I don’t do as well as I hoped”

“When I get a bad mark I’m often unsure how I’m going to avoid getting that mark again”

“Often the main reason I work at school is because I don’t want to disappoint my parents”

“I’ve pretty much given up being involved in things at university”

“I sometimes don’t study very hard before exams so I have an excuse if I don’t do as well as I hoped”

“When I get a bad mark I’m often unsure how I’m going to avoid getting that mark again”

“Often the main reason I work at university is because I don’t want to disappoint others”

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62 Motivation and Achievement: A Quantitative Synthesis Margaret E. Uguroglu and Herbert J. Walberg

W

hat is the typical correlation between measures of motivation and educational achievement? One recent review, discussed below, suggests a figure of approximately .50. But it is by no means clear that this figure would be estimated from a second sample of studies; and it remains to be shown whether certain motivation constructs such as locus of control are more valid than others, as claimed by some authors, or that the relation of motivation and achievement is stronger for some students, for example, boys or girls or older or younger participants, as held by others. The purpose of this study is to use new analytic techniques for synthesizing research to produce objective estimates of the motivation-achievement correlation and to examine its dependency on types of motivation, achievement, and samples that have been investigated in recent years. A complete discussion of motivation and learning in the context of a 6-factor theory of educational productivity is presented in Walberg & Uguroglu (1979).

Method Sample The analysis makes use of two samples of studies with correlations between motivation and achievement measures. The first set of data, which is termed the calibration sample, consists of 122 correlations compiled by Bloom Source: American Educational Research Journal, 16(4) (1979): 375–389.

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(1976, pp. 252–256) from 22 studies published between 1953 and 1974. Bloom does not explicitly describe the search and selection procedure. No systematic bias appears in the sampling; 56 of the 122 correlations in the calibration sample, however, were taken from a single large-scale study of mathematics achievement by Crosswhite in 1972 (Bloom, 1976), which was taken into account in the analysis in the present paper. The second sample of correlations, termed the validation sample, was obtained from two sources. (The validation sample contained the following 18 studies: Chang, 1976; Cobb, Chissom, & Davis, 1975; Cole, 1974; Frymier, Henning, Henning, Norris, & West, 1975; Handel, 1975; Kagan & Zahn, 1975; Kennelly & Kinley, 1975; Lewis & Adank, 1975; Portes & Wilson, 1976; Prawat, 1976; Prendergast & Binder, 1975; Primavera, Simon, & Primavera, 1974; Pugh, 1976; Schultz & Pomerantz, 1974; Simon & Simon, 1975; Satenner & Katzenmeyer, 1976; White & McConnel, 1974; White & Simmons, 1974.) To obtain a group of studies representative of current psychological research, all studies cited in Psychological Abstracts International for 1974, 1975, and 1976 under the descriptors Motivation, Self-concept, Self-esteem, Internal-External Locus of Control, and Academic Achievement Motivation were scanned. In addition, to obtain a representative set of studies on a basic school subject 1,578 studies discussed in Weintraub et al.’s (1974, 1975, 1976) “Summary of Investigations Relating to Reading,” published annually in Reading Research Quarterly, were searched. From these two sources, all studies were included that reported one or more Pearson product-moment correlations between any measure of motivation and any educational achievement or ability measure and that did not represent special populations, namely, juvenile delinquents, mentally or physically handicapped, the learning disabled, college students, or adults. Ability correlations were included for comparative purposes. This selection yielded 110 correlations from 18 studies. The combined samples contain 232 correlations between motivation measures and achievement measures based on 40 studies of a total of 36,946 students. These students were in addition to the more than 498,044 represented in four correlations from the Coleman, Campbell, Hobson, McPartland, Mood, Weinfeld, and York (1966) Equality of Educational Opportunity study in the calibration sample. Without the Coleman figures, the size of the samples ranged from 42 to 2,213 with a mean of 438 students and an even distribution across size.

Procedure Although physical scientists have long quantitatively summarized numerical results across studies, until recently there were few examples in psychological research (Jones & Fiske, 1953). Recent writings of Gage, Glass and Smith,

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Light and Smith, and Rosenthal, however, make a persuasive case for the need and value of such synthesis in educational and psychological research (see Glass, 1978). The present research makes use of the techniques developed by Glass. Twenty-seven characteristics (Table I) were recorded for each of the 232 correlations from the 40 studies so that the analysis could reveal how the sizes of the correlations are related to the characteristics of the samples and measures. Most of these characteristics are self-explanatory, but several require explanation. Concurrent validity studies are those that obtain motivation and achievement measures roughly at the same time; predictive validity studies are those which obtained motivation measures at least three months before the achievement measures. Reliability was recorded for all motivation measures for which it was reported in the original study; unreported reliabilities were estimated at .77, the mean of the reported reliabilities from the validation sample. The mean was considered a neutral value and used so that studies that did not report reliabilities would not be deleted from the analysis; special regressions, however, were run to test the possibility

Table I: Variable list and coding conventions Variable Sample Grade Size of sample Sex of sample Validity Motivation Measure Reliability Motivation Measure Reliability Source Nationality Observed Correlation

Coding of Categorical Variables and Range of Continuous Variables Calibration = 0, Validation = 1 1 through 12 42 through 200,000 Male = 1, Mixed = 1.5, Female = 2 Predictive = 0, Concurrent = 1 .48 through .91 Reported in study = 0, Estimated = 1 U.S. population = 0, other than U.S. population = 1 –.31 through .98

Motivation Measures General Self-concept Academic Self-concept Locus of Control/Field Dependence Achievement Motivation Mathematics Self-concept

(all coded: not present = 0, present = 1)

Achievement Measures General Achievement Test Reading Achievement Test Math Achievement Test Language Achievement Test Other Achievement Test General Grade Point Average Math Grade Index Language Grade Index Social Studies Grade Index Other Grade Index General Ability Verbal Ability Nonverbal Ability

(all coded: not present = 0, present = 1)

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that such estimation might cause misleading results. Mathematics Self-concept was coded separately because 56 correlations from one study of Crosswhite in 1972 (Bloom, 1976) involved this particular measure; none of the other motivation measures is specific to particular school subjects.

Results and Discussion Table II shows stem-and-leaf diagrams (Tukey, 1977) of the correlations for the two samples. The first decimal place of the correlations is represented on the stem on the left of the vertical line, and the second decimal place is represented as a leaf to the right of the line; for example, the highest and lowest outlying correlations for the calibration sample are .71 and .07. These Table II: Correlations between motivation and achievement Calibration sample

Validation sample

.9

.9

.9

.9

.8

.8

.8

.8

.7

8

.7

.7

1

.7

.6

77

.6

.6

12344

.6

.5

6889

.5

67

.5

011111111122344

.5

014

.4

55666678

.4

566778888

.4

00011122222334

.4

0000001122334

.3

555556677789999

.3

556788889999

.3

00000011123333334444

.3

000000011122223333444

.2

55566677777888899999

.2

5666777777889

.2

00011233334

.2

000011111233444

.1

556699

.1

577889

.1

00012234

.0

5

.0

.0

2

–.0

–.0

134

–.0

–.0

7

–.1

–.1

–.1

–.1

–.2

–.2

–.2

–.2

–.3

–.3

–.3

–.3

.1 .0

7

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diagrams, unlike the usual bar graphs, reveal each data point as well as the shape and irregularities of the distribution. The calibration sample reveals a slight tendency toward bimodality with peaks at about .30 and .51. The validation sample is more normally distributed but has negative correlations based on younger children in the primary grades. The validation sample also has two outlying correlations, –.31 and .98. The sources of these correlations (Cobb, Chissom & Davis, 1975; Prendergast & Binder, 1975) yield neither convincing reasons for the unusual results nor for deleting the correlations, although the possibility of their undue influence on the general results was checked in the analysis. Despite these irregularities, the central tendencies and spreads of the samples are reasonably close. The median correlation for the calibration and validation samples are .35 and .30 respectively. The middle 50% of the correlations of the samples are bounded by .28 and .46 and .21 and .40, yielding comparable interquartile ranges of .18 and .19. The median correlation for the combined sample, .325, suggests that motivation typically “accounts” for about 10.6% of the variance in achievement and ability. However, since the range of values after liberally trimming the extreme, perhaps questionable outlying values, is at least from .10 to .60 (Table II), the variance accounted for under partly identifiable conditions ranges from one to 36%. What characteristics of the sample and the measures account for this range of variation? To answer this question, the correlations for the different samples and categories may be compared. This is done in two ways in the present study: first, by tabulation of the means, standard deviations, and numbers of correlations for the subgroups to show how many times certain relations have been investigated (and also to introduce gradually the notion of comparison of averages of correlations, which may be unfamiliar to some readers); but because these simple comparisons do not control for the other variables investigated, a second method, multiple regression analysis, is employed.

Uncontrolled Comparisons Table III shows the results by grade level: the correlations are notably small in first grade, perhaps because of reading problems, and tend to be higher in the later grades, perhaps because of self-insight. The numbers of correlations are reasonably well distributed across grade levels, but there are fewer at the lower grade levels, an age level that has been less frequently investigated. Table IV shows that the correlations are slightly lower for boys, and that girls have been somewhat less often studied. Table V shows that concurrent and predictive mean correlations are approximately the same but concurrent correlations are considerably more frequently investigated and also more variable.

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Table III: Correlations by grade Grade

Mean correlation

Standard deviation

Number of correlations

1

.07

.19

9

2

.25

.14

4

3

.25

.08

15

4

.29

.07

6

5

.35

.10

27

6

.36

.12

37

7

.31

.14

40

8

.38

.09

12

9

.41

.18

26

10

.29

.08

13

11

.36

.11

19

12

.44

.18

24

Mean correlation

Standard deviation

Number of correlations

Males

.35

.15

66

Females

.37

.15

49

Mixed

.32

.13

117

Mean correlation

Standard deviation

Number of correlations

Concurrent

.34

.17

145

Predictive

.34

.11

87

Table IV: Correlations by sex

Note: Not all samples were separated by sex.

Table V: Correlations by type of validity

Table VI cross-classifies the two samples by motivation measure. With the exception of Academic Self-concept in the calibration sample, the averages of the correlations across the five categories of motivation lie in the limited range of .29 to .31. The calibration sample has correlations only for Academic and Mathematics Self-concept whereas the validation sample has a wider variety of measures. These results are due to the sampling procedures: Bloom (1976) restricted motivation to Academic Self-concept and obtained 56 correlations from an international study of mathematics achievement carried out in a number of countries by Crosswhite in 1972. The validation sampling more broadly reflects the educational and psychological literature, but also shows that recent studies have produced twice as many correlations for General Self-concept as the other three motivation measures combined.

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Table VI: Correlations by type of motivation measure Type of motivation measure Calibration Sample General Self-concept Academic Self-concept Locus of Control Achievement Motivation Mathematics Self-concept

Mean correlation

Standard deviation

Number of correlations

— .43 — — .31

— .14 — — .08

— 66 — — 56 122

Validation Sample General Self-concept Academic Self-concept Locus of Control Achievement Motivation Mathematics Self-concept

.29 .31 .32 .31 —

.17 .14 .13 .12 —

74 10 13 13 — 110

Total General Self-concept Academic Self-concept Locus of Control Achievement Motivation Mathematics Self-concept

.29 .41 .32 .31 .31

.17 .15 .13 .12 .08

74 76 13 13 56 232

Note: The Locus of Control category also includes Field Dependence.

The achievement measures (Table VII) are broadly categorized as achievement areas, grade indices, and ability tests. The grade indices correlated the highest with motivation measures which may suggest greater accuracy on the part of the teacher in evaluating student progress and also the role of the teacher in the motivation of the student.

Regression-controlled Results To control the effects of the independent variables for one another, multiple regression analysis was employed (Daniel & Wood, 1971). To find a parsimonious, nonredundant model that explains the maximum amount of variance and that is not distorted by aberrant, outlying data points or studies (Walberg & Rasher, 1976), 12 regressions were run. The first includes 25 variables (all 27 variables in Table I except Achievement Motivation and Other Grade Index whose effects are represented in the constant term) and accounts for 40.7% of the variance in the correlations. The final equation includes only the eight consistently significant variables and accounts for nearly as much variance: 39.2%. The variables deleted make no contribution to the explained variance because they are either insufficiently related to the size of the correlations, are redundant with the significant variables, or both.

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Table VII: Univariate statistics of correlations by type of achievement measure

Achievement Tests General Reading Math Language Other

Mean correlation

Standard deviation

Number of correlations

.28 .35 .30 .28 .44

.17 .16 .09 .17 .03

40 30 69 6 4 149

Grade Index General GPA Math Language Social Studies Other

.45 .40 .51 .50 .60

.15 .07 .09 .04 .01

38 18 2 2 2 62

Ability Tests General Verbal Non Verbal

.16 .39 .25

.16 .08 .07

7 5 9 21

Several intermediate steps deleted variables at successively higher significance levels until only those at the .05 level remain. Another step showed that deleting the two outlying correlations, .98 and –.31 (Table II), had little effect on the explained variance or the regression weights. Several steps tested the possibility of whether deleted variables that were at borderline significance at earlier steps would gain significance if entered one at a time. Another step set the largest number of cases from the Coleman et al. (1966) research to an intermediate level of 300 to test the possibility that the apparent tendency of larger studies to produce smaller correlations is attributable only to the Coleman et al. (1966) research, which has far higher sample sizes than the other studies. This was found to be true; the rushed schedule of the research and substandard administrative testing conditions might have accounted for the anomalous trend, which is not confirmed in the data from the other 39 studies. The penultimate step (discussed further below) entered the product of grade level and sex to determine if the two variables interact in the determination of the size of the correlation of motivation and achievement measures. The final equation shows that the magnitude of the correlation between motivation and achievement is parsimoniously predicted by the following regression weights for the significant variables (all probabilities less than .05; t-values given in parentheses):

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.48 + .10 (Calibration Sample) + .01 (Grade Level) (5.30) (5.45) – .16 (Math Self-concept) – .06 (General Achievement) (7.63) (2.73) + .11 (Math Grade Index) – .15 (General IQ Test) (3.81) (3.39) – .09 (Nonverbal IQ Test) – .34 (Motivation Reliability). (2.37) (3.09) The first weight after the constant means that, controlled for the other significant variables, the correlation for the calibration sample is .10 higher on average than in the validation sample. Table VI shows that the two selection procedures resulted in samples that differ in the types of motivational variables investigated; the 66 correlations for Academic Self-concept in the calibration sample which average .12 higher than in the validation sample largely explain the difference. Grade level has a weight of .01, which means that a one-unit rise in level adds this much to the estimated correlation; for example, 12th-grade samples, other things being equal, yield correlations .10 higher on average than second-grade samples (see Table III for the trend – uncontrolled for the other variables). The closer linkage between motivation and achievement among older students may be attributable to their wider and longer experience in comparing their ability and performance to age peers. Grade level is the only student characteristic that is significant, and it is notable that there is no significant difference on average between the correlations for boys and girls (see also Table IV). Some previous research indicates a possible interaction between age and sex in the determination of the relation of psychological traits and educational performance (Walberg, 1969); the association between the two could become closer with age in samples of girls than in samples of boys. To test this possibility, the product of grade level and sex was added to the 11th equation; it was found to be nonsignificant but showed the same trend as the previous research. Thus, from the large number of correlations analyzed in this research, it must be concluded that neither sex nor its interaction with grade level appears to be a significant determinant of the size of the correlations, although both these possibilities warrant further research. The next five coefficients identify characteristics of the motivation and educational outcome measures that significantly affect the correlations between them. Two of the weights reveal differences between Crosswhite’s (Bloom, 1976) 56 correlations involving mathematics achievement and the 176 correlations from the other 39 studies: correlations involving Mathematics Self-concept are on average .16 lower, and those involving Mathematics Grade Index are .11 higher than the other correlations with the other independent variables controlled. These controlled estimates do not correspond to the uncontrolled comparisons in Tables VI and VII because they are adjusted for differences between the calibration and validation samples as well as the

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large number of mathematics correlations obtained from high school samples. The significant differences associated with mathematics should be viewed as provisional since they are based only on a single study. The correlations of ability and motivation were included for comparative purposes. The regression equation indicates that correlations of general ability and nonverbal ability are, respectively, .15 and .09 lower than for verbal ability and the achievement correlations. These findings provide support for Cattell’s (1963) theoretical dimension of fluid and crystallized aptitudes: crystallized aptitudes such as verbal-educational achievement are more susceptible to internal influences such as motivation as well as external influences such as home environment and instruction. The last weight shows that more reliable motivation measures are less closely associated with achievement than are less reliable measures.1,2 Humphreys (1970) showed the reason for such a seemingly paradoxical finding in a general discussion of reliability assessment and factor analysis of psychological traits. Most reliabilities reported for psychological and educational tests, including those for the present samples, are indexes of internal consistency or factor purity rather than stability across occasions or forms. Because internal consistency estimation only requires one administration, it is most frequently used; indeed, item selection from pilot forms, based on correlations of items with the total test scores (a common practice) leads to unifactorial instruments that have lower validity, that is, correlations with external criteria, because the test content is narrowed. The regression weight for the motivation reliability in the present data supports this interpretation in that an increase in the reliability across the range from the lowest to the highest value, .48 to .91, is associated with a .15 drop in the correlation between motivation and achievement.3

Conclusion The mean correlation between motivation and achievement from samples of studies in psychological and educational literature is .338 (with a standard error of .009), which indicates that motivation measures on average “account” for 11.4% of the variance in achievement. There is considerable dispersion around the mean (Table II), and regression yields a more precise estimate and points to the significant factors that determine the size of the correlations associated with particular samples and measures. Perhaps the most general and useful estimates are .48 – .34 (.77) + .01 (1) = .24 and .48 – .34 (.77) + .01 (12) = .35 for the estimated correlation from the validation sample containing more recent and representative literature, for motivation measures with the average reliability of .77, and for first- and 12th-grade samples. These estimates are applicable to both sexes, to General Self-concept, Academic Self-concept,

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Locus of Control and Field Dependence, and Achievement Motivation, and most of the specific subject achievement measures and indexes. The exceptions to the estimates are: the correlations from the pre-1974, less representative literature that are .10 higher on average, the lower correlations for General and Nonverbal ability measures, and the mixed pattern for mathematics correlations that may be unique to a single large study. Motivation measures appear to be associated with less variance in educational achievement on average than are other factors in learning which are replicable correlates; the squares of the two estimates above yield figures ranging from 5% in first grade to 12% in twelfth grade. Similarly derived and partly overlapping estimates suggest that ability is associated with about 60% of the achievement variance; quality of instruction, about 15%; amount of time spent in learning, 15%; sociopsychological characteristics of the classroom group, 60%; and educationally-relevant aspects of the child’s home environment, about 60% on average in elementary and secondary school samples (Walberg, Note 1). The relations among the replicable, productive factors in learning, however, appear to be multiplicatively interactive; as a consequence, any factor such as motivation that is at very low level for a particular student or group can be a potent deterrent to learning. Although the present analysis shows that motivation measures are relatively weak correlates of learning, it also suggests that multiple or multifactorial, and hence more valid, measures of motivation rather than more internally consistent or homogeneous single measures, are likely to improve predictions. Because of their replicated correlations with achievement and potential for psychometric improvement, motivation measures clearly deserve inclusion in general research on classroom learning along with the other factors to determine the causal directions and weights for the factors as well as to point to the most effective ways to make learning more productive.

Notes 1. Since achievement reliabilities are rarely reported, the reliability of the achievement measures was set at .85 for all correlations. The reliabilities of the motivation measures were taken directly from each study or were set to .77 (the mean of all the motivation reliabilities from the validation sample) in cases in which a motivation reliability was not reported. Using the appropriate reliabilities, all correlations were corrected for attenuation (Guilford, 1954, p. 400) to see how strongly the results reported in the text would be affected after adjusting for the error of measurement. The mean correlation changed from .338 for the observed to .421 when corrected for attenuation; however, in the final regression model, the variance did not fluctuate in the first four decimal places. The b weights and t values were also similar for all variables except motivation reliability which is discussed in the text. 2. Using the Fisher Z transformation formula (Glass & Stanley, 1970, p. 265), all correlations were also adjusted for the uneven skewness which results when there is other than a normal distribution in analyses not discussed in the text. Unless a correlation is zero, skeawness occurs increasingly with the higher positive or lower negative correlations. The Z transformation adjusts the raw correlation so significance tests can be applied.

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The mean observed correlation changed from .338 to .366 when transformed; however, in the final regression model, the variance accounted for did not fluctuate. The b weights and t values were also similar for all variables. Thus, the Z transformation does not change the essential results reported in the text. 3. Various methods were used to check whether one correlation abnormally affected the others, but the most rigorous method used to see whether one study affected the others was the Tukey jackknife (Mosteller & Tukey, 1977, p. 135). Each study was weighted inversely to the number of correlations it contributed to the total 232, and then 40 regressions were run eliminating each study one after the other. The results of the final jackknifed regression showed the following: .31 + .25 (Calibration Sample) + .02 (Grade level) (1.34) (3.09) – .15 (Math Self-concept) – .05 (General Achievement) (5.14) (1.11) + .11 (Math Grade Index) – .14 (General IQ Test) (6.11) (1.90) – .11 (Nonverbal IQ Test) – .00 (Motivation Reliability). (1.81) (.03) The t values for the sample and general achievement became nonsignificant; it could be concluded that there was no difference between the calibration sample and the validation sample. Motivation Reliability according to the Tukey method makes no difference; however, the grade-level effect was strengthened from .01 to .02. Finally, except where just mentioned, all results were similar in sign, magnitude, and significance as stated in the text. The authors thank Maurice J. Eash, Dean of the College of Education; Harriet Talmage, Director of the Office of Evaluation Research; Sue Pinzur Rasher; Patricia Wang; and Aurelia Jones at the University of Illinois at Chicago Circle for institutional support; and Diane Schiller, Barbara K. Iverson, and Donna Hetzel for their ideas and encouragement. The research presented in this article was supported by the National Institute of Education (Grant No. NIE-G78-0090) and the National Science Foundation (Grant No. NSF-78-17374); the points of view and opinions stated do not necessarily represent the official position or policy of either agency.

Reference Note 1. Walberg, H. J. A psychological theory of educational productivity. Invited paper read at the annual meeting of the American Psychological Association, Toronto, September 1978.

References Bloom, B. S. Human characteristics and school learning. New York: McGraw-Hill, 1976. Catell, R. B. (Ed.). Handbook of multivariate experimental psychology. Chicago, Ill.: Rand McNally, 1963. Cattell, R. B., & Child, D. Motivation and dynamic structure. New York: Wiley, 1975. Chang, T. S. Self-concepts, academic achievement, and teacher’s rating. Psychology in the Schools, 1976, 13, 111–113. Cobb, P . R., Chissom, B. S., & Davis, M. W. Relationships among perceptual-motor, selfconcept, and academic measures for children in kindergarten, grades one and two. Perceptual and Motor Skills, 1975, 41, 539–546.

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Cole, J. L. The relationship of selected personality variables to academic achievement of average aptitude third grade. The Journal of Educational Research, 1974, 67, 329–333. Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, J., Mood, A. M., Weinfeld, F. D., & York, R. L. Equality of educational opportunity. Washington, D.C.: U.S. Department of Health, Education and Welfare, 1966. Daniel, C, & Wood, F. S. Fitting equations to data. New York: Wiley-Interscience, 1971. Frymier, J. R., Henning, M. J., Henning, W., Norris, L., & West, S. C. A longitudinal study of academic motivation. The Journal of Educational Research, 1975, 69, 63–66. Glass, G. V Integrating findings: The meta-analysis of research. In L. S. Schulman (Ed.), Review of Research in Education (Vol. 5). Itasca, Ill.: Peacock, 1978. Glass, G. V, & Stanley, J. C. Statistical methods in education and psychology. Englewood Cliffs, N.J.: Prentice-Hall, 1970. Guilford, J. P. Psychometric methods. New York: McGraw-Hill, 1954. Handel, A. Attitudinal orientations and cognitive functioning among adolescents. Developmental Psychology, 1975, 11, 667–675. Humphreys, L. G. A skeptical look at the factor-pure test. In C. E. Lunneborg (Ed.), Current problems and techniques in multivariate psychology. Seattle: University of Washington Press, 1970. Jones, L. V., & Fiske, D. W. Models for testing the significance of combined results. Psychological Bulletin, 1953, 50, 375 –382. Kagan, S., & Zahn, G. L. Field dependence and the school achievement gap between AngloAmerican and Mexican-American children. Journal of Educational Psychology, 1975, 67(5), 643–650. Kennelly, K., & Kinley, S. Perceived contingency of teacher administered reinforcements and academic performance of boys. Psychology in the Schools, 1975, 12, 449– 453. Lewis, J., & Adank, R. Intercorrelations among measures of intelligence, achievement, selfesteem and anxiety in two groups of elementary school pupils exposed to two different models of instruction. Educational and Psychological Measurement, 1975, 35, 499–501. Mosteller, F., & Tukey, J. W. Data analysis and regression. Reading, Mass.: Addison-Wesley, 1977. Portes, A., & Wilson, K. L. Black-white differences in educational attainment. American Sociological Review, 1976, 41, 414 – 431. Prendergast, M. A., & Binder, D. M. Relationships of selected self-concept and academic achievement measures. Measurement and Evaluation in Guidance, 1975, 8, 92–95. Primavera, L. H., Simon, W. E., & Primavera, A. M. The relationship between self-esteem and academic achievement: An investigation of sex differences. Psychology in the Schools, 1974, 11, 213–216. Pugh, M. D. Statistical assumptions and social reality: A critical analysis of achievement models. Sociology of Education, 1976, 49, 34 – 40. Schultz, C. B., & Pomerantz, M. Some problems in the application of achievement motivation to education: The assessment of motive to succeed and probability of success. The Journal of Educational Psychology, 1974, 66, 599– 608. Simon, W. E., & Simon, M. C. Self-esteem, intelligence and standardized academic achievement. Psychology in the Schools, 1975, 12, 97–100. Steanner, A. J., & Katzenmeyer, W. G. Self-concept, ability and achievement in a sample of sixth grade students. The Journal of Educational Research, 1976, 69, 270–273. Thorndike, R. L. (Ed.). Educational measurement. Washington, D.C.: American Council on Education, 1971. Tukey, J. W. Exploratory data analysis. Reading, Mass.: Addison-Wesley, 1977. Walberg, H. J. Physics, femininity and creativity. Developmental Psychology, 1969, 1, 47–54. Walberg, H. J., & Rasher, S. P. Improving regression models. Journal of Educational Statistics, 1976, 1, 253–277.

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Walberg, H. J., & Uguroglu, M. E. Motivation and educational productivity; theories, results, and implications. In L. J. Fyans, Jr. (Ed.), Achievement motivation; recent trends in theory and research. N.Y: Plenum, 1979. Weintraub, S., Robinson, H. M., Smith, H. K., Plessas, G. P., & Rowls, M. Summary of investigations relating to reading, July 1973 to June 1974. Reading Research Quarterly, 1974 –1975, ( Vol. 10). Weintraub, S., Robinson, H. M, Smith, H. K., Plessas, G. P., Roser, N. L., & Rowls, M. Summary of investigations relating to reading, July 1974 to June 1975. Reading Research Quarterly, 1975 –1976, ( Vol. 11). Weintraub, S., Robinson, H. M, Smith, H. K., Plessas, G. P., & Rowls, M. Summary of investigations relating to reading, July 1975 to June 1976. Reading Research Quarterly, 1976 –1977, (Vol. 12). White, W. F., & McConnel, J. Affective responses and school achievement among eighth grade boys and girls. Perceptual and Motor Skills, 1974, 38, 1,295–1,301. White, W. F., & Simmons, M. First grade readiness predicted by teachers perception of students maturity and students perception of self. Perceptual and Motor Skills, 1974, 39, 395–399.

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63 Academic Motivation and Achievement among Urban Adolescents Joyce F. Long, Shinichi Monoi, Brian Harper, Dee Knoblauch and P. Karen Murphy

T

he time of adolescence can be fraught with peril, particularly during the transition from middle school to high school. Moreover, the transitional adjustments of urban minority adolescents can be even more troublesome (Seidman, Aber, Allen, & French, 1996) because of already stressful home and neighborhood environments (Gillock & Reyes, 1996; Reyes, Gillock, Kobus, & Sanchez, 2000; Seidman, Allen, Aber, Mitchell, & Feinman, 1994). Upon entering high school, students often encounter a larger, more heterogeneous student body, whole-class instruction, higher levels of competition (Bryk & Thum, 1989), and rigid academic ability tracking (Seidman & French, 1997). In addition, there can be a loss of social status for ninth-grade students who are now the youngest in the school (Eccles et al., 1993). These transitions also can be accompanied by increased stress levels, decreased self-esteem (Alvidrez & Weinstein, 1993), academic underachievement, and social maladjustment (Reed, McMillan, & McBee, 1995). Furthermore, the size and bureaucracy of urban public schools (Seidman et al., 1994) may further exacerbate these transitional characteristics. If urban students are to successfully maneuver through the increasing challenges and academic rigors of high school, their motivation to learn must be supported throughout the transition. Motivation can be defined as a “temporal sequence that is started, sustained, directed, and finally terminated,” which examines “why people think and behave as they do” (Graham & Weiner, 1996, p. 63). Although motivational factors “are at the heart of contemporary Source: Urban Education, 42(3) (2007): 196–221.

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concerns about the status of African Americans in general and their academic achievements in particular” (Graham, 1994, p. 55), researchers know very little about how motivational variables relate to achievement in classrooms where African American students predominate. Several explanations postulated by sociologists, however, do address the underperformance of African American students relative to their Caucasian counterparts, and their conclusions include factors unique to this ethnic group (Steinberg, Dornbusch, & Brown, 1992). The intergenerational legacy of slavery and discrimination, for example, may force African Americans to develop an oppositional identity, which rejects the values of the dominant culture (Fordham & Ogbu, 1988). When African American students reject the effortful pursuit of academic excellence as “acting White,” this practice results in failure and estrangement from opportunities for mainstream success (Ogbu, 1988). In addition, negative stereotypes about their group of origin can be threatening to African American students and diminish their motivational beliefs (Aronson, Quinn, & Spencer, 1998). For example, images in the electronic and print media can stereotypically represent African Americans as being deficient in verbal and intellectual abilities compared with other ethnic groups. If students’ awareness of this stereotype is coupled with a deliberate affiliation toward this disparaged group, a stereotype threat can be initiated, which produces a specific psychological anxiety that inhibits the efficacy and cognitive performance of African American students (Aronson & Good, 2002; Steele & Aronson, 1995). This was clearly demonstrated in a series of experiments that examined negatively stereotyped intellectual abilities of African American students at Stanford University (Steele, Spencer, & Aronson, 2002). White and Black students were invited, one at a time, to enter a laboratory where they were administered a brief section of the Graduate Record Examination. Students in the treatment condition were informed that the test was a measure of intellectual prowess, whereas those in the control section were merely instructed to complete the examination to the best of their ability. The researchers hypothesized that for Black students in the treatment condition, the risk of confirming negative stereotypes about intellectual ability relative to other racial groups would heighten anxiety and impede performance. This was indeed the case: Black and White students in the control condition performed similarly on the examination, whereas White students outperformed Black students by a full standard deviation among those for whom the stereotype was made salient. As such, any model attempting to account for the academic achievement of African American students must attend to multiple influences and factors (Graham, 1994). Thus, this research project is directed toward understanding how three sources of motivation may relate to the academic achievement of predominantly African American urban students during their transition to high school. More specifically, the motivational variables selected for their

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association with achievement include interest (Schiefele, Krapp, & Winteler, 1992), self-efficacy (Bandura, 1997), and achievement goal orientation (Ames, 1992).

Interest William James (1958) remarked that a century ago, no other topic had received more pedagogical attention than interest. Conceptualized by Dewey (1899) as the formation of a relationship between a person and an object, some describe interest as being deep-seated and originating in the individual (e.g., Renninger, 2000; Schiefele, 1991). Others term interest to be a temporary response relative to the attractiveness of a situation or object (e.g., Hidi & Baird, 1988). Regardless of the source of origination, however, interest is described as energizing the underlying needs or desires of the learner (Alexander, Murphy, Woods, Duhon, & Parker, 1997) in a way that can positively influence the cognitive (Schiefele, 1996), affective (Sansone & Smith, 2000), and volitional (Dewey, 1899) components of individual learners. Interests are further categorized according to the degree of content specificity being considered. More precisely, topic interests focus on a single area, and domain interests relate to “a range of activities, text passages dealing with the field, or body of knowledge in general” (Tobias, 1994, p. 47). When interest is conceptualized as a domain-specific motivational variable, educators use this information to investigate why students are motivated to learn specific subject matter over others when all the activities appear to have the same value and provide similar challenges (Alexander & Murphy, 1998). However, interest’s potential for energizing learning appears to be limited when students possess lower levels of knowledge (Alexander, Kulikowich, & Schulze, 1994). Thus apparently positive findings that poor Black elementary students have higher science interest levels than their affluent White neighbors may not be cause for rejoicing because the White children possessed more knowledge of science (Wenner, 2003). This interest and knowledge partnership is also exemplified by decades of research into the juncture of interest and achievement (e.g., grades). One meta-analysis of these studies (Schiefele et al., 1992) revealed correlations ranging from .17 in literature to .35 in science among students from all grade levels, but the authors were unable to test for developmental differences because the distribution of studies across grade levels was unbalanced. More recently, empirical studies into this connection have continued in Europe, Australia, Africa, Canada, and the United States, indicating that academic interest is a cross-cultural phenomenon. However, the overwhelming majority of the work is done among Caucasian students, so we know considerably less about the academic interests of urban Black students and how they specifically relate to achievement.

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In addition, distinct gender differences appear to exist. Women, for example, can display a greater interest in music (Marjoribanks & Mboya, 2004), human biology, and social/moral issues, whereas men may exhibit preferences for scientific research and environmental preservation (Gardner & Tamir, 1989). However, the same meta-analysis cited earlier (Schiefele et al., 1992) indicated that the academic performance of female students was “less associated with their interests” than their male counterparts (p. 202). Of these studies, few have noted gender differences being related to academic transitions.

Self-Efficacy Beliefs Social cognitive theory (Bandura, 1977, 1997) suggests that self-efficacy beliefs powerfully influence the choices people make, the amount of effort they expend, and their level of persistence. Defining self-efficacy as “people’s judgments of their capabilities to organize and execute courses of action required to attain designated types of performances” (Bandura, 1986, p. 391), individuals with high self-efficacy beliefs view difficult tasks as challenges, remain committed to their goals, and increase their efforts when faced with failure. As such, their perseverance typically results in performance accomplishments. In contrast, individuals who have low self-efficacy beliefs do not embrace difficult tasks because they are seen as personal threats. When confronted with difficult tasks, individuals with low self-efficacy focus on their weaknesses, obstacles, or negative outcomes and easily give up. Because failure profoundly affects efficacy beliefs (Bandura, 1993), efficacy beliefs are correlated with academic choices, changes, and achievement. Consequently, efficacy beliefs can powerfully determine and predict the level of success that individuals will attain (Pajares, 1996). Schunk (1989) reported on the predictive utility of self-efficacy beliefs in regard to academic performance, noting that significant and positive correlations (rs = .33 to .42) were found between self-efficacy beliefs and the number of arithmetic problems that students completed during a lesson. Such correlations (rs = .27 to .84) were also found between self-efficacy and the proportion of problems solved correctly. Similarly, researchers discovered a strong correlation between self-efficacy beliefs and skill in reading and writing tasks among college students (Shell, Murphy, & Bruning, 1989). Gender differences in student academic self-efficacy beliefs have been reported, particularly in the domains of mathematics and writing. Pajares and Miller (1994) indicated that male undergraduates in their study expressed higher mathematical self-efficacy than did female undergraduates, whose poorer performance in math problems was “largely due to lower judgments of their capability” (p. 200). During elementary years, girls and boys exhibited no differences in their mathematics self-efficacy, but by middle school, boys displayed higher efficacy than did girls ( Wigfield, Eccles, & Pintrich, 1996). Furthermore, fifth-grade girls reported having higher writing self-efficacy

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than did their male counterparts, but neither girls nor boys differed in writing performance (Pajares & Valiante, 1997). By ninth grade, girls and boys still possessed similar writing performance levels, but boys expressed higher selfefficacy for writing (Pajares & Johnson, 1996). These gender results appear to indicate that efficacious beliefs can change over time. As such, Eccles et al. (1993) found that declines in academic performance after a transition to middle school were a reliable predictor of lower selfconcept, intrinsic motivation, and confidence in intellectual abilities. These researchers proposed that such declines resulted from a developmental mismatch between the early adolescents and their classroom environment, resulting in negative motivational outcomes especially for struggling students. In one study of poor African American elementary students, GPA significantly declined during the transition to middle school, but students “who felt more academically efficacious in sixth grade” had higher grades than did their peers (Gutman & Midgley, 2000, p. 237). A gap, however, exists in the literature regarding the efficacy beliefs of minority youth following their transition to high school.

Goal Orientations A goal orientation framework incorporates learning contexts, personal academic goal orientations, learning behaviors, and academic achievement (Anderman & Maehr, 1994; Eccles & Midgley, 1989). Specifically, research has found that students’ contextual goal structures were determinants of their achievement goal orientations, which in turn influenced their learning behaviors and academic achievement. Therefore, achievement goal orientations seem to be a predictive factor for adolescents’ academic performance across changes in learning environments, such as during the school transitions (Eccles & Midgley, 1989). Achievement goal orientations have reflected students’ reasons for engaging in academic tasks (Ames, 1992; Dweck & Leggett, 1988). Within the goal orientation literature, at least three conceptually distinct types of achievement goal orientations have been identified (Pintrich & Schunk, 2002): learning, performance, and work-avoidant. Learning goal orientations pertain to an individual’s willingness to master the skills necessary for academic tasks or to increase knowledge and understanding with effort (Pintrich, 2000). When students with a learning goal orientation “encounter difficulties, they are likely to seek help or if necessary to persist with their own self-regulated learning efforts, buoyed by the belief that these efforts are worthwhile and the confidence that they will pay off eventually ” (Brophy, 2004, p. 90). Performance goal orientations, on the other hand, represent social comparisons, such as a desire to gain favorable judgments from others while avoiding negative judgments of one’s competence (Dweck, 1986). When comparing the two goal orientations, learning goal orientations were

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considered to link to “a motivational pattern … likely to maintain achievement behavior,” whereas performance goal orientations tended to develop “a failureavoiding pattern of motivation” (Ames, 1992, p. 262). Generally, however, their contribution to achievement has yielded inconsistent research results (Brophy, 2004; Pintrich & Schunk, 2002). More recently, Elliot and Harackiewicz (1996) further distinguished performance goals by separating them into two types: performance-approach and performance-avoidance goals. They determined that performanceapproach goal orientations represented a desire to seek favorable judgments of competence, which is positively associated with academic achievement. In contrast, performance-avoidant goals represented a desire to avoid unfavorable judgments of competence and were negatively correlated with academic achievement. The third category of goal orientations, work-avoidant, focused on a student’s desire to finish assigned works with a minimum amount of effort (Meece, Blumenfeld, & Hoyle, 1988); this orientation is consistently reported as being detrimental to achievement behaviors. Researchers also have suggested that achievement goal orientations could change during school transitions. In a cross-sectional study across grade levels with predominantly White, middle-class adolescents, students were more oriented toward performance goals and less oriented toward learning goals in middle school than in elementary school (Midgley, Anderman, & Hicks, 1995). However, little is known about how the complete range of students’ achievement goal orientations and achievement behaviors may change during the transition to high school (Anderman, Austin, & Johnson, 2001; Newman, Myers, Newman, Lohan, & Smith, 2000), and current research has not frequently examined the predictive value of these goal orientations in urban African American students. Several studies, however, have examined gender differences in achievement goal orientations with mixed results. Some research (e.g., Meece & Miller, 2001; Middleton & Midgley, 1997) reported that adolescent gender differences existed only in work-avoidant goals (i.e., boys endorsing them more strongly than did girls). Contrary to these findings, other studies have noted significant gender differences in academic achievement goal orientations, suggesting that male adolescents were more oriented to performance goal orientations and less oriented to learning goal orientation than were female adolescents (e.g., Anderman & Midgley, 1997; Pajares, Britner, & Valiante, 2000). In sum, these inconsistent results indicate that additional research is necessary.

Integrative Impact How do these three motivational variables (i.e., interest, self-efficacy, and goal orientation) collectively interact and affect achievement? With regard to self-efficacy and goal orientation, judgments of competence or self-efficacy in middle school students “figure into motivation differentially depending on what

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goal (orientation) dominates” (Anderman & Maehr, 1994, p. 298). Studies examining the juncture of goal orientation and interest in middle school and college students found that positive relationships existed when interest (task or subject) was paired with learning or mastery goals (Gehlbach, 2006; Van Yperen, 2003) and the relationship was especially strong for adolescent girls. When goals were joined with achievement, however, the results appeared to developmentally differ: in middle school students, “increases in mastery goal orientation related to higher levels of content knowledge and better grades” (Gehlbach, 2006, p. 366), whereas college students consistently linked performance goals with grades (Harackiewicz, Durik, & Barron, 2005). Lent, Brown, and Hackett (1994) formulated a theory of career interest development that featured self-efficacy, interest, and goals. They envisioned the linear process as originating with self-efficacy, progressing to interests, and then affecting goals. An additional direct link from self-efficacy to goals represented their belief that self-efficacy has both a direct and an indirect effect on goals. After testing this theory among engineering students in contrasting Black and White university samples, Lent et al. (2005) found that Black students reported stronger self-efficacy, technical interests (e.g., reading books about engineering issues), and educational goals (e.g., becoming an engineering major). However, further research on the model indicated that only mathematics efficacy and interest were found to affect grade performance (Lent, Lopez, & Bieschke, 1993). Some conclude that goals precede interest (Krapp, 1999), and others identify goals as an outcome of interest (Lent et al., 1994). Consequently, these variations have led to conclusions that the relationship may be reciprocal rather than unidirectional (Hidi & Harackiewicz, 2000). In addition, the relationship between goals and interest may be moderated by socioeconomic status. One study among high school students in South Africa found a connection between mastery goals and interest only in middle class participants, whereas interest and performance goals were more strongly aligned in their lower class counterparts (Marjoribanks & Mboya, 2004). As such, these limited findings provide us with an inadequate road map for distinguishing how goal orientation, interest, and self-efficacy might affect the achievement of urban eighth- and ninth-grade students. Although we readily acknowledge that a number of variables are likely mediating cognitive, affective, or motivational processes involved in learning, we constructed a motivational model that conceptualized achievement goal orientations, gender, and self-efficacy as contributing to domain interests, which in turn affected academic achievement. Specifically, our research questions included the following inquiries: (a) To what extent do gender, achievement goal orientations, and self-efficacy predict domain interests in urban adolescents who are predominately African American? and (b) Within the same urban setting, to what extent do gender, achievement goal orientations, self-efficacy, and domain interests predict achievement?

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Methods Participants One site of this research project was a high school in a large urban district in the midwestern United States. Because students in the school have been performing below state proficiency levels in all subjects and grades (“Phi Delta,” 2002), the entire system was categorized as being in a state of “academic emergency.” This resulted in the curriculum of required courses (i.e., mathematics, English, social studies, and science) becoming more explicitly aligned with state proficiency exams, and teachers were expected to strictly adhere to the content of curriculum documents provided by central administration. The sample of eighth-grade students (n = 255) was drawn from three middle schools, which were feeder institutions for the single high school from which the ninth-grade sample (n = 159) was selected. The eighth-grade students consisted of 123 boys (48%) and 132 girls (52%). The ethnic breakdown was 87% African American or African American mix, 10% Caucasian, and 3% other (i.e., Hispanic, Native American, and Asian). Approximately 61% of the students received free or reduced lunches. For the ninth-grade sample, there were 83 boys (53%) and 75 girls (47%). Ethnically, students were 72% African American or African American mix, 22% Caucasian, and 6% Other. Almost 56% of the ninth-grade students received free or reduced lunches.

Measures Interest and self-efficacy. Students self-reported interest and efficacious belief levels in six subject domains: history, mathematics, science, reading, computer science, and art. The interest portion of the measure was composed of two items in which students identified their levels of interest and importance for each domain. Although not equivalent, researchers do consider value to be one valence of interest (Renninger, 2000). The self-efficacy portion consisted of three questions for each domain (e.g., ability to perform well in a math course, think through a math problem, and solve a math problem). Students selected from a 10-point modified Likert-type response scale ranging from 0 (strongly disagree) to 9 (strongly agree) and recorded their responses on a Scantron sheet. After the grade reports were gathered, we chose to compile composite interest and self-efficacy scores only for the four core domains (mathematics, science, reading, and history) required by the entire student population. The interest/self-efficacy scale had a Cronbach’s alpha of .92. Goal orientations. Achievement goal orientations were measured using 18 items related to learning, performance-approach, and performance-avoidance goals adapted from the Patterns of Adaptive Learning Survey (PALS; Midgley

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et al., 1998). An additional fourth achievement goal orientation (i.e., workavoidant) was assessed using a six-item scale adapted from previous work by Meece and colleagues (Meece et al., 1988). Specifically, some sample items are as follows: “I want to do as little school work as possible; I would feel successful in school if I did better than most of the other students.” The original scales for the four goals were assessed using 5-point Likert-type scales. In this study, however, a 10-point Likert-type scale was employed to make scales of measurement consistent among all variables. On the basis of factor analysis with varimax rotation, a three-factor structure was identified for both the eighth and ninth goal items. An examination of items loaded on the factors revealed that in eighth and ninth grades, the goal items were divided into three primary goals: learning, performance, and work-avoidant goals (Cronbach’s alpha reliability for these factors ranged from .77 to .86). For the subsequent analysis of this study, composite mean scores were calculated for each of these three achievement goal orientations. Additional data about gender and ethnicity were gathered using a demographic measure that was part of the test package. Academic records, including final grade reports and participation in school lunch programs, were collected from the schools at the end of each school year (both eighth and ninth grades). Grades for core subjects (reading/literature, history/social study, math, and science) were used as indices of academic achievement. They were coded using a 4-point scale and then averaged to calculate a composite GPA.

Procedures Members of a trained research team administered a test packet that was part of a longitudinal study on resilience. The eighth-grade participants were tested in May during regular class periods. In the following school year, ninth-grade students were tested either in December or February, depending on which semester they participated in a required core class – Exploration of Literature and Composition. In that way, every freshman student had the same opportunity to participate in the study and to become part of a larger longitudinal research project designed to focus on the transition from middle to high school.

Results and Discussion Descriptive Statistics and Correlational Analyses Tables 1 and 2 present the means, standard deviations, and correlations for eighth- and ninth-grade students. Mean student scores in both grades for the five motivational variables (three goal orientations, self-efficacy, and interest)

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Table 1: Bivariate correlations, means, and standard deviations for gender, motivational variables, domain interests, and academic achievements for the eighth graders (n = 255) 1

2

1. Gender — 2. Learning goal orientation .179** 3. Performance goal orientation 4. Work-avoidance goal orientation 5. Self-efficacy 6. Domain interests 7. GPA

–.116

3

4

M

SD

6

7

— 5.727

— 1.856

4.663

1.824

—

4.348

2.344

.588** .166** –.088 — .633** .153* –.121 .872** — .239** –.061 –.169** .204** .166**

5.774 5.875 2.130

1.950 1.951 0.823

— .137*

—

–.168** –.313** .407** –.024 .043 .192**

5

—

*p ≤ .05. **p ≤ .01.

Table 2: Bivariate correlations, means, and standard deviations for gender, motivational variables, domain interests, and academic achievements for the ninth graders (n = 159) 1 1. Gender 2. Learning goal orientation 3. Performance goal orientation 4. Work-avoidance goal orientation 5. Self-efficacy 6. Domain interests 7. GPA *p ≤ .05.

— –.072 –.302**

2

— .283**

–.243** –.112 –.101 –.069 .060

3

4

5

6

7

— .538**

—

.618** .277** .025 — .670** .309* –.016 .889** .154 –.095 –.217** .135

— .026

—

Μ

SD

— 5.627 4.663

— 1.924 1.883

4.340

1.916

5.921 5.896 1.472

1.814 1.884 1.091

**p ≤ .01.

were moderate in both grades, ranging in eighth grade from 4.348 for work avoidance to 5.875 for academic domain interests and in ninth grade from 4.340 for work avoidance to 5.921 for self-efficacy. Because no significant differences were detected between the eighth- and ninth-grade levels of any motivational variable, and we found no significant differences in interest or efficacy scores across the four core domains, we collapsed domain ratings into one composite score for interest as well as for self-efficacy. However, the mean GPA (2.130 for eighth grade) significantly decreased in ninth grade (1.427), as demonstrated by the independent t test, t(412) = 6.968, p < .001. This drastic drop in achievement is reminiscent of already-cited research among poor Black students where GPA significantly declined after the transition to middle school (Gutman & Midgley, 2000) and also corresponds to the literature’s contention that academic under-achievement can accompany the transition to high school (Reed et al., 1995). To assess the overall relationships among the variables in the study, we examined the zero-order intercorrelations for eighth- and ninth-grade students. All of the correlation values are interpreted as the Pearson correlation coefficients, although the correlations between gender, a dichotomous variable,

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and the remaining continuous variables were computed by the point biserial correlation formula. By assigning two different numerical values to each category (1 for male, 2 for female) of the dichotomous variable, the values of the point biserial correlation coefficients are numerically equivalent to those that are obtained by the Pearson correlation formula (Gravetter & Wallnau, 2000). Results (Tables 1 and 2) suggested that motivational patterns both correspond to and differ from previously published studies. Domain interests of these adolescents in both eighth and ninth grades significantly and moderately related to their learning goals (r = .633, .618), strongly correlated with self-efficacy (r = .872, 889), but were less intensely connected to performance goals (r = .153, .309). In addition, performance goals associated with learning goals at a low level (r = .137, .283) but were more powerfully connected with work-avoidant goals (r = .407, .538). Moreover, achievement correlated with domain interest (r = .166), self-efficacy (r = .204), and learning goals (r = .239), but only in eighth grade. The relationship between work-avoidant goals and achievement, however, existed in both grades (r = –.169, –.217). These findings contrast with studies in college students where performance goals correlated only to grades (Harackiewicz et al., 2005), but support other empirical work with middle school students that associated learning goals and interest (Gehlbach, 2006). Furthermore, the connection between performance goals and interest is corroborated by the South African high school study among lower class students. Nonetheless, the high correlation between interest and self-efficacy indicates that when these predominantly Black urban students believe they are competent in mastering materials within a domain, they also are likely to be interested in that domain. Moreover, the eighth-grade data does parallel research findings that support the ongoing relationship between self-efficacy and achievement (Pajares, 1996) as well as between domain interest and achievement (Schiefele et al., 1992). Gender differences did exist in eighth-grade learning and work-avoidance goal orientations (r = .179, –.168, respectively) and academic achievement (r = .192). Using gender as an independent variable and motivational variables as dependent variables, a multivariate analysis of variance (MANOVA) showed a significant main effect, Wilks’s = .897, F(6, 248) = 4.764, p < .001. Female eighth-grade students tended to hold stronger learning or masteryoriented goals and obtained higher GPA scores, whereas boys expressed stronger preferences for work-avoidant goals. Gender differences continued to be present in ninth-grade boys’ work-avoidant (r = –.243) and performance (r = –.302) goal orientations (i.e., significant main effect, Wilks’s l = .892), F(6, 152) = 3.055, p < .01. These results conflict with prior research reports noting insignificant gender differences in learning goals among middle school students (Middleton & Midgley, 1997) and yet support the tendency of male adolescents to endorse work-avoidant goals more strongly than their female counterparts (Meece & Miller, 2001).

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Contributors to Domain Interest and Achievement To answer the first research question, which examined the contribution of gender, self-efficacy, and goal orientation to domain interest, a regression analysis was employed. The five predictor variables (i.e., gender, learning, performance, work-avoidant goal orientations, and self-efficacy) were entered into the equation simultaneously. This approach allowed us to identify the unique contribution of each predictor to the designated outcome variable (Cohen, Cohen, West, & Aiken, 2003). Those results appear in Table 3 for eighth- and ninth-grade students. The predictors of the dependent variable, domain interests, accounted for 78% of the overall variance among eighthgrade students, and of those predictors, the learning goal orientation (β = .18) and academic self-efficacy (β = .77) were significant. Similarly, 81% of the variance of ninth-grader’s domain interests was explained by the same variables: learning goal orientation (β = .17) and academic self-efficacy (β = .77). The second research question, which focused on the predictive power of gender, goal orientations, self-efficacy, and domain interest on academic achievement, was addressed with a hierarchical regression analysis. An advantage of hierarchical regression analyses over simultaneous regression analysis is that this approach allowed us to examine the unique contribution of predictors to an outcome variable after controlling the overlaps among the five predictors established in the first regression equation (Cohen et al., 2003). These results are presented in Table 4. In step 1, the five predictors were entered in the equation: gender, learning, performance, work-avoidant goal orientations, and self-efficacy. Of these five predictors, gender (β = .16) significantly contributed to academic achievement, indicating that eighth-grade female students tended to have higher GPA than did their male counterparts. In step 2, domain interests were entered into the equation to control the contribution of gender on academic achievement. Gender remained a significant predictor (β = .16),

Table 3: Regression of domain interests on gender; learning, performance, and work-avoidance goal orientations; and academic self-efficacy Domain interests 8th Graders Gender Learning goal orientation Performance goal orientation Work-avoidance goal orientation Self-efficacy Total

β = .03 β = .18** β = .01 β = .01 β = .77** R2 = .78** (Adj. R2 = .78)

9th Graders β = .03 β = .17** β = .09 β = –.06 β = .77** R2 = .82** (Adj. R2 = .81)

**p ≤ .01.

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Table 4: Regression of academic achievement on gender; learning, performance, and work-avoidance goal orientations; academic self-efficacy; and domain interests Academic Achievement 8th Graders Step1 Gender Learning goal orientation Performance goal orientation Work-avoidance goal orientation Self-efficacy Total Step 2 Gender Learning goal orientation Performance goal orientation Work-avoidance goal orientation Self-efficacy Domain interests Total

β = .16* β = .11 β = –.05 β = –.08 β = .15 R2 = .10** (Adj. R2 = .08)

β = .02 β = .09 β = –.04 β = –.18 β = .10 R2 = .07* (Adj. R2 = .04)

β = .16* β = .14 β = –.05 β = –.07 β = .28* β = –.17 R2 = .11** (Adj. R2 = .09)

β = .04 β = .19 β = .01 β = –.22* β = .56** β = –.60** R2 = .14** (Adj. R2 = .10)

ΔR2 = .01 *p ≤ .05.

9th Graders

ΔR2 = .07**

**p ≤ .01.

academic self-efficacy became a significant predictor (β = .28), and domain interests did not significantly contribute to academic achievement. More specifically, eighth-grade students with higher academic self-efficacy beliefs were likely to receive higher GPA than those who were less efficacious. Overall, this model explained 9% of the variance of academic achievement for eighth-grade students. Not surprisingly, the ninth-grade data portrayed a different picture. In step 1, none of the five predictors were significant. Step 2 of the hierarchical regression analysis showed that the overall ninth-grade model accounted for 10% of the variance of academic achievement, and of this 10% variance, 4% was explained by the work-avoidant goal orientation (β = –.22) and academic self-efficacy (β = .56), whereas domain interests accounted for the remaining 6%. A notable finding, as shown in Table 4, is that both domain interests and work-avoidant goals negatively contributed to academic achievement (β = –.60). These results were likely skewed by the dramatic decrease in ninth-grade GPA because domain interest levels remained comparable in both grades (5.88 and 5.90, respectively). Nonetheless, it appears that ninth-grade students were more likely to endorse work-avoidant goals, resulting in lower GPA scores, but those who believed they were academically efficacious had higher GPA (Gutman & Midgley, 2000). To summarize, the data revealed the following findings about this primarily poor, urban, African American, adolescent sample. First, students

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expressed moderate levels of all three motivational variables (i.e., self-efficacy, domain interest, and personal goal orientations) in both grades, but grades were significantly lower in high school. Second, levels of efficacy and learning goals strongly predicted domain interest in both grades. Third, self-efficacy consistently contributed to achievement at either grade level. Fourth, although interest’s contribution to achievement could have been masked by selfefficacy and goal orientation in middle school, interest emerged as a significant (albeit negative) contributor to achievement in high school. Fifth, the negative effect of work-avoidant goals on achievement became prominent in high school. Sixth, gender’s affect on motivation and achievement varied between grades. These results confirm both Krapp (1999) and Lent et al.’s (1994) assumptions regarding the significant effect of goals and self-efficacy on interest. However, they do not support related findings by Lent and associates (1993) that both self-efficacy and interest positively affect grades. In addition, the overall contribution of motivational variables and gender represented only a small percentage of variance in achievement, confirming that achievement is a complex phenomenon composed of many factors that were not included in our model. Nonetheless, if motivation starts, sustains, and directs a sequence (Graham & Weiner, 1996), then its contribution to the learning process can be neither undervalued nor overestimated.

Conclusions and Implications Several provocative conclusions and implications emerge from these findings. First, if learning goals and self-efficacy significantly contribute to students’ domain interests, then interest’s power (Alexander et al., 1997) depends on positive beliefs about ability (Bandura, 1986), a willingness to master skills necessary for academic tasks, and an effortful investment into increasing levels of knowledge and understanding (Pintrich, 2000). As such, we could conclude that when student learning goals and self-efficacy are encouraged to grow, domain interests will likewise increase and empower achievement across a variety of subjects and domains. Unfortunately, however, domain interest’s consistent relationship with achievement across many cultures was lower in these eighth-grade students than the averages previously reported (Schiefele et al., 1992) and was insignificant in ninth grade. Furthermore, interest did not empower achievement at either grade level. Why did this occur? Although the literature has noted that declines in achievement can differentially affect motivation after developmental milestones (e.g., transition from elementary to middle school), self-efficacy’s effect increased (from low to moderate) but interest’s effect was not positive. Simply acknowledging that most interest and efficacy research has been conducted among Caucasian students, however, does not

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warrant our concluding that these disparate outcomes represent a cultural anomaly. Instead, we believe it may be more appropriate to suggest that their interest was simply more responsive to contextual factors. Until we know how interest develops, this suggestion remains provocatively unresolved. We do acknowledge, however, that students within a system characterized as being in an academic emergency may not have opportunities for their existing domain interests to be utilized during instruction. The vocational interests of 71 students from both eighth and ninth grades who participated in the entire 2-year study remained surprisingly consistent and stable longitudinally, yet few students believed their teachers could identify their individual or vocational interests (Long, 2003). Thus a student’s desire to pursue pediatric nursing, for example, would not necessarily empower domain interest or learning in science and math. Furthermore, if domain interest is unaccompanied by correspondingly high levels of knowledge (Alexander et al., 1994), then even students who are able to identify their levels of domain interest as being moderately strong are likely unable to capitalize on interest’s potential power to support their learning. Essentially, interest’s energizing ability is fueled by knowledge acquisition, which was low in eighth grade (M = 2.130) and plummeted in ninth grade (M = 1.427). Because new knowledge must be constructed from existing knowledge (Bransford, Brown, & Cocking, 1999), even students who possess the will, desire, and value for learning require substance with which to build cognitive schema and understanding. Thus, these levels of GPA indicate that students’ construction of knowledge is being severely hampered by other factors not examined in this empirical project. Second, because self-efficacy levels consistently contributed to domain interest as well as achievement at each grade level, we need to reexamine this outcome in light of previous findings in the literature. Typically, selfbeliefs among Caucasian students decline following a developmental school transition (Eccles et al., 1993), but this did not occur in our urban, minority sample. Conceptually regarded as being sensitive to experiences of failure, their levels of efficacious beliefs were moderately strong and surprisingly robust, but their skill and effort did not match the outcome (Bandura, 1986). This can occur in settings where social bias and inferior resources impair academic achievement and “self-efficacy may exceed actual performance,” indicating that rather than not knowing what to do, students “are unable to do what they know” (Pajares, 1996, p. 568). Such a scenario is reminiscent of the sociological premises referenced in the introduction of this article (i.e., oppositional identity, stereotype threat). As such, African American students who adopt oppositional identities to combat the negative impact of actual and perceived discrimination within their school setting may feel confident in their ability to successfully execute a given academic task or be interested in an academic domain and still simultaneously express a deliberate disdain for academic behaviors associated

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with successful outcomes. Steinberg (1991) examined beliefs about the rewards of success among African American high school students and concluded that it was extremely difficult for Black high school students to join a peer group that encouraged academic excellence. Thus the paramount importance of the peer group for African American students relative to other ethnic groups has led researchers to conclude that Black high school students who desire to excel academically may be faced with isolation or be cut off from the social networks that exist among their high school peers (Witherspoon, Speight, & Thomas, 1997). Third, neither learning goals (a contributor to domain interest) nor performance goals significantly factored into achievement at any grade level. Again, this contrasts with research in predominantly non-Black samples at comparable ninth-grade developmental levels (e.g., Gehlbach, 2006). However, these students’ work-avoidant goals did emerge as negatively influential on ninth-grade GPA. When faced with the challenges associated with high school (e.g., larger, more heterogeneous student body; rigid academic ability tracking), achievement was actually affected by students’ apparent desire to finish assigned works with a minimum amount of effort (Meece et al., 1988) rather than seeking help or persisting (Brophy, 2004). Even more important, although the literature and our eighth-grade findings support the notion that boys tended to hold work-avoidant goal orientations (Meece & Miller, 2001), these gender differences disappeared in high school. Within the present research design, it is impossible to determine if the more prominent adoption of work-avoidant goals actually encouraged the decline in GPA or resulted from the decline. We can only state that avoidant goals emerged as a significant factor among both ninth-grade boys and girls, sadly contrasting with the eighth-grade girls’ strength in higher grades and stronger learning goals. Although this finding could indicate the emergence of a cross-gender developmental trend, the literature neither corroborates nor negates this conclusion. Thus, this pattern may be unique to African American students, especially if their environment inadvertently encourages early acquisition of avoidance goals for strategic purposes. This possibility became evident to us when the first author was a reading tutor in one of the urban middle schools featured in this study. After a new female tutee was able to rather easily read the designated passage, the student was asked why she needed extra help. She responded by stating that their currently assigned novel was “very boring.” Therefore, she and a large group of her girlfriends complained and requested a replacement. When their pleas were denied because the book was mandated by the system’s language arts curriculum, they formulated a plan to finish the dull book more quickly. Their scheme consisted of pretending they could decipher only one-syllable words when it was their turn to read aloud. Their halting responses so frustrated the teacher that she took over reading the book, which was covered much more rapidly. As they

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moved to another text, the girls concluded that their strategy had been very successful. Unfortunately, their skills in constructing and implementing the work-avoidant goal later resulted in the group receiving lower reading grades and remediation (indicative of the negative relationship between avoidance and GPA). On one level, these middle school students’ proficiency in formulating strategies that utilized work-avoidant goals could appear to be the result of a developmental mismatch with their required text. However, there may be cultural reasons why African Americans may be particularly vulnerable to this goal orientation. More specifically, students of color attend more readily to curriculum presented in a humanized narrative form (Banks, 1988). In addition, Bennett (1990) reported that African American students tend to evidence a learning style that stresses a visual/global rather than a verbal/analytical approach as well as a preference for reasoning by inference rather than formal logic. Furthermore, Hale (2003) argued that culturally appropriate pedagogy must consider three interacting spheres of influence: classroom instruction, cultural enrichment, and instructional accountability. Boykin (1983) also found that Black students evidenced a preference for energetic involvement in several activities simultaneously rather than routine, step-by-step learning. Although these findings are well-known among ethnicity researchers, they are less apparent to educators and are rarely afforded pedagogical consideration, particularly in urban high schools where whole-class instruction and higher levels of competition (Bryk & Thum, 1989) sharply conflict with cultural differences in learning styles and communication preferences. In discussing how African American students may best reconcile the absence of these culturally relevant teaching practices, Ogbu (2003) recommends “accommodation without assimilation,” or the adoption of attitudes and behaviors that lend themselves to academic success in school setting while still embracing cultural norms that are acceptable in less formal settings. This alternative is preferred to other assimilationist behaviors that can significantly correlate with psychological distress (i.e., emulation of Whites, disguising true academic attitudes and behaviors, and the deliberate isolation from other African Americans). To reverse the apparent tendency for African American students to assume work-avoidant goal orientations, perhaps high school students need opportunities to value both the culture of the school and their African American community, stressing the value of one without undermining loyalty to the other. In sum, our findings indicate that students’ motivational beliefs and dispositions, similar to self-efficacy, domain interest, and achievement goal orientations, develop “partly as a consequence of the educational environments they experience” (National Research Council, 2004, p. 33). Thus, factors that hinder the relationship between motivation and achievement can consist of poor resources, dilapidated facilities or equipment, ineffective

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teachers (Pajares, 1996), or other indigenous factors often associated with academic transitions (i.e., increased stress levels, decreased self-esteem, and loss of social status). Qualitative methods (e.g., student interviews about motivation that capture their own words, close examinations of the climate of testing and its effect on motivation) as well as survey measures should be used in the future to distinguish why minority students’ existing levels of motivation fail to produce acceptable achievement levels. Furthermore, although the generalizability of these findings is limited to urban schools where there is an academic emergency that directly and indirectly affects all participants within the ecological educational system, our results suggest the need to further investigate such locales where a mandatory curriculum in all core courses is being implemented. A decade ago, Graham (1994) concluded that “Black subjects maintain undaunted optimism and positive self-regard even in the face of achievement failure” (p. 103), and our findings echo the same refrain today. Because these students possessed moderate levels of all three motivational variables, they cannot be technically classified as unmotivated. However, the purchasing power of their motivational resources seems reflective of an impoverished academic state. Some of our outcomes parallel developmental patterns discovered by other researchers after an academic transition (e.g., declines in achievement and shifts from learning goals), but other results (e.g., decline in the effectiveness of interest; both boys and girls utilizing work-avoidant goals) distinctively differ and seem more connected with cultural and contextual factors. Thus, we support suggestions by the National Research Council (2004) for fostering motivation in urban high schools. Their environmental recommendations included redesigning courses and instructional methods to increase engagement and learning, providing resources, assessing understanding and skills, creating smaller learning communities, coordinating communication within the community, and eliminating tracking. It is very likely that such improvements will not only support the growth of motivation but also contribute to its potency and effectiveness in empowering achievement.

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64 Intrinsic Motivation and School Misbehavior: Some Intervention Implications Howard S. Adelman and Linda Taylor

W

ith the “cognitive revolution” in psychology, new work on the construct of intrinsic motivation has emerged. This work has relevance for researchers and practitioners concerned with behavior and learning problems. The purpose of this article is to highlight the importance of understanding intrinsic motivation, with specific respect to research and practice focused on school misbehavior. The value of the concepts of self-determination, competence, and relatedness in studying motivation for devious and deviant behavior is discussed. Then, these concepts are applied to the problem of categorizing intrinsically motivated misbehavior. Finally, implications for intervention are explored to suggest directions for formal research and experimental practice.

An Intrinsic View of Motivation The following draws primarily on the work of Deci and his colleagues, because their theoretical ideas are consistent with a large amount of theory and research, and they consistently apply their work to schooling, clinical intervention, and special education populations (e.g., Deci, 1975, 1980; Deci & Chandler, 1986; Deci & Ryan, 1985; Ryan, Connell, & Deci, 1985). That perspective specifies three fundamental psychological needs motivating Source: Journal of Learning Disabilities, 23(9) (1990): 541–550.

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human activity – self-determination, competence, and relatedness (see Note 1). These are seen as the intrinsic motivating forces that lead individuals to seek out challenges; and, seeking and conquering challenges are widely viewed as fundamental to development of the internal structures that guide subsequent action. In contrast to growth-oriented-behavior theorists, intrinsic motivation theorists (e.g., Brehm & Brehm, 1981; Condry, 1977; Deci & Ryan, 1985; McGraw, 1978) emphasize that individuals are especially vulnerable to events that (a) exert pressure and control or (b) lead to repeated failure/ negative feedback, or to outcomes that are unpredictable or uncontrollable. Obvious examples are demands for conformity enforced with punishment for noncompliance. Less obvious examples are efforts to use material and social rewards to control behavior. For instance, even when students “shape up” to obtain an offered reward, they may perceive the situation as another effort to control them (i.e., limit their self-determination). Several research reviews concur that the use of rewards, surveillance, and deadlines, and other actions that exert pressure and control on an individual, can undermine feelings of self-determination and lead to psychological reactance (see Note 2) (e.g., Brehm & Brehm, 1981; Condry, 1977; Deci & Ryan, 1985; McGraw, 1978; Ryan et al., 1985). It should be stressed, however, that it is the surrounding context, not events themselves, that is seen as determining whether external control produces negative effects. As Deci and Chandler (1986) pointed out, In the realm of education, both the general classroom context and the specific context for any given child seem to be determined primarily by the teachers’ orientations and intentions. For example, when teachers offer rewards or impose deadlines with the intent of controlling the children’s behavior – of getting the children to do what they want them to do – the rewards and deadlines have predicted negative effects (e.g., Deci, Nezlek, & Sheinman, 1981). On the other hand, when these events are presented as informative structures, as ways of acknowledging independent achievement or creative initiations, for example, they do not have negative effects, (p. 589)

From the above perspective, then, a considerable amount of the motivation underlying school misbehavior can be understood in terms of a student’s (a) growth-oriented activity stemming from psychological needs for selfdetermination, competence, and relatedness, and (b) reactions to threats to these three psychological needs. In the latter instance, degree of threat is dependent on how the student perceives events and their context. The growing body of empirical support relevant to intrinsic motivation is too large to review here (see Deci & Ryan, 1985; Weiner, 1980). A few studies relevant to the concept of self-determination should suffice to illustrate the value of intrinsic motivation constructs in understanding school behavior problems. Much of the research generated by reactance theory is germane to selfdetermination (see Brehm & Brehm, 1981). Particularly pertinent are studies

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of the relation between hostility and defiance and one’s objective control over events. Most of these studies involve an experimental manipulation under laboratory conditions, wherein one person threatens the freedom of another. A representative study (Worchel, 1974) operationalizes threat by taking away a specific choice and replacing it with an alternative. Results show predicted increases in hostility due to frustrated expectations, with degree of hostility related to degree of disconfirmation of the expected choice and attractiveness of the alternative. Research on perceived control also is relevant to self-determination, in that having perceived control is necessary for people to feel self-determining (see Note 3). The bulk of work in this area emphasizes the relation of (a) positive perceptions of control to well-being and (b) negative perceptions to problem situations. For instance, research on stress and health has documented benefits of increased perceived control (see Langer, 1983); studies of school achievement and perceived control also report a strong positive relationship (see Stipek & Weisz, 1981; Weisz & Cameron, 1985). A specific example of direct relevance to school misbehavior is the work of Allen and Greenberger (1980) on destructive behavior. These investigators reported a series of laboratory studies on increases in sense of personal control as related to damage to the physical environment. In a task involving destruction of a block tower, undergraduates reported that (a) the act increased their feelings of perceived control and success, (b) the increase was greatest for those placed in a state of low general control (failure) prior to destruction, and (c) the degree of the complexity of and objective control over destruction was positively related to feelings of success. In the same report, data from an interview with a sample of one hundred twenty 18- to 20-year-old males regarding their motivation for vandalism relate such acts to a sense of self-determination. (Feelings of accomplishment about damaging the environment are illustrated by the respondent who had smashed a high school locker; he recalled thinking with pride each time he passed it, “There’s my little destruction to this brand new school.”) As Allen and Greenberger (1980) suggested, failure situations can lead to a state of low perceived control. Thus, students who do poorly would be expected to have lower levels of perceived control than those who achieve to their satisfaction. Indeed, our research group has found lower levels of perceived control among students diagnosed as learning disabled as contrasted to nonproblem student samples (Adelman, Smith, Nelson, Taylor, & Phares, 1986). Similarly, in a study at an inner city, lower SES area junior high, Nichols (1985) found decreasing levels of perceived control among a contrast group not enrolled in an experimental leadership-training program. Assuming that lower levels of perceived control reflect threats to self-determination, both theory and research suggest that students who do poorly at school may manifest some form of deviant or devious behavior in order to increase, at least temporarily, feelings of self-determination.

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Toward Categorizing Intrinsic Motivation for Misbehavior Once one adopts an intrinsic view of motivation, the concepts can be applied in efforts to categorize intrinsic motivational underpinnings of devious and deviant behavior. Before briefly illustrating the point with a working schema, however, the basic context for such work is stressed, namely, the problem of describing school misbehavior.

Categorizing School Misbehavior What makes a particular act at school deviant? Charles (1985) stated that classroom misbehavior is “behavior that the teacher judges to be inappropriate for a given time or place” (p. 4). That is, an act that bothers the teacher usually is seen as deviant. This is not to say the act should be identified as misbehavior, or that the student should be seen as a discipline problem. It is simply a recognition that, as a representative of a powerful system, the teacher’s perception generally prevails and is the starting point for intervention. Similarly, any effort to categorize and classify troublesome school behavior is influenced by the rationale used by the classifier. For example, one could simply categorize misbehavior in terms of designations used on behavior rating scales – arguing, high or low activity levels, crying, hitting and fighting, destroying things, not following rules and directions, not participating in class or not coming to school, lying, cheating, and so forth. (Such descriptive terms imply nothing about motivation.) Most practitioners and researchers, however, are not satisfied with descriptive terms. They prefer to classify misbehavior with respect to its disruptive influence or postulated underlying pathology. For instance, some refer to acting out (as contrasted with withdrawn) students with labels such as uncooperative, noncompliant, disrespectful, or inappropriately aggressive. These labels do not specify the motivational underpinnings for misbehavior, but the behavior commonly is explained in terms of youngsters’ desires to “get attention,” “flout authority,” or “avoid doing the assigned work.” In contrast, some professionals see deviant and devious behavior as a manifestation of disabilities associated with psychological and physiological malfunctioning. Thus, they use diagnostic labels such as attention deficithyperactivity disorder, oppositional defiant disorder, depression, and so forth. One implication of the underlying-pathology view is that misbehavior is not always rationally motivated; another related implication is that, although observers often infer rational intention, they may be in error. (Some intervenors offer these implications as a basis for not considering rational motives in dealing with misbehavior; others are unconcerned about motives because their practices are not based on differential diagnoses of cause.)

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Efforts to improve the classification of children’s psychological and educational problems involve an interplay of conceptual and empirical activity. Progress has accelerated with advances in methodology (Achenbach, 1988); however, the consensus among reviewers is that considerable problems remain, especially with respect to validation of taxonomies (Achenbach & Edelbrock, 1983; Dreger, 1982; Quay, 1986; Quay, Routh, & Shapiro, 1987; Rutter & Gould, 1985; Rutter & Tuma, 1988). As Quay et al. (1987) stressed in discussing the classification of childhood psychopathology, the field needs “categories or dimensions that (a) can be discriminated from other syndromes and thus reliably diagnosed or measured; and also meet one or more of the following criteria: (b) are associated with different causes, (c) have different outcomes, or (d) respond to different interventions” (p. 493). These reviewers conclude that “the application of these criteria would seem to rule out many of the categories of DSM-III and not a few of the empirically derived dimensions as well” (p. 493). One of our prerequisite tasks in exploring motivational underpinnings for school misbehavior is that of categorically describing manifestations of such behavior. Because of the deficiencies related to current schemes for classifying children’s problems, we have found it helpful for now to return to the use of basic dimensions to describe observable manifestations. The basic descriptive dimensions we use are (1) general type of behavior, (2) overtness, (3) energy level, (4) pervasiveness, and (5) frequency. That is, first we group the acts into the two major categories that are highlighted by multi-variate analytic studies (cf. Quay & Werry, 1986): (a) acting-out behavior and (b) withdrawal (physical and psychological). Second, we distinguish each category in terms of whether it is manifested overtly or covertly, whether the act is displayed in a highly intensive or passive manner, and whether it is seen in a narrow or broad range of situations. Then, we rate its frequency of occurrence. We find that this categorization provides sufficient descriptive differentiation among troublesome acts to allow us to proceed with exploring the motivational underpinnings for such behavior.

Categorizing Motivational Underpinnings As with learning problems, the largest proportion of deviant and devious behavior seen at school is not likely the result of internal pathology (Adelman & Taylor, 1983, 1986a; Winett, Stefanek, & Riley, 1983). Indeed, the majority of such behavior probably is motivated and rational, judging from the frequency with which misbehavior is characterized as an act of defiance, a diversion, revenge, an effort to deceive, manipulate, be nonconforming or anticonforming, and so forth. (We do recognize that some behavior problems are an unintentional by-product of efforts to cope without having acquired the skills for doing so appropriately. Purely unintentional misbehavior, however,

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seems most prevalent among young children who have not yet experienced a great deal of failure or frustration and among individuals with significant disabilities.) In categorizing intentional misbehavior, a motivational perspective requires distinguishing between motivational subgroups. To begin with, it is useful to differentiate proactive from reactive behavior. Proactive behavior is the individual’s effort to seek out or establish conditions that produce feelings of satisfaction. With respect to misbehavior, such actions reflect an approach tendency. Reactive behavior is seen in efforts to cope and defend against conditions that produce unpleasant feelings. With respect to misbehavior, such actions reflect an avoidance tendency. Furthermore, as conceived by Deci and his colleagues, the intrinsic motivational bases for most intentional behavior can be viewed as stemming from a desire to feel self-determining, competent, and related to others. From this theoretical base, a substantial portion of misbehavior at school can be understood in terms of students’ attempts to act in ways that increase feelings of control, competence, and connectedness. That is, some misbehavior reflects proactive efforts to do things that will lead to such feelings; other behavior reflects reactive efforts to deal with threats that interfere with such feelings (see Figure 1). For example, students often are compelled into situations in which they feel they cannot perform effectively and, under such circumstances, may react in negative or inappropriate ways, to avoid or protest what is happening. Over a period of time, this reactive behavior that was initially designed to defend against aversive situations can become an established pattern of coping (Adelman & Taylor, 1986a). The same action may reflect proactive or reactive motivation and stem from a desire to feel self-determining, competent, or related to others. The misbehavior may take the form of overt or covert actions. Examples of the former include open, direct defiance and aggression, direct physical or psychological withdrawal, and nonconformity or conformity to deviant models;

Proactive INTENTIONAL MISBEHAVIOR Reactive

SelfCompetence Relatedness determination TYPE OF INTRINSIC MOTIVE

Figure 1: Motivational underpinnings for intentional misbehavior in the classroom

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examples of the latter include manipulation, deception, passive withdrawal, and psychologically induced physical illness. The importance of distinguishing the underlying motivation for misbehavior at school can be illustrated by thinking about six students who are noncompliant, particularly when it comes to following school rules. Student A sets off the fire alarm in the hall. His action is based on a proactive effort to stir up some excitement. He is seeking the challenge of breaking the law and the feeling of competence that results from not getting caught. Student B paints graffiti all over the bathroom wall. Her act also is proactive; but the behavior is intended to increase her acceptance by a subgroup of peers who she believes expect her to defy the rules (i.e., she is trying to conform to the standards set by deviant role models). Student C proactively seeks to satisfy her need for self-determination by dressing according to her own view of what is best for her, rather than adhering to the school’s dress code. In contrast, Student D’s action is reactive: He defiantly breaks the dress code as a direct protest against what he views as a threat to his self-determination. Student E’s action is also reactive: She refuses to do classroom assignments, as a way of diverting attention from the fact that she lacks the skills to perform competently in class. Finally, Student F has been rebuffed in his efforts to establish a positive relationship with his teacher, and he reacts by withdrawing and giving her “the silent treatment.”

Implications for Intervention Interventions to deal with misbehavior can be categorized in terms of phases, namely, efforts to prevent and anticipate misbehavior, actions to be taken during misbehavior, and steps to be taken afterwards. An understanding of intrinsic motivation in general, and reactive and proactive deviance in particular, is critical for each of these intervention phases. For example, with respect to prevention, most professionals, regardless of theoretical orientation, recognize that social and school program improvements could reduce learning and behavior problems significantly. There is increasing acceptance that a primary preventive step involves normative changes in classroom programs. From the perspective of intrinsic motivation theory, such changes include designing classroom instruction to better match the broad range of differences in students’ intrinsic motivation, as well as their differences in capability. Indeed, such changes are considered to be an essential prerequisite to individual intervention (e.g., Adelman, 1989; Adel-man & Taylor, 1985, 1986b; Maher & Zins, 1987; Millman, Schaefer, & Cohen, 1981). However, even if primary and secondary preventive steps are taken, there remains the necessity of intervening with individuals who continue to be troublesome. Discussions of practices for dealing with such students often are organized around the topics of discipline, classroom management, and

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student behavioral self-management. An appreciation of the role intrinsic motivation plays in deviant and devious behavior suggests approaches to such behavior that go beyond current disciplinary and management practices. Before discussing these matters, however, it is important to acknowledge the necessity of dealing with the impact of misbehavior and to highlight practical and research implications related to minimizing negative motivational and behavioral repercussions.

Dealing with the Impact of Misbehavior: A Motivational Perspective The first concern of school personnel is almost always the impact of misbehavior, and rightly so. Such behavior disrupts; it may be hurtful; it may disinhibit others. Thus, when a youngster misbehaves, a natural reaction is to want that youngster to experience, and other students to see, the consequences of misbehaving, in hopes that the consequences will deter subsequent misbehavior. That is, because the impact of misbehavior is typically the first concern, the primary focus of intervention usually is on discipline (e.g., Charles, 1985; Dreikurs, Grunwald, & Pepper, 1982; Hyman, Flanagan, & Smith, 1982; Knoff, 1987; Wolfgang & Glickman, 1986). Given the primary role assigned to disciplinary practices in responding to school misbehavior, it is essential that their impact on intrinsic motivation be considered and investigated. Thus, some motivational concerns are highlighted here as a stimulus for practice and research. Discipline. Knoff (1987) presents three definitions of discipline as applied in schools: (a) ... a punitive intervention; (b) … a means of suppressing or elim inating inappropriate behavior, of teaching or reinforcing appropriate behavior, and of redirecting potentially inappropriate behavior toward acceptable ends; and (c) … a process of self-control whereby the (po tentially) misbehaving student applies techniques that interrupt inappropriate behavior, and that replace it with acceptable behavior. (p. 119)

In contrast to the first definition, which specifies discipline as punishment, Knoff (1987) sees the other two as nonpunitive, or, as he calls them, “positive, best practices approaches” (p. 119). He appears to make this distinction because of the general consensus that punishment is an undesirable form of discipline, to be used only in an emergency. School personnel often see punishment as the only recourse in dealing with a student’s misbehavior. That is, they use the most potent negative consequences available to them in a desperate effort to control an individual and make it clear to others that acting in such a fashion will not be tolerated.

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A demand for future compliance is usually made, along with threats of harsher punishment if compliance is not forthcoming. Furthermore, the discipline may be administered in a way that suggests that the student is officially seen as an undesirable person. As with many emergency procedures, the benefits of using punishment may be offset by a variety of negative consequences (e.g., increases in negative attitudes toward school and school personnel, which often lead to other forms of misbehavior). Thus, as soon as the emergency is resolved, the emphasis often shifts from punishment to implementing logical consequences. Logical Consequences and Recipient Perceptions. Guidelines for managing misbehavior generally emphasize the desirability of presenting discipline as reasonable, fair, and nondenigrating. Intrinsic motivation theory specifically stresses that recipients of positive, best-practices approaches experience them as legitimate disciplinary acts that neither denigrate one’s sense of worth nor reduce one’s sense of autonomy (e.g., Deci & Ryan, 1985). To these ends, discussions of classroom management practices usually emphasize establishing and administering logical consequences. This idea is evident in situations where there are naturally occurring consequences (e.g., if you touch a hot stove, you get burned). In classrooms, there may be little ambiguity about the rules; unfortunately, the same often cannot be said about “logical” penalties. Even when the consequence for a particular rule infraction has been specified ahead of time, its logic may be more in the mind of the teacher than in the eye of the student. Indeed, the distinctions made by Knoff (1987) reflect an observer’s perspective of discipline. In the recipient’s view, any act of discipline may be experienced as punitive (e.g., unreasonable, unfair, denigrating, disempowering). Basically, consequences involve depriving students of something they want and/or making them experience something they do not want to experience. Consequences usually take the form of (a) removal/deprivation (e.g., loss of privileges, removal from an activity); (b) reprimands (e.g., public censure); (c) reparations (to compensate for any losses arising from the misbehavior); and (d) recantations (e.g., apologies, plans for avoiding future problems). For instance, teachers commonly deal with acting-out behavior by removing a student from an activity. To the teacher, this step (often described as “time out”) may be seen as a logical way to stop the student from disrupting others by isolating him or her; or, the logic may be that the student needs a cooling-off period. It may be reasoned that (a) by misbehaving the student has shown that she or he does not deserve the privilege of participating (assuming the student likes the activity), and (b) the loss will lead to improved behavior in order to avoid future deprivation. Most teachers have little difficulty explaining their reasons for using a particular consequence. However, if the intent really is to have students perceive consequences as logical and nondebilitating, it seems logical to first

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determine whether the recipient sees a particular disciplinary act as a legitimate response to his or her misbehavior. Moreover, it is important to recognize the difficulty of administering consequences in a way that minimizes the negative impact on the recipient’s perceptions of self. That is, although the intent is to stress that it is the misbehavior and its impact that are bad, the student can too easily experience the process as a characterization of her or him as a bad person. Examples of an established, accepted set of consequences that gives major consideration to the recipients’ perceptions are organized sports, such as youth basketball and soccer. In these arenas, the referee is able to use the rules and related criteria to identify inappropriate acts and apply penalties; moreover, he or she is expected to do so with positive concern for maintaining the youngster’s dignity as well as engendering respect for others. For discipline to be seen as a logical consequence, it may be necessary to take steps to convey that (a) disciplinary responses are not personally motivated acts of power (e.g., an authoritarian action) and, at the same time, (b) the social order has established rational reactions to a student’s behavior that negatively affects others. Also, if the intent of the discipline is a longterm reduction in future misbehavior, it may be necessary to take steps to help students learn right from wrong, to respect others’ rights, and to accept responsibility. Toward these ends, motivational theorists suggest it may be useful to (a) establish a publicly accepted set of consequences, to increase the likelihood that students will experience them as socially just (e.g., reasonable, firm but fair), and (b) administer such consequences in ways that allow students to maintain a sense of integrity, dignity, and autonomy (e.g., Brehm & Brehm, 1981; Deci & Ryan, 1985). All of this is best achieved under conditions wherein students are empowered (e.g., are involved in deciding how to rectify the situation and avoid future misbehavior and are given opportunities for subsequent positive involvement and reputation-building at school). From a motivational perspective, then, it is essential to (a) gain a better understanding of recipient perceptions of discipline and (b) develop disciplinary practices that minimize negative repercussions. These are both areas where there is a dearth of direct research.

Addressing Underlying Motivation Beyond discipline, there is a need for research on interventions designed to address the roots of misbehavior, especially the underlying motivational bases for such behavior. Consider students who spend most of the day trying to avoid all or part of the instructional program. An intrinsic motivational interpretation of the avoidance behavior of many of these youngsters is that it reflects their perception that school is not a place where they experience

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a sense of competence, autonomy, and/or relatedness to others. Over time, these perceptions develop into strong motivational dispositions and related patterns of misbehavior. Relevant interventions for such problems begin with major changes in social and school programs. The aims of such changes vis-à-vis motivational problems are to (a) prevent and overcome negative attitudes toward school and learning, (b) enhance motivational readiness for learning and overcoming problems that arise, (c) maintain intrinsic motivation throughout learning and problem-solving processes, and (d) nurture the type of continuing motivation that results in students’ engaging in activities away from school that facilitate maintenance, generalization, and expansion of learning and problem solving. Failure to attend to these motivational concerns in a comprehensive, normative manner results in approaching passive and often hostile students with practices that can instigate and exacerbate many learning and behavior problems. After accomplishing broad programmatic changes to the degree feasible, intervention with a misbehaving student involves remedial steps directed at specific factors associated with unintentional, proactive, and/or reactive deviance. Because the concern here is with intentional behavior problems, the focus in the following sections is primarily on reactive and proactive misbehavior. First, a few implications for counseling and consulting are highlighted and then implications for general changes in school programs are discussed. Counseling and Consulting. Understanding the motivational ideas discussed above can profoundly influence research and practice focused on counseling individuals who misbehave and consulting with their teachers and parents. For instance, with intrinsic motivation in mind, the following assessment questions arise: 1. Is the misbehavior unintentional or intentional? 2. If it is intentional, is it reactive or proactive? 3. If the misbehavior is reactive, is it a reaction to threats to selfdetermination, competence, or relatedness? 4. If it is proactive, are there other interests that might successfully compete with the satisfaction derived from deviant behavior? The answers to these questions may be based on perspectives of cause that are related by teachers, parents, and the identified student. (In ruling out a skill deficit, data also are needed on the youngster’s basic abilities.) However, because of attributional biases, one can expect these interested parties to offer different causal views (Jones & Nisbett, 1972; Miller & Ross, 1975; Monson & Snyder, 1977). Rather than confounding assessment, such differences can help clarify the student’s underlying motivation and how others interpret that motivation. Both matters can be seen as central to planning corrective strategies aimed at affecting the student’s intrinsic motivation.

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That is, differing perceptions can compound a problem by resulting in different analyses of what is wrong and what should be done. Awareness of differences in perceived cause enables interveners to explore how those differences are affecting the actions of each interested party and clarify which perceptions may be counterproductive to resolving the problem. With respect to resolving the problem, intrinsic motivational theory suggests that individual corrective interventions for those who misbehave reactively require steps designed to reduce reactance and enhance positive motivation for participating in an intervention. For youngsters highly motivated to pursue deviance (e.g., those who proactively engage in criminal acts), even more is needed. Intervention might focus on helping these youngsters identify and follow through on a range of valued, socially appropriate alternatives to deviant activity. From the theoretical perspective presented above, such alternatives must be capable of producing greater feelings of self-determination, competence, and relatedness than usually result from the youngster’s deviant actions. To these ends, motivational analyses of the problem can point to corrective steps for implementation by teachers, clinicians, parents, or the students themselves. If misbehavior is unintentional, the focus of intervention at school, clinic, and home probably needs to be directed only at reducing stress and building skills. However, if the behavior is intentional, all interested parties probably should be encouraged to (a) eliminate situations leading to reactivity and establish alternative ways for the student to cope with what cannot be changed, or (b) establish activity options designed to redirect proactive misbehavior toward prosocial interests and behavior. For example, consultants might help teachers and parents understand the motivational bases for a youngster’s misbehavior and facilitate environmental and programmatic changes that would take into account the youngster’s need to feel self-determining, competent, and related. Similarly, in direct counseling with students whose misbehavior is intentionally reactive, short-term work might stress increasing a student’s awareness and how she or he can work with significant others to produce circumstances that better match his or her psychological needs. Comparable counseling might be provided to those exhibiting proactive deviance; however, evidence from delinquent populations suggests that short-term counseling in such cases is rather ineffective. Indeed, for both groups, it must be acknowledged that little is known about how effective even long-term psychotherapy or behavior change strategies might be. Nevertheless, long-term intervention generally provides the time necessary to deal with students’ affect, increasing their understanding of why they behave as they do, and exploring the possibility of change (Lambert, Shapiro, & Bergin, 1986). The question of how well such outcomes can be achieved awaits appropriate evaluative research (Adelman, 1986; Maher & Bennett, 1984).

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From a motivational perspective, an appropriate test of the efficacy of long-term psychotherapeutic and behavior change interventions for intentional misbehavior requires more than specifying what one wants youngsters to understand and do. It requires interventions that systematically address intrinsic motivation as key process or “enabling” objectives. To be specific, the intervention must deal with the initial attitudes these youngsters are likely to bring to the counseling situation. They are unlikely to approach the process positively or even neutrally; that is, there are negative attitudes to overcome. Assuming negative attitudes are overcome, the intervenor must be able to (a) enhance the youngster’s motivational readiness to develop a working relationship, and (b) maintain the youngster’s positive intrinsic motivation for as long as intervention is needed. In terms of the motivational concepts discussed above, from the beginning of the intervention until its successful completion, the process should strive to stimulate feelings of selfdetermination, competence, and interpersonal relatedness. Finally, the intervention should focus on intrinsic motivation as an outcome objective. That is, the process should nurture the type of ongoing intrinsic motivation that results in the youngster engaging in activities away from the intervention setting that facilitate maintenance and generalization of problem-solving behavior. School Program Changes to Deal with Reactive Misbehavior. A student who perceives school personnel and activities as threats to his or her self-determination, competence, or sense of relatedness to others may react in protective ways. For instance, a student who expects to do poorly on an assigned classroom task may misbehave as a way of avoiding the activity (Brehm & Brehm, 1981; Kaplan, 1980). If the teacher’s reaction to the misbehavior is to threaten or apply punitive measures, the student may react in increasingly negative ways. The case of Bret provides an example: Because of his many experiences of failure at school, Bret tends to perceive learning situations as threatening. Even before he knows much about a task, he expects to have difficulty coping. Thus, he feels vulnerable, fearful, and sometimes angry at being pushed into such situations. He would like to avoid them, and if he cannot do so directly, he tries indirect ways, such as diverting the teacher to a discussion of other matters. When he cannot manipulate the situation effectively, he engages in various acting-out behaviors, such as arguing, inciting the class to disruption, or regularly missing school. This often leads to a power struggle with the teacher and results in Bret being sent to the principal or home. After a number of such experiences, he has developed rather strong negative expectations and attitudes about school and teachers and has learned a rather large range of behaviors to protect himself from what he perceives as bad situations. Unfortunately, the more he displays such behavior, the more those around him tend to think of him as uncontrollable and incorrigible.

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A great deal of the negative behavior of students such as Bret may reflect reactions to immediate school pressures. Those with long or intense histories of school problems may develop the general expectation that most classroom experiences are hurtful. Consequently, a student may approach all classroom situations looking for the worst – and, thus, perceiving it. Even when a teacher offers “exciting” new opportunities, the student may not perceive them as such. If the intention is to address the motivational underpinnings for reactive misbehavior, two intervention objectives seem fundamental: (1) to minimize external demands to perform and conform (e.g., eliminate threats) and (2) to explore learning activities with the student in order to identify nonthreatening and interesting replacements (e.g., establish a program of intrinsically motivated activity). To these ends, intervention focuses first on assessing (if feasible) the nature of any perceived threats. Such an assessment is guided by motivational thinking about threats to perceived self-determination, competence, and relatedness. The data are then used to replace threatening situations and tasks with activity that produces positive perceptions with respect to identified psychological needs. Even if the specific areas of threat cannot be assessed, one can proceed to work with the student to eliminate aspects of the program that he or she appears to be reacting against. In making changes, it is important to realize that students with extremely negative perceptions of teachers and school programs are not likely to be open to “new” activities that look like “the same old thing.” There have to be vivid variations in the alternatives for students to perceive differences. Several key elements of such interventions are summarized after the following discussion of proactive misbehavior. School Program Changes to Deal with Proactive Misbehavior. Proactive misbehavior is aimed at directly producing feelings of satisfaction. That is, non-cooperative, disruptive, and aggressive behavior may be rewarding or satisfying to an individual because the behavior itself is exciting or because the behavior leads to desired outcomes (e.g., peer recognition, feelings of autonomy and /or competence). Intentional negative behavior stemming from such motivation can be viewed as the direct pursuit of deviance. In practice, it is not easy to differentiate reactive from proactive misbehavior. For example, one student may proactively engage in decorating school walls with graffiti because he or she finds it to be an interesting and exciting act; another may engage in the activity because of norms established by a valued peer group. Still another may reactively engage in such behavior because of anger toward school authorities. (Subsequently, this last student may fall in with negative role models, such as gang members, and adopt their pattern of proactive misbehavior, e.g., delinquent acts that are intrinsically interesting and exciting). And, of course, students involved in deviant behavior inevitably come into conflict with school authorities and soon manifest additional reactive misbehavior.

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Proactive misbehavior, such as staying home from school to watch TV or hang out with friends, participating with gangs, using drugs, and baiting authority, may be much more interesting and exciting to some students than any activity that school offers. That is probably why proactive misbehavior is so difficult to alter. From the perspective of intrinsic motivation theory, the fundamental objective of intervention in such cases is to establish a program of intrinsically motivated activity powerful enough to compete with the satisfaction gained from the misbehavior. This means the intervenor must be able to explore options well beyond the norm in offering nonthreatening and interesting learning activities to replace the student’s current school program. At the same time, because such students are unlikely to give up their pursuit of deviance quickly, it may be necessary, initially, to accommodate a wider range of behavior than typically is accepted in schools. That is, if the intention is to recapture the interest of such students, one may have to increase one’s tolerance, for a while, of certain “bad manners” (e.g., some rudeness, some swearing), eccentric mannerisms (e.g., strange clothing and grooming), and temporary nonparticipation. To be more specific, it may be necessary to begin by exploring with the student (a) topical interests (e.g., sports, rock music, movies and TV shows, computer games, auto mechanics) and (b) desired activities (e.g., working with certain individuals, use of nonstandard materials, special status roles). Discussion and sampling of the area of interest may have to be continued until the student identifies a specific facet that she or he would like to learn more about. Concomitantly, the intervenor may have to redefine rules and standards so that limits on behavior are expanded for such students (i.e., certain behaviors are tolerated and not treated as misbehavior). Failure to do so may account for the large proportion of these students who are pushed out or drop out due to constant conflict over misconduct. A case example suggests the extremes that may have to be attempted: Harry would come to school, but he had no interest in working on what his teachers had planned. He spent much of the time talking to friends and looking for exciting ways to make the time pass. He was frequently in the midst of whatever trouble was occurring in class. He was unresponsive to threats of punishment. He readily accepted suspensions. It seemed clear that unless something dramatic were done he would be expelled from school. Rather than letting the tragedy run its course, school personnel decided to try an experimental intervention. The teacher set aside time to help Harry identify one area of personal interest that he would like to learn more about. After some discussion, he indicated that he wanted to be a rock musician and would be interested in learning more about how people got into the field; he would also like to spend time improving his musical skills. Based on his stated interest, several interesting and realistic activities were identified that he would pursue, such as writing letters to musicians and agencies, instrument instruction and practice, and reading relevant publications. It was clear,

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however, that the one topic and those few activities would not hold his interest all day. Indeed, it was likely that what had been planned would involve him for only 1 to 2 hours a day. Thus, it was easy to anticipate that he would simply fall into his pattern of misbehaving for the remainder of the day, and the experimental effort to counter his misbehavior by building an intrinsically motivating program would be defeated. The solution devised for this problem was as simple as it was controversial. Harry was scheduled to come to school for only that period of time for which he had planned a program he intended to pursue. The reasoning for this approach was twofold: (1) It is clear that students such as Harry work only on what they have identified as desirable, and (2) they not only waste the rest of their time, but use it to pursue deviant behavior. If they are not at school for a full day, they are less likely to get into as much trouble at school. But, more important, the less that school personnel are in the position of coercing and punishing them, the less likely the problem will be confounded by misbehavior that is a reaction to such practices. Moreover, when such students no longer are expending energy misbehaving, they are in a better position to work with the teacher to develop an increasing range of academic interests. Indeed, it was a matter of only a few weeks before Harry indicated several additional areas of interest, including a desire to improve his reading. To accommodate his interests, his school day was expanded. Within a period of several months, he was regularly attending school all day, pursuing a combination of personally designated areas of interest and an increasing amount of the basic curriculum.

Clearly, there are many practical, economic, and legal problems involved in a strategy such as cutting back on the length of a student’s school day. However, those problems should be considered in the context of the cost to society (and individuals) of ignoring the fact that forcing certain students to stay in school all day might actually interfere with correcting their problems. It may be better to temporarily reduce a student’s time at school for positive reasons than for punishment (e.g., suspensions), or because of truancy. For older students, of course, a shortened day paired with a part-time job or apprenticeship is already an accepted and often productive strategy. Key Program Elements in Addressing Motivational Problems. As stated above, it is important to realize that students with extremely negative perceptions of teachers and school programs may not respond to changes that look like “the same old thing.” It seems necessary to, at the very least, make exceptional efforts to have students (a) view the teacher as supportive (rather than hostile and controlling or indifferent) and (b) perceive curricular and behavioral options as personally valuable and obtainable. Comprehensive, motivationally oriented intervention research is needed to clarify ways to produce major changes in students’ perceptions about such matters. (See Adelman & Taylor, 1983, 1986b, for a more extensive discussion.)

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Three key classroom program elements that warrant attention are highlighted below: 1. Options. Provision of a range of valued curricular and behavioral options is hypothesized as essential to establishing a classroom program that is a good match with a student’s psychological needs for self-determination, competence, and relatedness. (By definition, a good match means the program is nonthreatening.) In extreme cases it may be necessary to deemphasize temporarily the standard curriculum and pursue only activities to which the student makes a personal commitment. Furthermore, in extreme cases it may be necessary to accommodate, again temporarily, a wider range of deviant behavior than usually is tolerated from a student (e.g., limits set by existing standards and rules may have to be widened), to minimize psychological reactance and resultant increases in reactive behavior problems. 2. Student Decision Making. In terms of intrinsic motivation, options alone are insufficient. Also necessary is a structure that facilitates (e.g., supports) student decision making with respect to choosing options (and that allows for student-initiated changes in program plans). Thus, from a motivational perspective, another key element in dealing with misbehavior is student involvement in decision making about daily school activities and consequences for misbehavior. With respect to cause, it is hypothesized that students not included in decision making have little commitment to what is decided. Moreover, some of these students may perceive themselves as coerced and react in deviant ways in order to regain a sense of self-determination. With respect to correction, decision-making processes that maximize student perceptions of choice and control are essential. 3. Continuous Information on Functioning. Because of the potential negative impact of too much emphasis on extrinsic punishment and rewards, great care must be taken in providing student feedback on progress (see Deci, 1975, 1980; Deci & Ryan, 1985). The implications of research on this matter are that information given should highlight success not only in terms of task performance but with respect to student effectiveness in making good decisions and on how outcomes relate to the intrinsic reasons underlying student actions. Feedback, of course, is also supposed to clarify directions for future progress. Research is needed to clarify conditions that maximize the likelihood of feedback contributing to, rather than undermining, the student’s feelings of competence, self-determination, and relatedness.

Concluding Comments In the above brief discussion of interventions related to school misbehavior, we have been able to touch upon only a few major areas of practice. Table 1 provides a more comprehensive perspective on the nature and scope of needed intervention activity.

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As the preceding discussion underscores, and as is highlighted in Table 1, an understanding of the intrinsic motivational bases for deviant and devious behavior generates profound implications for intervention research and practice. For example, such an understanding points to assessment questions, classification concepts, and corrective strategies that might otherwise be ignored. It also highlights the need for comprehensive research programs to develop and evaluate interventions that address the motivational underpinnings of school misbehavior. Data from such research could shed considerable light on cause and correction with respect to all psychoeducational problems. Table 1: Focus of interventions for dealing with misbehavior I. Preventing Misbehavior A. Expand Social Programs 1. Increase economic opportunity for low income groups 2. Augment health and safety prevention and maintenance (encompassing parent education and direct child services) 3. Extend quality day care and early education B. Improve Schooling 1. Personalize classroom instruction (e.g., accommodating a wide range of motivational and developmental differences) 2. Provide status opportunities for nonpopular students (e.g., special roles as assistants and tutors) 3. Identify and remedy skill deficiencies early C. Follow Up All Occurrences of Misbehavior to Remedy Causes 1. Identify underlying motivation for misbehavior 2. For unintentional misbehavior, strengthen coping skills (e.g., social skills, problem-solving strategies) 3. If misbehavior is intentional but reactive, work to eliminate conditions that produce reactions (e.g., conditions that make the student feel incompetent, controlled, or unrelated to significant others) 4. For proactive misbehavior, offer appropriate and attractive alternative ways the student can pursue a sense of competence, control, and relatedness 5. Equip the individual with acceptable steps to take instead of misbehaving (e.g., options to withdraw from a situation or to try relaxation techniques) 6. Enhance the individual’s motivation and skills for overcoming behavior problems (including negative attitudes toward school) II. Anticipating Misbehavior A. Personalize Classroom Structure for High-Risk Students 1. Identify underlying motivation for misbehavior 2. Design curricula to consist primarily of activities that are a good match with the identified individual’s intrinsic motivation and developmental capability 3. Provide extra support and direction so the identified individual can cope with difficult situations (including steps that can be taken instead of misbehaving) B. Develop Consequences for Misbehavior That Are Perceived by Students as Logical (i.e., that are perceived by the student as reasonable, fair, and nondenigrating reactions that do not reduce one’s sense of autonomy) III. During Misbehavior A. Try to base response on understanding of underlying motivation. (If uncertain, start with assumption that the misbehavior is unintentional) B. Reestablish a calm and safe atmosphere 1. Use understanding of student’s underlying motivation for misbehaving to clarify what occurred. (If feasible, involve participants in discussion of events) 2. Validate each participant’s perspective and feelings 3. Indicate how the matter will be resolved, emphasizing use of previously agreed-upon logical consequences that have been personalized in keeping with understanding of underlying motivation

(Continued )

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Table 1: (Continued ) III. During Misbehavior (continued ) 4. If the misbehavior continues, revert to a firm but nonauthoritarian statement including that it must stop or else the student will have to be suspended 5. As a last resort use crisis back-up resources: a. If appropriate, ask student’s classroom friends to help b. Call for help from identified back-up personnel 6. Throughout the process, keep others calm by dealing with the situation with a calm and protective demeanor IV. After Misbehavior A. Implement Discipline – Logical Consequences/Punishment 1. Objectives in using consequences a. To deprive student of something he or she wants b. To make student experience something he or she does not want 2. Forms of consequences a. Removal/deprivation (e.g., loss of privileges, removal from activity) b. Reprimands (e.g., public censure) c. Reparations (e.g., of damaged or stolen property) d. Recantations (e.g., apologies, plans for avoiding future problems) B. Discuss the Problem with Parents 1. Explain how they can avoid exacerbating the problem 2. Mobilize them to work preventively with school C. Work Toward Prevention of Further Occurrences (see I & II)

Authors’ Note We want to thank Ed Deci and an anonymous reviewer for their valuable feedback and suggestions.

Notes 1. Deci and Ryan (1985) define self-determination as “a basic, innate propensity … that leads organisms to engage in interesting behaviors … out of choice rather than obligation or coercion, and those choices are based on an awareness of one’s organismic needs and a flexible interpretation of external events” (p. 38). They define competence as the need people have to be effective. Relatedness is the need for warmth from and involvement with others (Deci & Chandler, 1986). 2. Psychological reactance is Brehm’s term for the motivation to protect or restore options or freedoms (Brehm & Brehm, 1981). Such motivation is aroused when an option (freedom) that is important and believed to be available is removed or threatened. 3. There are significant variations in the way theorists have defined perceived control. In our work, we define it as the degrees of freedom one expects to have over processes that one believes must be pursued to accomplish specific outcomes (including decisionmaking processes and outcomes). In studying how such perceptions affect behavior, we also emphasize the importance of the degree of value one places on having control over a specific process or outcome (Adelman, Smith, Nelson, Taylor, & Phares, 1986).

References Achenbach, T. M. (1988). Integrating assessment and taxonomy. In M. Rutter, A. H. Tuma, & I. S. Lann (Eds.), Assessment and diagnosis in child psychopathology (pp. 28– 41). New York: Guilford.

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Achenbach, T. M., & Edelbrock, C. S. (1983). Taxonomic issues in child psychopathology. In T. H. Ollendick & M. Hersen (Eds.), Handbook of child psychopathology (pp. 65–93). New York: Plenum Press. Adelman, H. S. (1986). Intervention theory and evaluating efficacy. Evaluation Review, 10, 65–83. Adelman, H. S. (1989). Prediction and prevention of learning disabilities: Current state of the art and future directions. In L. Bond & B. Compas (Eds.), Primary prevention in the schools (pp. 106 –145). Newbury Park, CA: Sage. Adelman, H. S., Smith, D., Nelson, P., Taylor, L., & Phares, V. (1986). An instrument to assess students’perceived control at school. Educational and Psychological Measurement, 46, 1005–1017. Adelman, H. S., & Taylor, L. (1983). Learning disabilities in perspective. Glenview, IL: Scott, Foresman. Adelman, H. S., & Taylor, L. (1985). Toward integrating intervention concepts, research, and practice. In S. I. Pfeiffer (Ed.), Clinical child psychology: An introduction to theory, research, and practice (pp. 57–92). New York: Grune & Stratton. Adelman, H. S., & Taylor, L. (1986a). Children’s reluctance regarding treatment: Incompetence, resistance, or an appropriate response? School Psychology Review, 15, 91–99. Adelman, H. S., & Taylor, L. (1986b). An introduction to learning disabilities. Glenview, IL: Scott, Foresman. Allen, V. L., & Greenberger, D. B. (1980). Destruction and perceived control. In A. Baum & J. E. Singer (Eds.), Advances in environmental psychology: Vol. 2, Applications of personal control (pp. 85–109). Hillsdale, NJ: Erlbaum. Brehm, S. S., & Brehm, J. W. (1981). Psychological reactance: A theory of freedom and control. New York: Academic Press. Charles, C. M. (1985). Building classroom discipline: From models to practice (2nd ed.). New York: Longman. Condry, J. (1977). Enemies of exploration: Self-initiated versus other-initiated learning. Journal of Personality and Social Psychology, 35, 459–477. Deci, E. L. (1975). Intrinsic motivation. New York: Plenum Press. Deci, E. L. (1980). The psychology of self-determination. Lexington, MA: Lexington Books. Deci, E. L., & Chandler, C. L. (1986). The importance of motivation for the future of the LD field. Journal of Learning Disabilities, 19, 587–594. Deci, E. L., Nezlek, J., & Sheinman, L. (1981). Characteristics of the rewarder and intrinsic motivation of the rewardee. Journal of Personality and Social Psychology, 40, 1–10. Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum Press. Dreger, R. M. (1982). The classification of children and their emotional problems: An overview – II. Clinical Psychology Review, 2, 349–385. Dreikurs, R., Grunwald, B. B., & Pepper, F. C. (1982). Maintaining sanity in the classroom: Classroom management techniques (2nd ed.). New York: Harper & Row. Hyman, I., Flanagan, D., & Smith, K. (1982). Discipline in the schools. In C. R. Reynolds & T. B. Gutkin (Eds.), The handbook of school psychology (pp. 454 – 480). New York: Wiley. Jones, E., & Nisbett, R. (1972). The actor and the observer: Divergent perceptions of the causes of behavior. In E. E. Jones, D. E. Kanouse, H. H. Kelley, R. E. Nisbett, S. Valens, & B. Weiner (Eds.), Attribution: Perceiving the causes of behavior (pp. 79–94). Morristown, NJ: General Learning Press. Kaplan, H. B. (1980). Deviant behavior in defense of self. New York: Academic Press. Knoff, H. M. (1987). School-based interventions for discipline problems. In C. A. Maher & J. E. Zins (Eds.), Psychoeducational interventions in the schools (pp. 118–140). New York: Pergamon.

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Lambert, M. J., Shapiro, D. A., & Bergin, A. E. (1986). The effectiveness of psychotherapy. In S. L. Garfield & A. E. Bergin (Eds.), Handbook of psychotherapy and behavior change (3rd ed.). New York: Wiley. Langer, E. J. (1983). The psychology of control. Beverly Hills, CA: Sage. Maher, C. A., & Bennett, R. E. (1984). Planning and evaluating special education services. Englewood Cliffs, NJ: Prentice-Hall. Maher, C. A., & Zins, J. E. (Eds.). (1987). Psychoeducational interventions in the schools. New York: Pergamon. McGraw, K. O. (1978). The detrimental effects of reward on performance: A literature review and a prediction model. In M. R. Lepper & D. Greene (Eds.), The hidden costs of reward (pp. 33–60). Hillsdale, NJ: Erlbaum. Miller, D. T., & Ross, M. (1975). Self-serving biases in the attribution of causality: Fact or fiction? Psychological Bulletin, 82, 213–225. Millman, H. L., Schaefer, C. E., & Cohen, J. J. (1981). Therapies for school behavior problems. San Francisco: Jossey-Bass. Monson, T. C., & Snyder, M. (1977). Actors, observers, and the attribution process: Toward a reconceptualization. Journal of Experimental Social Psychology, 13, 89–111. Nichols, B. K. (1985). Self-perception of control and esteem as related to participation in a leadership training program. Unpublished doctoral dissertation, University of California, Los Angeles. Quay, H. C. (1986). A critical analysis of DSM-III as a taxonomy of psychopathology in childhood and adolescence. In T. Millon & G. L. Klerman (Eds.), Contemporary directions in psychopathology: Toward the DSM-IV (pp. 151–165). New York: Guilford. Quay, H. C., Routh, D. K., & Shapiro, S. K. (1987). Psychopathology of childhood: From description to validation. Annual Review of Psychology, 38, 491–532. Quay, H. C., & Werry, J. S. (Eds.). (1986). Psychopathological disorders of childhood (3rd ed.). New York: Wiley. Rutter, M., & Gould, M. (1985). Classification. In M. Rutter & L. Hersov (Eds.), Child and adolescent psychiatry: Modern approaches (2nd ed., pp. 304 –321). Oxford: Blackwell. Rutter, M., & Tuma, A. H. (1988). Diagnosis and classification: Some outstanding issues. In M. Rutter, A.H. Tuma, & I. S. Lann (Eds.), Assessment and diagnosis in child psychopathology. New York: Guilford. Ryan, R. M., Connell, J. P., & Deci, E. L. (1985). A motivational analysis of self-determination and self-regulation in education. In C. Ames & R. E. Ames (Eds.), Research on motivation in education: The classroom milieu (pp. 13–51). New York: Academic Press. Stipek, D. J., & Weisz, J. R. (1981). Perceived control and children’s academic achievement: A review and critique of the locus of control research. Review of Educational Research, 51, 101–137. Weiner, B. (1980). Human motivation. New York: Holt, Rinehart & Winston. Weisz, J. R., & Cameron, A. M. (1985). Individual differences in the student’s sense of control. In C. Ames & R. E. Ames (Eds.), Research on motivation in education: The classroom milieu (pp. 13–51). New York: Academic Press. Winett, R. A., Stefanek, M., & Riley, A. W. (1983). Preventive strategies with children and families: Small groups, organizations, communities. In T. H Ollendick & M. Hersen (Eds.), Handbook of child psychopathology (pp. 485–521). New York: Plenum Press. Wolfgang, C. H., & Glickman, C. D. (1986). Solving discipline problems: Strategies for classroom teachers (2nd ed.). Boston: Allyn & Bacon. Worchel, S. (1974). The effect of three types of arbitrary thwarting on the instigation to aggression. Journal of Personality, 42, 300 –318.

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65 Reinforcement, Reward, and Intrinsic Motivation: A Meta-Analysis Judy Cameron and W. David Pierce

R

einforcement theory has had a significant impact on education. Education professors routinely teach the basic elements of behavior theory. As a consequence, most classroom teachers have at least some rudimentary understanding of the principles of reinforcement. These principles are often used to promote learning and to motivate students. In recent years, however, there has been a growing concern over the application of reward systems in educational settings. Several researchers have presented evidence and argued that incentive systems based on reinforcement may have detrimental effects. The contention is that reinforcement may decrease an individual’s intrinsic motivation to engage in a particular activity. To illustrate, if a child who enjoys drawing pictures is externally reinforced (e.g., with points or money) for drawing, the child may come to draw less once the reward is discontinued. In other words, one alleged effect of reinforcement is that it undermines intrinsic interest in a task. The literature concerned with the effects of reinforcement on intrinsic motivation draws mainly from experimental investigations. In an article published in the American Psychologist, Schwartz (1990) cited the intrinsic motivation experiment of Lepper, Greene, and Nisbett (1973) and concluded that reinforcement has two effects. First, predictably it gains control of [an] activity, increasing its frequency. Second, … when reinforcement is later withdrawn, people engage in the activity even less than they did before reinforcement was introduced. (p. 10)

Source: Review of Educational Research, 64(3) (1994): 363–423.

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While several researchers agree with this conclusion (e.g., Kohn, 1993; Sutherland, 1993), others continue to favor the use of reinforcement principles in applied settings (e.g., Hopkins & Mawhinney, 1992). This is, obviously, an important issue. Incentive systems are often implemented (or not) in schools, industry, hospitals, and so forth on the basis of research findings and conclusions. The present article evaluates the literature concerned with the effects of reinforcement and reward on intrinsic motivation by a meta-analysis of the relevant experimental investigations. Several researchers draw a distinction between intrinsic and extrinsic motivation. Intrinsically motivated behaviors are ones for which there is no apparent reward except the activity itself (Deci, 1975). Extrinsically motivated behaviors, on the other hand, refer to behaviors in which an external controlling variable can be readily identified. According to Deci (1975), intrinsic motivation is demonstrated when people engage in an activity for its own sake and not because of any extrinsic reward. The result of such behavior is an experience of interest and enjoyment; people feel competent and self-determining, and they perceive the locus of causality for their behavior to be internal. Intrinsically motivated behavior is seen to be innate and is said to result in creativity, flexibility, and spontaneity (Deci & Ryan, 1985). In contrast, extrinsically motivated actions are characterized by pressure and tension and result in low self-esteem and anxiety (Deci & Ryan, 1985). A great deal of debate has surrounded the intrinsic/extrinsic distinction. Several critics (e.g., Guzzo, 1979; Scott, 1975) point out difficulties in identifying intrinsically motivated behaviors. Although many human behaviors appear to occur in the absence of any obvious or apparent extrinsic consequences, they may, in fact, be due to anticipated future benefits (Bandura, 1977) or intermittent reinforcement (Dickinson, 1989). From this perspective, intrinsically motivated behavior is simply behavior for which appropriate controlling stimuli have yet to be specified. In spite of these conceptual difficulties, other social scientists frequently accept the intrinsic/extrinsic distinction. In fact, a large body of research is concerned with the effects of extrinsic rewards and reinforcers on behavior that is thought to have been previously maintained by intrinsic motivation. The next section of this article presents a description of the early studies concerned with the effects of reward and reinforcement on intrinsic motivation, the various research designs used to further investigate the issue, the variables investigated, and major findings.

The Effects of Reward and Reinforcement on Intrinsic Motivation The terms reward and reinforcement have frequently been used synonymously. Although this is the case, behavioral psychologists make an important distinction between the two terms. A reinforcer is an event that

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increases the frequency of the behavior it follows. A reward, however, is not defined by its effects on behavior. Rewards are stimuli that are assumed to be positive events, but they have not been shown to strengthen behavior. Incentive systems (e.g., classroom token economies) may be based on reward or reinforcement and are designed to increase motivation. Because of these distinctions (between reward and reinforcement), this review separates those studies dealing with effects of reward from those concerned with the effects of reinforcement on intrinsic motivation.

The Early Studies The first laboratory investigations to test the effects of reward on intrinsic motivation were conducted by Deci (1971, 1972a, 1972b). In the first experiment (1971), 24 college students, fulfilling a course requirement, were presented with a puzzle-solving task (Soma, a commercial puzzle, produced by Parker Brothers, composed of seven different shapes that can be solved in a variety of ways). The Soma puzzle was chosen because it was believed that college students would be intrinsically interested in the task. The study was made up of three 1-hour sessions over a 3-day period. Twelve subjects were assigned to an experimental group; the other 12 to a control group. During each session, subjects were individually taken to a room and asked to work on the Soma puzzles in order to reproduce various configurations which were drawn on a piece of paper. Four puzzles were presented in a session, and subjects were given 13 minutes to solve each one. In the second session only, experimental subjects were told that they would receive $1.00 for each puzzle solved. Control subjects were offered no money. In the middle of each session, the experimenter made an excuse to leave the room for 8 minutes. Subjects were told that they could do as they pleased. During these 8-minute periods, the experimenter observed the subjects through one-way glass and recorded the time that each subject spent engaged on the Soma task. The amount of time spent on the task during the free periods was taken to be the measure of intrinsic motivation, the dependent variable. Deci hypothesized that reward (money) would interfere with subsequent intrinsic motivation and that subjects in the experimental group would spend less time on the task in the third session than they had in the first. He suggested that there would be a significant difference between the experimental and control subjects on this measure. Using a one-tailed t test, Deci found the difference between the two groups to be significant at p < .10. The rewarded group spent less time on the task than the control group. Although social scientists do not generally accept results at p > .05 as significant, and although Deci (1971) noted the marginal nature of his result, the data have been taken as support for the hypothesis that

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If a person is engaged in some activity for reasons of intrinsic motivation, and if he begins to receive the external reward, money, for performing the activity, the degree to which he is intrinsically motivated to perform the activity decreases. (Deci, 1971, p. 108)

Deci’s experiment is often cited as groundbreaking evidence for the negative effects of reinforcement on intrinsic motivation (e.g., Kohn, 1993). Given the distinction between reward and reinforcement, however, Deci’s (1971) experiment, at most, demonstrates that rewards may have a negative impact on a person’s interest in a task. Nonetheless, his study was the first to investigate an issue that was of prime concern to many psychologists. The experiment provided researchers with a way to measure intrinsic motivation and with a paradigm to investigate the negative effects of reward. In another experiment, Deci (1971; Experiment 3) used the same experimental paradigm to investigate the effects of verbal reward. The reward contingency introduced in the second session was verbal praise, rather than money. During the second phase, subjects in the experimental group were told after each trial that their performance was very good or much better than average. Deci found that the reinforced group spent significantly more time on the task (difference scores between Session 3 and Session 1) than those who received no praise (p < .05). These results suggest that social rewards may increase the motivation to perform an activity. One of the best known and most cited studies on the detrimental effects of reward on behavior is the work of Lepper, Greene, and Nisbett (1973). In this study, nursery school children were observed in a free-play period to determine their initial interest on an activity (drawing). Two observers sat behind a one-way glass and recorded the amount of time each child was engaged in the activity. Those children who spent the most time on the task were selected as subjects for the experiment. Three experimental conditions were employed. In the “expected-reward” condition, children were offered a “good-player” award, which they received for drawing with magic markers. Children in the “unexpected-reward” group received the award but were not promised it beforehand, and “no-reward” subjects did not expect or receive an award. In a subsequent free-play session, those children who were promised an award (expected-reward subjects) spent significantly less time drawing than the other two groups. Furthermore, the expected-reward group spent less time drawing in the postexperimental session than they had in the initial session (preexperimental free-play session). The unexpected-reward and no-reward subjects showed slight increases in time on task from preexperimental to postexperimental sessions. Lepper et al. (1973) concluded that their results provided “empirical evidence of an undesirable consequence of the unnecessary use of extrinsic rewards,” (p. 136). However, those who received an unexpected reward spent more time on the task during the postexperimental free-play period than either the

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expected-reward or the control group. Because the unexpected- and expectedreward groups are both reward conditions, the conclusion that these results demonstrate the negative effects of reward may not be warranted. This is because reward was held constant in the unexpected-reward and expectedreward groups; what differed was promise or no promise. That is, the promises made or the instructions given could have produced these results. Nonetheless, the findings of Lepper et al.’s (1973) study are frequently cited in journal articles and introductory psychology textbooks as evidence that extrinsic rewards and reinforcement undermine intrinsic interest in a task. The early studies by Deci (1971) and Lepper et al. (1973) have raised a number of issues and controversies that have generated considerable research. Some psychologists have claimed that the original findings provide evidence for the view that reinforcement decreases intrinsic motivation (e.g., Schwartz, 1990). Others recognize that not all types of reinforcement undermine intrinsic interest (e.g., Deci & Ryan, 1985). Still others argue that one must demonstrate that rewards are, in fact, reinforcers before any statements about the effects of reinforcement can be made (Feingold & Mahoney, 1975; Mawhinney, 1990). Several researchers are cautious about equating reward with reinforcement; their focus has been to discover when and under what conditions reward is detrimental (Bates, 1979; Morgan, 1984). In order to address these issues, researchers have employed a variety of research paradigms.

Between-Group Designs Studies designed to assess the effects of reward on intrinsic motivation have been conducted using between-group designs. Typically, one of two methods is employed. The first method, referred to as a before-after design (Deci & Ryan, 1985), involves a three-session paradigm. In these studies, a baseline measure of intrinsic motivation on a particular task is taken. This entails measuring time on task in the absence of extrinsic reward, usually from a session of short duration (e.g., 10 minutes). Subjects are then assigned to a reward or no-reward (control) condition, and an intervention with extrinsic rewards is carried out. Following this, reward is withdrawn, and time on task is again measured. The procedure is identical for both groups except that control subjects do not experience the intervention in the second session. Mean differences in time on task between pre- and postintervention are calculated for each group, and the scores for the experimental and control subjects are then statistically compared. Any difference between the two groups is considered evidence of the effects of withdrawal of reward. One advantage to the before-after procedure is that it allows the researcher to examine differences within groups from pre- to postexperimental sessions as well as differences between groups. In most studies of this type, however, only differences between groups are investigated. This is because the before-after

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procedure has generally been used to identify individuals who show an initial interest in a specific task; those people are then selected as subjects for the study. In such cases, differences between rewarded and nonrewarded subjects are usually measured in the after-reward session only. Most researchers have used an after-only between-groups experimental design to assess the effects of rewards on intrinsic motivation. In this approach, no pretreatment measure of intrinsic interest is collected. In the typical experiment, subjects are presented with a task that is assumed to be intrinsically motivating – solving and assembling puzzles, drawing with felt-tipped pens, word games, and so on. Experimental subjects are rewarded with money or grades, candy, praise, good-player awards, and so forth for performing the activity. In some studies, the reward is delivered contingent on a certain level of performance on the task; in others, subjects are simply rewarded for participating in the task. Control subjects are not rewarded. The reward intervention is usually conducted over a 10-minute to 1-hour period. All groups are then observed during a nonreward period. This usually occurs immediately after the experimental session, although some researchers have observed subjects several weeks later. If experimental subjects spend less time on the task (during the postreward observation) than the controls, reinforcement/reward is said to undermine intrinsic motivation. The amount of time subjects spend on the task during the nonreward period is one of the major ways in which intrinsic motivation has been measured, and it is usually referred to as free time on task.

Findings from the Group Design Studies Generally, the results of the group design studies examining the main effects of rewards are conflicting. While some researchers have found that rewards lead to decreased time on the task relative to control groups (e.g., Deci, 1971; Fabes, 1987; Morgan, 1981), others report the opposite (e.g., Brennan & Glover, 1980; Deci, 1972a; Harackiewicz, Manderlink & Sansone, 1984). Some studies report no significant differences (e.g., Amabile, Hennessey, & Grossman, 1986; DeLoach, Griffith & LaBarba, 1983). Not all studies use the free-time measure of intrinsic motivation. Other dependent variables have included self-reports of task enjoyment, interest, and satisfaction; performance during the free time period (number of puzzles/ problems solved, number of drawings completed, etc.); and willingness to volunteer for future projects without reward. Overall, the results from studies employing these measures are conflicting and do not help to clarify the issue of whether reward leads to decreased intrinsic motivation. A number of reviewers (e.g., Bates, 1979; Deci & Ryan, 1985; Dickinson, 1989; Morgan, 1984) have noted the contradictory nature of the findings and have attempted to identify the conditions under which extrinsic reward produces decrements in intrinsic motivation. Some of the conditions thought to be critical in determining the impact of rewards include the type of reward (tangible or verbal), reward expectancy (whether reward is expected – i.e., offered

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beforehand or received unexpectedly), and reward contingency (whether reward is delivered simply for performing the task or is contingent on some specified level of performance). Although this categorization system is useful, an examination of the literature within each category reveals conflicting results.

Type of Reward When verbally praised subjects are compared to a control group, some researchers have found an increase in intrinsic motivation (e.g. Deci, 1971) while others report no significant differences (e.g., Orlick & Mosher, 1978). The same holds true when subjects receiving tangible rewards are compared to controls. While some results provide evidence for a decrease in intrinsic motivation following the receipt of a tangible reward (e.g., Danner & Lonkey, 1981), others indicate an increase (e.g., Rosenfìeld, Folger, & Adelman, 1980).1 Reward Expectancy Comparisons between subjects who receive an unexpected tangible reward and subjects who receive no reward are also not clear cut. Some results indicate that unexpected-reward subjects show a decrease in intrinsic motivation (e.g., Orlick & Mosher, 1978); others have found no significant differences (e.g., Greene & Lepper, 1974). Experiments designed to investigate the effects of expected tangible rewards are also contradictory. Some studies, comparing subjects offered an expected reward to nonrewarded controls, show a negative effect of reward on intrinsic motivation (e.g., Deci, 1971; Lepper, Greene, & Nisbett, 1973). Others, however, demonstrate that expected-reward subjects show an increase in intrinsic motivation relative to controls (e.g., Brennan & Glover, 1980). Reward Contingency Morgan (1984) and Deci and Ryan (1985) suggest that reward contingency may play a critical role in determining the negative effects on intrinsic motivation. Again, however, results from such studies vary. When rewards are delivered contingent on some level of performance, some researchers have found a positive effect (e.g., Karniol & Ross, 1977); others report negative findings (e.g., Ryan, Mims, & Koestner, 1983). When rewards are delivered contingent on engagement in the task regardless of subjects’ level of performance, an undermining effect has been found in some studies (e.g., Lepper, Greene & Nisbett, 1973; Morgan, 1983, Experiment 1). Others report no decrease in intrinsic motivation (e.g., Pittman, Emery & Boggiano, 1982).

Within-Subject Designs One of the criticisms of the group design research is that researchers employing such a design often refer to their reward manipulation as a reinforcement procedure. By definition, a reinforcer is an event that

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increases the frequency of the behavior it follows. In most studies on intrinsic motivation, researchers have not demonstrated that the events used as rewards increased the frequency of the behavior studied. In addition, critics (e.g., Feingold & Mahoney, 1975; Mawhinney, 1990) suggest that the measurement phases in the group design research are too brief to detect any temporal trends and transition states. In order to address these issues, a few studies have been conducted using a repeated measures, within-subject design. In this paradigm, the amount of time subjects spend on a particular task is measured over a number of sessions. Reinforcement procedures are then implemented over a number of sessions. In the final phase, reinforcement is withdrawn, and time on task is again repeatedly measured. Intrinsic motivation is indexed as a difference in time on task between pre- and postreinforcement phases where differences are attributed to the external reinforcement. In general, no substantial differences have been found when rate of performance and time on task in postreinforcement sessions are compared to pre-reinforcement phases (although, see Vasta & Stirpe, 1979). The advantage of within-subjects designs is that the researcher can determine whether the rewards used are actual reinforcers – that is, whether behavior increases during the reinforcement phase. Statements can then be made about the effects of reinforcement, rather than reward. However, only a handful of studies have been conducted using this type of design. Critics of within-subject research (e.g., Deci & Ryan, 1985) suggest that results from these designs are not generalizable because so few subjects are studied in any one experiment. A further criticism has to do with the lack of a control group. The argument is that in the within-subject designs there is no group that performs the activity without reinforcement; thus, one cannot know if there is an undermining effect relative to a control group. Finally, for these studies, the definition of a reinforcer is necessarily circular. That is, reward becomes reinforcement only after its effects are shown to increase behavior.

Theoretical Accounts of the Literature Although the results of laboratory investigations into the effects of reward and reinforcement on intrinsic motivation appear contradictory and confusing, a general contention in many textbooks and journal articles is that reward and/or reinforcement is detrimental to an individual’s intrinsic motivation (e.g., Kohn, 1993; McCullers, 1978; Schwartz, 1990; Zimbardo, 1988). In an attempt to account for the disparate outcomes, a few psychologists have offered theoretical explanations. Three major accounts are outlined below.

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The Overjustifìcation Hypothesis One explanation that has been put forth to account for the detrimental effects of reward is termed the overjustifìcation effect (Lepper, Greene, & Nisbett, 1973). This hypothesis is largely based on attribution (Kelly, 1967) and selfperception (Bem, 1972) theories. A person’s perceptions about the causes of behavior are hypothesized to influence future motivation and performance. In the presence of external controls, people attribute their behavior to an external agent; when this is removed, future motivation and performance decrease. Conversely, behavior is attributed to internal causes in the absence of obvious external controls. In this case, motivation and performance are not affected. A decrease in intrinsic motivation following the withdrawal of a reward has been termed the overjustifìcation effect because it is thought that an external reward provides overjustifìcation for participating in an already attractive activity. Put another way, when individuals are rewarded for engaging in an already interesting activity, their perceptions shift from accounting for their behavior as self-initiated to accounting for it in terms of external rewards. That is, they are faced with too many reasons (justifications) for performing the activity, and the role of intrinsic motivation is discounted resulting in a decline in intrinsic motivation. Lepper (1981) has suggested that extrinsic rewards lead to a decrease in intrinsic motivation when they allow perceptual shifts of causality. According to Lepper, this occurs when there is sufficient initial interest in an activity, when the extrinsic rewards are salient, and when rewards do not increase perceived competence.

Cognitive Evaluation Theory Deci and Ryan (1985) suggest that the overjustifìcation hypothesis should not be considered a theory of motivation. They argue that self-attributions may affect intrinsic motivation, but they do not see them as necessary mediators. Instead, Deci and Ryan offer cognitive evaluation theory as an explanation for intrinsic motivation. Cognitive evaluation theory is based on the assumption that people have innate needs for competence and self-determination. From this perspective, a person’s intrinsic motivation is affected by changes in feelings of competence and self-determination. According to Deci and Ryan (1985), events facilitate or hinder feelings of competence and self-determination depending on their perceived informational, controlling, or amotivational significance. Events seen as informational indicate skill in performing a task; hence, competence is facilitated, which leads to increased intrinsic motivation. A controlling event is one perceived as an attempt to determine behavior. This type of event diminishes an individual’s self-determination

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and intrinsic motivation. An amotivational event provides negative feedback, indicating a lack of skill, which reduces one’s competence and intrinsic motivation. Cognitive evaluation theory focuses on a person’s experiences of an activity. For this reason, Deci and Ryan (1985) emphasize the importance of self-report measures of task interest, satisfaction, and enjoyment as more indicative of intrinsic motivation than the free time-on-task measure. According to cognitive evaluation theory, rewards are not always harmful. Verbal rewards may be informational and lead to an increase in intrinsic motivation. Tangible rewards, on the other hand, are seen as controlling when their delivery is stated before the reward period (expected rewards). This is because the cognitive evaluation process is believed to begin while the rewarded activity is occurring. Further, rewards promised to persons for engaging in a task without a performance criterion (referred to as expected task contingent rewards by Deci & Ryan, 1985) are controlling and decrease intrinsic motivation. Deci and Ryan suggest that rewards delivered to a person contingent on a specified level of performance are more complicated. This type of reward can be informational or controlling, but the difficulty is that its function can only be determined by how well a person performs in relation to the specified standard. If the individual performs well, the reward is informational, and, if performance is poor, it is controlling. Rummel and Feinberg (1988) conducted a meta-analysis to assess cognitive evaluation theory. Subjects who received rewards that were defined to convey “controlling” information were compared to groups receiving other types of rewards or no reward. The dependent measure of intrinsic motivation was a combination of both free time-on-task measures and self-reports of satisfaction and task interest. Results provided support for cognitive evaluation theory. Rummel and Feinberg concluded that controlling, extrinsic rewards have detrimental effects on intrinsic motivation. In Rummel and Feinberg’s meta-analysis, rewards were defined as controlling after the fact. That is, when a reward was found to produce a negative effect, it was seen as controlling, and the study was selected for the analysis. This exemplifies the major difficulty with cognitive evaluation theory. Rewards are defined as controlling or informational after their effect on performance has been measured. Although cognitive evaluation theory may account for the diverse findings of the effects of reward on intrinsic motivation, there are difficulties with this interpretation. One problem is that feelings of competence and self-determination are seen as causes of changes in intrinsic motivation, but they are not measured. They are assumed to be operating because behavior changes. In other words, the existence of competence, selfdetermination, and intrinsic motivation is inferred from the very behavior it supposedly causes. Rewards are defined as controlling if measures of

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intrinsic motivation decrease and informational when the dependent variable indexes an increase in motivation.

Behavioral Accounts An operant analysis of behavior involves consideration of a prior learning history and the three-term contingency, the SD: R → Sr relationship. The three terms are: (a) discriminative stimulus (SD) or setting event, (b) the response (R) or behavior, and (c) contingent reinforcement (Sr). Flora (1990) has suggested that all of the empirical results of the intrinsic motivation research can be accounted for by considering the promised reward procedures (expected reward) as discriminative stimuli. That is, telling a person that he or she will receive a reward is a stimulus event that precedes the operant and, as such, is a discriminative stimulus rather than a reinforcer. From this perspective, if behavior is regulated by its consequences (i.e., reinforcement), no loss of intrinsic motivation is expected. When individuals who are engaged in a task are reinforced for doing the task, they will spend as much time on the activity as they originally did once the reinforcer is withdrawn. A behavioral view suggests that it is only when rewards function as discriminitive stimuli that one might expect to observe a decline in intrinsic motivation. Although discriminitive stimuli are part of the three-term contingency and affect the probability of an operant, they can and do have very different effects from reinforcers. Task performance evoked by instructions and promises of reward (SDs) can be influenced by a number of factors such as the subject’s history with respect to whether promised rewards were actually received, the subject’s verbal repertoire, the nature of prior exposure to the object being offered as the reward, and so on (Dickinson, 1989).

Summary The overjustifìcation effect, cognitive evaluation theory, and recent behavioral explanations each attempt to account for the disparate effects of reward and reinforcement on intrinsic motivation. Given the diverse findings reported in this literature, however, it is not clear at this point what effect reward or reinforcement has on intrinsic motivation. Reviewers on all sides of the issue tend to be highly critical of research designed outside of their own paradigm, and, more often than not, findings from studies in opposing camps are not considered relevant. For these reasons, the literature and its interpretations are still contentious. Because a substantial number of experimental studies have been carried out to assess the effects of reward and reinforcement on intrinsic motivation, one way to evaluate their effects is to conduct a meta-analysis.

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The Present Meta-Analysis The primary purpose of the present meta-analysis is to make a causal statement about the effects of extrinsic rewards and reinforcement on intrinsic motivation. This analysis should be useful in addressing a number of concerns. Of major importance is whether the bulk of evidence suggests that extrinsic rewards and/or reinforcement produce decrements in intrinsic motivation. If so, what is the size of the relationships being uncovered? Also, do different patterns emerge with different reward types (e.g., tangible, verbal rewards), reward expectancies (expected, unexpected), or reward contingencies (e.g., rewards delivered for engaging in a task, competing or solving a task, or meeting a specified level of performance)? In the following sections of this article, the research questions addressed in the present meta-analysis are outlined, the steps involved in conducting the meta-analysis are described, and the findings are presented and discussed.

Research Questions The following questions have been addressed in this meta-analysis: 1. Overall, what is the effect of reward on intrinsic motivation? In order to answer this question, a meta-analysis of the group design experiments was conducted. Subjects who received a tangible reward and/or an extrinsic verbal reward were compared to a nonrewarded control group. This analysis should shed light on the overall effects of reward on intrinsic motivation. 2. What are the effects of specific features of reward on intrinsic motivation? Several researchers note that reward interacts with other variables to produce increments or decrements in intrinsic motivation. That is, intrinsic motivation is affected differently by the type of reward implemented, the reward expectancy and the reward contingency. Specifically, researchers have investigated the following: (a) the effect of reward type on intrinsic motivation (i.e., whether rewards are verbal or tangible), (b) the effect of reward expectancy on intrinsic motivation (i.e., whether rewards are expected – promised and delivered to subjects or unexpected – delivered to subjects but not promised), (c) the effect of reward contingency on intrinsic motivation (i.e., whether rewards are delivered to subjects for participating in an experimental session regardless of what they do, for engaging in a task, for completing or solving a task, or for attaining a specific level of performance). All analyses performed on these features were conducted with group design studies in which a rewarded group was compared to a control group. These

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analyses should lead to a greater understanding of the specific conditions under which reward affects intrinsic motivation. Although the present analyses present a breakdown of several features of reward, there are other moderator variables mentioned in the literature (e.g., salience of reward, task type, reward attractiveness, goals of individuals, etc.). These conditions may interact with reward to affect intrinsic motivation. Unfortunately, these variables appear in only one or two studies and are, thus, not amenable to a meta-analysis. At this point in time, placing emphasis on interaction effects that have few replications would not be beneficial to an understanding of reward and intrinsic motivation. 3. Overall, what is the effect of reinforcement on intrinsic motivation? One of the criticisms of the group designs has been that reward is frequently cited as synonymous with reinforcement, yet no evidence has been provided to indicate that the rewards used in group designs are actual reinforcers. In the single-subject, repeated measures designs, researchers have demonstrated that the rewards administered increased behavior and can be considered as reinforcers. For this reason, a separate analysis was conducted with the single-subject designs where subjects served as their own controls. This analysis should allow a more definitive statement to be made about the effects of reinforcement on intrinsic motivation.

Method Selection of Studies A basic list of studies was assembled by conducting a computer search of the psychological literature (PSYCH LIT) using intrinsic motivation as the search term. The meta-analysis started with Deci (1971), and relevant articles published up to September 1991 were identified. Studies not listed on the computer database were identified through the bibliographies of review articles, chapters, books, and papers located in the original search. Two sets of studies were collected (between-group designs and withinsubject designs). The main analysis entailed assessing the overall effects of reward on intrinsic motivation from studies involving group designs. Criteria for including studies in the sample were: (a) that the study involve an experimental manipulation of a reward condition and include a nonrewarded control group; (b) that any characteristics of rewarded subjects be either held constant or varied but be represented identically for both rewarded and control groups; and (c) that studies be published (no unpublished documents were collected) and written in English.2

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In addition, only studies that measured intrinsic motivation as a dependent variable were included. Intrinsic motivation has been measured as free time on task after withdrawal of reward; self-reports of task interest, satisfaction, and/or enjoyment; performance during the free time period (number of puzzles/problems solved, number of drawings completed, etc.); and subjects’ willingness to participate in future projects without reward. One study which met the criteria was excluded (Boggiano & Ruble, 1979) because the statistical contrasts used in the article were not logical given the sample size of the study.3 Other studies were omitted from the sample if some subjects in a reward condition were not actually given a reward (e.g., Pritchard, Campbell, & Campbell, 1977). The resulting sample consisted of 83 documents, reporting 96 independent studies. A major criticism of the meta-analytic technique has been that researchers often lump different measures together. This has been referred to as the apples-and-oranges problem in that it is argued that logical conclusions cannot be drawn from comparisons of studies using different measures of the dependent variable (see Glass, McGaw, & Smith, 1981). In order to avoid this problem, separate analyses were conducted on the overall effect of reward for each measure of intrinsic motivation. Using this strategy, 61 studies compared a rewarded group to a control group on the free-time measure; 64 studies investigated the attitude (task interest, enjoyment, and satisfaction) measure; 11 studies assessed the willingness to volunteer for future studies without reward measure, and 12 studies measured performance during the free-time period. In order to assess the impact of specific features of reward, further analyses were conducted with data from the 96 group design studies. In these analyses, subjects assigned to different types of rewards (tangible, verbal), reward expectancies (unexpected, expected), and reward contingencies were compared to nonrewarded control groups. The second meta-analysis was conducted on studies that employed a within-subject, multiple-trials design. In this type of design, subjects served as their own controls. These experiments are conducted in three phases with a number of sessions in each phase. Baseline measures of intrinsic motivation are taken in the first phase; reinforcement procedures are then implemented over a number of sessions, and in the third phase reinforcement is withdrawn. Changes in intrinsic motivation are measured as differences between the pre- and postreinforcement phase. Single-subject studies were included in this analysis when a reinforcement effect was demonstrated (i.e., the rewards used showed an increase in behavior) and when baseline, reinforcement, and postreinforcement phases involved repeated measures. One study reporting a reinforcement effect was excluded (Vasta, Andrews, McLaughlin, Stirpe, & Comfort, 1978, Experiment 1) because the authors reported only one measure of behavior

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during the postreinforcement phase. Two studies used a repeated measures group design to assess the effects of reinforcement between and within groups (Greene, Sternberg & Lepper, 1976; Mynatt, Oakley, Arkkelin, Piccione, Margolis, & Arkkelin, 1978). Although subjects’ performance in these studies was measured repeatedly as in the single-subject designs, only group effects were reported. In addition, the rewards used in these studies were not shown to be reinforcers for some of the rewarded groups. Thus, these two studies were not included in the meta-analysis of single-subject designs (Mynatt et al., 1978, are included in the meta-analysis of group designs because their study included a nonrewarded control group). In all, five studies were selected for the within-subject meta-analysis. A list of studies included in the meta-analyses is presented in Appendix A.

Coding of Studies Once all relevant articles had been collected, each study was read and coded. The following general information was extracted from each report: (a) author(s), (b) date of publication, (c) publication source, (d) population sampled (children or adults), (e) sample size, (f) type of experimental design (before-after groups design, after-only groups design, or single-subject multiple-trial design), and (g) type of task used in the study. The following aspects of the independent variable were also coded: (a) reward type (tangible or verbal), (b) reward expectancy (expected or unexpected) and (c) reward contingency. Reward contingency was coded according to Deci and Ryan’s (1985) taxonomy. Task noncontingent rewards referred to rewards delivered to subjects for participating in an experimental session regardless of what they did in the session. The term task contingent reward was used to mean that a reward was given for actually doing the task and/or for completing or solving the task. Performance contingent rewards were defined as rewards delivered for achieving a specified level of performance. In addition to using Deci and Ryan’s classification, contingency was also coded in accord with a behavioral perspective. Using operant definitions, rewards were defined as noncontingent or contingent. Noncontingent rewards referred to rewards delivered for participating in the study or engaging in the task regardless of any level of performance. Contingent rewards were defined as rewards dependent on performance (i.e., rewards given for completing a puzzle, solving a task, and/or attaining a specified level of performance). Other characteristics of studies that were coded were: (a) type of dependent measure (e.g., free time on task, task interest, etc.), (b) whether experimenter was blind to conditions, and (c) whether experimenter was present or absent during the post-reward phase. As well, statistical information was recorded, and effect sizes were calculated from appropriate contrasts.

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Descriptive characteristics and effect sizes of the reviewed studies are summarized in Appendix C.

Intercoder Reliability From the pool of relevant studies, 10 were randomly selected and independently coded by the second author. A standardized coding form4 was created that allowed the second coder to extract information regarding independent variables (reward type, reward expectancy, reward contingency), dependent variables (measures of intrinsic motivation), sample sizes, type of task used in the study, and calculation of effect sizes for available contrasts. Reliability calculated as percentage agreement was 93.4%. For 6 of the 10 studies, agreement was 100%. Disagreements in the other four studies involved (a) miscommunication of formulas to use for calculating effect size (for two studies), (b) mislabeling of reward expectancy (in one study), and (c) a misreading of the number of subjects in a group (in one study). Disagreements were resolved through discussion and a more careful reading of the studies and coding criteria.

Computation and Analysis of Effect Sizes The procedures used in the meta-analysis of the group design studies followed those of Hedges and Olkin (1985). Meta-analysis is a statistical technique for aggregating the results of many experimental studies which compare two groups on a common dependent measure. Once the studies and groups to be compared are identified, the statistical result of each study is transformed into a measure called an effect size. An effect size is found by converting the findings from each study into a standard deviation unit. The effect size indicates the extent to which experimental and control groups differ in the means of a dependent variable at the end of a treatment phase. In its simplest form, the effect size calculated, g, is the difference between the means of the rewarded group and a nonrewarded control group divided by the pooled standard deviation of this difference. When means or standard deviations were not available from reports, effect size was calculated from t tests, F statistics, and p-level values (e.g., p < .05) by using Hedges and Becker’s (1986) formulas. Formulas for calculating effect size are listed in Appendix B. One problem that arises in conducting a meta-analysis is determining effect sizes from studies with limited information. In a few studies, for example, contrasts are simply reported as t or F < 1.00. In such cases, effect size estimates were calculated by making t or F equal to a number between 0.01 and 1.00 chosen from a random numbers table. When results from a study were not reported or were reported as nonsignificant and when t or F values were not available but means and/or direction of means were

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known, a random number between 0.01 and the critical value of t or F at p = .05 was chosen to calculate an estimate of effect size. When results for an outcome measure were not reported or were reported as nonsignificant and when means and direction were unknown, the effect size for that measure was set at 0.00 (indicating exactly no difference between rewarded and nonrewarded groups). For each analysis, results were calculated with 0.00 values included and with 0.00 values omitted. For several studies, more than one effect size was calculated. For example, if a single study contained two measures of intrinsic motivation (e.g., free time on task, attitude) and two types of reward groups plus a control group (e.g., tangible reward, verbal reward), a total of four effect sizes was calculated (e.g., free time-tangible reward, free time-verbal reward, attitude-tangible reward, attitude-verbal reward). In order to satisfy the independence assumption of meta-analytic statistics (Hedges & Olkin, 1985), only one effect size per study was entered into each analysis. When two or more effect sizes from one study were appropriate for a particular analysis, these effect sizes were averaged. To illustrate, for the estimate of the overall effect of reward on the free-time measure of intrinsic motivation, some studies assessed the effects of several types of rewards. If a single study, for example, contained two or more reward groups (e.g., expected reward, unexpected reward) and a control condition, the two effect sizes were averaged so that the study contributed only one effect size to the overall analysis of reward. For an analysis of the effects of expected reward on intrinsic motivation, only the one appropriate effect size from the study would be used. This strategy retained as much data as possible without violating the assumption of independence. Average effect sizes were obtained by weighting each g index by the number of participants on which it was based (see Cooper, 1989). As was previously mentioned, in the single-subject, repeated measure designs, there is no separate control group; subjects serve as their own controls. An increase or decrease in intrinsic motivation is indexed by a difference in the amount of time spent on the task between baseline and postreinforcement sessions. Effect sizes for these studies were calculated by subtracting the average time spent by all subjects in the baseline phase from the average time spent by all subjects in the postreinforcement phase. This number was then divided by the pooled standard deviation. After all effect sizes were calculated, the analyses were run on the computer program Meta (Schwarzer, 1991). Results reported in this article are based on the weighted integration method (Hedges & Olkin, 1985). Using this technique, effect sizes g are converted to ds by correcting them for bias (g is an overestimation of the population effect size, particularly for small samples; see Hedges, 1981). To obtain an overall effect size, each effect size is weighted by the reciprocal of its variance, and the weighted ds are averaged. This procedure gives more weight to effect sizes that are more

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reliably estimated. Once mean effect sizes are calculated, 95% confidence intervals are constructed around the weighted mean. In order to verify the accuracy of the computer program, one analysis (the overall effect of reward on free time) was hand calculated. All obtained values from the meta-analysis program and the hand calculations were identical within rounding error. To determine whether each set of effect sizes in a sample shared a common effect size (i.e., was consistent across studies), a homogeneity statistic, Q, was calculated. Q has an approximate chi-square distribution with k–1 degrees of freedom, where k is the number of effect sizes (Hedges & Olkin, 1985). The null hypothesis is that the effect sizes are homogeneous (i.e., effect sizes in a given analysis are viewed as values sampled from a single population; variation in effect sizes among studies is merely due to sampling variation). For purposes of the present analyses, samples were considered homogeneous at p > .01. When samples are not homogeneous, studies can be classified by characteristics, such that effect sizes within categories are homogeneous. This strategy was undertaken by examining the effects of different types of rewards, reward expectancies, and reward contingencies. As a supplementary analysis, homogeneity was attained by removing outliers. That is, studies were omitted when they provided estimates that were inconsistent with those from other studies. Outliers in each data set were first identified using Tukey’s (1977) procedure. These outliers were then omitted from the analysis. If homogeneity was still not attained, other studies that reduced the homogeneity statistic by the largest amount were removed. Hedges (1987) has pointed out that this is a common procedure in both the physical and social sciences. In one area of physics, for example, Hedges (1987) found that data from 40% of the available studies were omitted from calculations. For meta-analyses of psychological topics, Hedges (1987) notes that removal of up to 20% of the outliers in a group of heterogeneous effect sizes usually results in a high degree of homogeneity. In an article in Psychological Bulletin, McGraw and Wong (1992) noted that one of the problems with effect size statistics (e.g., d) is that many readers of meta-analyses have difficulty interpreting the meaning and generalizability of findings. McGraw and Wong have introduced another way to look at effect size, by a statistic they call the common language effect size indicator (CL). CL refers to the probability that a score sampled from one distribution will be greater than a score sampled from some other distribution. McGraw and Wong suggest that CL is a useful way to talk about effect size because it is easily interpretable. They provide an example in which a sample of young adult men is compared to a sample of young adult women on the variable height. A CL of .92 indicates the probability of a male being taller than a female. Put another way, in any random pairing of young adult men and women, the male will be taller than the female 92 out of 100 times.

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CL is calculated from means and standard deviations. Additionally, an effect size, d, can be converted to CL by multiplying d by 1/√2 or 0.707 to obtain a Z value (K.O. McGraw, personal communication, April 24, 1992). The upper tail probability associated with this value corresponds to CL and can be calculated using the unit normal curve. To test the robustness of the CL statistic, McGraw and Wong (1992) conducted a series of 118 tests (simulations) to determine the implications of violating the assumption that sample data come from populations of values that are normally distributed with equal variances. They found small discrepancies between the estimate of CL under the normality assumption and the estimate of CL when the normality assumption was violated in terms of skewness and kurtosis. The worst case discrepancy was 0.1 which occurred with a large violation of the equal variance assumption, considerable negative skewness, and a large violation of kurtosis. Given the robustness of CL and the ease with which it can be interpreted, results from the present analyses have also been expressed using the CL statistic. The meta-analytic procedures used in the present review include: (a) the estimation of average effect sizes and 95% confidence intervals, (b) homogeneity analyses to determine whether effect sizes are drawn from the same population, (c) removal of outliers to attain homogeneity, and (d) conversion of average effect sizes to the common language statistic (CL). Note that outliers are included and excluded in each analysis.

Results from Group Designs The Overall Effect of Reward on Intrinsic Motivation To assess the overall effect of reward on intrinsic motivation, descriptive and meta-analytic procedures were performed on each of the four different measures of intrinsic motivation (free time on task, attitude, performance during the free-time period; willingness to volunteer for future studies without reward). For each measure, negative effects represent a decrement in intrinsic motivation; positive effects indicate an increment.

Direction of Effects The number of studies collected for each analysis of the overall effects of rewards on intrinsic motivation and the direction of their effects is presented in Table 1. On the free-time measure, the majority of studies showed that reward decreased intrinsic motivation. However, when intrinsic motivation was measured by attitude toward a task, performance during the free-time period, or willingness to volunteer for future studies without reward, more studies showed positive effects.

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Table 1: Number of studies and direction of effects for reward versus control groups on four measures of intrinsic motivation Free time

Attitude

Performance in free time

Willingness to volunteer

Showing a positive effect of reward

22

31

6

6

Showing a negative effect of reward

34

15

4

4

Showing no effect

1

1

–

–

With lack of sufficient information to calculate effects

4

17

2

1

61

64

12

11

Number of studies

Total

Distribution of Effect Sizes Frequency distributions of the data are shown in Figure 1. Studies that found no significant differences but did not provide sufficient information to calculate effect sizes are not portrayed in the graphs. When intrinsic motivation was measured as time on task following the removal of a reward (free time), effect sizes ranged from –1.94 to 1.06. The bulk of experiments found effects between – 0.59 and 0.19. Using Tukey’s (1977) procedure, one negative outlier was identified in the free-time data. This effect (g = –1.94) was calculated from a study conducted by Morgan (1983, Experiment 1). In this study, subjects who received an expected, task contingent (noncontingent), tangible reward were compared to no-reward control subjects. The large negative effect could be due to the type of reward (tangible), the reward expectancy, and/or the reward contingency. All of these features are examined in further analyses. In addition, this study was somewhat different from other studies in that subjects who performed the activity for a reward were observed by other subjects. That is, subjects were offered a reward for engaging in an activity while their performance on the task was being watched. Thus, the large negative effect could be a result of an interaction of reward type, expectancy, contingency, and surveillance. The attitude measure of intrinsic motivation refers to subjects’ self-reports of task interest, enjoyment, and/or satisfaction. Effect sizes ranged from –0.69 to +1.98 with the majority of effects falling between –0.19 and +0.59. Two positive outliers in this data set come from studies conducted by Vallerand (1983) and Butler (1987). In both of these studies, extrinsic verbal reward is compared to a no-reward group. The effect of verbal reward on intrinsic motivation is investigated in a subsequent analysis. Effect sizes on the performance measure ranged from –3.72 to + 0.96; the median was +0.03. One large negative outlier (–3.72) comes from a study conducted by Deci (1971, Experiment 2). This study differed from others in that it was a field experiment where students working for a college newspaper were paid to write headlines. Only eight subjects participated,

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12 11 10 9 8 7 6 5 4 3 2 1 0

199

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−0.79 to −0.60 −0.59 to −0.40 −0.39 to −0.20 −0.19 to −0.01 0.00 to 0.19 0.20 to 0.39 0.40 to 0.59 0.60 to 0.79 0.80 to 0.99 1.00 to 1.19 1.20 to 1.39 1.40 to 1.59 1.60 to 1.79 1.80 to 1.99

FREE TIME Number of Studies

10 9 8 7 6 5 4 3 2 1 0

Reinforcement, Reward, and Intrinsic Motivation

−1.99 to −1.80 −1.79 to −1.60 −1.59 to −1.40 −1.39 to −1.20 −1.19 to −1.00 −0.99 to −0.80 −0.79 to −0.60 −0.59 to −0.40 −0.39 to −0.20 −0.19 to −0.01 0.00 to 0.19 0.20 to 0.39 0.40 to 0.59 0.60 to 0.79 0.80 to 0.99 1.00 to 1.19

Number of Studies

Cameron and Pierce

Effect sizes

Effect sizes

PERFORMANCE

Effect sizes

0.60 to 0.79

0.40 to 0.59

0.20 to 0.39

0.00 to 0.19

−0.19 to −0.01

−0.39 to −0.20

0

−0.59 to −0.40

1

3

2

1

0

−3.79 to −3.60 −3.59 to −3.40 −3.39 to −3.20 −3.19 to −3.00 −2.99 to −2.80 −2.79 to −2.60 −2.59 to −2.40 −2.39 to −2.20 −2.19 to −2.00 −1.99 to −1.80 −1.79 to −1.60 −1.59 to −1.40 −1.39 to −1.20 −1.19 to −1.00 −.99 to −.80 −.79 to −.60 −0.59 to −0.40 −0.39 to −0.20 −0.19 to −0.01 0.00 to 0.19 0.20 to 0.39 0.40 to 0.59 0.60 to 0.79 0.80 to 0.99

Number of Studies

2

−0.79 to −0.60

Number of Studies

WILLINGNESS TO VOLUNTEER

Effect sizes

Figure 1: Frequency distributions of effect sizes for overall reward versus control groups on four measures of intrinsic motivation

and two subjects in the control group dropped out and were not included in the analysis. On the willingness-to-volunteer measure, effect sizes ranged from –0.63 to +0.68. There were no outliers in this sample. To establish whether the CL statistic (McGraw & Wong, 1992) could be used confidently in the analyses, the extent to which the free-time distribution of effect sizes deviated from normality was determined. Obtained values for skewness and kurtosis were –0.21 and 0.55, respectively (where normal skewness and kurtosis equal 0.00). McGraw and Wong tested the effect that violations from normality would have on CL. Based on their findings and the skewness and kurtosis values obtained here, in the metaanalysis of effect sizes for the free-time measure, one could expect, at worst, an underestimate of 0.02 and an overestimate of 0.04 for CL. Given this small discrepancy, the implication is that the CL statistic can be used and interpreted without any serious concern about violations of normality and homogeneity of variance.

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Meta-Analysis of Effect Sizes The overall meta-analysis of effect sizes presented in Table 2 allows one to determine whether rewarded subjects showed less intrinsic motivation than nonrewarded subjects as measured by time on task following the removal of reward (free time); self-reports of task interest, satisfaction, and enjoyment (attitude); performance during the free-time period; and willingness to volunteer for future studies without reward. For each measure of intrinsic motivation, an analysis was conducted which included all studies that provided sufficient information to calculate effect sizes (see “All known effects” in Table 2). When samples were not homogeneous, outliers were identified and removed using Tukey’s (1977) procedure. If samples were still significantly heterogeneous, additional outliers were removed. Homogeneity was attained for the free-time and attitude measures by omitting approximately 20% of the effect sizes, a typical meta-analytic procedure. An examination of Table 2 indicates that the procedure of including and excluding outliers does not drastically alter mean effect sizes. On the free-time measure, rewarded subjects showed less intrinsic motivation than nonrewarded controls (mean weighted d = –0.04), but this effect was not significant (i.e., the confidence interval included 0.00). When the mean effect of the homogeneous sample was converted to CL, results indicate that, given a sample of studies designed to investigate the effects of reward on time on task, 51 out of 100 studies would show that overall, rewarded subjects spend less time on the task than nonrewarded controls (assuming that all studies are of equal importance and have the same characteristics). Results from the attitude measure indicate greater intrinsic motivation for rewarded subjects. This effect was small at 0.14 (from the homogeneous sample) but differed significantly from the value of 0.00 (i.e., the confidence interval did not include 0.00). The CL statistic was .54 and can be interpreted to mean that, in comparisons of rewarded to nonrewarded subjects, rewarded subjects will show a more positive attitude toward a task than nonrewarded subjects in 54 out of 100 studies. Rewarded subjects also showed a tendency to score higher on performance measures and to volunteer for the future projects more than nonrewarded subjects, but these effects were not significant. Studies that could not be represented with effect sizes were given a value of 0.00. When these studies were included in the overall analyses (see “All reports” in Table 2), the mean effect size for each measure was little changed. Overall, the results show that reward does not significantly affect intrinsic motivation as measured by free time on task following removal of reward, by performance during the free-time period, or by subjects’ willingness to volunteer for future projects without reward. When intrinsic motivation is measured by attitude toward a task, rewarded subjects report higher intrinsic motivation than nonrewarded subjects. It is important to point out that these main effect

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Table 2: Overall effect of reward versus control groups on four measures of intrinsic motivation Analysis Free time on task All known effects (zeros excluded) Outliers removed using Tukey’s procedure (zeros excluded) Additional outliers removed (no zeros) All reports (zeros and outliers included) Attitude All known effects (zeros excluded) Outliers removed using Tukey’s procedure (zeros excluded) Additional outliers removed (no zeros) All reports (zeros and outliers included) Performance during free time period All known effects (zeros excluded) Outliers removed using Tukey’s procedure (zeros excluded) Additional outliers removed (no zeros) All reports (zeros and outliers included) Willingness to volunteer All known effects (zeros excluded) All reports (zeros and outliers included)

k

Sample size

Mean weighted d

95% CI for d

Q

CL

57

3539

–0.06

−0.13 to 0.01

225.51*

.48

56

3459

–0.03

−0.10 to 0.04

177.40*

.49

44 61

2634 3858

–0.04 –0.06

−0.12 to 0.04 −0.12 to 0.01

66.39 225.80*

.49 .48

47 45

3184 3034

+0.21 +0.17

0.14 to 0.29 0.09 to 0.24

167.50* 110.70*

.56 .55

39 64

2680 4431

+0.14 +0.15

0.06 to 0.22 0.09 to 0.21

58.03 177.07*

.54 .54

10 9

575 569

+0.08 +0.09

−0.09 to 0.25 −0.08 to 0.26

27.90* 21.63*

.52 .52

8 12

509 770

–0.0004 +0.06

−0.18 to 0.18 −0.09 to 0.21

11.73 28.07*

.50 .52

10 11

561 609

+0.05 +0.05

−0.12 to 0.23 −0.12 to 0.22

17.38 17.42

.52 .52

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase. k = number of effect sizes; sample size = sum of n in all studies; mean weighted d = mean of weighted effect sizes (weighted by sample size); CI = confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01.

results should be viewed with caution. This is because many studies show interaction effects that are obscured when results are aggregated. Previous reviewers (e.g., Deci & Ryan, 1985; Morgan, 1984) have suggested that reward type, reward expectancy, and reward contingency may influence the effect of reward on intrinsic motivation. In subsequent analyses, effect sizes have been partitioned into groups based on these characteristics in an attempt to test potential moderator variables and to establish homogeneity of variance.

Interactions: Effect Size as a Function of Reward Characteristics5 In the following section, type of reward and its impact on effect size are presented. Studies are included that measured the effects of either verbal or tangible reward (e.g., money) on intrinsic motivation. The second part

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of this section involves an analysis of reward expectancy (i.e., expected and unexpected rewards). Finally, reward contingency is assessed. Specifically, the question here is whether effect size varies as a function of reward delivered for engaging in a task, completing or solving a task, or achieving a certain level of performance. Studies that could not be represented as effect sizes due to lack of sufficient information are not included in further analyses presented in this article.6

Type of Reward The purpose of the present analyses is to assess the effects of different types of rewards (i.e., tangible and verbal) on intrinsic motivation. Because few studies assessed intrinsic motivation as a function of “performance during the free-time period” and “willingness to volunteer,” no further analyses on these measures have been conducted. Effect sizes for both types of reward on the free-time and attitude measures are presented in funnel distributions in Figure 2. Funnel graphs are used to plot effect size against sample size of the study. The advantage of a funnel display is that it capitalizes on a well-known statistical principle (Light & Pillemer, 1984). That is, the larger the sample, the closer the effect size will come to represent the true underlying population value; variability due to sampling error decreases. Conversely, smaller samples are more prone to sampling error and are likely to deviate considerably about the true mean. For these reasons, the distribution is expected to take the shape of an inverted funnel. An inspection of the funnel distribution of effect sizes for the free-time measure indicates that, overall, larger samples tend to concentrate around zero; greater variation is evident with smaller samples. Verbal reward appears to produce a positive effect. Results of tangible reward suggest a negative effect. These differences suggest that, on the free-time measure, the effects of reward depend on the type of reward. On the attitude measure, positive effects emerge from both tangible and verbal reward studies; verbal reward appears to produce a slightly more positive effect. There is no indication of a publication bias because studies with small sample sizes and near zero effects are represented in the funnel distribution (for a discussion of this issue, see Light & Pillemer, 1984). Although it is not possible to rule out experimenter bias (Rosenthal, 1966), the funnel graphs demonstrate that sampling variability may account for the fact that some researchers find reward has a detrimental effect while others do not. The results from the meta-analysis of the effects of reward type presented in Table 3 indicate that, when studies compared subjects who received a verbal reward (i.e., praise or positive feedback) to those who did not receive a reward, rewarded subjects demonstrated significantly higher intrinsic motivation as measured by both time on task and attitude. On the time measure,

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FREE TIME

160 Tangible Verbal

140 Sample size

120 100 80 60 40 20 0

−2

−1

+1

0 Effect size

+2

160 ATTITUDE 140

Tangible Verbal

Sample size

120 100 80 60 40 20 0

−3

−2

−1

0 Effect size

+1

+2

+3

Figure 2: Funnel distributions of effect sizes for tangible and verbal reward on two measures of intrinsic motivation

homogeneity was attained by removing one outlier. This extreme positive value (+1.61) was obtained from a study conducted in India (Tripathi & Agarwal, 1985). Because all other studies in this analysis came from North America, the large effect size may have been due to differences in the population studied.7 Three outliers from studies measuring the effects of verbal reward were removed to achieve homogeneity on the attitude measure. Inspection of these outliers suggested that they did not differ in obvious ways from other studies in the sample except for their tendency to generate extreme values of effect size. From these analyses, one can estimate that the probability of a sample of verbally rewarded subjects’ being more highly intrinsically motivated than nonrewarded subjects is 0.61 (CL) as measured by time on task and attitude toward task.

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Table 3: Effect size as a function of the type of reward delivered Reward type

Analysis

Free time on task Verbal All known effects Verbal Outliers removed using Tukey’s procedure Tangible All known effects Tangible Outliers removed using Tukey’s procedure Tangible Additional outliers removed Attitude Verbal All known effects Verbal Outliers removed using Tukey’s procedure Verbal Additional outliers removed Tangible All known effects Tangible Outliers removed using Tukey’s procedure

k

Sample size

Mean Weighted d

95% CI for d

Q

CL

15 14

958 918

+0.42 +0.38

0.29 to 0.56 0.25 to 0.52

29.37* 18.96

.62 .61

51 47

2983 2761

–0.20 –0.22

–0.28 to –0.12 –0.30 to –0.14

181.01* 97.55*

.44 .44

43

2591

–0.21

–0.29 to –0.13

63.53

.44

15 13

1024 874

+0.45 +0.30

0.31 to 0.58 0.15 to 0.43

69.71* 26.75*

.63 .58

12

785

+0.39

0.24 to 0.53

8.73

.61

37 33

2362 2149

+0.09 +0.05

0.004 to 0.17 –0.04 to 0.13

143.29* 50.56

.52 .52

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase. k = number of effect sizes; sample size = sum of n in all studies; mean weighted d = mean of weighted effect sizes (weighted by sample size); CI = confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01.

Studies assessing the effects of tangible reward on intrinsic motivation show a decrease on the free-time measure as indicated by a negative mean effect size that differed significantly from 0.00. The CL statistic of .44 implies that subjects who receive a tangible reward will show a decrease in intrinsic motivation as measured by time on task in 56 out of 100 studies. The mean effect size on attitude for subjects given a tangible reward was positive, but once outliers were removed, the mean did not differ significantly from 0.00. In summary, subjects rewarded with verbal praise or positive feedback show significantly greater intrinsic motivation than nonrewarded subjects. Those who receive a tangible reward evidence significantly less intrinsic motivation than nonrewarded subjects, as measured by time on task, but they do not differ in their reports of task interest or enjoyment. The next step in the analysis involves a further breakdown of the effects of tangible reward. The goal is to identify variables that may moderate the effects of tangible reward on intrinsic motivation and to establish within-group

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homogeneity. One factor that may impact effect size is whether the rewards implemented in the studies were promised to subjects prior to the experimental sessions or whether they were received unexpectedly.

Reward Expectancy Within the intrinsic motivation literature, researchers draw a distinction between expected and unexpected reward. Expected rewards refer to a procedure whereby subjects are offered a reward prior to the experimental session and delivered the reward following the session. Subjects who receive an unexpected reward have not been promised the reward beforehand. These terms are generally used to describe procedures involving the administration of tangible rewards. In most studies on verbal reward, praise was delivered unexpectedly and was not contingent on any specified level of performance. The few studies on verbal reward that did employ expected and/or contingency procedures did not produce effect sizes that deviated much from the mean effect size presented in Table 3. For this reason, no further subdivision of effect sizes from verbal reward studies was undertaken. The following analyses concern the effects of tangible reward. Results are displayed in Table 4. Only six studies assessed the effects of unexpected tangible reward on the time measure of intrinsic motivation; five studies investigated attitude. The average effect sizes for unexpected tangible reward versus control groups on free time and attitude were slightly positive but did not differ from 0.00. These results indicate that subjects receiving an unexpected reward do not differ significantly from nonrewarded control subjects on measures of intrinsic motivation. For the expected tangible reward versus control comparisons, expected reward subjects demonstrated significantly less intrinsic motivation on the free-time measure. On attitude, when homogeneity was attained, the two groups did not differ. In the following section of this article, studies comparing expected, tangible reward groups to nonrewarded controls were further subdivided into groups based on reward contingency.

Reward Contingency In some studies, subjects were promised a tangible reward that was delivered for participating in the study or for engaging in a specific task. In other studies, a tangible reward was offered for solving a puzzle, completing a task, and/or attaining a certain level of performance. Rewards administered in these various ways have been labeled by Deci and Ryan (1985) as task noncontingent (rewards offered for participating in the study regardless of what subjects do), task contingent (rewards offered for engaging in a task, and/or

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Table 4: Effect size as a function of reward expectancy for tangible reward versus control comparisons Reward expectancy

Analysis

k

Sample size

Free time on task: Tangible reward versus control Unexpected All known 6 275 effects Expected All known 50 2825 effects Expected Outliers 46 2603 removed using Tukey’s procedure Expected Additional 42 2408 outliers removed Attitude: Tangible reward versus control Unexpected All known 5 311 effects

Mean weighted d

95% CI for d

Q

CL

+0.01

−0.24 to 0.25

7.38

.50

−0.23

−0.30 to −0.15

185.48*

.44

−0.25

−0.33 to −0.17

101.36*

.43

−0.25

−0.33 to −0.16

64.78

.43

+0.06

−0.16 to 0.28

12.42

.52

135.26*

.53

50.48

.52

Expected

All known effects

35

2126

+0.10

0.01 to 0.19

Expected

Outliers removed using Tukey’s procedure

32

1961

+0.07

−0.02 to 0.16

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase. k = number of effect sizes; sample size = sum of n in all studies; mean weighted d = mean of weighted effect sizes (weighted by sample size); CI = confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01.

completing or solving a task), and performance contingent (rewards offered for attaining a specified level of performance). Table 5 presents results from the meta-analysis of these comparisons. Table 5 indicates that when subjects who are promised a tangible reward regardless of what they do in the study (task noncontingent) are compared to nonrewarded controls, no significant difference emerges on the free-time measure of intrinsic motivation. No analyses were conducted with this type of reward contingency on the attitude measure because only two studies of this type assessed attitude. Subjects who receive an expected tangible reward for doing, completing, or solving a task (task contingent) show significantly less intrinsic motivation than controls, as measured by time on task, once reward is withdrawn. On attitude, they show less intrinsic motivation, but this difference is not significant. When rewards are delivered contingent on a certain level of performance, there is no significant effect on the free-time measure; subjects in this condition do, however, report a more positive attitude than controls.

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Table 5: Effect size as a function of reward contingency (as defined by Deci & Ryan, 1985) for expected tangible reward versus control comparisons Reward contingency

Analysis

k

Sample size

Mean weighted d

Free time on task: Expected tangible reward versus control Task nonAll known effects 6 225 +0.55 contingent Task nonOutliers removed 4 124 +0.10 contingent Task All known effects 45 2257 –0.32 contingent Task Outliers removed 44 2177 –0.28 contingent using Tukey’s procedure Task Additional outliers 40 2015 –0.23 contingent removed Performance All known effects 10 484 –0.12 contingent Performance Outliers removed 8 439 –0.13 contingent using Tukey’s procedure Attitude: Expected tangible reward versus control Task All known effects 21 1217 –0.07 contingent Outliers removed Task 20 1157 –0.01 contingent using Tukey’s procedure Task Additional outliers 19 1058 –0.08 contingent removed Performance All known effects 14 819 +0.38 contingent Performance Outliers removed 13 762 +0.29 contingent using Tukey’s procedure Performance Additional outliers 11 682 +0.19 contingent removed

95% CI for d

Q

CL

+0.27 to 0.83

20.02*

.65

–0.26 to 0.45

1.86

.53

–0.41 to –0.24

130.90*

.41

–0.37 to –0.19

94.99*

.42

–0.32 to –0.14

62.08*

.44

–0.31 to 0.06

26.22*

.47

–0.34 to 0.06

17.83

.46

–0.18 to 0.05

53.75*

.48

–0.13 to 0.10

36.24*

.49

–0.20 to 0.04

21.76

.48

0.24 to 0.52

70.03*

.61

0.14 to 0.43

27.35*

.58

0.04 to 0.35

11.54

.55

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase. k = number of effect sizes; sample size = sum of n in all studies; Mean weighted d = mean of weighted effect sizes (weighted by sample size); CI = confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01. No effect size was calculated for the attitude measure of task noncontingent rewards because there were only two studies that fit in this category.

Studies employing various reward contingencies were also categorized using behavioral definitions. Rewards delivered for participating in a study or for engaging in a task are referred to as noncontingent rewards. Rewards are called contingent when they are offered for solving a puzzle, completing a task, or reaching a specified level of performance. The results of this analysis are shown in Table 6. The findings indicate that, when reward contingency is defined behaviorally, subjects demonstrate a decrease in intrinsic motivation on the

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free-time measure when expected tangible rewards are not contingent on successful performance. On the attitude measure, noncontingent rewards produce no significant effect. Rewards contingent on successful performance do not produce significant effects on either the free-time or attitude measures. The major difference between a behavioral classification of contingency and Deci and Ryan’s categorization system concerns those studies where subjects are given a reward for completing or solving a task. The first experiment conducted by Deci (1971) is an example of a study coded as task contingent using Deci and Ryan’s categories and contingent using a behavioral framework. In this study, subjects were paid money for each puzzle they solved. Deci and Ryan classified such reward procedures as task contingent because the rewards were not contingent on how well subjects performed relative to some standard. From a behavioral perspective, however, completion or solution of a task is seen as dependent on successful performance; these studies were labeled contingent. Thus, performance contingent rewards as

Table 6: Effect size as a function of reward contingency (as defined behaviorally) for expected tangible reward versus control comparisons Reward contingency

Analysis

k

Sample size

Mean weighted d

Free time on task: Expected tangible reward versus control Contingent All known effects 18 906 –0.12

95% CI for d

Q

CL

–0.26 to 0.01

37.44*

.47

861 2017

–0.13 –0.27

–0.26 to 0.01 –0.35 to – 0.18

29.06 167.05*

.46 .42

Outliers removed 38 1894 using Tukey’s procedure NonAdditional outliers 34 1728 contingent removed Attitude: Expected tangible reward versus control

–0.26

–0.35 to – 0.16

100.86*

.43

–0.26

–0.36 to – 0.16

54.66

.43

Contingent Noncontingent Noncontingent

Outliers removed All known effects

16 40

Contingent

All known effects

20

1224

+0.24

0.12 to 0.36

88.64*

.57

Contingent

Outliers removed using Tukey’s procedure All known effects

17

1087

+0.11

–0.01 to 0.23

22.24

.53

17

913

–0.04

–0.17 to 0.09

50.14*

.49

Outliers removed using Tukey’s procedure Additional outliers removed

16

853

+0.03

–0.10 to 0.17

31.52*

.49

15

833

+0.05

–0.08 to 0.19

27.91

.48

Noncontingent Noncontingent Noncontingent

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase. k = number of effect sizes; sample size = sum of n in all studies; Mean weighted d = mean of weighted effect sizes (weighted by sample size); CI= confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01.

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Table 7: Effect size as a function of rewards contingent on task completion or solution for expected tangible reward versus control comparisons Measure

k

Sample size

Mean weighted d

95% CI for d

Q

CL

Free time Attitude

8 6

423 405

–0.12 –0.05

–0.32 to 0.08 –0.25 to 0.14

11.21 6.89

.47 .48

Note: Negative effect sizes indicate a decrease in intrinsic motivation for reward/reinforcement groups; positive effect sizes indicate an increase, k = number of effect sizes; sample size = sum of n in all studies; Mean weighted d = mean of weighted effect sizes (weighted by sample size); CI = confidence interval; Q = homogeneity statistic for mean effect sizes; CL = common language effect size statistic. *Significance indicates rejection of the hypothesis of homogeneity. *p < .01.

defined by Deci and Ryan (1985) include only those studies where subjects are offered a reward for attaining a certain level of performance; using a behavioral definition, studies coded as contingent include both rewards that are contingent on completing or solving a task and rewards that are contingent on reaching a specified level of performance. Because these two types of reward contingencies may have opposite effects on intrinsic motivation, a separate analysis was conducted on studies in which reward was delivered for completing or solving a task. Results given in Table 7 show no significant differences between rewarded and control groups on the free-time or attitude measures for this type of reward contingency. These findings suggest that contingent rewards (which include performance contingent rewards), as defined behaviorally, do not harm intrinsic motivation.

Summary of Results from Group Designs A summary of the various analyses conducted on the group design studies and the major findings is given in Figure 3.8 When all types of reward are aggregated, overall, the results indicate that reward does not negatively affect intrinsic motivation on any of the four measures (free time on task once reward is withdrawn, self-reports of attitude, performance during the free-time measure, willingness to volunteer for future studies without reward). When rewards are subdivided into reward type (verbal, tangible), reward expectancy (expected, unexpected), and reward contingency, the findings demonstrate that people who receive a verbal reward spend more time on a task once the reward is withdrawn; they also show more interest and enjoyment than nonrewarded persons. Tangible reward produces no decrement in intrinsic motivation when it is received unexpectedly. Expected tangible rewards produce differing effects depending on the manner in which they are administered. Individuals who receive an expected reward for solving or completing a task or for achieving a specific level of performance do not spend less time on a task than controls once the reward is withdrawn. They do, however, report more interest,

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INTRINSIC MOTIVATION

Attitude

Performance

Willingness to Volunteer

Reward (dw = 0.14)

Reward n.s.

Reward n.s.

Free Time

Reward n.s.

Verbal (dw = 0.38)

Tangible (dw = −0.21)

Unexpected n.s.

Verbal (dw = 0.39)

Expected (dw = −0.25)

Contingent n.s.

Tangible n.s.

Unexpected n.s.

Noncontingent (dw = −0.26)

Contingent on task completion or solution n.s. Performance contingent n.s.

Task contingent (dw = −0.23)

Expected n.s.

Contingent n.s.

Noncontingent n.s.

Contingent on task completion or solution n.s. Task noncontingent n.s.

Performance contingent (dw = 0.19)

Task contingent n.s.

Note: dw = mean weighted effect size (based on homogeneous samples); n.s. = not significant; analyses in regular type indicate no effect; analyses in bold indicate a negative effect; underlined analyses indicate a positive effect. When no dw is reported, there was no significant effect. No analyses were conducted on the attitude measure for task noncontingent reward because only two studies assessed this measure.

Figure 3: A summary of the meta-analysis of the effects of reward versus control groups on intrinsic motivation

satisfaction, and enjoyment of the task when the reward is given for a certain level of performance. The detrimental effects of reward appear when rewards are offered to people simply for engaging in a task, independent of successful performance. Under these conditions, once the reward is removed, individuals spend less time on the task than controls; they do not, however, report a less favorable attitude toward the task.

Results from Single-Subject Designs To determine the effects of reinforcement on intrinsic motivation, an analysis was conducted on effect sizes from single-subject, repeated measures designs where the rewards used were shown to be reinforcers for each

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subject in the study. That is, rewards were shown to increase behavior during a reinforcement phase. An increase or decrease in intrinsic motivation was measured as a difference between behavior during the pre- and postreinforcement phases. Five studies contributed an effect size to this analysis. Four studies showed that subjects spent more time on the task during the postreinforcement phase than the baseline phase. One study (Vasta & Stirpe, 1979) showed a decrease in time on task immediately following the removal of reward but an increase in time when intrinsic motivation was measured 2 weeks later. To make this analysis comparable to the analysis of group design studies, however, only differences between the immediate postreinforcement phase and baseline were analyzed. The average effect size and confidence interval for this analysis was +0.34 (– 0.28, 0.96) indicating no significant change in intrinsic motivation from baseline to postreinforcement phases. Effect sizes were homogeneous (Q = 2.96, df = 4). These results suggest that reinforcement does not alter people’s intrinsic motivation. As noted previously, two studies used a between- and within-group repeated measures design to assess the effects of reinforcement on intrinsic motivation (Greene, Sternberg, & Lepper, 1976; Mynatt, Oakley, Arkkelin, Piccione, Margolis, & Arkkelin, 1978). Although these studies did not meet the criteria for inclusion in the meta-analysis of within-subject designs, it is possible to assess the within-group effects for reward conditions that were comparable in both studies. Both Greene et al. (1976) and Mynatt et al. (1978) included a group of subjects rewarded for playing with activities that they had spent the most time with during baseline phases (high interest condition) and a group that was rewarded for playing with activities they had spent the least time with during baseline (low interest condition). In terms of the high interest conditions, Mynatt et al. did not find a reinforcement effect but reported a decrease in intrinsic motivation from baseline to postreward phases. Greene et al. reported a reinforcement effect for the high interest group and a decrease in intrinsic motivation between baseline and postreinforcement sessions. It is difficult to draw conclusions from only two studies. Nonetheless, because a decline in intrinsic motivation occurred with or without a reinforcement effect, it may be that reinforcement is not the critical variable. Both studies reported a reinforcement effect for the low interest conditions, but there was no change in intrinsic motivation from baseline to postreinforcement phases. Again, conclusions based on two studies are tenuous. One interpretation, however, is that the time spent on low interest activities was so low that a decline in intrinsic motivation could not be detected. Alternatively, reinforcement does not interrupt intrinsic motivation for low interest activities.

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Discussion A major contention in education and psychology is that rewards and reinforcement negatively impact a person’s intrinsic motivation. The view is that, if people are reinforced or rewarded for activities they already spend time on and enjoy, they will be less motivated to engage in the activity than they were prior to the introduction of reward, once the reward is no longer forthcoming. In other words, rewards and reinforcement are said to decrease people’s intrinsic motivation. Over the past 20 years, dozens of studies have been conducted to investigate this issue. The primary objective of this article was to assess the research findings by conducting a meta-analysis of results from experiments on the effects of reward and reinforcement on intrinsic motivation. What follows is a discussion of the results obtained from the meta-analysis. The vast majority of studies have assessed the effects of reward on intrinsic motivation by using group designs. Rewarded subjects are compared to nonrewarded controls. Intrinsic motivation is measured by differences between groups on attitude, time spent on a task following the removal of reward (free time), performance during the free-time period, and willingness to volunteer for future studies without reward. The main meta-analysis reported in this article was conducted on results from these studies. This analysis concerned assessing the overall effects of reward on intrinsic motivation as well as the effects of a number of reward characteristics. The results suggest that in the laboratory, overall, reward does not negatively impact intrinsic motivation on any of the four measures analyzed here. A separate analysis was conducted using single-subject, repeated measures designs. A few researchers employed this type of design to evaluate the effects of reinforcement on intrinsic motivation. The rewards used in these studies were shown to be reinforcers, and intrinsic motivation was indexed as differences in subjects’ behavior between pre- and postreinforcement sessions. Results from the meta-analysis indicate no effect of reinforcement on intrinsic motivation. That is, the evidence suggests that reinforcement does not decrease a person’s intrinsic motivation to engage in an activity. In terms of rewards and extrinsic reinforcement, our overall findings suggest that there is no detrimental effect on intrinsic motivation. These findings are based on laboratory experiments, but a similar conclusion was reached by Workman and Williams (1980) in their review of the effects of extrinsic rewards on intrinsic motivation in the classroom. Generally, on task behavior, Workman and Williams found that external reinforcement increased and maintained intrinsic motivation for prolonged periods (up to 12 months). Thus, it no longer seems appropriate to argue against the use of incentive systems in applied settings. The findings from both experimental and applied research run contrary to the views expressed by many psychologists and educators (e.g., Deci & Ryan,

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1985; Kohn, 1993; Levine & Fasnacht, 1974; Schwartz, 1990). For example, Deci and Ryan (1987) state that: In general [italics added], rewards have been found to undermine intrinsic motivation. When people received rewards for working on an interesting activity, they tended to display less interest in and willingness to work on that activity after the termination of the rewards than did people who had worked on the activity without receiving a reward. (p. 1026)

Results from the present meta-analysis suggest that this statement is erroneous. The findings indicate that, in general, rewarded people are not less willing to work on activities and they do not display a less favorable attitude toward tasks than people who do not receive rewards. When rewards are broken down into reward type, expectancy, and contingency, results indicate that, on the free-time measure, verbal reward produces an increase in intrinsic motivation; tangible rewards produce no effect when they are delivered unexpectedly, and they are not detrimental when they are expected and contingent on level of performance or completing or solving a task. Expected tangible rewards produce a decrease in intrinsic motivation as measured by free time on task when they are given to individuals simply for engaging in an activity. On the attitude measure, verbal reward produces an increase in intrinsic motivation, and tangible rewards do not lead to a decrease in intrinsic motivation under any conditions. An increase in intrinsic motivation is shown on the attitude measure when individuals are offered a reward for performing to a set of standards. Thus, the present results suggest that rewards are detrimental only under a highly specified set of circumstances. That is, when subjects are offered a tangible reward (expected) that is delivered regardless of level of performance, they spend less time on a task than control subjects once the reward is removed. The same condition has no effect on attitude. Given these results, why is it that one commonly finds general statements condemning reinforcement and/or reward in journal articles and introductory textbooks? The present meta-analysis makes it clear how circumscribed the negative effect of reward really is. One possibility is that terms such as tangible, expected, unexpected, contingent and noncontingent become very confusing to a reader sorting through this literature. Consider, at its simplest, a study investigating the effects of expected reward on intrinsic motivation. Suppose the results showed a negative effect for expected reward. When discussing findings, do the researchers talk about the negative effects of the promise of reward or about the negative effects of reward, in general? There is no doubt that conclusions reached from such studies are often made about reward or reinforcement in general, not promise of reward. This has led to a great deal of misunderstanding about the overall effects of reward and reinforcement on intrinsic motivation. Even an informed reader can have difficulty keeping in mind what a particular study is investigating. It may be for this reason that rewards are

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often equated with reinforcers and, overall, have come to be seen as harmful. It is hoped that the present meta-analysis has helped to clarify the issue.

Theoretical Implications How do results from the present meta-analysis fit in with the various theories that have been formulated to account for the negative effects of rewards on intrinsic motivation? Advocates of cognitive evaluation theory (e.g., Deci & Ryan, 1985) would probably not have difficulty reconciling results from the free-time measure of intrinsic motivation. According to cognitive evaluation theory, competence and self-determination underlie intrinsic motivation. Rewards can facilitate or hinder competence and self-determination depending on whether they are perceived as informational, controlling, or amotivational. From this perspective, results from the meta-analysis would suggest that verbal rewards increase a person’s intrinsic motivation because of their informational value. Verbal praise would be seen to lead an individual to feel competent in performing a task; hence, intrinsic motivation would increase. Because the cognitive evaluation process is said to take place while the rewarded activity is occurring, unexpected rewards would not alter a person’s intrinsic motivation. On the other hand, rewards offered to people for participating in a task, in spite of how well they perform, would be perceived as controlling and would decrease intrinsic motivation. The problem for cognitive evaluation theory arises when one considers results from the attitude measure of intrinsic motivation. Deci and Ryan (1985) suggest that interest, enjoyment, and satisfaction are central emotions that accompany intrinsic motivation. A person’s experience of an activity is a focal point of cognitive evaluation theory. In other words, cognitive evaluation theory depends on an internal attitude change that is later expressed behaviorally as time on task. Results from the present meta-analysis indicate that reward does not negatively affect attitude. Individuals who receive verbal praise report greater interest than nonrewarded people. Tangible rewards produce no change in attitude when they are given for doing, completing, or solving a task; a positive effect is evident when rewards are contingent on a specified level of performance. One way of mitigating the findings for cognitive evaluation theory is to question the reliability of the attitude measure. In many studies, the attitude measure was obtained from a single-item Likert scale. An additional problem is that the questions designed to assess attitude toward the task may have been unable to separate subjects’ liking of the reward from their liking of the task. If the attitude measures are unreliable, they will fail to reflect true differences between rewarded and nonrewarded groups. This may be one way to handle the puzzling results; however, it also suggests that there has been no test of the major mediator proposed by the theory.

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The problem of operationalizing the construct of intrinsic motivation was recently addressed in a meta-analysis by Wiersma (1992).9 Results from Wiersma’s study depended on whether intrinsic motivation was operationalized as a free-time measure or as a task performance during rewarded period measure. Free-time measures showed a decline in intrinsic motivation; performance measures showed an increase. As noted, in the present analyses, results from the attitude measure do not coincide with the free-time measure. Additionally, measures of intrinsic motivation as performance during free time or as willingness to volunteer for future studies do not clarify the issue of operationalization of intrinsic motivation. Given the lack of covariation among the measures, it seems appropriate to devote further research to clarifying the concept of intrinsic motivation and to developing suitable measures. A different solution is offered by Rigby, Deci, Patrick, and Ryan (1992) who suggest that attention be directed toward the concept of self-determination rather than a pursuit of the intrinsic/ extrinsic dichotomy. Others concur but suggest that researchers should focus on goal definitions (Sansone & Morgan, 1992). A final alternative would be to agree that constructs such as self-determination, goal definition, and intrinsic motivation are scientifically unclear and that it would be more appropriate to deal with the effects of reward and reinforcement on behavior (e.g., Bandura, 1977, 1986; Dickinson, 1989). Such a course of action would mean abandoning cognitive evaluation theory. Another theoretical explanation that has been proposed to account for the effects of rewards on intrinsic motivation is the overjustification effect (Lepper, Greene, & Nisbett, 1973). The view is that people’s perceptions about the causes of their behavior influence future motivation. Rewards lead to a decrease in intrinsic motivation when people’s perceptions shift from accounting for their behavior as self-initiated to accounting for it in terms of external reward. Because the present analysis did not evaluate subjects’ perceptions about the causes of their behavior, it is impossible to determine whether overjustification explains the results. Further research that measures subjects’ attributions to internal and external factors is warranted. Finally, how would the findings of the meta-analysis be interpreted from a behavioral perspective? The results from single-subject designs indicate that reinforcement does not produce decrements in intrinsic motivation. This finding is compatible with a behavioral view. That is, behaviorists maintain that behavior returns to baseline after reinforcement is withdrawn. If the rewards used in the groups’ design studies are reinforcers, one would expect behavior to eventually return to baseline. Research designed to investigate the effects of reward on intrinsic motivation has typically measured time on task for a brief 8- to 10-minute period, immediately following the removal of reward. Thus, if verbal praise were a reinforcer, one might interpret the positive effect as a carryover of the reinforcement procedure. Another interpretation is that the positive effect is the result of an extinction burst. That is, when reinforcement is first withdrawn, the immediate, short-term effect is that rate

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of response increases. After a period of time, behavior would return to baseline. In terms of the negative effect of expected, noncontingent, tangible reward, some writers (e.g., Dickinson, 1989; Flora, 1990) have suggested that such a reward procedure does not represent a reinforcement contingency. The promise of a reward is seen by behaviorists as a discriminative stimulus (SD), and the negative effect is understood as the result of a bribe. A difficulty with this interpretation is that it does not account for findings from other conditions where promise of reward does not produce a negative effect. Further research is necessary to determine when and under what conditions promises of rewards function as bribes. Our data suggest that promises linked to noncontingent reward may function as bribes rather than as positive incentives.

Practical Implications The present findings suggest that verbal praise and positive feedback enhance people’s intrinsic interest. This is an important finding. Most social interaction in business, education, and clinical settings involves verbal feedback from managers, teachers, and therapists. When praise and other forms of positive feedback are given and later removed, people continue to show intrinsic interest in their work. In contrast to recent claims made by Kohn (1993, p. 55), verbal praise is an extrinsic motivator that positively alters attitudes and behavior. Rewards can have a negative impact on intrinsic motivation when they are offered to people for engaging in a task without consideration of any standard of performance. In a classroom, this might occur if a teacher promised students tangible rewards simply for doing an activity. For example, a teacher who promises stars or other awards to students for spending time doing math problems may undermine intrinsic motivation. In such a case, one could expect rewarded individuals to enjoy the task as much as those who are not offered an incentive. But, they may spend less time on the activity in a study period when the reward is no longer forthcoming. According to our results, this would not occur if the teacher used the same rewards but made them contingent on successful completion of the problems. Overall, the present review suggests that teachers have no reason to resist implementing incentive systems in the classroom. This conclusion is based on our findings, which show that verbal praise enhances intrinsic motivation and that other rewards and reinforcement leave intrinsic motivation largely unaffected. A small negative effect occurs when tangible rewards are promised without regard to a standard of performance. Under this circumstance, the promise of reward may act as a bribe. Importantly, on a practical level, the implication is that reward offered in educational and other settings should be delivered contingent on performance.

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Notes 1. Although there was an overall positive effect of tangible reward on intrinsic motivation, Rosenfìeld et al. (l980) also found that rewards that did not indicate ability led to less intrinsic interest. 2. In addition to studies reported in English, five relevant Japanese experiments were identified by the CD-ROM search. The information in the abstracts was not adequate to code the findings. Therefore, these studies are not included in the meta-analysis. 3. Boggiano and Ruble (1979) reported that 147 children participated in the study. There were two reward conditions (task contingent, performance contingent) and a nonrewarded control group. The contrast for the control versus task-contingent reward groups on the free-time measure is reported as t(l30) = 2.0, p < .05; the contrast for the control versus performance-contingent reward groups is reported as t(130) = 1.16, n.s. 4. A copy of the coding form is available on request from the first author. 5. A list of the experiments included in each interaction is available on request from the first author. 6. Further analyses which include studies that index effect size as 0.00 are available in Cameron (1992). 7. The present review does not assess cultural differences in the impact of reward on intrinsic motivation. However, it is interesting to note that, although the study from India (Tripathi & Agarwal, 1985) shows an extreme positive value for the effect of verbal praise on the free-time measure, the direction of the result is consistent with the North American studies. 8. A few researchers have assessed the effects of expected tangible rewards on intrinsic motivation relative to unexpected tangible rewards (e.g., Enzle & Ross, 1978; Fazio, 1981; Lepper & Greene, 1975). Other researchers have conducted studies comparing expected noncontingent reward groups to expected contingent reward groups (e.g., Farr, 1976; Phillips & Lord, 1980; Pinder, 1976). Such studies concern direct comparisons between the two types of reward expectancies (expected versus unexpected) and the two types of reward contingencies (noncontingent versus contingent) without reference to a nonrewarded control group. Results from meta-analyses conducted on these comparisons and a list of studies included in such analyses can be obtained in Cameron (1992). One significant effect emerged from these analyses; subjects who received an expected tangible reward showed less intrinsic motivation on the free-time measure than subjects who received an unexpected tangible reward. The average effect size and confidence interval for this comparison was –0.26 (–0.45, –0.06). 9. Wiersma (1992) reported results of a meta-analysis of 23 experiments on reward and intrinsic motivation. These studies make up a subset of those analyzed in the present article. Effect sizes from Wiersma’s study were not always based on a comparison of a reward condition to a no-reward condition. This makes it impossible to directly compare our findings with those of Wiersma.

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Mynatt, C., Oakley, D., Arkkelin, D., Piccione, A., Margolis, R., & Arkkelin, J. (1978). An examination of overjustification under conditions of extended observation and multiple reinforcement: Overjustification or boredom? Cognitive Therapy and Research, 2, 171–177. Orlick, T. D., & Mosher, R. (1978). Extrinsic awards and participant motivation in a sport related task. International Journal of Sport Psychology, 9, 27–39. Phillips, J. S., & Lord, R. G. (1980). Determinants of intrinsic motivation: locus of control and competence information as components of Deci’s cognitive evaluation theory. Journal of Applied Psychology, 65, 211–218. Pinder, C. C. (1976). Additivity versus nonadditivity of intrinsic and extrinsic incentives: Implications for work, motivation, performance, and attitudes. Journal of Applied Psychology, 61, 693–700. Pittman, T. S., Emery, J., & Boggiano, A. K. (1982). Intrinsic and extrinsic motivational orientations: reward-induced changes in preference for complexity. Journal of Personality and Social Psychology, 42, 789–797. Pritchard, R. D., Campbell, K. M., & Campbell, D. J. (1977). Effects of extrinsic financial rewards on intrinsic motivation. Journal of Applied Psychology, 62, 9–15. Rigby, C. S., Deci, E. L., Patrick, B. C., & Ryan, R. M. (1992). Beyond the intrinsic–extrinsic dichotomy: Self-determination in motivation and learning. Motivation and Emotion, 16, 165–185. Rosenfield, D., Folger, R., & Adelman, H. F. (1980). When rewards reflect competence: A qualification of the overjustification effect. Journal of Personality and Social Psychology, 39, 368–376. Rosenthal, R. (1966). Experimenter effects in behavioral research. New York: AppletonCentury-Crofts. Rummel, A., & Feinberg, R. (1988). Cognitive evaluation theory: A meta-analytic review of the literature. Social Behavior and Personality, 16, 147–164. Ryan, R. M., Mims, B., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45, 736–750. Sansone, C., & Morgan, C. (1992). Intrinsic motivation and education: Competence in context. Motivation and Emotion, 16, 249–270. Schwartz, B. (1990). The creation and destruction of value. American Psychologist, 45, 7–15. Schwarzer, R. (1991). Meta: Programs for secondary data analysis, MS-DOS Version 5.0 [Computer program]. Dubuque, IA: Wm. C. Brown. Scott, W. E., Jr. (1975). The effects of extrinsic rewards on “intrinsic motivation.” Organizational Behavior and Human Performance, 25, 311–335. Sutherland, S. (1993). Impoverished minds. Nature, 364, 767. Tripathi, K. N., & Agarwal, A. (1985). Effects of verbal and tangible rewards on intrinsic motivation in males and females. Psychological Studies, 30, 77–84. Tukey, J. W. (1977). Exploratory data analysis. Reading, MA: Addison-Wesley. Vallerand, R. J. (1983). The effect of differential amounts of positive verbal feedback on the intrinsic motivation of male hockey players. Journal of Sport Psychology, 5, 100–107. Vasta, R., Andrews, D. E., McLaughlin, A. M., Stirpe, L. A., & Comfort, C. (1978). Reinforcement effects on intrinsic interest: A classroom analog. Journal of School Psychology, 16, 161–168. Vasta, R., & Stirpe, L. A. (1979). Reinforcement effects on three measures of children’s interest in math. Behavior Modification, 3, 223–244. Wiersma, U. J. (1992). The effects of extrinsic rewards in intrinsic motivation: A metaanalysis. Journal of Occupational and Organizational Psychology, 65, 101–114. Workman, E. A., & Williams, R. L. (1980). Effects of extrinsic rewards on intrinsic motivation in the classroom. Journal of School Psychology, 18, 141–147. Zimbardo, P. G. (1988) Psychology and life (11th ed.). Glenview, IL: Scott, Foresman.

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Appendix A: Studies Included in the Meta-Analysis of Group Designs Amabile, T. M., Hennessey, B. A., & Grossman, B. S. (1986). Social influences on creativity: The effects of contracted-for reward. Journal of Personality and Social Psychology, 50, 14–23. Anderson, R., Manoogian, S. T., & Reznick, J. S. (1976). The undermining and enhancing of intrinsic motivation in preschool children. Journal of Personality and Social Psychology, 34, 915–922. Anderson, S., & Rodin, J. (1989). Is bad news always bad? Cue and feedback effects on intrinsic motivation. Journal of Applied Social Psychology, 19, 449–467. Arkes, H. R. (1979). Competence and the overjustification effect. Motivation and Emotion, 3, 143–150. Arnold, H. J. (1976). Effects of performance feedback and extrinsic reward upon high intrinsic motivation. Organizational Behavior and Human Performance, 17, 275–288. Arnold, H. J. (1985). Task performance, perceived competence, and attributed causes of performance as determinants of intrinsic motivation. Academy of Management Journal, 28, 876–888. Blanck, P. D., Reis, H. T., & Jackson, L. (1984). The effects of verbal reinforcement of intrinsic motivation for sex-linked tasks. Sex Roles, 10, 369–386. Boal, K. B., & Cummings, L. L. (1981). Cognitive evaluation theory: an experimental test of processes and outcomes. Organizational Behavior and Human Performance, 28, 289–310. Boggiano, A. K., Harackiewicz, J. M., Besette, J. M., Main, D. S. (1985). Increasing children’s interest through performance contingent reward. Social Cognition, 3, 400–411. Boggiano, A. K., & Hertel, P. T. (1983). Bonuses and bribes: mood effects in memory. Social Cognition, 2, 49–61. Boggiano, A. K., Ruble, D. N., & Pittman, T. S. (1982). The mastery hypothesis and the overjustification effect. Social Cognition, 1, 38–49. Brennan, T. P., & Glover, J. A. (1980). An examination of the effect of extrinsic reinforcers on intrinsically motivated behavior: experimental and theoretical. Social Behavior and Personality, 8, 27–32. Broekner, J., & Vasta, R. (1981). Do causal attributions mediate the effects of extrinsic rewards on intrinsic interest? Journal of Research in Personality, 15, 201–209. Butler, R. (1987). Task-involving and ego-involving properties of evaluation: Effects of different feedback conditions on motivational perceptions, interest, and performance. Journal of Educational Psychology, 79, 474–482. Calder, B. J., & Staw, B. M. (1975). Self-perception of intrinsic and extrinsic motivation. Journal of Personality and Social Psychology, 31, 599–605. Crino, M. D., & White, M. C. (1982). Feedback effects in intrinsic/extrinsic reward paradigms. Journal of Management, 8, 95–108. Daniel, T. L., & Esser, J. K. (1980). Intrinsic motivation as influenced by rewards, task interest, and task structure. Journal of Applied Psychology, 65, 566–573.

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Motivation

Danner, F. W., & Lonkey, E. (1981). A cognitive developmental approach to the effects of rewards on intrinsic motivation. Child Development, 52, 1043–1052. Deci, E. L. (1971). Effects of externally mediated rewards on intrinsic motivation. Journal of Personality and Social Psychology, 18, 105–115. Deci, E. L. (1972a). Intrinsic motivation, extrinsic reinforcement, and inequity. Journal of Personality and Social Psychology, 22, 113–120. Deci, E. L. (1972b). The effects of contingent and noncontingent rewards and controls on intrinsic motivation. Organizational Behavior and Human Performance, 8, 217–229. DeLoach, L. L., Griffith, K., & LaBarba, R. C. (1983). The relationship of group context and intelligence to the overjustification effect. Bulletin of the Psychonomic Society, 21, 291–293. Dollinger, S. J., & Thelen, M. H. (1978). Overjustification and children’s intrinsic motivation: comparative effects of four rewards. Journal of Personality and Social Psychology, 36, 1259–1269. Earn, B. M. (1982). Intrinsic motivation as a function of extrinsic financial rewards and subjects’ locus of control. Journal of Personality, 50, 360–373. Fabes, R. A. (1987). Effects of reward contexts on young children’s task interest. Journal of Psychology, 121, 5–19. Fabes, R. A., Eisenberg, N., Fultz, J., & Miller, P (1988). Reward, affect and young children’s motivational orientation. Motivation and Emotion, 12, 155–169. Freedman, S. M., & Phillips, J. S. (1985). The effects of situational performance constraints on intrinsic motivation and satisfaction: the role of perceived competence and self-determination. Organizational Behavior and Human Decision Processes, 35, 397–416. Greene, D., & Lepper, M. R. (1974). Effects of extrinsic rewards on children’s subsequent intrinsic interest. Child Development, 45, 1141–1145. Griffith, K. M., DeLoach, L. L., & LaBarba, R. C. (1984). The effects of rewarder familiarity and differential reward preference in intrinsic motivation. Bulletin of the Psychonomic Society, 22, 313–316. Hamner, W. C., & Foster, L. W. (1975). Are intrinsic and extrinsic rewards additive: A test of Deci’s cognitive evaluation theory of task motivation. Organizational Behavior and Human Performance, 14, 398–415. Harackiewicz, J. M. (1979). The effects of reward contingency and performance feedback on intrinsic motivation. Journal of Personality and Social Psychology, 37, 1352–1363. Harackiewicz, J. M., Abrahams, S., & Wageman, R. (1987). Performance evaluation and intrinsic motivation: The effects of evaluative focus, rewards, and achievement orientation. Journal of Personality and Social Psychology, 53, 1015–1023. Harackiewicz, J. M., & Manderlink, G. (1984). A process analysis of the effects of performance-contingent rewards on intrinsic motivation. Journal of Experimental Social Psychology, 20, 531–551. Harackiewicz, J. M., Manderlink, G., & Sansone, C. (1984). Rewarding pinball wizardry: effects of evaluation and cue value on intrinsic interest. Journal of Personality and Social Psychology, 47, 287–300. Hom, H. L. (1987). A methodological note: time of participation effects on intrinsic motivation. Personality and Social Psychology Bulletin, 13, 210–215.

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Karniol, R., & Ross, M. (1977). The effect of performance relevant and performance irrelevant rewards on children’s intrinsic motivation. Child evelopment, 48, 482–487. Koestner, R., Zuckerman, M., & Koestner, J. (1987). Praise, involvement, and intrinsic motivation. Journal of Personality and Social Psychology, 53, 383–390. Kruglanski, A. W., Alon, S., & Lewis, T. (1972). Retrospective misattribution and task enjoyment. Journal of Experimental Social Psychology, 8, 493–501. Kruglanski, A. W., Friedman, I., & Zeevi, G. (1971). The effects of extrinsic incentive on some qualitative aspects of task performance. Journal of Personality, 39, 606–617. Kruglanski, A. W., Riter, A., Amitai, A., Margolin, B. S., Shabatai, L., & Zaksh, D. (1975). Can money enhance intrinsic motivation?: A test of the contentconsequence hypothesis. Journal of Personality and Social Psychology, 31, 744–750. Lepper, M. R., Greene, D., & Nisbett, R. E. (1973). Undermining children’s intrinsic interest with extrinsic reward: A test of the “overjustification” hypothesis. Journal of Personality and Social Psychology, 28, 129–137. Loveland, K. K., & Olley, J. G. (1979). The effect of external reward on interest and quality of task performance in children of high and low intrinsic motivation. Child Development, 50, 1207–1210. Luyten, H., & Lens, W. (1981). The effect of earlier experience and reward contingencies on intrinsic motivation. Motivation and Emotion, 5, 25–36. McGraw, K. O., & McCullers, J. C. (1979). Evidence of a detrimental effect of extrinsic incentives on breaking a mental set. Journal of Experimental Social Psychology, 15, 285–294. McLoyd, V. C. (1979). The effects of extrinsic rewards of differential value on high and low intrinsic interest. Child Development, 50, 1010–1019. Morgan, M. (1981). The overjustification effect: A developmental test of selfperception interpretations. Journal of Personality and Social Psychology, 40, 809–821. Morgan, M. (1983). Decrements in intrinsic interest among rewarded and observer subjects. Child Development, 54, 636–644. Mynatt, C., Oakley, D., Piccione, A., Margolis, R., & Arkkelin, J. (1978). An examination of overjustification under conditions of extended observation and multiple reinforcement: Overjustification or boredom? Cognitive Therapy and Research, 2, 171–177. Ogilvie, L., & Prior, M. (1982). The overjustification effect in retarded children: durability and generalizability. Australia and New Zealand Journal of Developmental Disabilities, 8, 213–218. Orlick, T. D., & Mosher, R. (1978). Extrinsic awards and participant motivation in a sport related task. International Journal of Sport Psychology, 9, 27–39. Palack, S. R., Costomotis, S., Sroka, S., & Pittman, T. S. (1982). School experience, reward characteristics, and intrinsic motivation. Child Development, 53, 1382–1391. Pittman, T S., Cooper, E. E., & Smith, T. W. (1977). Attribution of causality and the overjustification effect. Personality and Social Psychology Bulletin, 3, 280–283.

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Pittman, T. S., Davey, M. E., Alafat, K. A., Wetherill, K. V., & Kramer, N. A. (1980). Informational versus controlling verbal rewards. Personality and Social Psychology Bulletin, 6, 228–233. Pittman, T. S., Emery, J., & Boggiano, A. K. (1982). Intrinsic and extrinsic motivational orientations: reward-induced changes in preference for complexity. Journal of Personality and Social Psychology, 42, 789–797. Porac, J. F., & Meindl, J. (1982). Undermining overjustification: Inducing intrinsic and extrinsic task representations. Organizational Behavior and Human Performance, 29, 208–226. Pretty, G. H., & Seligman, C. (1984). Affect and the overjustification effect. Journal of Personality and Social Psychology, 46, 1241–1253. Reiss, S., & Sushinsky, L. W. (1975). Overjustification, competing responses, and the acquisition of intrinsic interest. Journal of Personality and Social Psychology, 31, 1116–1125. Rosenfield, D., Folger, R., & Adelman, H. F. (1980). When rewards reflect competence: A qualification of the overjustification effect. Journal of Personality and Social Psychology, 39, 368–376. Ross, M. (1975). Salience of reward and intrinsic motivation. Journal of Personality and Social Psychology, 32, 245–254. Ross, M., Karnio, R., & Rothstein, M. (1976). Reward contingency and intrinsic motivation in children: a test of the delay of gratification hypothesis. Journal of Personality and Social Psychology, 33, 442–447. Ryan, R. M., Mims, B., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive evaluation theory. Journal of Personality and Social Psychology, 45, 736–750. Salinick, G. R. (1975). Interaction effects of performance and money on selfperception of intrinsic motivation. Organizational Behavior and Human Performance, 13, 339–351. Sansone, C. (1986). A question of competence: the effects of competence and task feedback on intrinsic interest. Journal of Personality and Social Psychology, 51, 918–931. Sansone, C. (1989). Competence feedback, task feedback, and intrinsic interest: An examination of process and context. Journal of Experimental Social Psychology, 25, 343–361. Sansone, C, Sachau, D. A., & Weir, C. (1989). Effects of instruction on intrinsic interest: The importance of context. Journal of Personality and Social Psychology, 57, 819–829. Sarafino, E. P. (1984). Intrinsic motivation and delay of gratification in preschoolers: the variables of reward salience and length of expected delay. British Journal of Developmental Psychology, 2, 149–156. Shanab, M. E., Peterson, D., Dargahi, S., & Deroian, P . (1981). The effects of positive and negative verbal feedback on the intrinsic motivation of male and female subjects. The Journal of Social Psychology, 115, 195–205. Shapira, Z. (1976). Expectancy determinants of intrinsically motivated behavior. Journal of Personality and Social Psychology, 34, 1235–1244.

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Smith, T. W., & Pittman, T. S. (1978). Reward, distraction, and the overjustification effect. Journal of Personality and Social Psychology, 36, 565–573. Staw, B. M., Calder, B. J., Hess, R. K., & Samdelands, L. E. (1980). Intrinsic motivation and norms about payment. Journal of Personality, 48, 1–14. Swann, W. B., Jr., & Pittman, T. S. (1977). Moderating influence of verbal cues on intrinsic motivation. Child Development, 48, 1128–1132. Taub, S. I., & Dollinger, S. J. (1975). Reward and purpose as incentives for children differing in locus of control expectancies. Journal of Personality, 43, 179–195. Tripathi, K. N., & Agarwal, A. (1985). Effects of verbal and tangible rewards on intrinsic motivation in males and females. Psychological Studies, 30, 77–84. Tripathi, K. N., & Agarwal, A. (1988). Effect of reward contingency on intrinsic motivation. The Journal of General Psychology, 115 (3), 241–246. Vallerand, R. J. (1983). The effect of differential amounts of positive verbal feedback on the intrinsic motivation of male hockey players. Journal of Sport Psychology, 5, 100–107. Vallerand, R. J., & Reid, G. (1984). On the causal effects of perceived competence on intrinsic motivation: A test of cognitive evaluation theory. Journal of Sport Psychology, 6, 94–102. Weinberg, R. S., & Jackson, A. (1979). Competition and extrinsic rewards: Effect on intrinsic motivation and attribution. Research Quarterly, 50, 494–502. Weiner, M. J. (1980). The effect of incentive and control over outcomes upon intrinsic motivation and performance. The Journal of Social Psychology, 112, 247–254. Weiner, M. J., & Mander, A. M. (1978). The effects of reward and perception of competency upon intrinsic motivation. Motivation and Emotion, 2, 67–73. Wicker, F. W., Brown, G., Wiehe, J. A., & Shim, W. Y. (1990). Moods, goals, and measures of intrinsic motivation. The Journal of Psychology, 124, 75–86. Williams, B. W., (1980). Reinforcement, behavior constraint and the overjustification effect. Journal of Personality and Social Psychology, 39, 599–614. Wimperis, B. R., & Farr, J. L. (1979). The effects of task content and reward contingency upon task performance and satisfaction. Journal of Applied Social Psychology, 9 (3), 229–249. Zinser, O., Young, J. G., & King, P. E. (1982). The influence of verbal reward on intrinsic motivation in children. The Journal of General Psychology, 106, 85–91.

Studies Included in the Meta-Analysis of Single-Subject Designs Davidson, P., & Bucher, B. (1978). Intrinsic interest and extrinsic reward: The effects of a continuing token program on continuing nonconstrained preference. Behavior Therapy, 9, 222–234. Feingold, B. D., & Mahoney, M. J. (1975). Reinforcement effects on intrinsic interest: Undermining the overjustification hypothesis. Behavior Therapy, 6, 357–377.

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Motivation

Mawhinney, T. C., Dickinson, A. M., & Taylor, L. A. (1989). The use of concurrent schedules to evaluate the effects of extrinsic rewards on “intrinsic motivation.” Journal of Organizational Behavior Management, 10, 109–129. Vasta, R., Andrews, D. E., McLaughlin, A. M., Stirpe, L. A., & Comfort, C. (1978). Reinforcement effects on intrinsic interest: A classroom analog. Journal of School Psychology, 16, 161–168. Vasta, R., & Stirpe, L. A. (1979). Reinforcement effects on three measures of children’s interest in math. Behavior Modification, 3, 223–244.

Appendix B: Formulas for calculating effect size, g 1.

g=

X E − XC Sp where XE = mean of experimental group XC = mean of control group Sp = pooled standard deviation

Sp2 =

(nE − 1)SE2 + (nC − 1)SC2 nE + nC − 2 where

Sp2= pooled variance SE2 = variance of experimental group SC2 = variance of control group nE = sample size of experimental group nC = sample size of control group

2.

g=t

2 n

g=t

1 1 + nE nC

3.

4.

g= F

Salkind_Chapter 65.indd 226

for equal ns; n = sample size of each group

for unequal ns

nE + nC nE nC

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Salkind_Chapter 65.indd 227

JPSP

Child dev

Greene, Lepper (1974)

Ross (1975) Exp. 2

Child dev

Greene, Lepper (1974)

JPSP

JPSP

Lepper et al. (1973)

Ross (1975) Exp. 1

JPSP

Lepper et al. 1973)

Child dev

J. Exp. Soc Psych

Kruglanski et al. (1972)

JPSP

Org Beh & Hum Perf

Deci (1972b)

Ross (1975) Exp. 1

JPSP

Deci (1972a)

Greene, Lepper (1974)

JPSP

JPSP

J of Pers.

Kruglanski et al. (1971)

Deci (1972a)

J of Pers.

Kruglanski et al. (1971)

Deci (1972a)

JPSP

JPSP

Deci (1971) Exp. 2

JPSP

JPSP

Deci (1971) Exp. 1

Deci (1971) Exp. 3

JPSP

Deci (1971) Exp. 1

Deci (1971) Exp. 3

Journal

Author(s)

Children

Children

Children

Children

Children

Children

Children

Children

Children

Adults

Adults

Adults

Adults

15–16 yrs

15–16 yrs

Adults

Adults

Adults

Adults

Adults

Subjects

Drum

Playing drum

Playing drum

Drawing

Drawing

Drawing

Drawing

Drawing

5 games

Soma

Soma

Soma

Soma

Creativity & recall

Creativity & recall

Soma

Soma

Writing headlines

Soma

Soma

Task

T

T

T

T

T

T

T

T

T

T

T

T

V

T

T

V

V

T

T

T

Reward type

E

E

E

U

U

E

U

E

U

E

E

E

U

E

E

U

U

E

E

E

Expectancy

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Not, NC

Cont, TC

Cont, TC

Not, TC

Not, TC

Cont. TC

Cont, TC

Cont, TC

Contingency

Free time

Free time

Free time

Free time

Free time

Free time

Free time

Free time

Attitude

Free time

Free time

Free time

Free time

Volunteer

Attitude

Attitude

Free time

Performance

Attitude

Free time

Dep. measure

52

20

20

13

13

15

18

18

36

24

32

32

48

16

16

12

12

4

12

12

N exp.

14

20

20

15

15

15

15

15

33

16

32

32

48

16

16

12

12

2

12

12

–0.81

+0.56

–0.54

+0.22

+0.06

–0.70

+0.57b

–0.72

–0.66

+0.08b

–0.10

+0.75

+0.29

–0.63

–0.69

0.00a

+0.82

–3.72

0.00a

– 0.54

Effect size (g)ab

(Continued )

N control

Reinforcement, Reward, and Intrinsic Motivation

A/O

A/O

A/O

A/O

A/O

A/O

B/A

B/A

A/O

A/O

A/O

A/O

A/O

A/O

A/O

B/A

B/A

Field study

B/A

B/A

Design

Appendix C: Characteristics of Studies Included in the Meta-Analysis Cameron and Pierce 227

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Salkind_Chapter 65.indd 228

JPSP

Calder, Staw (1975)

Org Beh & Hum Perf

Org Beh & Hum Perf

JPSP

JPSP

Arnold (1976)

Arnold (1976)

Ross et al. (1976)

Ross et al. (1976)

Adults

Adults

Adults

Adults

Adults

Adults

Children

15–16-yr.olds

14–15-yr.olds

Children

Subjects

A/O

A/O

Multiple trials

Multiple trials

Children

Children

Adults

Adults

Children

Children

SS Repeated Children measures

A/O

A/O

A/O

B/A

Org Beh & Hum Perf

Hamner, Foster (1975)

A/O

JPSP

Org Beh & Hum Perf

Hamner, Foster (1975)

A/O

B/A

Org Beh & Hum Perf

Salanick (1975)

A/O

JPSP

Org Beh & Hum Perf

Salanick (1975)

A/O

Anderson et al. (1976) Anderson et al. (1976)

JPSP

Reiss, Sushinski (1975)

A/O

JPSP

JPSP

Kruglanski et al. (1975) Exp. 2

A/O

Behavior Therapy

JPSP

Kruglanski et al. (1975) Exp. 1

A/O

Feingold, Mahoney (1975)

J of Pers

Taub, Dollinger (1975)

Design

Calder, Staw (1975)

Journal

Author(s)

Appendix C: (Continued )

Drawing

Drawing

Computer game

Computer game

Drawing

Drawing

Dot-to-dot connections

Puzzles

Puzzles

Scoring questions

Scoring questions

Train game

Train game

Listening to songs

2 tasks

2 tasks

Coding

Task

T

T

T

T

T

V

T

T

T

T

T

T

T

T

T

T

T

Reward type

E

E

E

E

E

U

E

E

E

E

E

E

E

E

E

E

E

Expectancy

Not, NC

Not, TC

Not, TC

Not, TC

Not, TC

Cont

Not, TC

Not, TC

Cont, TC

Not, NC

Cont, PC

Cont, PC

Not, TC

Cont, PC

Cont, PC

Cont, PC

Contingency

Free time

Free time

Volunteer

Attitude

Free time

Free time

# of connections

Volunteer

Attitude

Attitude

Attitude

Attitude

Free time

Free time

Attitude

Attitude

Attitude

Dep. measure

12

12

17

17

36

18

5

20

20

37

31

38

38

16

40

24

124

N exp.

12

12

36

36

46

46

–

20

20

30

30

39

39

16

40

24

124

N control

+0.44

–0.64

+0.02

0.00a

+0.04

+1.07

+0.34

+0.28

+0.22b

+0.19

–0.23

–0.01b

–0.12b

–0.84

+0.39

+1.15

0.00a

Effect size (g)ab

228 Motivation

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Salkind_Chapter 65.indd 229

Child Dev

Child Dev

Child Dev

Child Dev

Per & Soc Psy Bull

Cog Ther & Res

Mot & Emotion

Mot & Emotion

Mot & Emotion

Mot & Emotion

Mot & Emotion

Mot & Emotion

Int J. of Sport Psy

Int J. of Sport Psy

Swann, Pittman (1977) Exp. 1

Swann, Pittman (1977) Exp. 2

Karniol, Ross (1977)

Karniol, Ross (1977)

Pittmann et al. (1977)

Mynatt et al. (1978)

Weiner, Mander (1978)

Weiner, Mander (1978)

Weiner, Mander (1978)

Weiner, Mander (1978)

Weiner, Mander (1978)

Weiner, Mander (1978)

Orlick, Mosher (1978)

Orlick, Mosher (1978)

B/A

B/A

A/O

A/O

A/O

A/O

A/O

A/O

B/A mult. trials

A/O

A/O

A/O

A/O

A/O

A/O

Int J. of Sport Psy B/A

Child Dev

Swann, Pittman (1977) Exp. 1

A/O

Children

Children

Children

Adults

Adults

Adults

Adults

Adults

Adults

Children

Adults

Children

Children

Children

Children

Children

Adults

Stabilometer

Stabilometer

Stabilometer

Decoding cartoons

Decoding cartoons

Decoding cartoons

Decoding cartoons

Decoding cartoons

Decoding cartoons

Educ games

Gravitation

Slide show

Slide show

Drawing

Drawing

Drawings

Soma

V

T

T

T

T

T

T

T

T

T

T

T

T

T

T

T

T

U

U

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

Cont, TC

Cont, PC

Not, TC

Cont, PC

Not, TC

Cont, PC

Not, TC

Not, TC

Cont, PC

Cont, PC

Not, TC

Not, TC

Not, NC

Not, NC

Cont, PC

Free time

Free time

Free time

Performance

Performance

Attitude

Attitude

Free time

Free time

Free time

Attitude

Free time

Free time

Free time

Free time

Free time

Attitude

11

12

14

30

30

30

30

30

30

10

60

20

17

39

20

20

30

12

12

12

30

30

30

30

30

30

10

20

20

20

26

20

20

30

(Continued )

–0.22

–0.82

–0.34

–0.39b

–0.39b

000a

0.00a

–0.54

–0.34

+1.01

–0.20

+0.15

–0.04

–0.15b

–0.78b

–0.21b

+0.41b

Reinforcement, Reward, and Intrinsic Motivation

Orlick, Mosher (1978)

JPSP

Shapira (1976)

Cameron and Pierce 229

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Salkind_Chapter 65.indd 230

SS Repeated Children measures

Behavior Therapy

J of School Psych

Mot & Emotion

Mot & Emotion

Child Dev

JPSP

JPSP

Child Dev

Child Dev

J. Applied Soc Psych

J. Applied Soc Psych

J. Applied Soc Psych

Davidson, Bucher (1978)

Vasta et al. (1978)

Arkes (1979)

Arkes (1979)

Loveland, Olley (1979)

Harackiewicz (1979)

Harackiewicz (1979)

McLoyd (1979)

McLoyd (1979)

Wimperis, Farr (1979)

Wimperis, Farr (1979)

Wimperis, Farr (1979)

Research Quarterly

A/O

Dollinger, Thelan (1978) JPSP

Weinberg, Jackson (1979)

A/O

JPSP

A/O

A/O

A/O

A/O

A/O

A/O

B/A

B/A

A/O

A/O

A/O

Adults

Adults

Adults

Adults

Children

Children

16-yr.-olds

16-yr.-oIds

Children

Adults

Adults

SS Repeated Children measures

Children

Adults

Adults

Smith, Pittman (1978)

A/O

JPSP

Subjects

Smith, Pittman (1978)

Design

Journal

Author(s)

Appendix C: (Continued )

Stabilometer

Erector sets

Erector sets

Erector sets

Reading books

Reading books

Hidden puzzles

Hidden puzzles

Drawing

Soma

Soma

Coloring

Playing with clown

Mazes

Labyrinth

Labyrinth

Task

T

T

T

T

T

T

T

V

T

T

T

T&V

T

T&V

T

T

Reward type

E

E

E

E

E

E

E

U

E

E

E

U

E

E

E

E

Expectancy

Cont, PC

Both

Cont, PC

Not, TC

Cont, TC

Cont, TC

Not, TC

Not, TC

Cont, TC

Cont, TC

Not

Both

Cont, TC

Cont, TC

Contingency

Attitude

Volunteer

Attitude

Attitude

Performance

Free time

Attitude

Attitude

Free time

Attitude

Free time

Time

# of responses

Attitude

Performance

Attitude

Dep. measure

40

32

16

16

36

36

31

31

12

32

32

6

3

48

66

66

N exp.

40

16

16

16

18

18

31

31

12

32

32

–

–

12

33

33

N control

0.00a

+0.69

+1.36

+0.56

–0.40

–0.22

–0.38

+0.59

0.00a

+0.03

–0.16

+0.74

+1.83

0.00a

0.00a

–0.10b

Effect size (g)ab

230 Motivation

9/4/2010 7:05:35 PM

Behavior Mod

Soc Beh & Pers

J of Soc Psych

J of Soc Psych

J of Soc Psych

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

J of Pers

J of Pers

JPSP

JPSP

J Applied Psych A/O

J Applied Psych A/O

J Applied Psych A/O

JPSP

JPSP

Vasta, Stirpe (1979)

Salkind_Chapter 65.indd 231

Brennan, Glover (1980)

Weiner (1980)

Weiner (1980)

Weiner (1980)

Rosenfield et al. (1980)

Rosenfield et al. (1980)

Rosenfield et al. (1980)

Rosenfield et al. (1980)

Rosenfield et al. (1980)

Rosenfield et al. (1980)

Staw et al. (1980)

Staw et al. (1980)

Williams (1980)

Williams (1980)

Daniel, Esser (1980)

Daniel, Esser (1980)

Daniel, Esser (1980)

Morgan (1981) Exp. 1

Morgan (1981) Exp. 1

Adults

Adults

Children

Children

Adults

Adults

Adults

Children

Children

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Puzzles

Puzzles

Puzzles

Puzzles

Puzzles

4 games

4 games

Puzzles

Puzzles

Ad Lib

Ad Lib

Ad Lib

Ad Lib

Ad Lib

Ad Lib

Anagrams

Anagrams

Anagrams

Soma

Math problems

Water jar problem

Water jar problem

T

T

T

T

T

T

T

T

T

T

T

T

V

V

V

T

T

T

T

T

T

T

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

E

Not, TC

Not, TC

Cont, TC

Cont, TC

Cont, TC

Not, TC

Not, TC

Not, TC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Not, NC

Not

Cont, PC

Cont, PC

Attitude

Free time

Volunteer

Attitude

Free time

Attitude

Free time

Volunteer

Attitude

Volunteer

Attitude

Free time

Volunteer

Attitude

Free time

Performance

Volunteer

Attitude

Free time

Time

Volunteer

Attitude

27

27

32

32

32

24

24

47

47

30

30

30

30

30

30

24

24

24

19

4

18

20

27

27

32

32

32

24

24

46

46

27

27

27

59

59

59

24

24

24

39

–

17

20

0.00a

(Continued )

–0.31

–0.98

+0.08

–0.19b

–0.52

0.00a

–0.32

+0.34

+0.19

+0.27

+2.80

+0.65

–0.76

–0.64

+0.48

+0.35

0.00a

+1.06

–0.46

–0.43b

–0.04

Reinforcement, Reward, and Intrinsic Motivation

A/O

A/O

B/A

B/A

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

B/A

SS Repeated Children measures

A/O

J Exp Soc Psych

McGraw, McCullers (1979)

A/O

J Exp Soc Psych

McGraw, McCullers (1979)

Cameron and Pierce 231

9/4/2010 7:05:35 PM

Journal

JPSP

JPSP

J of Res in Pers

J of Res in Pers

Pers & Soc Psych Bull

J of Soc Psych

J of Soc Psych

Child Dev

Child Dev

Child Dev

Child Dev

Org Beh & Hum Perf

Org Beh & Hum Perf

Mot & Emotion

Author(s)

Morgan (1981) Exp. 2

Morgan (1981) Exp. 2

Brockner, Vasta (1981)

Brockner, Vasta (1981)

Pittman et al. (1980)

Shanab et al. (1981)

Shanab et al. (1981)

Danner, Lonkey (1981)

Danner, Lonkey (1981)

Danner, Lonkey (1981)

Danner, Lonkey (1981)

Boal, Cummings (1981)

Boal, Cummings (1981)

Luyten, Lens (1981)

Appendix C: (Continued )

Salkind_Chapter 65.indd 232

A/O

Field study

Field study

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

Design

Adults

Adults

Adults

Children

Children

Children

Children

Adults

Adults

Adults

Adults

Adults

Children

Children

Subjects

Wood models

Coding data

Coding data

Class inclusion

Class inclusion

Class inclusion

Class inclusion

Soma

Soma

Soma

Soma

Soma

Puzzles

Puzzles

Task

T

T

T

V

V

T

T

V

V

V

T

T

T

T

Reward type

E

E

E

U

U

E

E

U

U

U

E

E

E

E

Expectancy

Not, TC

Cont, TC

Not, NC

Not, TC

Not, TC

Cont, TC

Cont, TC

Not, TC

Not, TC

Contingency

Free time

Free time

Free time

Attitude

Free time

Attitude

Free time

Attitude

Free time

Free time

Attitude

Free time

Attitude

Free time

Dep. measure

10

21

21

30

30

30

30

20

20

24

25

26

20

20

N exp.

10

22

22

30

30

30

30

20

20

12

26

26

20

20

N control

–0.96

+0.38

+1.64

–0.08

–0.10

–1.23

–1.33

+0.43

+0.64

+0.80

–0.58

–0.37

+0.04

–0.77

Effect size (g)ab

232 Motivation

9/4/2010 7:05:35 PM

Salkind_Chapter 65.indd 233

Child Dev

Child Dev

Child Dev

Pallack et al. (1982)

Pallack et al. (1982)

JPSP

Pittman et al. (1982) Exp. 2

Pallack et al. (1982)

JPSP

Pittman et al. (1982) Exp. 1

Org Beh & Hum Perf

Porac, Meindl (1982)

JPSP

J General Psych

Zinser et al. (1982)

Pittman et al. (1982) Exp. 1

Social Cognition

Boggiano et al. (1982)

JPSP

Am. J Psych

Fabes et al. (1981)

Pittman et al. (1982) Exp. 1

Mot & Emotion

Luyten, Lens (1981)

J of Pers

Mot & Emotion

Luyten, Lens (1981)

J of Pers

Mot & Emotion

Luyten, Lens (1981)

Earn (1982)

Mot & Emotion

Luyten, Lens (1981)

Earn (1982)

Mot & Emotion

Luyten, Lens (1981)

A/O

A/O

A/O

A/O

A/O

Children

Children

Children

Children

Children

Children

Children

Adults

Adults

Adults

Children

Children

Adults

Drawing

Drawing

Drawing

Drawing

Matching games

Matwzching games

Matching games

Anagrams

Anagrams

Soma

Hidden pictures

Hidden pictures

Algorithms heuristic tasks

Wood models

Wood models

Wood models

Wood models

Wood models

T

V

V

T

T

T

T

T

T

T

V

T

T

T

T

T

T

T

U

E

U

E

E

E

E

E

E

E

U

E

E

E

E

E

E

E

Not, TC

Not, TC

Not, TC

Not, NC

Not, TC

Not, TC

Not, TC

Not, TC

All

Cont, PC

Cont, PC

Cont, PC

Not, TC

Not, TC

Free time

Free time

Free time

Free time

Attitude

Free time

Free time

Attitude

Free time

Free time

Free time

Free time

Performance

Volunteer

Attitude

Free time

Volunteer

Attitude

15

14

14

28

20

10

10

40

40

40

64

81

57

10

10

10

10

10

12

12

12

28

10

10

10

20

20

20

32

84

19

10

10

10

10

10

(Continued )

–0.44

+0.32

–0.48

–0.05

0.00a

+0.25

+0.37

+0.18

–0.28

–0.21

+0.08

+0.28

–0.53

+1.08

+0.08

–0.91

–1.15

–0.88

Reinforcement, Reward, and Intrinsic Motivation

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

Adults

Adults

Adults

Adults

Adults

Cameron and Pierce 233

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Salkind_Chapter 65.indd 234

J Management

Aust & N.Z. J Dev. Dis.

Social Cognition

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

Child Dev

Child Dev

Child Dev

Boggiano, Hertel (1983)

Ryan et al. (1983)

Ryan et al. (1983)

Ryan et al. (1983)

Ryan et al. (1983)

Ryan et al. (1983)

Ryan et al. (1983)

Morgan (1983) Exp. 1

Morgan (1983) Exp. 1

Morgan (1983) Exp. 2

J Management J Management

Crino, White (1982) Crino, White (1982)

Ogilvie, Prior (1982)

J Management

Crino, White (1982)

Child Dev

Pallack et al. (1982)

Journal

Crino, White (1982)

Author(s)

Appendix C: (Continued )

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

B/A

A/O

A/O A/O

A/O

A/O

Design

Children

Children

Children

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Children

Adults

Adults Adults

Adults

Children

Subjects

Puzzles

Puzzles

Puzzles

Hidden puzzles

Hidden puzzles

Hidden puzzles

Hidden puzzles

Hidden puzzles

Hidden puzzles

Memory task

Drawing

Puzzles

Puzzles Puzzles

Puzzles

Drawing

Task

T

T

T

V

V

T

T

T

T

T

T

V

V V

V

T

Reward type

E

E

E

E

E

E

E

E

E

U

E

U

U U

U

E

Expectancy

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Cont, PC

Cont, PC

Not, TC

Contingency

Free time

Attitude

Free time

Attitude

Free time

Attitude

Free time

Attitude

Free time

Attitude

Free time

Volunteer

Volunteer Attitude

Attitude

Free time

Dep. measure

40

40

40

64

64

16

16

32

32

46

26

20

20 20

20

15

N exp.

40

20

40

32

32

16

16

32

32

46

26

10

10 10

10

12

N control

–0.59

–0.27b

–1.94

0.00a

+0.47

0.00a

–0.35

0.00a

–0.46

+.0.02

–0.08

+0.64

+0.49 +0.07

+0.01

–0.16

Effect size (g)ab

234 Motivation

9/4/2010 7:05:35 PM

B/A

B/A

Bull Psych Society

Sex Roles

Sex Roles

Br. J Dev Psych

Br. J Dev Psych

J Exp. Psych

Bull Psych Society

Bull Psych Society

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

DeLoach et al. (1983)

Blanck et al. (1984)

Blanck et al. (1984)

Sarafino (1984)

Sarafino (1984)

Harackiewicz et al. (1984)

Griffith et al. (1984)

Griffith et al. (1984)

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 1

Pretty, Seligman (1984) Exp. 2 Pretty, Seligman (1984) Exp. 2

B/A

Salkind_Chapter 65.indd 235

B/A

B/A

B/A

B/A

B/A

B/A

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

J Sport Psych

Vallerand (1983)

A/O

Child Dev

Morgan (1983) Exp.2

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Children

Children

16-yr.-olds

Children

Children

Adults

Adults

Children

Children

Children

Soma

Soma

Soma

Soma

Soma

Soma

Soma

Soma

Reading books

Reading books

Hidden puzzles

Riddles

Riddles

Word game

Word game

Connect dots

Slideshow game

Puzzles

T

T

T

V

V

T

T

T

T

T

T

T

T

T

V

V

T

V

E

E

E

U

U

U

U

E

E

E

E

E

E

U

U

E

E

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Cont, PC

Not, TC

Not, TC

Not, TC

Not, TC

Attitude

Attitude

Free time

Attitude

Free time

Attitude

Free time

Attitude

Free time

Performance

Free time

Attitude

Attitude

Free time

Attitude

Free time

Free time

Attitude

20

30

30

30

30

30

30

30

30

64

64

47

85

85

70

70

26

40

20

30

30

30

30

30

30

30

30

32

32

47

15

15

69

69

26

10

0.00

Reinforcement, Reward, and Intrinsic Motivation

(Continued )

–0.16

–0.13

+0.46

+0.35

+0.42

+0.06

–0.05

–0.75

0.00a

0.00a

+0.33

0.00a

–0.41

+0.46

+0.56

0.00a

+1.98

Cameron and Pierce 235

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Journal

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

JPSP

J Sport Psych

Acad. Man. J.

Author(s)

Pretty, Seligman (1984) Exp. 2

Pretty, Seligman (1984) Exp. 2

Harackiewicz et al. (1984) Exp. 1

Harackiewicz et al. (1984) Exp. 1

Harackiewicz et al. (1984) Exp. 1

Harackiewicz et al. (1984) Exp. 2

Harackiewicz et al. (1984) Exp. 2

Harackiewicz et al. (1984) Exp. 2

Harackiewicz et al. (1984) Exp. 2

Harackiewicz et al. (1984) Exp. 3

Harackiewicz et al. (1984) Exp. 3

Vallerand, Reid (1984)

Arnold (1985)

Appendix C: (Continued )

Salkind_Chapter 65.indd 236

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

B/A

Design

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Subjects

Computer game

Stabilometer

Pinball

Pinball

Pinball

Pinball

Pinball

Pinball

Pinball

Pinball

Pinball

Soma

Soma

Task

T

V

T

T

T

T

T

T

T

T

T

T

T

Reward type

E

E

E

E

U

U

E

E

E

E

E

U

U

Expectancy

Both

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Cont, PC

Contingency

Attitude

Attitude

Performance

Attitude

Performance

Attitude

Performance

Attitude

Performance

Attitude

Free time

Attitude

Free time

Dep. measure

26

28

26

26

15

15

15

15

32

32

32

30

30

N exp.

16

28

26

26

15

15

15

15

32

32

32

30

30

N control

–0.04

+0.53 b

+0.04

+0.32

+0.44

+0.15

–0.43

+0.18

+0.16

+0.03

+0.07

+0.38

+0.06

Effect size (g)ab

236 Motivation

9/4/2010 7:05:35 PM

Social Cognition

Social Cognition

Org Beh & Hum Dec P

Org Beh & Hum Dec P

Psych Studies

Psych Studies

Psych Studies

Psych Studies

Psych Studies

Psych Studies

JPSP

JPSP

JPSP

JPSP

JPSP

Boggiano et al. (1985)

Boggiano et al. (1985)

Freedman, Phillips (1985)

Salkind_Chapter 65.indd 237

Freedman, Phillips (1985)

Tripathi, Agarwal (1985)

Tripathi, Agarwal (1985)

Tripathi, Agarwal (1985)

Tripathi, Agarwal (1985)

Tripathi, Agarwal (1985)

Tripathi, Agarwal (1985)

Sansone (1986) Exp. 1

Amabile et al. (1986) Exp.1

Amabile et al. (1986) Exp. 1

Amabile et al. (1986) Exp. 3

Harackiewicz et al. (1987)

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

16-yr.-olds

Adults

Children

Children

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Adults

Children

Children

Puzzles

3 tasks

3 tasks

3 tasks

Identify names

Puzzles

Puzzles

Puzzles

Puzzles

Puzzles

Puzzles

Proof reading

Proof reading

Puzzles

Puzzles

T

T

T

T

V

V

V

V

T

T

T

T

T

T

T

E

E

E

E

U

E

E

E

E

E

E

E

E

E

E

Cont, PC

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Not, TC

Cont, PC

Not, TC

Cont, PC

Not, TC

Attitude

Attitude

Attitude

Free time

Attitude

Performance

Attitude

Free time

Performance

Attitude

Free time

Attitude

Attitude

Free time

Free time

24

30

56

56

44

20

20

20

20

20

20

47

52

26

26

27

30

57

57

11

20

20

20

20

20

20

47

47

13

13

Reinforcement, Reward, and Intrinsic Motivation

(Continued )

–0.10

0.00a

0.00a

0.00a

+0.68

+0.54

+0.48

+1.61

+0.54

+0.54

+0.41

+0.68

+0.75

–0.10

–0.79

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JPSP

JPSP

J Ed Psych

J Ed Psych

J Gen Psych

J Gen Psych

Koestner et al. (1987)

Koestner et al. (1987)

Butler (1987)

Butler (1987)

Tripathi, Agarwal (1988)

Tripathi, Agarwal (1988)

J of Psych

Fabes (1987) Exp. 1

J of Psych

Pers & Soc Psych Bull

Hom (1987) Exp. 2

J of Psych

Pers & Soc Psych Bull

Hom (1987) Exp. 1

Fabes (1987) Exp. 2

Pers & Soc Psych Bull

Hom (1987) Exp. 1

Fabes (1987) Exp. 1

Journal

Author(s)

Appendix C: (Continued )

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

A/O

Design

Adults

Adults

Children

Children

Adults

Adults

Children

Children

Children

Adults

Adults

Adults

Subjects

Problem solving

Problem solving

Problem solving

Problem solving

Hidden puzzles

Hidden puzzles

Block building

Block building

Block building

Solving anagrams

Pursuit rotor task

Pursuit rotor task

Task

T

T

V

V

V

V

T

T

T

V

T

T

Reward type

E

E

U

U

U

U

E

E

E

?

?

?

Expectancy

Cont, PC

Not, TC

Not, TC

Cont, PC

Not, TC

?

Not

Not

Contingency

Free time

Free time

Performance

Attitude

Attitude

Free time

Free time

Free time

Free time

Performance

Attitude

Free time

Dep. measure

20

20

50

50

35

35

14

19

18

28

26

26

N exp.

10

10

50

50

18

18

14

19

19

28

26

26

N control

+1.18

+0.03

+0.39

+1.59

0.00a

+0.51

–0.45

–0.87

–0.82

–0.37

0.00a

+0.11b

Effect size (g)ab

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JPSP

J App Soc Psych A/O

J Org Beh SS Repeated Adults Management measures

J of Psych

J of Psych

Sansone et al. (1989)

Anderson, Rodin (1989)

Mawhinney et al. (1989)

Wicker et al. (1990)

Wicker et al. (1990)

Adults

Adults

Adults

Adults

Adults

Think Tac Toe

Think Tac Toe

Video game

Brain teasers

Computer games

Identify names

Beanbag game

Problem solving

T

T

T

V

V

V

T

T

E

E

E

U

U

U

E

E

Not, TC

Not, TC

Not

Not, TC

Both

Attitude

Free time

Time

Attitude

Attitude

Attitude

Free time

Attitude

Notes: Design: B/A = before-after groups design, A/O = after-only groups design, SS = single-subject design Reward type: T = tangible, V = verbal Reward expectancy: E = expected, U = unexpected Reward contingency: cont = contingent, not = not contingent; NC = nontask contingent, TC = task contingent, PC = performance contingent a indicates effect sizes given a value of zero (nonsignificant results with no report of means or direction of means) b indicates estimated effect sizes JPSP = Journal of Personality and Social Psychology J of Pers = Journal of Personality Org Beh & Hum Perf = Organizational Behavior and Human Performance J Exp Soc Psych = Journal of Experimental Social Psychology Child Dev = Child Development Per & Soc Psy Bull = Personality and Social Psychology Bulletin Cog Ther & Res = Cognitive Therapy and Research Mot & Emotion = Motivation and Emotion Int J of Sport Psy = International Journal of Sport Psychology J of School Psych = Journal of School Psychology J Applied Soc Psych = Journal of Applied Social Psychology

A/O

A/O

A/O

J Exp Soc Psych A/O

Sansone (1989)

Children

Mot & Emotion A/O

Adults

Fabes et al. (1988)

A/O

J Gen Psych

Tripathi, Agarwal (1988)

29

29

3

10

40

82

14

40

29

29

–

10

40

41

14

10

(Continued )

0.00a

0.00a

+0.15

+0.90

+0.12

+0.46

–1.34

+0.26b

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Appendix C: (Continued )

Behavior Mod = Behavior Modification Soc Beh & Pers = Social Behavior and Personality J of Soc Psych = Journal of Social Psychology J Applied Psych = Journal of Applied Psychology J of Res Pers = Journal of Research in Personality J General Psych = Journal of General Psychology J Management = Journal of Management Aust & N.Z. J Dev Dis = Australia and New Zealand Journal of Developmental Disabilities J Sport Psych = Journal of Sport Psychology Bull Psych Society = Bulletin of the Psychonomic Society Br J Dev Psych = British Journal of Developmental Psychology J Exp Psych = Journal of Experimental Psychology Acad Man J = Academy of Management Journal Org Beh & Hum Dec P = Organizational Behavior and Human Decision Processes Psych Studies = Psychological Studies J Org Beh Management = Journal of Organizational Behavior Management

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66 Motivation in Transition Barbara Stauber

Introduction

T

his article focuses on young people’s motivation in their transitions from school to work. The context for these considerations is a general pattern of change in youth transitions throughout Europe, which, as recent research shows, have become more prolonged, fragmented, insecure and, in many respects, reversible – a process described as ‘Yoyoisation’ (EGRIS, 2001).The metaphor of the yo-yo illustrates the phenomenon that youth transitions no longer represent a (linear) status passage from youth to adulthood, but include forward and backward movements, ups and downs, between both statuses, creating an ever longer transitional period of semi-dependencies. Broadly speaking, this change is part of a general destandardization of the life course that has taken place during the last 30 years (Hurrelmann, 2003), in conjunction with major changes in two fundamental social institutions: work and family, and their gendered notions of what constitutes ‘normal’ male work and ‘normal’ female family life (Stauber, 2006). These changes address increasing demands to the younger generation in terms of their life decisions and career orientations, and increasingly these demands have to be coped with by the individual. Formal qualifications are necessary, but no longer sufficient. Concepts such as self-organization or even self-socialization (Heinz, 2002) are indicative of these new demands. Such concepts are, however, predicated on certain material and psychological conditions, above all a sense of ‘social hold’ in order to provide what Anthony

Source: Young: Nordic Journal of Youth Research, 15(1) (2007): 31– 47.

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Giddens (1991) has called ‘ontological security’. And it is precisely these soft prerequisites that are missing in risky transitions: stable and reliable relationships and a certain amount of social capital, providing opportunities which help to respectively create and maintain a certain level of motivation. The article draws from some recently completed research (Walther et al., 2006; see also Walther, 2006) in which the interrelation between motivation and conditions for transitions into work has been explored in depth – the European research project Yo-Yo (Youth Policy and Participation). After introducing some basic concepts, I will briefly present three of its case study projects in order to discuss exemplary research findings on the issue of motivational change in youth transitions. The analysis will focus on a series of modes of participation identified in the research, which have general relevance for motivating young people.

The European Research Project Yo-Yo – Youth Policy and Participation The Yo-Yo-project can be situated at the interface of several European discourses on young people: the debate on social exclusion; the respective policy recommendations, such as (lifelong) learning, informal/nonformal learning, youth citizenship and ‘activation’; and, connected to these discourses, the topic of participation. Focusing on the latter, the project sought to attain a more profound understanding of what participation means, to enable young people to regain a pro-active attitude towards their individual transition project. The Yo-Yo-research project was carried out at a comparative level, encompassing nine European countries.1 Methodologically it is based on case study analysis (28 case studies), which included semi-structured qualitative interviews with 365 young people (with around 70 per cent interviewed twice), expert interviews (141 individuals working in the field of school to work transitions) and video projects with young people. The key research question for the Yo-Yo-project was whether motivational change could be facilitated by increased possibilities of participation. This core question was broken down into two further questions: what are the prerequisites, settings and, above all, opportunity structures in case study projects which facilitate motivational experiences for young people? How do different projects provide for different target groups in different contexts? A broad range of projects was selected for case study analysis, clustered in respect to their function in the transition system and their profile as either gravitating towards soft’ youth work (highly participatory but without direct career related outcomes in terms of transitions) or ‘hard’ employment-centred schemes (intended to lead to integration into work, but not necessarily in a participatory way).2 All projects represent best practice and have been chosen

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because, compared to normal practice in the respective transition systems, they are exemplary in terms of facilitating participation. Key concepts of the research are motivation, participation and, for comparative purposes, the concept of transition regimes.

Motivation Motivation theory has widely influenced educational research and practice (Maehr and Meyer, 1997), but has still not really been incorporated into transition research and policies. This is all the more surprising since motivation has to be regarded as an increasingly important, but at the same time scarce, resource that can easily be lost, for instance in institutionally misled trajectories (see Walther et al., 2002). Corresponding with the expectancy-value approach of Allan Wigfield and Jaquelynne Eccles (2000), we started with a double theoretical assumption that motivation, in the sense of a proactive attitude among young people towards their transitions, can result on the one hand from subjective needs and interests, and on the other hand from the perceived probability of achieving subjectively relevant goals. Both aspects are contextual (Ryan and Deci, 2000), namely they relate to different contextual conditions, and are open to experiences and potential change: needs or interests generate from interaction with the social world, and these may change with their fulfilment, giving rise to other needs or interests. The perception of the probability of achieving goals depends on the facilities available to young people to experience self-efficacy (Bandura, 1994). The notion of motivational change corresponds with the way we look at young people’s transitions. In this respect, change is perceived as the natural order, namely is normal and common, whereas stability represents a more unusual condition (Abbott, 1997) – a perspective which has become increasingly salient under the conditions of late modernity and destandardized life-courses (Fornäs, 1995). In these motivational changes, intrinsic and extrinsic aspects of motivation are interwoven: the desire to engage in a specific activity (e.g. enjoying dance) often has extrinsic aspects related to the instrumental quality or consequences of this activity.3 Young people’s eagerness to engage with their own transition biographies is in constant flux, depending on their experiences. With these sets of experiences, every young person, throughout his or her transition to adulthood, develops a personal motivational career,4 influenced by empowering or frustrating experiences, facilitated or hindered by more or less favourable contexts – in their broader social surroundings as well as in concrete pedagogical settings (du Bois-Reymond and Stauber, 2005). With this dynamic, biographical concept, motivation gets embedded in social contexts, which are partly shaped by individuals, but, to a far greater extent, are out of their hands. This relates to opportunity structures, which in the Yo-Yo-project focused on the perspective of participation.

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Participation In order to define this widely discussed concept, we used two central lines of distinction: • participation as active influence versus passive (or formalized) involvement; and • participation as a principle versus participation as a goal of policies. Our concept of participation contrasts with formal participation models, which in theory ‘allow’ young people to have a voice without considering the unequal distribution of tools and access structures that enable them to do so in practice. Instead, we include concepts such as ‘active citizenship’ (European Commission, 2001), which considers the scopes and tools for active engagement. Such an understanding of participation is missing in young people’s transitions from school to work. Above all in the hard policy sector (education, training and labour market policies), with its powerful gatekeepers, young people are channelled into trajectories in which participation is postponed to an ill-defined ‘later’. Instead, we argue that participation should be an integral principle of policies from design to practice. Translated to the supporting measures for youth transitions, which are at the forefront of our research, this principle would encompass four dimensions: (1) voluntary (and thereby self-chosen) attendance at the project; (2) involvement in project related decision-making; (3) social and civic engagement by means of citizenship tools within a community approach; and (4) biographical selfdetermination. The latter is the most demanding level of participation, but the dimensions are interrelated: as soon as young people start to feel that they can ‘participate in the making of their own future’ (Project Leader of Cityteam, The Netherlands), the other dimensions can become subjectively meaningful. This could also operate the other way around: as soon as young women and men feel that they are part of a community, have influence within it, are recognized, acknowledged and mirrored’, their subjective goals and abilities can become clearer to them. Here, both the expectation of success and the needs/interests aspect of motivation are equally relevant – eventually they are interlinked in the sense that the probability of achieving goals influences the definition of interests (Cocks and Watt, 2004).

Transition Regimes The concept of transition regimes is necessary for transnational comparison. This concept draws on the famous regime differentiation of Gösta EspingAnderson (1990), enlarged by Duncan Gallie and Serge Paugam (2000), and transfers it to the transition topic (Walther et al., 2006).Transition regimes refer to the interplay of socio-economic structures, institutions, cultural norms

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and the agency of pedagogues, parents, peers and young people themselves in all areas that are relevant for youth transitions. Such areas include labour markets and the ways in which they are regulated; education and training and how they are organized; compensatory programmes for unemployed youth and concepts of ‘disadvantage’; as well as mechanisms relating to gender, ethnicity and, more generally, difference. Considering, that the regimeapproach relates more to the general ‘Gestalt’ (Kaufmann, 2003) than to specificities of individual transition systems, and that there are considerable variations and also developmental dynamics within and between the countries subsumed under one regime, we refer to five broad regime types: the universalistic transition regime of the Scandinavian countries, the liberal regime of the Anglo-Saxon countries, the employment-centred regime in Continental countries, the sub-protective regime in Southern Europe, and the postsocialist transition regime, including a very heterogeneous group of East-European countries. These regimes can also be distinguished by the ways in which they deal with young people’s motivation, in which they function as motivational systems, and the ways in which they regulate flows of students and applicants by encouraging or ‘cooling’ individual aspirations (Goffman, 1963). Very roughly, we can distinguish the universalistic regime, which predominantly works through motivating young people to find out what suits them best, allows for biographical orientation and empowers individual aspirations, from the work-oriented regime types which operate much more through the principle of sorting out and thus dampening down the motivation of those who do not fit into certain educational clusters. The starting point of our research was the view that in destandardized transitions from school to work, individual motivation to orient one’s career, take decisions, complete education or training, or seek alternatives if chosen careers turn out to be inappropriate, is an increasingly important prerequisite. It is, above all, in such critical situations that maintaining a proactive attitude becomes crucial. This was confirmed by both groups of young people we interviewed, most visibly by the young women and men who managed to develop alternative careers in terms of choice biographies, and which they may realize due to personal competencies and family resources, coupled with favourable conditions on the local labour market. But the need for a proactive attitude was also confirmed by those young women and men whose options were restricted by low qualification levels, few family resources and a labour market segmentation that is unfavourable to them. This second group of young women and men who we accessed in the projects mostly look back on a turbulent motivational career with severe drawbacks. In identifying their motivational changes, we applied two strategies. Where they formed part of young people’s narratives in the interviews, motivational careers could be reconstructed by interview analysis. In other cases, we combined evaluation of the interviews with other sources of information (expert interviews, and

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observation of these young people via the video projects). Although the following section highlights mostly positive motivational change, the concept (and our results) includes upward as well as downward movement in motivational careers. Thus, motivational change cannot simply be equated with progress or success.

Exemplary Cases from the Yo-Yo-Project There is insufficient space in this article to carry out an extended case-study analysis. Instead, we have selected a few exemplary practices from a rather complex and comprehensive research project, restricting our analysis to only three (out of 28) case study examples in order to show the interrelationship between participation and motivation: the Italian project ArciRagazzi, located in the context of the sub-protective transition regime; the Dutch Cityteam project, located in a mixed regime type, including elements from employment-centred, liberal, but also universalistic regimes; and the project Lifting the Limits in Northern Ireland, exemplary of the liberal regime. We will briefly outline these case study projects by looking at their structures, goals, target groups and also some contextual conditions for evaluating their specific participatory strengths, but also some limitations.

ArciRagazzi, Palermo (Italy) – A Youth Work Project5 ArciRagazzi in Palermo, Sicily, is a youth association organizing leisure and cultural activities. It is a local branch of a national association, partly financed through membership fees, partly by public and private funding. A prime objective is to provide young people, especially those living in deprived neighbourhoods or having been released from detention centres, with life perspectives beyond unemployment and/or involvement with the Mafia. This is even more important in a context where youth unemployment stands at 60 per cent and the expectation is of a prolonged waiting period before entering a regular job. Typically for southern Italy this affects young people from all educational levels. Therefore, participants at ArciRagazzi are heterogeneous in terms of class and education. Taking a specific community approach, the project sets up participatory planning initiatives in which children, adolescents and families collaborate to improve public buildings and spaces, and offers all kinds of cultural activities based on young people’s skills and wishes, such as handicrafts workshops, fairs and concerts. Through debates, meetings and assemblies to decide even on project management guidelines, young people are given tools for active participation. The project initiates career orientation and transitions to work, and in this regard also aims at the enhancement of entrepreneurship. In fact, several young men and women have made semi-professional careers from simple engagement in voluntary activities to become freelance project leaders.

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Cityteam ( The Netherlands) – Preparing Young People for Training and Work Cityteam is based in three cities in The Netherlands: Utrecht, Zoetermeer and Rotterdam – the latter, however, has closed down owing to local funding problems which arose during the timeframe of the research. Cityteam provides professional orientation through a flexible combination of workshops, voluntary work and internships in private companies, accompanied by career counselling and coaching, in order to open up individual pathways for each of its participants. The target group of Cityteam consists of a mix of young people from different ethnic backgrounds and mostly with risk-full transitions (such as school dropout, low qualification levels and unemployment). A smaller number of participants, mostly younger than 20 and with at most secondary qualifications, are less at risk but felt they needed to take time out for orientation. The share of the first group of young people has increased lately, owing to the fact that Cityteam has become more involved in providing programmes for publicly funded reintegration trajectories. Cityteam may be regarded as an example of an independent transition institution and public-private partnership, the latter occurring more and more often in The Netherlands; but it seems to be becoming increasingly dependent on scarce public funding. This endangers its approach because of the predominance of youth at risk in what has up to now been rather balanced groups. With its focus on temporary work it also reflects the trend in labour market policies towards greater flexibility. However, it still has a strong focus on biographical participation.

Lifting the Limits, Armagh (Northern Ireland) – A Participatory Training Scheme Lifting the Limits is a year-long programme for young mothers between 16 and 25 in the countryside of Armagh, Northern Ireland. Financed by public funding, it is under increasing pressure due to funding shortfalls and problems finding new premises. It combines personal empowerment and support for these young mothers, with formally acknowledged training guaranteeing their inclusion in future employment. Facilitating structures for their participation are carefully adapted to their needs: a salary of about 8400 Euros per annum, reduced working hours of 25 hours a week, and contributions towards childcare and travel. The participating young women are trained by two peer support workers (former participants of the programme) to do outreach work as community leaders. They directly implement their training into practical work with the same group they themselves belong to: young mothers. The prospect of an immediate switch from trainee to peer-educator creates a highly empowering space for personal and interpersonal development, such as leadership skills, initiative skills and problem solving directly linked to the community. In the female peer-context young mothers can use

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each other as a type of gender role-model that is different from the norm. While a lot of their learning is informal, it is at the same time formally recognized, and successful completion of the training even provides access to higher education qualifications in community youth work, social work and community development. Leaving aside other important areas for analysis, we will immediately move on to modes of participation, which were revealed to be important for (re-)motivating disengaged young people (Walther et al., 2006).

Modes of Participation Through case-study analysis, based on the accounts of both project workers and young people and drawing together subjective experiences and the specific approaches of the projects, different modes of participation were identified. It is important to note that some of these modes correspond to the original function and objectives of the projects, whereas others are a result of the courage of the project workers to transcend institutionally set boundaries.

Choice The first thing is, nobody should tell you: ‘do this and that’ but it’s you in the first place who has to take decisions … a sort of self-experimentation. We also realize we have made some mistakes during this project, but it was nice, even making mistakes, growing up, it was like self-training. (Pamela, female, 21, ArciRagazzi)

The importance of choice was striking in the accounts of the young people interviewed. One could say that for young people choice represents a metaprinciple of self-determination and participation in late modern societies. Because it belongs to the set of cultural demands for individualization, choice has also been described as an ambiguous concept, above all if the necessary prerequisites to take decisions are neglected. From the perspective of the Foulcauldian discourse on governmentality, the issue of choice is even suspected of underpinning the advanced liberal model of social control (Rose, 1999). This meta-discussion in a way passes over issues of subjective relevance, which of course are influenced by ideological discourses, but which have to be respected anyway. The projects under discussion furthermore minimize the ambiguity of choice, because most of them provide a sensitive network of support and companionship for young people in their decisionmaking, which helps to mitigate the inequalities of advanced liberalism. Choice operates at various levels: it concerns an individual’s decision to attend a project or not, but also how to ‘use’ a project to meet individual

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needs and interests. Both aspects have been identified as being crucial for young people in order to identify with a project and to regard it as integral element of their transition. Of course, we find different conditions in the various projects to provide for both dimensions: those that aim to help young people find their orientation most often provide more options in this regard than those that stick to a specific task – for example training – for which they are paid. Biographical participation comes into play, whenever projects allow participants to develop individually appropriate and subjectively meaningful relations not only with the project, but with their life-decisions as well. This interrelation is expressed most clearly by those responsible for the Lifting the Limits project, working with young single mothers: … a kind of self-determination – having freedom in choices around things like … that they don’t want more children, or what they want for their children, or that they don’t have to be with the father of the children, or that they do want to be with the father of their children despite family opposition. And it’s having the courage and the self-confidence to stand up and say, ‘These are the choices I want to make and these are the right choices’, and not to be bullied as a result of that. (Project worker, female, Lifting the limits)

Such empowerment includes a strong notion of negotiating gender. Motivation built up by such empowerment may include financial or material incentives or wages that meet financial needs to build a bridge to more intrinsic experiences. In the case of Lifting the Limits, the access given to paid work and higher education can be regarded as such a bridge. Choice in some projects is explicitly related to a low threshold approach – especially when they attempt to attract young people at risk, who may have already developed a negative street-life-attitude. It is even more important for such projects to reflect on regime-related structures, which young people have experienced as pressure, force or as stigmatization. They need to understand the cycle of de-motivation and try to move beyond it by offering alternative structures.

Flexible Outcomes and Biographical Fit In existing arrangements, if a young person comes in, it is already assumed that he will become a painter. I think this is nonsense. A young person should decide for him- or herself what he or she would like to become. (Cityteam director, male)

Especially with regard to the pre-vocational sector, the degree to which projects either allow for open outcomes or pre-define outcomes can be seen as a criterion for distinguishing participatory from non-participatory

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approaches in terms of biographical self-determination. At the same time, it may be seen as a criterion for being more or less ascribing in terms of gendered vocational routes. However, this criterion has to be combined with biographical fit to the young person concerned. One of the original principles of the Cityteam project is that the young person him or herself is supported to find out what personal goals to follow and what transition steps to take within the project and afterwards. This principle unites what has been worked out as basic motivational factors: sticking with young people’s interests and needs, and providing them with encouraging experiences to achieve their goals, which they can get closer to step-by-step. This demands a high level of flexibility, by which projects are sometimes overburdened. In the case of Cityteam, for example, this could mean that internships cannot be organized at the right time – which could then lead to a withdrawal of motivation: When I started at Cityteam I was looking forward to it a lot. But after a few months, I didn’t care to get up anymore to go there. Because you didn’t do anything and you might as well be sitting at home. (Liv, female, 21, Cityteam)

Projects targeting youth at risk are particularly reliant on the principle of open outcomes, whereas in participatory training and employment projects the situation is completely different: young people enter these projects because they have a clearly defined objective, and because it is attractive to them to achieve it. In these cases, there is no contradiction between a predefined outcome and motivation, but how the means of achieving their goal is presented, and how much room there is for participation and personal decision-making are all the more crucial.

Individuality and Strengths One mode of participation is even more basic in terms of motivating young people: the mode of personal acceptance – ‘come as you are’ is a principle that acknowledges individual needs, peculiarities, obligations and constraints across different transitional strands (Thomson et al., 2002), which young people have to cope with and shape. This counter-strategy to negative labelling and stigmatization responds to young women’s and men’s need for recognition as individuals with normal aspirations. As long as this need is ignored, young people feel alienated from the start and cannot look at the next steps as their own.6 Lifting the Limits is an example of a project that manages to positively represent the situation of young single mothers by underlining the expertise deriving from the daily practice of these young women. This shift from what is normally considered a ‘deficit’ (all the more in terms of labour market

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opportunities) into a competence which gives access to paid work and even higher education, is highly empowering: When Lydia, 23, recalls being faced for the first time with having to take a group of young women in her local community for training: ‘It first was a big “no way” … I didn’t think I could do it’ – but then it felt amazing that I could do that’, which ‘showed me that I could do everything I wanted to do, despite having a child … The training has given me a sense of independence and shown me that I have a choice in how I live my life.’ Indeed, Lydia uses the opportunity of getting access to higher education by planning to study community work after her time in the project. Crucially, the success of this example depends to a great extent on an open access structure within the educational system, as can be found in the liberal transition regime. Similarly, very different projects such as Cityteam and ArciRagazzi have in common a refusal to ascribe stagnation and disadvantage in young people’s transitions to individual deficits, but rather focus on strengths instead. This attitude is even more important in case study projects, which address youth at risk (such as homeless youth), because it forms a basic prerequisite for gaining access to young people who would otherwise be excluded. These projects shift attention from areas in which young people fail to meet formal standards to activities in which they are strong. The approach allows for experiences of success. Instead of lowering the education and training level, which would mean reproducing rather than overcoming the deficit-perspective, it changes the ‘subject’ by shifting attention away from areas in which persons fail to meet formal standards to activities which they can excel at – precisely because they are related to their subjective interests. This approach avoids the mistaken assumption, often made in formal learning settings, that the issue of increased self-efficacy is isolated from the subjective relevance of goals, and combines them instead. Of course, focusing on strengths is closely related with the principle of allowing project-related decision-making and a general climate that incorporates more holistic approaches, in which young people can feel acknowledged in their (special) strengths as well as in their (special) problems.

Space I believe experimenting is important in the transition from school to work, namely having time and opportunities to realize what you like, what you don’t, and what you want to do in life … Experimenting also involves the possibility of making mistakes, and discovering your potential. (Project worker, male, ArciRagazzi)

We found various constellations whose interpretation of participation involved providing places and spaces to be appropriated and shaped by young people themselves according to their own needs and interests. Projects such as ArciRagazzi and Lifting the Limits focus on community-related participation

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and enlarge these spaces to the local level. They aim at fostering a mutual relationship between subjective motivation and active (youth) citizenship. By offering young people the possibility of influencing, shaping and changing their social environment, they provide a means of both constructing personal biographies and actively participating in the community. In ArciRagazzi the connection between space as part of the wider community and space as something to be jointly shaped and decided upon is enacted by internal democratic procedures which are realized in the external community. Participation in practical terms means that project workers do not make full use of their decision-making power, but share it with the young women and men involved in the projects who actively co-decide. This democratic principle is closely related with the rationale of focusing on young people’s strengths and requires confidence in young people’s competencies and in their willingness to invest them in a cooperative context. Giving young people space expresses belief in their strength. As one German project-worker puts it: If you would just let them do, far better things would come about than you and I would even consider. (Project worker, Kompass-Job-in-Club, East Germany)

What this translates to is an explicit empowerment of young people to create spaces for experimentation, in which experiences of self-efficacy can be made. Thus, the issue of space extends to the issue of accessibility (choice) and individuality (recognition). This relates to the external qualities of spaces to represent cool places (Skelton and Valentine, 1998), laden with youth cultural value, as in the case of ArciRagazzi, which is an attractive place for young people to meet. Such projects can be used by young people for self-presentation, which is an important aspect of identity work and thereby closely related to subjective needs and interests as an important level of motivation (Stauber, 2004). But equally important is the recognition that these locations also seem to represent warm places: by providing reliable bonds and a warm and welcoming atmosphere these spaces can in some cases even replace missing families and become homes for young people during a certain phase of their transitions. This second meaning points to a deeper need of young people and represents the ‘holding’ component to cool places. This is reaffirmed by the way some projects offer opportunities for self-presentation, allowing young people to decide for themselves how far they want to become visible, and when they prefer to draw back into the secure backstage of the project. The fashion-shows related to the dressmaking training provided by the German project, La Silhouette, are a perfect symbol of this balance, with individual young women stepping into the spotlight, but cushioned by the bigger group. The combination of cool and warm places allows for the creation of ‘communities of practice’ in which the interplay of meaning, belonging and identity (Wenger, 1998) can even produce a stronger desire for engagement.

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Responsibility Giving young people possibilities to use and to shape space, implies giving them a share of responsibility for what is happening in the project. This means taking them seriously, addressing them as adults. Participation in projectrelated decision-making, involving shared responsibility for the group, a common task or goal, enhances in young people a feeling of making a personal contribution and being socially important, which increases self-esteem. This is most obvious in the case of ArciRagazzi where young people at an early age can become project leaders and run a child recreation centre quasi autonomously. In this case, the task for the project workers is to keep a balance between transferring responsibility to the young people and maintaining a holding frame together with an encouraging atmosphere in which mistakes are accepted as learning experiences. Such a combination of personal challenge and a feeling of security encourages young people and motivates them to take on more responsibility with time.7 The motivating effect of approaches that balance of self-responsibility and support is aptly described by a young women of LaSilhouette: They help you to a certain degree, but you have to make your own contribution. Which means they don’t take the thing out of your hands. No matter, how clumsy your problem is. (Dani, 20, female, Germany)

Taking responsibility does not necessarily have to be an individual project, but often happens in interaction between individuals and their social surroundings. Some projects consciously use this link between social responsibility and biographical meaningfulness by providing opportunities for social engagement: work with children, with the elderly, social engagement in communities, for example.

Trust and Reliability I haven’t got this kind of relation either with my father or with my mother, I can’t talk about certain things with them … He [the project worker] made me see what it really means to listen to somebody. (Pamela, female, 21, ArciRagazzi)

In one way or another, all constellations of participation are based on and depend on relationships of trust. Trust is both a basic need, which grows with the experience of social marginalization, and a prerequisite for increased self-efficacy. First of all, this relates to the relationships between young people and project workers. Project workers often represent an alternative type of adult compared to teachers, employment officers and parents. This ‘otherness’ is based not only on professional habitus, for example as youth worker, but also on a ‘different’ socio-cultural orientation towards liberal,

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alternative milieus, relying more on personal authority than on power. As Pamela’s quote shows, coming to know these project workers can represent a new intergenerational experience. But also at the peer-level of other participants, ‘significant others’ (Mead, 1934) represent a strong motivational force: We were very supportive of each other all the time. There was a few of us went through different things during the project, you know, outside of work, and everyone was always involved in supporting each other. (Laura, female, 19, Lifting the Limits)

As mentioned above, in some cases projects could provide a substitute for missing families and become homes for young people during a certain phase of their transitions. Correspondingly, many young people referred to the projects they were involved in as a ‘family’. Nevertheless, reference to relationships of trust cannot simply be equated with harmony. In contrast, in the context of de-standardized transitions it is rather unlikely that young people’s and project worker’s values, views and interests will converge. Allowing for a culture of conflict in most projects is therefore an important issue, and respective learning experiences are a necessary prerequisite for participation in its project-, community- and biographyrelated dimensions.

Concluding Discussion: Motivators and Motivational Changes To conclude our discussion, it first has to be stressed that those modes of participation, which were shown to be relevant for motivating disengaged young people, are not equally applied by the projects under discussion because of their different functions and positions within the transitional system and their level of equipment and resources. These structural differences are also reflected in the different dimensions through which participation is actually foreseen and facilitated by the projects. It is even more important to underline the relative strength of these projects, which in different aspects and constellations show what could be important contributions to a successful link between participation and motivation. A second observation is that, as far as the link between participation and motivation is concerned, the major common trait shared by the various participants’ experiences is the activation of a virtuous circle in which participatory and motivational aspects are interconnected with each other. However, the projects have different approaches to the two aspects which are regarded as driving forces for motivation – namely to start from individual needs and interests, and to enhance the probability of achieving self-chosen goals.

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Some projects relate to and start from existing interests and desires expressed by young people; others pick up what remaining motivation young people have maintained despite a series of de-motivating experiences; others provide the opportunity for young people to access the space and develop their own interests. Some projects start out by addressing young people’s competencies and raise their feeling of self-efficacy through an advance in terms of trust. Also, the legitimate possibility of taking ‘time out’ may be experienced as breaking with past experiences of failure. And there are projects which, by providing different (material and immaterial) resources and support, increase the means which the concerned young women and men have at their disposal to reach their goals. The third observation is that most of the projects explicitly follow a biographical perspective, aiming at providing (and using) the chance of a ‘fresh start’, without ignoring the hindering/de-motivating factors of the transitional process to date. This new process starts with personal recognition, which gives young people a sense of their personal expertise and a general sense that they ‘matter’ (Amundson, 1998). It goes on to provide young people with spaces and opportunity structures by which they can find out what is subjectively meaningful to them in order to take subjectively relevant decisions (also ‘against’ the logic of institutions). This implies an awareness of structural conditions, namely the availability of means which are necessary for shaping their own trajectory (in terms of opportunities, resources and competencies). And it often implies an increased reflexivity regarding gender roles and respective scopes of agency. Most rewarding, in this regard are projects (such as Lifting the Limits) in which new competencies get certified and officially acknowledged. Getting in contact with meaningfulness in a biographical sense does not necessarily have to be an individual project, but often comes about in the interaction between individuals and their social surroundings. Personal counselling and coaching appear to be valuable resources in this respect, helping to incorporate individual experiences and learning steps into the broader framework of biographical development. This enables young women and men to develop what has been called ‘biographicity’: to acknowledge their biography to date, and to develop confidence in their biographical progress in future, while being open to biographical change (Alheit and Dausien, 1999). Biographicity can be regarded as crucial for motivational management, above all, if it includes reflection on personal limits and structural borders and leads to a sense of realism regarding the possible scope of one’s own efforts. In this way it becomes obvious that motivation is a prerequisite of (biographical) learning, whereas motivational change itself has to be seen as a learning process. Fourth, in all projects, the modes of participation that turned out to be motivators for young people go hand in hand with the perception of social

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hold, allowing the achievement of a sense of belonging and relatedness: self-determination is not realized in an individualized way but is combined with companionship, support and reliable relationships. It is striking that all these participatory modes rely on this interactive dimension – regardless of whether they are more related to needs fulfilment or the expediency of goals. Choice is an issue of trust (in someone’s ability to make the right decisions) and thus implies a strong interactive component. The same applies to flexible outcomes. Focusing on individuality and strengths means trusting in young people’s abilities from the start, which shapes the framework of interaction from the beginning. Giving young people responsibility for project procedures and space links the motivational issues of increased self-efficacy and subjectively relevant goals, and puts them into the interactive framework of reliable relationships. It is this link between freedom and bonds, choice and hold, weak ties and strong ties (Granovetter, 1977), self-determination and reliable support which makes the difference both to the individualized situation which young people failed to cope with and the negative ascriptions they have experienced in the past. So, participatory approaches are shown to be the most sustainable motivators, as long as they include these soft qualities of social bonds and reliability. This last point has some theoretical consequences. Whereas the issue of biographical meaningfulness is not new in motivation theory, the importance of relatedness must be highlighted, since it is often underestimated. Through such an appreciation, we can come to a much more differentiated understanding of the motivation of young people (Ryan and Deci, 2000).

Perspectives While focusing on the question of how to motivate young, disengaged people, our research has produced interrelated findings, which have some general relevance for theorizing youth transitions. Transitions have to be considered much more from the perspective of motivational careers, which demands a contextualized understanding of structural and biographical (de-)motivators. As regards the latter, active participation is revealed to be an important motivator, as long as participatory measures have a strong interactive dimension of relationships of trust, and as long as the activities they are involved in have biographical relevance for young people. These insights into the development of motivational careers – as the biographical motors of transitions – are basic, but nevertheless their latent potential is underestimated. Theoretical and, to an even greater extent, political discourses on changed demands of (youth) transitions often neglect the ‘soft’ prerequisites to cope with new transition-related demands; discourses on (lifelong) learning, competence building and (key) competences often overlook the unspectacular yet crucial components of subjective relevance and intersubjective support.

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This is exactly what these projects under discussion have understood and why they are outstanding examples – despite some limitations – of best practice: late modern youth needs biographical orientation in connection with social hold and reliability – perhaps much more than previous generations. But the increasingly precarious financial situation these projects find themselves in after three years of evaluation shows that their approaches are far from being established. On the contrary, the specific qualities which enable them to make a difference in transition systems, and which in some countries represent the very spirit and soul of the projects, have in some cases already been abolished (e.g. Danish Open Youth Education) or are in acute danger of being reduced to mere pre-vocational education or vocational training (as in Northern Ireland and Germany) in the context of current workfare trends in transition policies.

Notes 1. The United Kingdom, Ireland, Portugal, Spain, Italy, Romania, Denmark, The Netherlands and, distinguished because of still existing differences, East and West Germany. The research (2002–04) was funded by the European Commission under the 5th Framework Program. For further information see www.iris-egris.de/yoyo. 2. The projects under discussion have been clustered into: • • • • •

3. 4.

5. 6.

7.

youth work projects; projects focusing on the integration of youth at risk; projects focusing on preparation for training and work; projects which represent training and employment schemes; and projects with a highly participatory approach to training and employment.

Whereas the first two categories can be associated with the soft sectors of youth policies, the last three belong, via their link to training and employment, to the hard sector of transition policies. For the fluid boundaries between intrinsic and extrinsic motivation, see the continuum model in Ryan and Deci (2000:72). See Erving Goffman’s idea of career’, related to any social strand of any person’s course through life’ (Goffman, 1968: 119), and also Bloomer and Hodkinson (2000), who have adopted it in their concept of learning careers. For these classifications see Note 2. The importance of such personal recognition was also apparent during the interviews. As soon as interviewees felt encouraged to present themselves as experts of their own life situation, a more participatory climate developed and the communication situation became more symmetrical. This shows the difference between the right to take responsibility (including tools and opportunity structures) and the rights and responsibilities approach on which repressive workfare policies rely.

References Abbott, Andrew (1997) ‘On the Concept of Turning Point’, Comparative Social Research 16: 85–105. Alheit, Peter and Dausien, Bettina (1999) ‘Biographicity as a Basic Resource of Lifelong Learning’, paper presented at the European Conference Lifelong Learning inside and

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outside Schools, Bremen, 25–27 February, URL (consulted November 2005): http:// www.erill.unibremen.de/lios/sections/s4_alheit.html Amundson, Norman (1998) Active Engagement: Enhancing the Career Counselling Process. Richmond: Ergon Communcations. Bandura, Albert (1994) Self-efficacy: The Exercise of Control. New York: Freeman. Bloomer, Martin and Hodkinson, Phil (2000) ‘Learning Careers: Continuity and Change in Young People’s Dispositions to Learning’, British Educational Research Journal 26(5): 583–97. Cocks, Rachel J. and Watt, Helen M.G (2004) ‘Relationships among Perceived Competence, Intrinsic Value and Mastery Goal Orientation in English and Maths’, Australian Educational Researcher 31(2): 81–112. du Bois-Reymond, Manuela and Stauber, Barbara (2005) ‘Biographical Turning Points in Young People’s Transitions to Work Across Europe’, in Helena Helve and Gunilla Holm (eds) Contemporary Youth Research: Local Expressions And Global Connections, pp. 63–75. Aldershot: Ashgate. EGRIS (European Group of Integrated Social Research) (2001) ‘Misleading Trajectories: Transition Dilemmas of Young Adults in Europe’, Journal of Youth Studies 4(1): 101–18. Esping-Andersen, Gösta (1990) The Three Worlds of Welfare Capitalism. Cambridge: Cambridge University Press. European Commission (2001) A New Impetus for European Youth. European Commission White Paper, URL (consulted August 2005): http://europa.eu.int/comm/dgs/ education_culture/publ/pdf/youth-wb/en.pdf Fornäs, Johan (1995) Cultural Theory and Late Modernity. London: Sage. Gallie, Duncan and Paugam, Serge (eds) (2000) Welfare Regimes and the Experience of Unemployment in Europe. Oxford: Oxford University Press. Giddens, Anthony (1991) Modernity and Self-Identity. Cambridge: Polity Press. Goffman, Erving (1963) On “Cooling the Mark Out”: Some Aspects of Adaptation and Failure’, in Arnold Rose (ed.) Human Behaviour and Social Processes, pp. 482–505. Boston, MA: Houghton Mifflin. Goffman, Erving (1968) Asylums: Essays on the Social Situation of Mental Patients and Other Inmates. Harmondsworth: Penguin. Granovetter, Marc (1977) ‘The Strength of ‘Weak Ties’, American Journal of Sociology 78(6): 1360–80. Heinz, Walter R. (2002) ‘Self-socialisation and Post-traditional Society ’, in Richard A. Settersten and Timothy J. Owens (eds) New Frontiers of Socialisation, pp. 41–64. Oxford: Elsevier Science. Hurrelmann, Klaus (2003) ‘Der entstrukturierte Lebenslauf. Die Auswirkungen der Expansion der Jugendphase’ (The De-standardized life Course. Effects of the Expansion of Youth), Zeitschrift für Soziologie der Erziehung und Sozialisation (Journal for Sociology of Education and Socialization) 23(2): 115–26. Kaufmann, Franz-Xaver (2003) Varianten des Wohlfahrtsstaats. Der deutsche Sozialstaat im internationalen Vergleich (Variations of Welfare State. The German Sozialstaat in International Comparison). Frankfurt: Suhrkamp. Maehr, Martin L. and Meyer, Heather A. (1997) Understanding Motivation and Schooling: Where We’ve Been, Where We Are, and Where We Need to Go’, Educational Psychology Review 9(4): 371– 403. Mead, George Herbert (1934) Mind, Self and Society. Chicago, IL: C.W Morris. Rose, Nikolas (1999) Powers of Freedom: Refraining Political Thought. Cambridge: Cambridge University Press. Ryan, Richard M. and Deci, Edward L. (2000) ‘Self-Determination Theory and the Facilitation of Intrinsic Motivation, Social Development, and Well-Being’, American Psychologist 55(1):68–78.

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Skelton, Tracey and Valentine, Gill (eds) (1998) Cool Places: Geographies of Youth Cultures. London and New York: Routledge. Stauber, Barbara (2004) Junge Frauen und Männer in Jugendkulturen. Selbstinszenierungen und Handlungspotentiale (Young Women and Men in Youth Cultures. Performing Selves and Agency Potentials). Opladen: Leske and Budrich. Stauber, Barbara (2006) ‘Biography and Gender in Youth Transitions’, in Manuela du-Bois Reymond and Lynne Chisholm (eds) The Modernization of Youth Transitions in Europe, New Directions for Child and Adolescent Development 113: 63–75. Thomson, Rachel, Bell, Robert, Holland, Janet, Henderson, Sheila, McGrellis, Sheena and Sharpe, Sue (2002) ‘Critical Moments: Choice, Chance and Opportunity in Young People’s Narratives of Transition to Adulthood’, Sociology 36(2): 335–54. Walther, Andreas (2006) ‘Regimes of Youth Transitions, Choice, Flexibility and Security in Young People’s Experiences Across Different European Contexts’, Young 14(2): 119 – 41. Walther, Andreas, du-bois Reymond Manuela and Biggart, Andy (eds) (2006) Participation in Transition: Motivation of Young Adults in Europe for Learning and Working. Frankfurt: Peter Lang. Walther, Andreas, Stauber, Barbara, Biggart, Andy, du Bois-Reymond, Manuela, Furlong, Andy, Lòpez Blasco, Andreu, Morch, Sven Pais and José Machado (eds) (2002) Misleading Trajectories – Integration Policies for Young Adults in Europe? An EGRIS Publication, Opladen: Leske + Budrich. Wenger, Etienne (1998) Communities of Practice. Learning, Meaning, and Identity. Cambridge: Cambridge University Press. Wigfield, Allan and Eccles, Jacquelynne S. (2000) ‘Expectancy-value Theory of Achievement Motivation’, Contemporary Educational Psychology 25(1): 68–81.

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Section IV: Research Design, Measurement and Statistics and Evaluation

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67 Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing Raymond Hubbard and R. Murray Lindsay

The most important task before us in developing statistical science is to demolish the P-value culture, which has taken root to a frightening extent in many areas of both pure and applied science, and technology. (Nelder, 1999, p. 261) My personal view is that p-values should be relegated to the scrap heap and not considered by those who wish to think and act coherently. (Lindley, 1999, p. 75)

M

uch empirical work in psychology focuses on hypothesis testing. The typical empirical paper develops, tests, and reports the results of a number of explicit hypotheses relating to the topic at hand. The outcomes of these hypothesis tests are said to contribute toward the creation of a body of knowledge within the discipline. For the most part, psychology researchers rely on p values from statistical significance tests when evaluating the merits of their hypotheses. Based on an annual random sample of issues from 12 American Psychological Association journals for the period 1990–2002, for example, Hubbard (2004) estimated that 94% of empirical papers used significance tests. Given their universality, it seems reasonable to presume that p values play an integral part in knowledge development. In addition, the ubiquity of p values strongly suggests that researchers are intimately familiar with their capabilities. But this is not always the case. Thus, for instance, many Source: Theory & Psychology, 18(1) (2008): 69–88.

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investigators erroneously believe that the p value indicates the probability that (1) the results occurred because of chance, (2) the results are replicable, (3) the alternative hypothesis is true, (4) the results are important, and (5) the results will generalize. (For specific examples showing where each of these five misuses of p values may be found in the psychology literature, see Bakan, 1966; Carver, 1978; Cohen, 1994; Falk & Greenbaum, 1995; Gigerenzer, 1993; Gigerenzer, Krauss, & Vitouch, 2004; Krämer & Gigerenzer, 2005; Krantz, 1999; Krueger, 2001; Nickerson, 2000; Schmidt, 1996; and Thompson, 1999, among others.)1 This paper is not concerned with such misinterpretations of p values, damaging though they are. Rather, it examines the inherent problems associated with the p value as a plausible measure of evidence per se. Although the origin of the modern p value is generally credited to Karl Pearson (1900), who introduced it in his c2 test (he actually called it the P , c2 test), it was Sir Ronald Fisher who was responsible for popularizing statistical significance testing and p values in the many editions of his classic books Statistical Methods for Research Workers and The Design of Experiments, first published in 1925 and 1935, respectively. Fisher used discrepancies in the data to reject the null hypothesis, that is, he calculated the probability of the data on a true null hypothesis, or Pr(x | H0). Formally, p = Pr(T(X) ≥ T(x) | H0). P is the probability of getting a test statistic T(X) greater than or equal to the observed result, T(x), in addition to more extreme ones, conditional on a true null hypothesis, H0, of no effect or relationship. (Disturbingly, Freund and Perles [1993] remark that differences in the definition of the p value abound in textbooks. See Good [1981], also.) So, the p value is a measure of the (im)plausibility of the actual observations (as well as more extreme and unobserved ones) obtained in an investigation, assuming a true null hypothesis. The rationale is that if the data are seen as being rare or highly discrepant under H0, this constitutes inductive evidence against H0. The idea that rare occurrences comprise evidence against a hypothesis has a pedigree dating back to the first ‘significance test’ by John Arbuthnot in 1710 concerning the birth rates of males and females in London, and is continued in the work of Mitchell, LaPlace, and Edgeworth, among others. (See Baird, 1988 and Gigerenzer et al., 1989 for synopses of this early history of statistical testing.) Traditionally, a p value of .05 has been used as a benchmark to indicate inductive evidence against the null hypothesis, with values like p < .01, p < .001, etc., furnishing even stronger evidence against H0. Fisher (1959) considered the p value to be an objective way for researchers to assess the (im)plausibility of the null hypothesis: ... the feeling induced by a test of significance has an objective basis in that the probability statement on which it is based is a fact communicable to and verifiable by other rational minds. The level of significance in such cases fulfils the conditions of a measure of the rational grounds for the disbelief [in the null hypothesis] it engenders. (p. 43)

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But a critical question remains: does the p value, in fact, provide an objective, useful, and unambiguous measure of evidence in hypothesis testing? We argue in this paper that it does not. More specifically, a review of the statistics literature points to several reasons – statistical, logical, the relative nature of evidence, etc. – why the p value fails visibly as a credible measure of evidence. Our premise is simple: that p values continue to saturate empirical work is taken as prima facie testimony that most psychology (and other) scholars – W. Edwards, Lindman, and Savage (1963), Gigerenzer and his colleagues (Gigerenzer, 1993; Gigerenzer et al., 2004; Gigerenzer & Murray, 1987; Gigerenzer et al., 1989), and Nickerson (2000) being notable exceptions – remain unaware of many of the reasons why this index is a defective measure of evidence. To illustrate this, even those who see value in statistical significance testing (e.g., Abelson, 1997; Chow, 1996, 1998; Cortina & Dunlap, 1997; Hagen, 1997; and Mulaik, Raju, & Harshman, 1997) simply never bring up, much less defend, the issue of the adequacy of the p value as a measure of evidence qua evidence. We hope that the present review will help to rectify this situation. As a secondary goal we propose, like Cohen (1994) and Loftus (1996), that instead of/along with reporting p values in individual studies, researchers should provide estimates of sample statistics, effect sizes, and their confidence intervals. We also stress, following Fisher, the importance of replication with extension research (the grist for meta-analyses) in developing a cumulative knowledge base. For comparisons of population estimates from this research, we recommend the criterion of overlapping confidence intervals. Sufficiently overlapping confidence intervals indicate reasonable estimates of the same population parameter.

Why P Values Are an Inadequate Measure of Evidence in Statistical Significance Testing P Values Exaggerate the Evidence against the Null Hypothesis This is the most damning criticism of the p value as a measure of evidence.

Two-Sided Nulls P values exaggerate the evidence against a two-sided (point null or ‘small interval’) hypothesis (Berger, 1986; Berger & Sellke, 1987). An exact, or point null, hypothesis takes the form H0: q = q0 versus HA: q ≠ q0, where q0 is a specific value of q. More realistically, Berger and Delampady (1987) argue, exact hypotheses are better represented as tests such as H0: |q – q0| ≤ e versus HA: |q – q0| > e, where e is ‘small’.

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Using a Bayesian significance test for a normal mean, James Berger and Thomas Sellke (1987, pp. 112–113) showed that for p values of .05, .01, and .001, respectively, the posterior probabilities of the null, Pr(H0 | x), for n = 50 are .52, .22, and .034. For n = 100 the corresponding figures are .60, .27, and .045. Clearly these discrepancies between p and Pr(H0 | x) are pronounced, and cast serious doubt on the use of p values as reasonable measures of evidence. In fact, Berger and Sellke (1987) demonstrated that data yielding a p value of .05 in testing a normal mean nevertheless resulted in a posterior probability of the null hypothesis of at least .30 for any objective (symmetric priors with equal prior weight given to H0 and HA) prior distribution. It is important at this juncture to emphasize the distinction between the p value, Pr(x | H0), and the posterior probability of the null, Pr(H0 | x).2 The p value gives the probability of the observed (and more extreme) data conditional on a true null hypothesis. Even though it may sound similar, this is not the same thing as the probability of the null being true conditional on the observed data. There is an asymmetric relationship between Pr(x | H0) and Pr(H0 | x). Despite this, a number of psychologists, including Carver (1978), Cohen (1994), and Nickerson (2000), note that many researchers are confused over the meaning of the two expressions, and tend to view the p value as the probability that the null is true. Berger and Sellke (1987) put this succinctly: ‘Indeed, most nonspecialists interpret p precisely as Pr(H0 | x)’ (p. 114). Berger and Sellke’s (1987) research led them to conclude that p values can be highly misleading measures of evidence. That is, the use of p values makes it relatively easy to obtain statistically significant findings, such that p = .05 can indicate no evidence against H0. Researchers and practitioners, on the other hand, tend to interpret a .05 value as constituting much greater evidence against the null. Continuing in the same vein, Berger and Delampady (1987) found similar discrepant results between p values versus Pr(H0 | x) in both normal and binomial situations. This prompted them to recommend that formal use of p values should be abandoned when testing precise (point null and small interval) hypotheses, a conclusion supported by Nester (1996). And, of course, psychologists overwhelmingly test point null and small interval hypotheses. George Casella and Roger Berger (1987), however, showed that Berger and Sellke’s (1987) results for two-sided hypotheses do not necessarily extend to the one-sided testing problem. This outcome maintained hope for the efficacy of the p value as a measure of evidence, at least in more restricted circumstances. Casella and Berger believe that the p value is useful as a quick and crude inferential index. Berger and Sellke (1987) responded: Our basic view of the Casella–Berger article, however, is that it pounds another nail into the coffin of P values. To clarify why, consider what it is that makes a statistical concept valuable; of primary importance is that the concept must convey a well-understood and sensible message for the vast majority of problems to which it is applied. (p. 135)

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Berger and Sellke find no such ‘well-understood and sensible message’ with respect to p values because they do not provide easily interpretable measures of evidence against H0 over the spectrum of everyday testing problems. Dickey (1987) agreed with Berger and Sellke’s position regarding the drawbacks of p values, while Dollinger, Kulinskaya, and Staudte (1996) found them wanting even as a measure of evidence for normal data in a one-sided testing context. And in any case, surely science requires more than the quick, crude, restrictive form of inference that Casella and Berger (1987) appear willing to settle for. In light of the above discussion, one would have to concur with Berger and Berry’s (1988) sobering opinion that there should be concern about the validity of research findings based on moderately small, including .05, p values.

Frequentist ‘Calibration’ of P Values It is conceivable that the work cited above raising serious doubts on the usefulness of p values may be ignored or dismissed by mainstream (Neyman–Pearson) frequentist statisticians because of its ‘subjective’ Bayesian orientation (see Neyman, 1977). But what if p values are found wanting as a measure of evidence among those espousing ‘objective’ relative frequency approaches to statistical testing. Here, Sellke, Bayarri, and Berger’s (2001) findings should serve as a salutary warning even to entrenched (Neyman–Pearson) frequentists. To fully appreciate the importance of this issue requires some background information, supplied below. It is not understood by many researchers that in classical statistical testing there are two, quite different, measures of ‘statistical significance.’ One is Fisher’s p value, which is an inferential index of the strength of the evidence against H0, is a data-based random variable, and is applicable to individual studies. On the other hand there is the a level from a Neyman–Pearson hypothesis test. This test is concerned with minimizing Type II, or b, errors (i.e., false acceptance of a null hypothesis) subject to a bound on Type I, or a, errors (i.e., false rejections of a null hypothesis). In addition, a is a prescription for behaviors (accepting or rejecting H0), not a means of assessing evidence; is a pre-selected fixed measure, not a random variable; and applies only to long-run repeated random sampling from the same population, not to single experiments (Hubbard, 2004). The Neyman–Pearson theory of hypothesis testing, with a as the significance level, is generally accepted as constituting frequentist statistical orthodoxy (Hogben, 1957; Nester, 1996; Royall, 1997).3 So the Neyman–Pearson model is the one typically portrayed in statistics textbooks. Conversely, social science methods texts, in a misguided attempt to present a single, unified model of statistical testing, have tended to anonymously mix together the two incompatible measures of statistical significance, p’s and a ’s. Needless to say, this has resulted in massive confusion among members of the scholarly

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community about exactly what ‘statistical significance’ means – is it denoted by a p value, an a level, and/or the ubiquitous p < a criterion (Hubbard & Armstrong, 2006)? The upshot is that many researchers (e.g., Bayarri & Berger, 1999, 2000, 2004; Berger, 2003; Berger & Sellke, 1987; Gigerenzer, 1993; Goodman, 1993, 1999; Hubbard, 2004; Hubbard & Bayarri, 2003a, 2003b, 2005) state that the p value is routinely misinterpreted as a frequentist Type I error probability. An empirical literature in which p values and a levels are erroneously seen to be interchangeable, but in which investigators overwhelmingly report p’s rather than a ’s required of Neyman–Pearson frequentist orthodoxy (see Hubbard, 2004), sets the backdrop for Sellke et al.’s (2001) study. As seen above, Berger and his colleagues had already shown the p value to be a poor gauge of evidence in a Bayesian context. They now wanted to determine if p values are useful measures of evidence against H0 when considered from a Neyman–Pearsonian perspective. Accordingly, Sellke et al. (2001) devised a method for ‘calibrating’ p values so that they can be interpreted as Neyman–Pearson frequentist error probabilities.4 The end result of this calibration is as follows: a( p) = (1 + [−e p log ( p)]–1)–1. Consequently, p = .05 translates into frequentist error probability a (.05) = .289 in rejecting H0 – a result suggesting no evidence against H0. Even a (.01) = .111. These findings convey in a non-Bayesian manner the severe problems involved in using p values as credible measures of evidence against the null hypothesis.

Frequentist Performance of P Values As reported in a number of studies (e.g., Berger, 2003; Hubbard & Bayarri, 2003a; and especially Sellke et al., 2001), a simulation of the frequentist performance of p values is revealing. Whereas a ’s can be constrained to some pre-assigned (e.g., .05) level, p values share no similar obligation. That is, p’s do not behave in the frequentist manner of a ’s. This is dramatically illustrated by accessing an applet at www.stat.duke.edu/~berger, which permits a simulation of the frequentist properties of p values. As an example, suppose we wish to conduct some tests on the effectiveness of a new psychotherapy, P-T. The statistical test would be H0: P-T = 0 versus HA: P-T ≠ 0. The simulation, based on a long series of such tests on normal data (variance known), records how often H0 is true for p values in given ranges, say p approximately equal to .05 or .01. Otherwise expressed, this frequentist simulation of the behavior of p values demonstrates that even when we obtain ‘statistically significant’ outcomes near the .05 or .01 levels, these results often arise from true null hypotheses of no effect or association. More specifically, assuming that one-half of the null hypotheses in the P-T tests are true, Sellke et al. (2001, p. 63) warned that:

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1. Of the subset of P-T tests for which the p value is close to the .05 level, at least 22% (and typically about 50%) come from true nulls. 2. Of the subset of P-T tests for which the p value is close to the .01 level, at least 7% (and typically about 15%) come from true nulls.5 As Berger (2003) understated the case: ‘The harm from the common misinterpretation of p = 0.05 as an error probability is apparent’ (p. 4). A p value of .05 may provide no evidence against the null hypothesis.

P Values and Sample Size P Values and Small Versus Large Samples Sample size is hugely influential in determining significance levels. Royall (1986), for example, cites well-known statisticians whose interpretations of p values in small versus large sample studies are totally contradictory: some argue that a given p value in a small sample study is stronger evidence against H0 than the same p value in a large sample study, and vice versa. As such, a given p value does not have a fixed, objective meaning – it is contingent upon (at least) the sample size. Indeed, as Marden (2000) points out, the p value is not very useful with large sample sizes. Because almost no null hypothesis is exactly true (Tukey, 1991), when sample sizes are large enough almost any null hypothesis will have a tiny p value. Hand’s (1998) concerns about the relevance of significance testing in data-mining situations, where every p value will be statistically significant to several zeros following the decimal point, is simply Marden’s observation writ bold.

Lindley’s ‘Paradox’ Moreover, the problems with p values and sample sizes do not end here. We must consider also Lindley’s ‘paradox’ (Lindley, 1957). He showed that for any level of significance, p, and for any nonzero prior probability of the null hypothesis, Pr(H0), a sample size can be found such that the posterior probability of the null, Pr(H0 | x), is 1 – p. That is, a null hypothesis that is soundly rejected at, say, the .05 level by a Fisherian significance test can nevertheless have 95% support from a Bayesian viewpoint. That these inferences are diametrically opposed is the paradox. The rationale behind this conundrum, Johnstone (1986) explains, is that no matter how small the p value, the likelihood ratio Pr(x | H0)/Pr(x | HA) approaches infinity as the sample size increases. Consequently, for large n, a small p value can actually be interpreted as evidence in favor of H0 rather than against it. The question of the objectivity and usefulness of the p value as a measure of evidence is shattered by this argument.

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Experimental Designs and P Values How different investigators might conceive the planning and execution of a study can also lead to p values with widely varying magnitudes. As an example of this, let us examine Fisher’s (1935, ch. 2) classic experiment of the ‘lady tasting tea,’ as described by Lindley (1993). The lady in question claimed she could distinguish between whether milk or tea had been poured first into a cup (of tea). In the experiment, the lady is presented with six pairs of cups of tea, and she must determine whether milk or tea entered the cup first. The null hypothesis – that she cannot, in fact, discriminate – is that she would guess 50% right (R) and 50% wrong (W). Suppose that she gets the first five results right and the last one wrong, or RRRRRW. The p value for this outcome, Lindley notes, is 7(½)6, or .110, which is not statistically significant at the .05 level. This p value, like all of them, consists of two parts. In this case: 6(½)6 = .094 (probability of observed outcomes) + (½)6 = .016 (probability of more extreme outcomes). The justification for the inclusion of the latter in the calculation of p values is given in a later section of the paper. Suppose instead of the above design, another researcher decides to repeat the experiment until the lady makes her first mistake. In this case, and with the same RRRRRW data, the p value is now statistically significant at the .032 level [(½)6 + (½)6 = .016 + .016 = .032]. The two parts of this p value are explained as follows: (½)6 = .016 (probability of observed outcomes) – but without this expression being multiplied by 6 because the mistaken choice, W, must always come at the end (see, e.g., Goodman, 1999) – + (½)6 = .016 (probability of more extreme outcomes). Of course, these experimental results make no sense. The exact same data, obtained in the exact same sequence, should yield the exact same p values. But they do not. And all because two different investigators held alternate conceptions as to how the experiment should be run.

Effect Sizes and P Values One must surely question the p value as a measure of evidence when it has nothing to say about the effect size obtained in a study (Gelman & Stern, 2006). For instance, a small sample study with a large effect can yield the same p value as a large sample study with a small effect size. To illustrate this, consider Freeman’s (1993) hypothetical medical trials in which all patients receive both treatments A and B and are asked to express their preference (see Table 1). The results of trial 1, with its 75% preference rate for A over B, would be considered as indicative of a potentially enormous preference for A. Trial 4, on the other hand, with a 50.07% preference rate for A, would be

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Table 1 Trial 1 2 3 4

No. preferring A 15 114 1,046 1,001,455

No. preferring B 5 86 954 998,555

% preferring A 75.0 57.0 52.3 50.07

regarded as overwhelming evidence that preferences for A versus B are all but identical. Very few researchers would view the results of these four trials as being equivalent, yet they all produce a p value of .041. (Freeman does not specify which particular statistical test was used in making these comparisons.) Gibbons’ (1986) assertion, therefore, in an article titled ‘P-Values’, that ‘An investigator who can report only a P value conveys the maximum amount of information contained in the sample...’ (p. 367) is seen to be incredulous. Far from conveying such information, Berger, Boukai, and Wang (1997) caution that the interpretation of p values will change drastically from problem to problem. Contrary to Fisher’s claims, the p value is not an objective measure of evidence against a hypothesis, a topic that is pursued below.

P Values and Subjectivity A further example of the fallibility of the p value as an objective measure of evidence is seen in the choice of one-sided versus two-sided statistical significance tests (Goodman & Royall, 1988; Royall, 1997). Although twosided tests are the norm, researchers are sometimes advised that if they expect a departure from H0 in a specific direction they can halve the p value, say from .05 to .025. That is, Goodman and Royall (1988) comment, even though the data are the same, the p value is altered by the researcher’s subjective impressions about the likely outcome of the study. They also note that similar changes to p values occur when the research involves multiple comparisons.

P Values Are Logically Flawed P Values Are Logically Flawed Measures of Support for Hypotheses Schervish (1996) demonstrated that p values fail to meet the simple logical condition required by a measure of support, namely, that if hypothesis H implies hypothesis H′, there should be at least as much support for H′ as there is for H. In the course of this work, he lamented that he had been

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unable to construct a consistent interpretation of the p value as anything resembling a measure of support for a hypothesis even in simple, much less multiparameter, problems. Schervish warned that ‘common informal use of P values as measures of support or evidence for hypotheses has serious logical flaws’ (p. 203). Further, because they are not as different as they might have seemed (i.e., point null and one-sided hypotheses are, in fact, at opposite ends of a continuum of hypotheses spanned by interval hypotheses), Schervish argued that the interpretation of the p value as a measure of evidence should be consistent across the different hypotheses tested – point null, one-sided, and interval. This, of course, is not the case. Thus, Schervish’s research supports the claim of Berger and Sellke (1987) and Bayarri and Berger (2000) that the p value is not amenable to a reasonably objective interpretation as evidence over the spectrum of testing problems. And this, together with much other information presented in this paper, runs counter to Frick’s (1996) claim that a p value creates a common measure of strength of evidence across statistical tests.

The P Value Computes Not the Probability of the Observed Data under H0 , But This Plus the Probability of More Extreme Data This is a major weakness regarding the usefulness of p values. Because they are defined as a procedure for establishing the probability of an outcome, as well as more extreme ones, on a null hypothesis, significance tests are affected by how the probability distribution is spread over unobserved outcomes in the sample space. That is, the p value denotes not only the probability of what was observed, but also the probabilities of all the more extreme events that did not arise. How is it that these more extreme, unobserved, cases are involved in calculating the p value? To find out, we revisit Lindley’s (1993) analysis of the ‘lady tasting tea’. Recall the lady was right (R) about the outcomes of the first five experiments, and wrong (W) about the sixth, i.e., RRRRRW. This result has probability (½)6, or a statistically significant p value of .016. But, Lindley continues, Fisher saw the flaw in this argument because every possible result with the six pairs of cups is significant at p = .016. To guard against this, Fisher proposed that any result where just one W occurs out of six supports the lady’s ability to discriminate, and should be included in the calculation of the p value. There are six possibilities, including RRRRRW, so the p value is now 6(½)6 = .094, which is not significant. Fisher’s significance rationale is no longer the p value for a given outcome on a true null hypothesis, but that and similar outcomes; in our case, one mistake in six taste tests. He was aware, however, that this situation also was not feasible. This is because the most likely result in the which comes first – milk or tea – taste test is sheer guessing: 50% R and

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50% W. For example, Lindley asserts, for 128 taste tests (64 R, 64 W) the p value – 128C64(½)128 – is approximately .05. But this brings us back to square one; if this result is the most likely, then all other outcomes have a smaller probability. That is, all 128 taste tests will be significant at the p = .05 level. In order to circumvent this issue, Fisher suggested that if one error in six (RRRRRW) is significant, more extreme outcomes, such as no mistakes at all (RRRRRR), must necessarily be significant. Therefore, these more extreme results should be incorporated when calculating the p value. For the outcome RRRRRW, with probability (½)6 or p = .016, there are five others (RRRRWR, RRRWRR, etc.) as extreme, and one (RRRRRR) more extreme, so the overall probability is 7(½)6 = .110, which is not significant. And this p value has two components: 6(½)6 = .094 (probability of observed data) plus (½)6 = .016 (probability of more extreme data). This p = .110 is, of course, the same value cited earlier. Many statisticians (e.g., Berger & Berry, 1988; Berger & Delampady, 1987; Freeman, 1993; Goodman, 1999; Royall, 1997) charge that a valid measure of strength of evidence cannot be dependent on the probabilities of unobserved outcomes. Jeffreys (1939) acknowledged this illogic in p values: What the use of P implies ... is that a hypothesis that may be true may be rejected because it has not predicted observable results that have not occurred. This seems a remarkable procedure. (p. 316)

Royall (1997) insists that there is no value to Fisherian significance tests because they are at odds with the law of likelihood and its implication of the ‘irrelevance of the sample space’ (p. 68). As he explains: The law of likelihood says that the evidence in an observation, X = x, as it pertains to two probability distributions labeled q1 and q2, is represented by the likelihood ratio, f(x; q1) ⁄ f(x; q2 ). In particular, the law implies that for interpreting the observation as evidence for hypothesis H1: q = q1 vis-à-vis H2: q = q2, only the likelihood ratio is relevant. What other values of X might have been observed, and how the two distributions in question spread their remaining probability over the unobserved values is irrelevant – all that counts is the ratio of the probabilities of the observation under the two hypotheses. (p. 22)

Or as Freeman (1993) says, echoing Birnbaum’s (1962) and A.W.F. Edwards’ (1992) seminal contributions: … the likelihood principle is the one secure foundation for all statistics. I find the arguments in favour of it compelling and the counterarguments unconvincing. Since p-values and all other frequentist methods violate this principle, they must necessarily be unsatisfactory tools of statistical inference. (pp. 1444 –1445)6

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Specification of an Alternative Hypothesis Evidence Is Relative When an alternative hypothesis is specified, it is possible to identify those outcomes as extreme or more so than the observed event. Consequently, Royall (1997) states, it is not low probability under A that makes an observation evidence against A. Rather, it is low probability under A compared with the probability under a different hypothesis B, and this makes it evidence against A versus B. This line of reasoning necessitates a weighing of the evidence between two rival hypotheses, a situation impossible in Fisherian significance tests, where there is only the null hypothesis. Fisher never saw the need for an alternative hypothesis, and vigorously opposed its later inclusion by Jerzy Neyman and Egon Pearson (Gigerenzer & Murray, 1987; Hubbard & Bayarri, 2003a). Note, then, Johnstone’s (1986) observation that the law of likelihood provides a better measure of evidence than p values for evaluating the plausibility of two (or more) rival hypotheses.7 More specifically, if the likelihood ratio Pr(x | H0)/Pr(x | HA ) exceeds 1, then the evidence is in favor of H0 over HA, and vice versa. Unfortunately, Fisher’s disjunction only addresses Pr(x | H 0 ); it is silent about Pr(x | HA). The p value is a tail-area probability and not a likelihood ratio.

Our Interest Is in the Alternative Hypothesis Specifying an alternative hypothesis is not just a means of covering values more extreme than those observed on a null hypothesis. The alternative (research) hypothesis is the one the investigator is interested in. Berkson (1942) recognized this when posing an early challenge to Fisher’s paradigm of null hypothesis testing: In the null hypothesis schema we are trying only to nullify something . . . . But ordinarily evidence does not take this form. With the corpus delicti in front of you, you do not say, ‘Here is evidence against the hypothesis that no one is dead’. You say, ‘Evidently, someone has been murdered’. (p. 326)

For statistical tests to be scientifically useful they should speak to the research hypothesis, and not be fixated with rejection of the null hypothesis. This is consistent with Goodman and Royall’s (1988) complaint that p values blinker us into thinking that a hypothesis can only be weakened, rather than strengthened, by the data. But Fisher’s methodology denies the existence of an alternative/research hypothesis. In this matter, it is sometimes thought that Fisherian significance testing has an implicit alternative hypothesis that is simply the complement of the null. But, as Hubbard and Bayarri (2003a) point out, this is difficult to formalize. For instance, what is the complement of an N (0, 1) model? Is it the mean differing from 0, the variance differing

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from 1, the model not being Normal? Formally, Fisher only had the null model in mind, and wanted to see if the data were compatible with it.

Confidence Intervals, Not P Values The foregoing discussion makes it clear that p values are neither objective nor credible measures of evidence in statistical significance testing. Moreover, the authenticity of many published studies with p < .05 findings must be called into question. Rather than the preoccupation with p values and testing, the goal of empirical research in individual studies should be the estimation of sample statistics, effect sizes, and the confidence intervals (CIs) surrounding them. CIs underscore the superiority of estimation over testing. Scientific advance typically necessitates plausible estimates of the magnitude of effect sizes in the population (A.W.F. Edwards, 1992; Lindsay, 1995), and the CI provides this. CIs also indicate the precision or reliability of the estimate via the width of the interval. Also, because they are in the same metric as the point estimate, CIs make it easier to see whether the results are substantively, rather than statistically, significant. And, of course, a CI can be used as a significance test; a 95% CI not including the null value is equivalent to rejecting the hypothesis at the .05 level. Furthermore, initial results need to be replicated and extended. Here again, CIs assume a pivotal role. Specifically, we advocate the criterion of overlapping CIs around point estimates across similar studies as a measure of replication success. Substantially overlapping CIs suggest tenable estimates of the same population parameter, and we applaud the very useful recent work in this area (e.g., Cumming & Finch, 2001, 2005; Fidler, Thomason, Cumming, Finch, & Leeman, 2005; Goldstein & Healy, 1995; Schenker & Gentleman, 2001; Schmidt, 1996; Smifhson, 2003; Thompson, 2002; Tryon, 2001).8 It is the systematic replication and extension of the results of previous studies, and not p values from individual ones, that fosters cumulative knowledge development. That this statement appears to have eluded many applied researchers, as well as editors and reviewers, is puzzling because Fisher (1966) himself put only provisional stock in statistically significant results from single studies: ‘we thereby admit that no isolated experiment, however significant in itself, can suffice for the experimental demonstration of any natural phenomenon’ (p. 13). Fisher was a major proponent of replication: ‘Fisher had reason to emphasize, as a first principle of experimentation, the function of appropriate replication in providing an estimate of error’ (Fisher Box, 1978, p. 142). Indeed, Fisher Box (1978) insinuates that Fisher coined the term ‘replication’: ‘The method adopted was replication, as Fisher called it; by his naming of what was already a common experimental practice, he called attention to its functional importance in experimentation’ (p. 142). Fisher (1966) encouraged in particular the importance of replication with extension research: ‘we may, by

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deliberately varying in each case some of the conditions of the experiment, achieve a wider inductive basis for our conclusions, without in any degree impairing their precision’ (p. 102). It is easy, therefore, to imagine Fisher agreeing with the sentiments put forward in both the psychology (e.g., Falk, 1998; Hubbard, 2004; Neuliep & Crandall, 1990, 1993; Rosenthal, 1990; Rosnow & Rosenthal, 1989; Sohn, 1998; Thompson, 1994) and statistics (e.g., Bayarri & Mayoral, 2002; Chatfield 1995; Ehrenberg & Bound, 1993; Guttman, 1977; Lindsay & Ehrenberg, 1993; Nelder, 1986, 1999; Ottenbacher, 1996; Rosenbaum, 1999, 2001) disciplines that there is an urgent need for more replication with extension research.

Conclusions Over the last few decades a considerable literature has emerged in psychology critical of the misuse of statistical significance testing. Much of the literature has dealt with how researchers invest these tests with far greater capabilities than they possess. Moreover, this frequently involves gross misinterpretations of the meaning of p values. Works like these are to be welcomed. During this same time, however, little has appeared in psychology (or elsewhere in the social sciences) about the severe limitations of the p value as a measure of evidence per se. In other words, it is bad enough for researchers to misuse a measure that is useful: But it strains credulity to do so when that measure is seriously flawed in itself. And this paper has demonstrated – from a multitude of perspectives – that the p value is just that. Hence Nelder’s (1999) call to ‘demolish’ the p value culture. In concluding, we note that there is more than a hint of irony in the fact that Fisher’s sanctioning of the vital role of replication has been overlooked, while at the same time his widely misunderstood and defective p values blanket the empirical literature. This has occurred, even though, as Steiger (1990) expressed: ‘An ounce of replication is worth a ton of inferential statistics’ (p. 176). It is past time to redress this imbalance. Accordingly, we hope that the present paper will help stimulate further public discussion on methods of data analysis and knowledge development within the field.

Notes 1. These works, particularly Nickerson’s (2000) tour de force, also offer excellent reviews of the statistical significance testing controversy in psychology. 2. From Bayes’ theorem, the posterior probability of the null hypothesis using our terminology is calculated as follows: Pr(H 0 | x) =

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4. 5.

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Readers are referred to several articles in the psychology literature making use of this formula (e.g., Cohen, 1994; Falk & Greenbaum, 1995; Hagen, 1997; Nickerson, 2000). Fisher is also a frequentist in the sense that a p value of .05 on a true null hypothesis yielded in a single study would be interpreted to mean that the probability of obtaining such an observed value (and more extreme ones) is only 5%. He is not, however, a frequentist in the long-run repeated sampling mode like Neyman–Pearson. See Hubbard and Bayarri (2003a) for further discussion of this. The details of this calibration are too involved to consider here. They can be found in Sellke et al. (2001). Interested readers are encouraged to experiment with the applet, where one can specify the initial percentage of true nulls, the small ranges of p values to investigate (e.g., p = .05 might be chosen as p between .049 and .05), and the value of the normal means, m’s, that occur under HA in the simulation. Freeman’s (1993) appraisal of the usefulness of p values in data analysis is instructive, reflecting as it does a 180° change of opinion: This paper started life as an attempt to defend p-values ... I have, however, been led inexorably to the opposite conclusion, that the current use of p values as the ‘main means’ of assessing and reporting the results of clinical trials is indefensible. (p. 1443)

7. See also Glover and Dixon’s (2004) support of the likelihood principle as a means of adjudicating knowledge claims in psychology. 8. Despite the advantages in using CIs over p values, reforms in statistical practice in psychology have been problematic (Hubbard & Ryan, 2000). Fidler, Thomason, Cumming, Finch, and Leeman (2004), for example, report on the difficulties encountered by Loftus (1993) in his efforts to decrease the emphasis on significance testing while editor of Memory & Cognition. During his tenure, Fidler et al. (2004) note, the proportion of articles using error bars (both CIs and standard error bars) increased to 41% as compared with 7% under his predecessor. Unfortunately, after Loftus left his editorial position, this proportion fell to 24%. Clearly, effecting changes in the manner in which statistical evidence is presented in the literature will be no easy task. Yet it is surely an important one.

References Abelson, R.P. (1997). A retrospective on the significance test ban of 1999 (If there were no significance tests, they would be invented). In L.L. Harlow, S.A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 117–141). Mahwah, NJ: Erlbaum. Baird, D. (1988). Significance tests, history and logic. In S. Kotz & N.L. Johnson (Eds.), Encyclopedia of statistical sciences (pp. 466– 471). New York: Wiley. Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 66, 423– 437. Bayarri, M.J., & Berger, J.O. (1999). Quantifying surprise in the data and model verification (with comments). In J.M. Bernardo, J.O. Berger, A.P. Dawid, & A.F.M. Smith (Eds.), Bayesian statistics ( Vol. 6, pp. 53–82). Oxford: Clarendon. Bayarri, M.J., & Berger, J.O. (2000). P values for composite null models. Journal of the American Statistical Association, 95, 1127–1142. Bayarri, M.J., & Berger, J.O. (2004). The interplay of Bayesian and frequentist analysis. Statistical Science, 19, 58– 80. Bayarri, M.J., & Mayoral, A.M. (2002). Bayesian design of ‘successful’ replications. The American Statistician, 56, 207–214.

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Berger, J.O. (1986). Are p-values reasonable measures of accuracy? In I.S. Francis, B.F.J. Manly, & F.C. Lam (Eds.), Pacific Statistical Congress (pp. 21–27). Amsterdam: Elsevier. Berger, J.O. (2003). Could Fisher, Jeffreys and Neyman have agreed on testing? (with comments). Statistical Science, 18, 1–32. Berger, J.O., & Berry, D.A. (1988). Statistical analysis and the illusion of objectivity. American Scientist, 76, 159–165. Berger, J.O., Boukai, B., & Wang, Y. (1997). Unified frequentist and Bayesian testing of a precise hypothesis (with comments). Statistical Science, 12, 133–160. Berger, J.O., & Delampady, M. (1987). Testing precise hypotheses (with comments). Statistical Science, 2, 317–352. Berger, J.O., & Sellke, T. (1987). Testing a point null hypothesis: The irreconcilability of p values and evidence (with comments). Journal of the American Statistical Association, 82, 112–139. Berkson, J. (1942). Tests of significance considered as evidence. Journal of the American Statistical Association, 37, 325–335. Birnbaum, A. (1962). On the foundations of statistical inference (with comments). Journal of the American Statistical Association, 57, 269–326. Carver, R.P. (1978). The case against statistical significance testing. Harvard Educational Review, 48, 378–399. Casella, G., & Berger, R.L. (1987). Reconciling Bayesian and frequentist evidence in the one-sided testing problem (with comments). Journal of the American Statistical Association, 82, 106–139. Chatfield, C. (1995). Model uncertainty, data mining and statistical inference (with comments). Journal of the Royal Statistical Society A, 158, 419– 466. Chow, S.L. (1996). Statistical significance: Rationale, validity and utility. Thousand Oaks, CA: SAGE. Chow, S.L. (1998). Précis of statistical significance: Rationale, validity and utility (with comments). Behavioral and Brain Sciences, 21, 169–239. Cohen, J. (1994). The earth is round ( p < .05). American Psychologist, 49, 997–1003. Cortina, J.M., & Dunlap, W.P. (1997). On the logic and purpose of significance testing. Psychological Methods, 2, 161–172. Cumming, G., & Finch, S. (2001). A primer on the understanding, use and calculation of confidence intervals that are based on central and noncentral distributions. Educational and Psychological Measurement, 61, 532–574. Cumming, G., & Finch, S. (2005). Inference by eye: Confidence intervals and how to read pictures of data. American Psychologist, 60, 170–180. Dickey, J.M. (1987). Comment on Berger and Sellke. Journal of the American Statistical Association, 82, 129–130. Dollinger, M.B., Kulinskaya, E., & Staudte, R.G. (1996). When is a p-value a good measure of evidence? In H. Rieder (Ed.), Robust statistics, data analysis and computer intensive methods (pp. 119–134). New York: Springer Verlag. Edwards, A.W.F. (1992). Likelihood (Expanded ed.). Baltimore, MD: Johns Hopkins University Press. Edwards, W., Lindman, H., & Savage, L.J. (1963). Bayesian statistical inference for psychological research. Psychological Review, 70, 193–242. Ehrenberg, A.S.C., & Bound, J.A. (1993). Predictability and prediction (with comments). Journal of the Royal Statistical Society A, 156, 167–206. Falk, R. (1998). Replication – A step in the right direction: Commentary on Sohn. Theory & Psychology, 8, 313–321. Falk, R., & Greenbaum, C.W. (1995). Significance tests die hard: The amazing persistence of a probabilistic misconception. Theory & Psychology, 5, 75–98.

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Hubbard, R. (2004). Alphabet soup: Blurring the distinctions between p’s and a ’s in psychological research. Theory & Psychology, 14, 295–327. Hubbard, R., & Armstrong, J.S. (2006). Why we don’t really know what statistical significance means: Implications for educators. Journal of Marketing Education, 28, 114–120. Hubbard, R., & Bayarri, M.J. (2003a). Confusion over measures of evidence ( p’s) versus errors (a ’s) in classical statistical testing (with comments). The American Statistician, 57, 171–182. Hubbard, R., & Bayarri, M.J. (2003b). P values are not error probabilities. Institute of Statistics and Decision Sciences, Working Paper, No. 03–26. Durham, NC: Duke University Working Papers Series, 27708–0251. Hubbard, R., & Bayarri, M.J. (2005). Comment on Christensen. The American Statistician, 59, 353. Hubbard, R., & Ryan, P.A. (2000). The historical growth of statistical significance testing in psychology – and its future prospects. Educational and Psychological Measurement, 60, 661–681. Jeffreys, H. (1939). Theory of probability. Oxford: Clarendon. Johnstone, D.J. (1986). Tests of significance in theory and practice (with comments). The Statistician, 35, 491–504. Krämer, W., & Gigerenzer, G. (2005). How to confuse with statistics or: The use and misuse of conditional probabilities. Statistical Science, 20, 223–230. Krantz, D.H. (1999). The null hypothesis testing controversy in psychology. Journal of the American Statistical Association, 44, 1372–1381. Krueger, J. (2001). Null hypothesis significance testing: On the survival of a flawed method. American Psychologist, 56, 16–26. Lindley, D.V. (1957). A statistical paradox. Biometrika, 44, 187–192. Lindley, D.V. (1993). The analysis of experimental data: The appreciation of tea and wine. Teaching Statistics, 15, 22–25. Lindley, D.V. (1999). Comment on Bayarri and Berger. In J.M. Bernardo, J.O. Berger, A.P. Dawid, & A.F.M. Smith (Eds.), Bayesian Statistics ( Vol. 6, p. 75). Oxford: Clarendon. Lindsay, R.M. (1995). Reconsidering the status of tests of significance: An alternative criterion of adequacy. Accounting, Organizations and Society, 20, 35–53. Lindsay, R.M., & Ehrenberg, A.S.C. (1993). The design of replicated studies. The American Statistician, 47, 217–228. Loftus, G.R. (1993). Editorial comment. Memory & Cognition, 21, 1–3. Loftus, G.R. (1996). Psychology will be a much better science when we change the way we analyze data. Current Directions in Psychological Science, 5, 161–171. Marden, J.I. (2000). Hypothesis testing: From p values to Bayes factors. Journal of the American Statistical Association, 95, 1316–1320. Mulaik S.A., Raju, N.S., & Harshman, R.A. (1997). There is a time and a place for significance testing. In L.L. Harlow, S.A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 65–115). Mahwah, NJ: Erlbaum. Nelder, J.A. (1986). Statistics, science and technology (with comments). Journal of the Royal Statistical Society A, 149, 109–121. Nelder, J.A. (1999). From statistics to statistical science (with comments). The Statistician, 48, 257–269. Nester, M.R. (1996). An applied statistician’s creed. The Statistician, 45, 401– 410. Neuliep, J.W., & Crandall, R. (1990). Editorial bias against replication research. Journal of Social Behavior and Personality, 5, 85–90. Neuliep, J.W., & Crandall, R. (1993). Reviewer bias against replication research. Journal of Social Behavior and Personality, 8, 22–29. Neyman, J. (1977). Frequentist probability and frequentist statistics. Synthese, 36, 97–131.

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68 Alphabet Soup: Blurring the Distinctions between p’s and a’s in Psychological Research Raymond Hubbard

It is my personal belief that an objective look at the record will show that Fisher contributed a large number of statistical methods, but that Neyman contributed the basis of statistical thinking. (Lucien LeCam, quoted in Reid, 1982, p. 268)

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xtensive confusion prevails among psychologists concerning the reporting and interpretation of results of classical statistical tests. The reason for this confusion is that textbooks on statistical methods in psychology and the social sciences usually present the subject matter as a single, comprehensive, uncontroversial theory of statistical inference. These texts rarely allude to the fact that classical statistical inference as it is generally portrayed is in fact an anonymous hybrid consisting of the union of the ideas developed by Ronald Fisher, on the one hand, and Jerzy Neyman and Egon Pearson, on the other (Gigerenzer, 1993; Gigerenzer & Murray, 1987; Gigerenzer et al., 1989; Huberty, 1993; Huberty & Pike, 1999). It is a union that neither side would have agreed to, given the pronounced philosophical and methodological differences between them. In fact, a bitter debate raged over the years between the Fisherian and Neyman–Pearson camps. The seminal work of Gigerenzer and his colleagues (Gigerenzer, 1993; Gigerenzer & Murray, 1987; Gigerenzer et al., 1989) and Huberty (1993; Huberty & Pike, 1999) notwithstanding, most researchers in psychology and

Source: Theory & Psychology, 14(3) (2004): 295–326.

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elsewhere remain uninformed about the historical development of methods of statistical inference, and of the mixing of Fisherian and Neyman – Pearson concepts. In particular, there is widespread failure to acknowledge the incompatibility of Fisher’s evidential p value (actually, Karl Pearson [1900] introduced the modern p value, but Fisher popularized it) with Neyman–Pearson’s Type I error rate, α (Goodman, 1993). The difference between evidence ( p’s) and errors (α’s) is not some semantic splitting of hairs. Rather, it points to the basic distinctions between Fisher’s notions of significance testing and inductive inference, versus Neyman–Pearson’s ideas on hypothesis testing and inductive behavior. But since statistics textbooks often surreptitiously blend concepts from both sides, misunderstandings concerning the reporting and interpretation of statistical tests are virtually guaranteed. Adding insult to injury, the confusion over measures of evidence versus errors is so completely ingrained that it is not even seen as being a problem among the rank and file of researchers. As proof of this, even critics of statistical testing often fail to distinguish between p’s and α’s, and the repercussions this has on the meaning of empirical results. Thus, many of these critics (e.g. Carver, 1978; Kirk, 1996; Krueger, 2001; Rozeboom, 1960; Wilkinson & the APA Task Force on Statistical Inference, 1999) unwittingly adopt a Fisherian stance inasmuch as they talk almost exclusively in terms of p, as opposed to α, values. Still other critics or discussants of statistical testing (e.g. American Psychological Association, 1994, 2001; Cohen, 1990, 1994; Dar, Serlin, & Omer, 1994; Falk & Greenbaum, 1995; Loftus, 1996; Mulaik, Raju, & Harshman, 1997; Nickerson, 2000; Rosnow & Rosenthal, 1989; Schmidt, 1996) are inclined, erroneously, to use p’s and α’s interchangeably. Additional examples of recent studies critiquing the merits of statistical testing that nonetheless continue to offer incorrect advice regarding the interpretation of p’s and α’ s are readily adduced from the literature (e.g. Chow, 1996, 1998; Clark, 1999; Daniel, 1998; Dixon & O’Reilly, 1999; Finch, Cumming, & Thomason, 2001; Grayson, Pattison, & Robins, 1997; Hyde, 2001; Macdonald, 1997; Nix & Barnette, 1998). The varying levels of confusion exhibited in so many articles dealing with the meaning and interpretation of classical statistical tests points to the need to become familiar with their historical development. Krantz (1999) would surely agree with this assessment. In light of the above concerns, the present paper addresses how the confusion between p’s and α’s came about. I do this by first reporting on the major differences in the structure of the Fisherian and Neyman– Pearson schools of thought. In doing so, I typically let the protagonists speak for themselves. This is an absolute necessity given that their own names are conspicuously absent from the textbooks used to help teach psychologists about statistical methods. Because textbook authors almost uniformly do not cite and discuss Fisher’s and Neyman–Pearson’s respective contributions to the statistics literature, it is hardly surprising to learn that present researchers are not familiar with them. Second, I show how

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the competing ideas from the two camps have been inadvertently merged. The upshot is that although Neyman–Pearson theory claimed the mantle of statistical orthodoxy some fifty or so years ago (Hogben, 1957; LeCam & Lehmann, 1974; Nester, 1996; Royall, 1997; Spielman, 1974), it is Fisher’s influence which dominates statistical testing procedures in psychology today. Third, empirical evidence is gathered from a random sample of articles in 12 psychology journals for the period 1990–2002 detailing the widespread confusion among researchers caused by the mixing of Fisherian and Neyman–Pearson perspectives. This evidence is manifested in how researchers, through their misunderstandings of the differences between p’s and α’s, almost universally misreport and misinterpret the outcomes of statistical tests. They can scarcely help it, for such misreporting and misinterpretation is virtually sanctioned in the advice found in APA Publication Manuals (1994, 2001). The end result is that applications of classical statistical testing in psychology are largely meaningless. And this signals the need for changes in the way in which it is taught in the classroom. More specifically, hopes for eliminating (or at least drastically reducing) the mass confusion over the meanings of p’s and α’s must rest on acquainting students with the fundamentals of the historical development of Fisherian and Neyman–Pearson statistical testing. The present paper attempts to do this.

Comparing and Contrasting the Fisherian and Neyman–Pearson Paradigms Fisher’s Paradigm of Significance Testing Fisher’s ideas on significance testing, popularized in the many editions of his widely influential books Statistical Methods for Research Workers (1925) and The Design of Experiments (1935a), were enthusiastically received by practitioners. At the heart of his conception of inductive inference is what he termed the null hypothesis, H0. Although briefly dabbling with Bayesian approaches (Zabell, 1992), Fisher quickly renounced the methods of inverse probability, or the probability of a hypothesis (H) given the data (x), Pr(H | x), instead championing the direct probability, Pr(x | H). In particular, Fisher used disparities in the data to reject the null hypothesis, that is, the probability of the data conditional on a true null hypothesis, or Pr(x | H0). Consequently, a significance test is a means of determining the probability of a result, in addition to more extreme ones, on a null hypothesis of no effect or relationship. In Fisher’s model the researcher proposes a null hypothesis that a sample comes from a hypothetical infinite population with a known sampling distribution. As Gigerenzer and Murray (1987) comment, the null hypothesis is

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rejected ‘if our sample statistic deviates from the mean of the sampling distribution by more than a criterion, which corresponds to alpha, the level of significance’ (p. 10)1 In other words, the p value from a significance test is regarded as a measure of the implausibility of the actual observations (as well as more extreme and unobserved ones) obtained in an experiment or other study, assuming a true null hypothesis. The rationale for the significance test is that if the data are seen as being rare or highly discrepant under H0 this constitutes inductive evidence against H0. Fisher (1966) noted that ‘It is usual and convenient for experimenters to take 5 per cent as a standard level of significance, in the sense that they are prepared to ignore all results which fail to reach this standard’ (p. 13). Thus, Fisher’s significance testing revolves around the rejection of the null hypothesis at the p ≤ .05 level. If, in an experiment, the researcher obtains a p value of, say, .05 or .01 on a true null hypothesis, it would be interpreted to mean that the probability of obtaining such an extreme (or more extreme) value is only 5% or 1%. (Hence, Fisher is a frequentist, but not in the same sense as Neyman–Pearson.) For Fisher (1966), then, ‘Every experiment may be said to exist only in order to give the facts a chance of disproving the null hypothesis’ (p. 16). In the Fisherian paradigm, an event is deemed established when we can conduct experiments that rarely fail to yield statistically significant ( p ≤ .05) results. As mentioned earlier, Fisher considered p values from single experiments as supplying inductive evidence against the null hypothesis, with smaller p values indicating greater evidence (Johnstone, 1986, 1987b; Spielman, 1974). According to Fisher’s famous disjunction, a p value ≤ .05 on the null hypothesis shows that either a rare event has occurred or else the null hypothesis is false (Seidenfeld, 1979). Fisher was sure that statistics could play a major role in fostering inductive inference, that is, drawing inferences from the particular to the general, from samples to populations. According to him, ‘Inductive inference is the only process known to us by which essentially new knowledge comes into the world’ (Fisher, 1966, p. 7). But Fisher (1958) was wary that mathematicians (certainly Neyman) did not necessarily subscribe to his inductivist viewpoint: In that field of deductive logic, at least when carried out with mathematical symbols, [mathematicians] are of course experts. But it would be a mistake to think that mathematicians as such are particularly good at the inductive logical processes which are needed in improving our knowledge of the natural world, in reasoning from observational facts to the inferences which those facts warrant. (p. 261)

Fisher never wavered in his belief that inductive reasoning was the chief mechanism of knowledge development, and for him the p values from significance tests were evidential.

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Neyman–Pearson’s Paradigm of Hypothesis Testing The Neyman–Pearson (1928a, 1928b, 1933) statistical paradigm is widely accepted as the norm in classical statistical circles (Carlson, 1976; Hogben, 1957; LeCam & Lehmann, 1974; Nester, 1996; Oakes, 1986; Royall, 1997; Spielman, 1974). Their work on hypothesis testing, terminology they preferred to distinguish it from Fisher’s ‘significance testing’, was quite distinct from the latter’s framework of inductive inference. The Neyman–Pearson approach postulates two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (HA). In justifying the need for an alternative hypothesis, Neyman (1977) wrote: … in addition to H [the null hypothesis] there must exist some other hypotheses, one of which may conceivably be true. Here, then, we come to the concept of the ‘set of all admissible hypotheses’ which is frequently denoted by the letter Ω. Naturally, Ω must contain H. Let H denote the complement, say Ω – H = H. It will be noticed that when speaking of a test of the hypothesis H, we really speak of its test ‘against the alternative H.’ This is quite important. The fact is that, unless the alternative H is specified, the problem of an optimal test of H is indeterminate. (p. 104)

Neyman–Pearson considered Fisher’s usage of the occurrence of rare or implausible results to reject H0 to be an inadequate vehicle for hypothesis testing. Something more was needed. They wanted to see whether this same improbable outcome under H0 is more likely to occur under a competing hypothesis. As Pearson later explained: ‘The rational human mind did not discard a hypothesis until it could conceive at least one plausible alternative hypothesis’ (E.S. Pearson, 1990, p. 82). Even William S. Gosset (‘Student’, of t test fame), a man whom Fisher admired, saw the need for an alternative hypothesis. In response to a letter from Pearson, Gosset wrote that ‘the only valid reason for rejecting any statistical hypothesis, no matter how unlikely, is that some alternative hypothesis explains the observed events with a greater degree of probability’ (quoted in Reid, 1982, p. 62). The inclusion of an alternative hypothesis by Neyman–Pearson critically distinguishes their approach from Fisher’s, and this was an issue of great contention between the two camps over the years. In Neyman–Pearson theory, the investigator selects a (typically point) null hypothesis and tests it against the alternative hypothesis. Their work introduced the probabilities of committing two kinds of error, namely false rejection (Type I error) and false acceptance (Type II error) of the null hypothesis. The former probability is called α, while the latter probability is called β. Eschewing Fisher’s ideas about hypothetical infinite populations, Neyman–Pearson results are predicated on the assumption of repeated

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random sampling from a defined population (Gigerenzer & Murray, 1987). Therefore, Neyman–Pearson theory is best equipped for handling situations where repeated random sampling has meaning, such as in the case of quality-control experiments. In these narrow circumstances, the Neyman– Pearson frequentist interpretation of probability makes sense: α is the long-run relative frequency of Type I errors conditional on the null being true and β is the counterpart for Type II errors. The Neyman–Pearson theory of hypothesis testing introduced the entirely new concept of the power of a statistical test. The power of a test, or (1 – β), is the probability of rejecting a false null hypothesis. Since the power of a test to detect a particular effect size in the population can be calculated before conducting the research, it is useful in the design of experiments. In Fisher’s significance-testing scheme, however, there is no alternative hypothesis (HA), making the ideas about Type II errors and the power of the test irrelevant. Fisher (1935b) pointed this out when rebuking Neyman and Pearson without naming them: ‘In fact … “errors of the second kind” are committed only by those who misunderstand the nature and application of tests of significance’ (p. 474). And he subsequently added: The notion of an error of the so-called ‘second kind,’ due to accepting the null hypothesis ‘when it is false’ … has no meaning with respect to simple tests of significance, in which the only available expectations are those which flow from the null hypothesis being true. (Fisher, 1966, p. 17)

Fisher denied the need for an alternative hypothesis, and strenuously opposed its incorporation by Neyman–Pearson (Gigerenzer & Murray, 1987; Hacking, 1965). Fisher (1966), however, touches upon the concept of the power of a test when discussing the ‘sensitiveness’ of an experiment: By increasing the size of the experiment we can render it more sensitive, meaning by this that it will allow of the detection of a lower degree of sensory discrimination, or, in other words, of a quantitatively smaller departure from the null hypothesis. Since in every case the experiment is capable of disproving, but never of proving this hypothesis, we may say that the value of the experiment is increased whenever it permits the null hypothesis to be more readily disproved. (pp. 21–22)

And Neyman (1967) was, of course, familiar with this: ‘The consideration of power is occasionally implicit in Fisher’s writings, but I would have liked to see it treated explicitly’ (p. 1459). Whereas Fisher’s view of inductive inference centered on the rejection of the null hypothesis, Neyman and Pearson had no time at all for the very idea of inductive reasoning. Their concept of inductive behavior sought to provide rules for making decisions between two hypotheses,

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regardless of the researcher’s belief in either one. Neyman (1950) made this quite explicit: Thus, to accept a hypothesis H means only to decide to take action A rather than action B. This does not mean that we necessarily believe that the hypothesis H is true … [while rejecting H] … means only that the rule prescribes action B and does not imply that we believe that H is false. (pp. 259–260)

Neyman–Pearson theory, therefore, substitutes the idea of inductive behavior for that of inductive inference. According to Neyman (1971): The description of the theory of statistics involving a reference to behavior, for example, behavioristic statistics, has been introduced to contrast with what has been termed inductive reasoning. Rather than speak of inductive reasoning I prefer to speak of inductive behavior. (p. 1)

And ‘The term “inductive behavior” means simply the habit of humans and other animals (Pavlov’s dogs, etc.) to adjust their actions to noticed frequencies of events, so as to avoid undesirable consequences’ (Neyman, 1961, p. 148; see also Neyman, 1962). Further defending his preference for inductive behavior over inductive inference, Neyman (1957) acknowledged his suspicions about the latter ‘because of its dogmatism, lack of clarity, and because of the absence of consideration of consequences of the various actions contemplated’ (p. 16). In presenting his decision rules for taking action A rather than B, Neyman (1950) emphasized that ‘the theory of probability and statistics both play an important role, and there is a considerable amount of reasoning involved. As usual, however, the reasoning is all deductive’ (p. 1). The deductive character of the Neyman–Pearson model proceeds from the general to the particular. They came up with a ‘rule of behavior’ for selecting between two alternative courses of action, accepting or rejecting the null hypothesis, such that ‘in the long run of experience, we shall not be too often wrong’ (Neyman & Pearson, 1933, p. 291). Whether to accept or reject the hypothesis in their framework depends on the cost trade-offs involved with committing a Type I or Type II error. These costs are independent of statistical theory. They must be estimated by the researcher in the context of each particular problem. Neyman and Pearson (1933) advised: … in some cases it will be more important to avoid the first [type of error], in others the second [type of error]. . . . From the point of view of mathematical theory all we can do is to show how the risk of errors may be controlled or minimised. The use of these statistical tools in any given case, in determining just how the balance should be struck, must be left to the investigator. (p. 296)

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After heeding such advice, the researcher would design an experiment to control the probabilities of the α and β error rates, with the ‘best’ test being the one that minimizes β subject to a bound on α (Lehmann, 1993). In determining what this bound on α should be, Neyman (1950) later stated that the control of Type I errors was more important than that of Type II errors: The problem of testing statistical hypotheses is the problem of selecting critical regions. When attempting to solve this problem, one must remember that the purpose of testing hypotheses is to avoid errors insofar as possible. Because an error of the first kind is more important to avoid than an error of the second kind, our first requirement is that the test should reject the hypothesis tested when it is true very infrequently. . . . To put it differently, when selecting tests, we begin by making an effort to control the frequency of the errors of the first kind (the more important errors to avoid), and then think of errors of the second kind. The ordinary procedure is to fix arbitrarily a small number α … and to require that the probability of committing an error of the first kind does not exceed α. (p. 265)

Consequently, α is specified or fixed prior to the collection of the data. Because of this, Neyman–Pearson methodology is sometimes labeled the fixed α (Huberty, 1993), fixed level (Lehmann, 1993) or fixed size (Seidenfeld, 1979) approach. This contrasts α with Fisher’s p value, which is a random variable whose distribution is uniform over the interval [0, 1] under the null hypothesis. The α and β error rates define a ‘critical’ or ‘rejection’ region for the test statistic, say z or t > 1.96. If the test statistic falls in the critical region, H0 is rejected in favor of HA, otherwise H0 is retained (Goodman, 1993; Huberty, 1993). Furthermore, descriptions of Neyman–Pearson theory refer to the rejection of H0 when H0 is true – the Type I error probability, α – as the ‘significance level’ of a test. As we shall see below, calling the Type I error probability the significance level of a statistical test was something quite unacceptable to Fisher. It has also helped to create enormous confusion among researchers concerning the meaning and interpretation of ‘statistical significance’. Recall that Fisher regarded his significance tests as constituting inductive evidence against the null hypothesis in single experiments (Johnstone, 1987a; Kyburg, 1974; Seidenfeld, 1979). Neyman–Pearson hypothesis tests, on the other hand, do not permit an inference to be made about the outcome of any individual hypothesis that the researcher is examining. Neyman and Pearson (1933) were unequivocal about this: ‘We are inclined to think that as far as a particular hypothesis is concerned, no test based upon the theory of probability can by itself provide any valuable evidence of the truth or falsehood of that hypothesis’ (pp. 290–291). But since scientists are in the business of gleaning evidence from individual studies, this limitation of Neyman–Pearson theory is acute. Nor, for that matter, does the Neyman– Pearson model allow an inference to be made in the case of ongoing, repetitive studies. Thus, Grayson, Pattison and Robins (1997) were incorrect when they stated that ‘one implication of a strictly frequentist [Neyman–Pearson]

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approach is that we can only make inferences on the basis of a long run of repeated trials’ (p. 68). Neyman–Pearson theory is strictly behavioral; it is non-evidential in both the short and long runs. As Neyman (1942) wrote: ‘it will be seen that the theory of testing hypotheses has no claim of any contribution to … “inductive reasoning” ’(p. 301). Fisher (1959) recognized this, commenting that the Neyman–Pearson ‘procedure is devised for a whole class of cases. No particular thought is given to each case as it arises, nor is the tester’s capacity for learning exercised’ (p. 100). Instead, the investigator is only allowed to make a decision about the likely outcome of a hypothesis as if it had been subjected, as Fisher (1956) observed, to ‘an endless series of repeated trials which will never take place’ (p. 99). In the vast majority of applied work, repeated random sampling does not occur; empirical findings are usually limited to a single sample. Fisher conceded that Neyman and Pearson’s contribution, which he referred to as an ‘acceptance procedures’ approach, had merit in the context of quality-control decisions. For example, he acknowledged: ‘I am casting no contempt on acceptance procedures, and I am thankful, whenever I travel by air, that the high level of precision and reliability required can really be achieved by such means’ (Fisher, 1955, p. 69). This concession aside, Fisher (1959) was resolute in his objections to Neyman–Pearson ideas about hypothesis testing as an appropriate method for guiding scientific research: The ‘Theory of Testing Hypotheses’ was a later attempt, by authors who had taken no part in the development of [significance] tests, or in their scientific application, to reinterpret them in terms of an imagined process of acceptance sampling, such as was beginning to be used in commerce; although such processes have a logical basis very different from those of a scientist engaged in gaining from his observations an improved understanding of reality. (pp. 4–5)

He insisted that … the logical differences between [acceptance procedures] and the work of scientific discovery by physical or biological experimentation seem to me so wide that the analogy between them is not helpful, and the identification of the two sorts of operation is decidedly misleading. (Fisher, 1955, pp. 69–70)

In further distancing himself from Neyman–Pearson methodology, Fisher (1955) drew attention to the fact that: From a test of significance, however, we learn more than that the body of data at our disposal would have passed an acceptance test at some particular level; we may learn, if we wish to, and it is to this that we usually pay attention, at what level it would have been doubtful; doing this we have a genuine measure of the confidence with which any particular opinion may be held, in view of our particular data. From a strictly realistic

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viewpoint we have no expectation of an unending sequence of similar bodies of data, to each of which a mechanical ‘yes or no’ response is to be given. What we look forward to in science is further data, probably of a somewhat different kind, which may confirm or elaborate the conclusions we have drawn; but perhaps of the same kind, which may then be added to what we have already, to form an enlarged basis for induction. (p. 74)

The above discussion shows that Fisher and Neyman–Pearson disagreed vehemently over both the nature of statistical methods and their approaches to the conduct of science per se. Indeed, ongoing exchanges of a frequently acrimonious nature passed between Fisher and Neyman–Pearson as both sides promulgated their respective conceptions of statistical analysis and the scientific method.

Minding One’s p’s and a’s Users of statistical techniques in the social and medical sciences are almost totally unaware of the distinctions, described above, between Fisher’s ideas on significance testing and Neyman–Pearson thoughts on hypothesis testing (Gigerenzer, 1993; Goodman, 1993, 1999; Huberty, 1993; Royall, 1997). This is through no fault of their own; after all, they have been taught from numerous well-regarded textbooks on statistical methods. Unfortunately, many of these same textbooks combine, without acknowledgement, incongruous ideas from both the Fisherian and Neyman–Pearson camps. This is something that both sides found appalling. Ironically, as will be seen, the end result of this unintentional mixing of Fisherian with Neyman–Pearson ideas is that although the latter’s work came to be accepted as statistical orthodoxy about fifty years ago (Hogben, 1957; Spielman 1974), it is Fisher’s methods that flourish today. As Royall (1997) observed: The distinction between Neyman–Pearson tests and [Fisher’s] significance tests is not made consistently clear in modern statistical writing and teaching. Mathematical statistical textbooks tend to present Neyman–Pearson theory, while statistical methods textbooks tend to lean more towards significance tests. The terminology is not standard, and the same terms and symbols are often used in both contexts, blurring the differences between them. (p. 64)

Johnstone (1986) and Keuzenkamp and Magnus (1995) maintain that statistical testing usually follows Neyman–Pearson formally, but Fisher philosophically. For instance, Fisher’s notion of disproving the null hypothesis is taught along with the Neyman–Pearson concepts of alternative hypotheses, Type II errors and the power of a statistical test. In addition, textbook descriptions of Neyman–Pearson theory often refer to the Type I error probability as the ‘significance level’ (Goodman, 1999; Kempthorne, 1976; Royall, 1997). But the quintessential example of the bewilderment caused by the forging of Fisher’s ideas on inductive inference with the Neyman–Pearson principle of

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inductive behavior is the widely unappreciated fact that the former’s p value is incompatible with the Neyman–Pearson hypothesis test in which it has become embedded (Goodman, 1993). Despite this fundamental incompatibility, the end result of this mixing is that the p value is now indelibly linked in researchers’ minds with the Type I error rate, α. And this is precisely what Fisher (1955) had earlier complained about when he accused Neyman– Pearson of attempting ‘to assimilate a test of significance to an acceptance procedure’ (p. 74). Because of this assimilation, much empirical work in psychology and the social and biological sciences proceeds in the following manner: the investigator states the null (H0) and alternative (HA) hypotheses, the Type I error rate/significance level, α, and presumably – but rarely – calculates the statistical power of the test (e.g. t). These steps are in accordance with Neyman–Pearson convention. After this, the test statistic is computed for the sample data, and in an effort to have the best of both worlds, an associated p value (significance probability) is calculated. The p value is then erroneously interpreted as a frequency-based ‘observed’ Type I error rate, α (Goodman, 1993), and at the same time as an incorrect (i.e. p < α) measure of evidence against H0.

The p Value as a Type I Error Rate Even staunch critics of, and other commentators on, statistical testing in psychology occasionally commit this error. Thus Dar et al. (1994) noted: ‘The sample p value, in the context of null hypothesis testing, is involved … in determining whether the predetermined criterion of Type I error, the alpha level, has been surpassed’ (p. 76). Likewise, Meehl (1967) misreported that the investigator ‘gleefully records the tiny probability number “p < .001,” and there is a tendency to feel that the extreme smallness of this probability of a Type I error is somehow transferable’ (p. 107, my emphasis). Nickerson (2000) also makes the mistake of drawing a parallel between p values and Type I error rates: ‘The value of p that is obtained as the result of NHST is the probability of a Type I error on the assumption that the null hypothesis is true’ (p. 243). He later goes on to compound this mistake by adding: ‘Both p and α represent bounds on the probability of Type I error … p is the probability of a Type I error resulting from a particular test if the null hypothesis is true’ (p. 259, my emphasis). Here, Nickerson misinterprets a p value as an ‘observed’ Type I error rate, something which is impossible since the latter applies only to long-run frequencies, not to individual instances. Neyman (1971) expressed this as follows: It would be nice if something could be done to guard against errors in each particular case. However, as long as the postulate is maintained that the observations are subject to variation affected by chance (in the sense of frequentist theory of probability), all that appears possible to do is to control the frequencies of errors in a sequence of situations. (p. 13)

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The p value is not a Type I error rate, long-run or otherwise; it is a measure of inductive evidence against H0. Type I errors play no role in Fisher’s paradigm. This misinterpretation of his evidential p value as a Neyman–Pearson Type I error rate severely upset Fisher, who was adamant that the significance level of a statistical test had no ongoing sampling interpretation. With regard to the .05 level, Fisher (1929) early on warned that this does not mean that the researcher ‘allows himself to be deceived once in every twenty experiments. The test of significance only tells him what to ignore, namely all experiments in which significant results are not obtained’ (p. 191). The significance level, for Fisher, was a measure of evidence for the ‘objective’ disbelief in the null hypothesis; it had no long-run frequentist characteristics. Again, Fisher (1950) protested that his tests of significance … have been most unwarrantably ignored in at least one pretentious work on ‘Testing Statistical Hypotheses’ … Pearson and Neyman have laid it down axiomatically that the level of significance of a test must be equated to the frequency of a wrong decision ‘in repeated samples from the same population.’ This idea was foreign to the development of tests of significance given by the author in 1925. (p. 35.173a, my emphasis)

Seidenfeld (1979) exposed the difference between the two schools of thought on this crucial matter: … such a frequency property has little or no connection with the interpretation of the [Fisherian significance] test. To repeat, the correct interpretation is through the disjunction, either a rare event has occurred or the null hypothesis is false. (p. 79)

In highlighting the discrepancies between p’s and α’s, Gigerenzer (1993) offered the following: For Fisher, the exact level of significance is a property of the data (i.e., a relation between a body of data and a theory); for Neyman and Pearson, alpha is a property of the test, not of the data. Level of significance [p value] and alpha are not the same thing’ (p. 317, my emphasis)

Despite the above cautions about p values not being Type I error rates, it is sobering to note that even well-known statisticians such as Barnard (1985), Gibbons and Pratt (1975) and Hinkley (1987) nevertheless make the mistake of equating them. Yet, as Berger and Delampady (1987) warn, the interpretation of the p value as an error rate is strictly forbidden: P-values are not a repetitive error rate … A Neyman–Pearson error probability, α, has the actual frequentist interpretation that a long series of α level tests will reject no more than 100α% of true H0, but the datadependent-P-values have no such interpretation. (p. 329)

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At the same time it must be underlined that Neyman–Pearson would not endorse an inferential or epistemic interpretation of statistical testing, as manifested in a p value. Their theory is behavioral, not evidential, and they would likewise complain that the p value is not a Type I error rate. It should therefore be pointed out that in his effort to partially resolve discrepancies between the Fisherian and Neyman–Pearson programs, Lehmann (1993) similarly fails to distinguish between measures of evidence versus error. He refers to the Type I error rate as the significance level of the test, when for Fisher this was determined by p values and not α’s. And we have seen that misconstruing the evidential p value as a Neyman–Pearson Type I error rate was anathema to both Fisher and Neyman–Pearson.

The p Value as a Quasi-measure of Evidence against H0 (p < α) While the p value is being erroneously reported as a Neyman–Pearson Type I error rate, it will be interpreted simultaneously in an incorrect quasi-Fisherian manner as evidence against H0. If p < α, a statistically significant finding is announced, and the null hypothesis is disproved. For example, Leavens and Hopkins’ (1998) declaration that ‘alpha was set at p < .05 for all tests’ (p. 816) reflects a common tendency among researchers to confuse p’s and α’s in a statistical significance testing framework. Clark-Carter (1997) goes further in this regard by invoking the great man himself: ‘According to Fisher, if p were greater than a then the null hypothesis could not be accepted’ (p. 71). Fisher, of course, would have taken umbrage at such a statement, just as he would have with Clark’s (1999) assertion that ‘in Fisher’s original work in agriculture, alpha was set a priori’ (p. 283), with Huberty (1993, p. 328) and Huberty and Pike’s (1999, p. 11) suggestions that Fisher encouraged the use of α = .05, with Cortina and Dunlap’s (1997) recommendation to compare observed probabilities with predetermined α cut-off values, and with Chow’s (1996) error of investing his (Fisher’s) tests with both p’s and α’s. Fisher had no use for the concept α. Again, in an otherwise thoughtful article titled ‘The Appropriate Use of Null Hypothesis Testing’, Frick (1996) nonetheless makes the mistake of using p’s and α’s interchangeably: ‘Finally, the obtained value of p is compared to a criterion alpha, which is conventionally set at .05 … When p is less than .05, the experimenter has sufficient empirical evidence to support a claim’ (p. 385). In addition, Nickerson (2000), who earlier misinterpreted a p value as a Type I error rate, follows Frick (1996) in ascribing an evidential meaning to the p value when it is directly compared with this error rate: A specified significance level conventionally designated α (alpha) serves as a decision criterion, an the null hypothesis is rejected only if the value

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of p yielded by the test is not greater than the value of α. If α is set at .05, say, and a significance test yields a value of p equal to or less than .05, the null hypothesis is rejected and the result is said to be statistically significant at that level. (Nickerson, 2000, pp. 242–243)

Nix and Barnette (1998) do likewise in a paper subtitled ‘A Review of Null Hypothesis Significance Testing’: ‘As such, p values lower than the alpha value are viewed as a rejection of the null hypothesis, and p values equal to or greater than the alpha value are viewed as a failure to reject’ (p. 6). Yet we have seen that interpreting p values as evidence against the null hypothesis in a single experiment is impossible in the Neyman–Pearson framework. Their approach centers on decision rules with a priori stated error rates, α and β, which are limiting frequencies based on long-run repeated sampling. If a result falls into the critical region, H0 is rejected and HA is accepted, otherwise H0 is accepted and HA is rejected (Goodman, 1993; Huberty, 1993). Interestingly, this last claim contradicts Fisher’s (1966) remark that ‘the null hypothesis is never proved or established, but is possibly disproved, in the course of experimentation’ (p. 16). In the Neyman– Pearson framework one can indeed ‘accept’ the null hypothesis. Neyman’s (1942) advice makes this plain: … we may say that any test of a statistical hypothesis consists in a selection of a certain region, w0, in the n dimensional experimental space W, and in basing our decision as to the hypothesis H0 on whether the experimental point E', determined by the actual observations, falls within w0 or not. If it does, the hypothesis H0 will be rejected, if it does not, it will be accepted. (p. 303, my emphasis)

Note, then, the distinctly Fisherian bent adopted by Wilkinson and the APA Task Force on Statistical Inference (1999) when they recommend: ‘Never use the unfortunate expression “accept the null hypothesis” ’ (p. 599). To reiterate, this advice is at odds with, ostensibly, Neyman–Pearson statistical convention.

Further Confusion over p’s and α’s In the Neyman–Pearson decision model the researcher is only allowed to say whether or not the result fell in the critical region, not where it fell, as might be indicated by a p value. Thus, if the Type I error rate, α, is fixed at it usual .05 value before (as it must be) the study is carried out, and the researcher subsequently obtains a p value of, say, .0014, this exact value cannot be reported in a Neyman–Pearson hypothesis test (Oakes, 1986). This is because, Goodman (1993, 1999) explains, α is the probability of a set of possible results that may fall anywhere in the tail area of the distribution under the null hypothesis, and we cannot know in advance which of these particular results will arise. This differs from the tail area for the p value,

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which is known only after the result is observed, and which, by definition, will always lie exactly on the border of that tail area. Consequently, a predetermined Type I error rate cannot be conveniently renegotiated as a measure of evidence after the result is observed (Royall, 1997). Despite the above, Wilkinson and the APA Task Force on Statistical Inference (1999) again display a Fisherian, rather than a Neyman–Pearsonian, orientation when they state: ‘It is hard to imagine a situation in which a dichotomous accept–reject decision is better than reporting an actual p value’ (p. 599). But it is not at all difficult to imagine such a situation in a Neyman– Pearson context. On the contrary, the dichotomous accept–reject decision is all that is possible in their statistical calculus. Furthermore, p values, exact or otherwise, play no role in the Neyman–Pearson model. By the same reasoning, it is not permissible to report what Goodman (1993, p. 489) calls ‘roving alphas’, whereby p values are assigned a limited number of categories of Type I error rates, such as p Ps 1 1 + (1 − P1 )s1 .

To do this computation, examinees must know both P1 and P2 (at least for their own values of θ), as well as the item scores. But the examinees, when choosing an item, do not know their probability of responding correctly to the item. Instead, they have some subjective idea of that probability. We have already provided strong evidence indicating that some examinees do not choose wisely. Moreover, we have seen that the propensity for making optimizing choices varies by sex and ethnic group. As we have already seen, choice items, as currently prepared, are typically not of equal difficulty. This fact, combined with the common practice of not equating choice items for their differential difficulty, yields the inescapable conclusion that it matters what choice an examinee makes. Examinees who chose the more difficult question will, on average, get lower scores than would have been the case had they chosen the easier item. The fact that all examinees do not choose those items that will show their proficiency to best advantage completes this unhappy syllogism: examinee choice is not likely to yield credible estimates of θMax.

What Can We Do to Improve Matters? There appear to be two paths that can be followed: eliciting wiser choices by examinees or equating test forms. The second option removes the necessity for the first; in fact, it makes examinee choice unnecessary. How can we improve examinees’ judgment about which items to select? Estimation of θMax can be done optimally only by asking examinees to answer all items and then scoring just those responses that yield the highest estimate of performance. This strategy is not without its drawbacks. First, it takes more testing time, and choice is often instituted to keep testing time within practical limits. Second, many examinees, on hearing that “only one of the six items will be counted” will only answer one. Thus, this strategy may commingle measures of grit, choice wisdom, and risk aversion with those of proficiency. A more practical approach might be to try to improve the instructions to the examinees about how the test is graded, to guide their choices better. It would be well if the instructions about choice made it clear that there is no advantage to answering a hard item correctly relative to answering an easy one, if such is indeed the case. Current instructions do not address this issue.

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For example, the instructions about choice on the 1989 AP Chemistry Test (CEEB, 1990, p. 23), reproduced in their entirety are: Solve ONE of the two problems in this part. (A second problem will not be scored.)

Contrast this with the care that is taken to instruct examinees about the hazards of guessing. These are taken from the same test (p. 3): Many candidates wonder whether or not to guess the answers to questions about which they are not certain. In this section of the examination, as a correction for haphazard guessing, one-fourth of the number of questions you answer incorrectly will be subtracted from the number you answer correctly. It is improbable, therefore, that mere guessing will improve your score significantly; it may even lower your score, and it does take time. If, however, you are not sure of the correct answer but have some knowledge of the question and are able to eliminate one or more of the answer choices as wrong, your chance of getting the right answer is improved, and it may be to your advantage to answer such a question.

Perhaps, with better instructions, the quality of examinee choices can be improved. At the moment, there is no evidence supporting the conjecture that they can be, or if so, by how much. An experimental test of the value of improved instructions could involve one randomly selected group with the traditional instructions and another with a more informative set; which group has higher average scores on the choice section? A more complex experiment could use a paradigm much like that employed by Wang, Wainer, and Thissen (1993) in which examinees were asked to choose from among several items but were then required to answer all of them. This sort of experiment would allow a detailed examination of the change in choice behavior due to the instructions. While more explicit instructions may help matters somewhat, and ought to be included regardless of their efficacy, we are not sanguine about this option solving the problem of getting  θ Max closer to θ Max . To do this requires reducing the impact of unwise choice. As we pointed out, there are two terms in the calculation of subjective posterior density. The first is the subjective probability of getting the item correct; improved instructions may help this. The second requires that the examinee have an accurate idea of the relative difficulty of the choice items. Pretesting, when it is possible, would allow us to present to examinees each item’s difficulty in the pretest population. It will not help to characterize the individual variations in item difficulty that are the principal reason for allowing choice. A more promising path seems to be to make all of the choice problems equally difficult (from the point of view of the entire examinee population) and allow the choice to be governed by whatever special knowledge or proficiency each individual examinee might possess. In this way, we can be sure that, at least on average, the items are as fair as

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possible. The problem is that it is at least difficult, and perhaps impossible, to build items that empirically turn out to be exactly equal in difficulty. Another option is to adjust the scores on the choice items statistically for their differential difficulty. We will refer to this statistical adjustment as equating, although the way it is carried out may not satisfy the strict rules that are sometimes associated with that term.

How Does Equating Affect the Examinee’s Task? Equating appears, at first blush, to make the examinee’s task of choosing more difficult still. If no equating is done, the instructions to the examinee should be: Answer that item that seems easiest to you

(and we hope that the examinees choose correctly, but we will not know if they do not). If we equate the choice items (give more credit for harder items than easier ones), the instructions should be: Pick that item which, after we adjust, will give you the highest score.

This task could be akin to the problem faced by competitive divers, who choose their routine of dives from within various homogeneous groups of dives. The diver’s decision is informed by: • • •

knowledge of the degree of difficulty of each dive, knowledge of the concatenation rule by which the dive’s difficulty and the diver’s performance rating are combined (they are multiplied), and knowledge, obtained through long practice, of what his or her score is likely to be on all of the dives.

Armed with this knowledge, the diver can select a set of dives that is most likely to maximize his or her total score. The diver scenario is one in which an individual’s informed choice provides a θ Max that seems to be close enough to θMax for useful purposes. Is a similar scenario possible within the plausible confines of standardized testing? Let us examine the aspects of required knowledge point by point. Specifying how much each item will count in advance is possible, either by calculating the empirical characteristics of each item from pretest data or, as is currently the case, by specifying how much each one counts by fiat. We favor the former, because it allows each item to contribute to total score in a way that minimizes measurement error. An improvident choice of a priori weights can have a serious deleterious effect on

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measurement accuracy (see Lukhele, Thissen, & Wainer, 1993; Wainer & Thissen, 1993a). Specifying the concatenation rule (how examinee performance and item characteristics interact to contribute to the examinee’s score) in advance is also possible but may be quite complex, for example, if IRT is used. Perhaps a rough approximation can be worked out, or perhaps one could present a graphical solution like that shown in Figure 4, but for now this remains a question. The difficulties that we might have with specifying the concatenation rule are largely technical, and workable solutions could probably be developed. A much more formidable obstacle is providing the examinees with enough information so that they can make wise choices. This seems completely out of reach, for, even if examinees know how much a particular item will, if answered correctly, contribute to their final score, it does no good unless the examinees have a good idea of their likelihood of answering the item correctly. The extent to which such knowledge is imperfect would then correspond to the bias (used in its statistical sense) associated with the θ Max to estimate θMax. The nature of security associated with modern use of  large-scale tests makes impossible the sort of rehearsal that provides divers with accurate estimates of their performance under various choice options. The prospect appears bleak for simultaneously allowing choice and satisfying the canons of good practice that require the equating of test forms of unequal difficulty. The task that examinees face in choosing items when they are adjusted seems too difficult. But is it? There remain two glimmers of hope. The brighter of these rests on the possibility of successfully equating the various choice forms. If we can do this, the examinees should be indifferent as to which items they answer, because successful equating means that an examinee will receive, in expectation, the same score regardless of the form administered. This is happy but ironic news, for it appears that we can allow choice and have fair tests only when choice is unnecessary. A dimmer possibility is to try to improve examinees’ estimates of their success on the various choices. In a computer administered test, it may be possible to provide some model-based estimates of an examinee’s probable score on each item. To the extent that these estimates are accurate, they might help. Of course, if really good estimates were available, we would not need to test further. Moreover, the value of choice would be greatest when an examinee’s likelihood of success is very different than that predicted from the rest of the test. To answer the question posed at the beginning of this section: When we do not equate selected items, the problem of choice faced by the examinee can be both difficult and important. When we do equate, the selection problem simultaneously becomes much more difficult but considerably less important. This conclusion naturally brings us to the next question.

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Under What Conditions Can We Equate Choice Items? How? Let us reconsider Harold Gulliksen’s (1950, p. 338) advice, “Alternative questions should always be avoided.” We have discussed one possible reason for this – that it makes the examinee’s task too difficult. Our conclusion was that, while it does make the task difficult, this difficulty becomes irrelevant for most uses of the test score if the alternate forms thus constructed can be equated. This raises a second possible explanation for this advice: The equating task is too difficult. Certainly this explanation was the one favored by Tucker (quoted earlier). The only way to equate test forms that are created by choice is to make some (untestable) assumptions about the structure of the missing data that have resulted from the choice behavior. One possible assumption is missing-completely-at-random. Underlying this assumption is the notion that, if we had the examinee’s responses to all of the items, a random deletion of some portion of them would yield, in expectation, the same score as was obtained through the examinee’s choice. In simple terms, we assume that the choice had no effect on the examinee’s score. If we really believed missing-completely-at-random, we could equate without any anchor items because an important consequence of missingcompletely-at-random is that all choice groups will have the same proficiency distribution. Data gathered from all of the Advanced Placement exams (Pomplun, Morgan, & Nellikunnel, 1992) suggest that this is not credible. Thus, it is imperative to use required anchor items to establish a common scale for the choice items. This can be done using traditional or IRT methods (see Dorans, 1990) and is justified if we believe that the missing responses yielded by examinee choice are generated by a process that is, in Little and Rubin’s (1987) terminology, conditionally ignorable.4 What we mean by this weaker assumption is that the probability of an examinee choosing any particular item is independent of his or her likelihood of getting that item correct, conditional on θ. In graphical terms, this means that an item’s trace lines are the same for those individuals who chose it as they would have been for those who omitted it. Subsequent discussions will be clearer if we repeat our characterization of the missing data assumption and the logic surrounding their genesis with more precision. Therefore, suppose yi is the score on test item Yi, and Ri is a choice function that takes the value 1 if Yi is chosen and 0 if not.

In a choice situation, we can observe the distribution of scores, f1( y), for those who opted to take an item. This can be denoted f1( y) = P(Y = y|R = 1).

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What we do not know, but what is crucial if we are to be able to equate the different choice items, is the distribution of scores, f0( y), for those who did not take the item. This is denoted f0( y) = P( Y = y|R = 0). To be able to equate, we need to know the distribution of scores in the unselected population, g( y) = P( Y = y). Note that we can represent g( y) as g( y) = f1( y) × P(R = 1) + f0( y) × P(R = 0).

(l)

The only piece of this which is unknown is f0( y), the distribution of scores among those individuals who chose not to answer it. Unless one engages in a special data gathering effort, in which those examinees who did not answer Yi are forced to, f0( y) is not only unknown but unknowable. Thus, the conundrum is that we must equate to ensure fairness, but we cannot equate without knowing f0( y). One approach to such problems, mixture modeling (Glynn, Laird, & Rubin, 1986), involves a hypothesized structure for f0( y). It is convenient to assume that the function f0( y) is the same as f1( y). In formal terms, f1( y) = P( Y = y|R = l, θ) = P( Y = y|R = 0, θ) = f0( y) = P( Y = y|θ).

(2)

Or: We assume that the trace lines for the choice item would have been the same for those who didn’t choose it as it was for those who did. If we could gather the appropriate data (forcing those who opted not to answer it to do so), this hypothesis could easily be tested using standard DIF technology (Holland & Wainer, 1993). Although the conditional independence, given R and θ, expressed in Equation 2 has a surface similarity to the conditional independence, given θ, that underlies all of IRT, Equation 2 expresses a much stronger assumption that may or may not be true: Equation 2 states that, if θ is known, knowledge of whether the examinee chooses to answer an item or not does not affect the modeled probability of each response. This assumption is certainly contrary to the perceptions of examinees, who often feel that their choice of an item optimizes their score. However, there is little evidence available that illuminates the relationship between examinees’ preference for a particular item and their eventual score. Contrary to widespread belief, what little experimental evidence there is supports the assumption expressed in Equation 2. Thus, in the absence of contrary data, and because this assumption allows us to employ the existing technology of IRT to equate, we shall use it. For a fuller description of the structure and consequences of assumptions about missing data, the reader is referred to Allen and Holland (1993); of special importance in the examinee choice situation is their distinction between ignorable and forgettable nonresponse.

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What Other Assumptions Are Necessary for Equating? While ignorable nonresponse is the only assumption that is new to this circumstance, it is not the only assumption required. In addition, we need to assume unidimensionality and fit to the test scoring model employed. These two latter assumptions are well known and can be tested with the test data ordinarily gathered; ignorable nonresponse cannot be. To test ignorable nonresponse requires a special data gathering effort. One example is the sort of data gathering scheme that Wang, Wainer, and Thissen (1993) employed: asking examinees to choose items but then requiring them to answer some of the items they did not choose. This is called sampling from the unselected population and will be discussed in greater detail later. Equating test forms constructed by examinee choice can be straightforward once we have made some assumptions about the unobserved distribution of scores f0( y). While one can derive a formal equating procedure for many assumed characterizations of the missing data, the assumption of conditionally ignorable nonresponse allows us to immediately use the existing machinery for IRT equating. One merely enters the various vectors of item responses and treats what’s missing as having not been presented to the individual. We establish a common scale by requiring a subset of items that all examinees must answer. This anchor test provides a set of items drawn from the unselected population on which we can also test model fit and unidimensionality.

How Can We Test Our Assumptions? The special assumption required to equate choice items involves the distribution of scores on the choice items from those who did not answer them: f0( y). This distribution is necessary to estimate g( y), the distribution of scores in the unselected population. There are many ways to test the viability of this assumption, but they all require some sort of special data gathering. We will describe two experimental designs that can be used to accomplish this. Design 1: Within subjects. In a randomly chosen subset of the examinee population, examinees must be required to indicate their choice but then required to answer all items. This design allows us to estimate all three parts of Equation 1 and so allows an explicit test of the assumption stated as Equation 2. A variant of this design was employed by Wang (1992) which asked examinees their choices both before and after they answered the questions. This design is subject to the criticism that examinees might not be particularly judicious in their choices when they know that they will have to answer all the questions anyway. If this conjecture is true, it is likely to affect the estimates of f0( y) and f1( y) more than that of the composite g( y). Using

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the good estimates of f1( y) we can get from the operational choice test and the estimates of g( y) from the experimental administration, we can derive f0( y) through Equation 1. We used this design to test the assumption of ignorable nonresponse among some choice items in the 1989 AP Chemistry Test ( Wang, Wainer, & Thissen, 1993), using IRT-based DIF technology (Thissen, Steinberg, & Wainer, 1988, 1993; Wainer, Sired, & Thissen, 1991). Figure 5 shows the estimated trace lines for choice Items 11 and 12 for those examinees that chose each item [ f1( y)] as well as for those that did not [ f0( y)]. The apparent difference between the two trace lines for Item 11 2 is somewhat unlikely (χ(2) = 4), whereas there is no difference at all between the two trace lines for Item 12. Operationally, this means the ordinarily untestable assumption that we used to equate choice forms may be untrue for Item 11. A more extensive experiment seems in order. Note that the differences observed in the trace lines for Item 11, although not quite achieving nominal levels of statistical significance, suggest that Item 11 is easier for those who chose it than for those examinees who did not. This is not always the case. As part of the same study, we Tracelines for item 11 1.0 Those

em 11

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Figure 5: Graphical tests of the ordinarily untestable assumption that choice items have the same trace lines for those who chose them as for those who did not

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found that for another pair of choice items the reverse was true. In none of the cases examined were the differences between f1( y) and f0( y) so large as to generate errors in the equating larger than would have been the case had we not equated. Design 2: Between subjects. In a randomly chosen subset of the examinee population, examinees must be randomly assigned to each of the choice items. This will provide us with unbiased estimates of g( y) for each of the choice items and allow us to equate. It will not provide direct estimates of f0( y) and f1( y), but those can be obtained from the portion of the exam in which choice is allowed. As of this writing, an experiment that will have this format is currently being considered for the GRE Writing Test.

Test Dimensionality Because of the increasing interest in the development of tests that combine the psychometric advantages of multiple-choice items with other features of constructed response items, the following two questions assume importance: 1. Are we measuring the same thing with the constructed response items that we are measuring with the multiple-choice questions? 2. Is it meaningful to combine the scores on the constructed response sections with the multiple-choice score to yield a single reported total score? Answers to these questions are necessary to build appropriate score-reporting strategies for such hybrid tests. As we shall see, answering these questions is more difficult when the examinee is permitted to choose to answer a subset of the time-consuming constructed response questions (Wainer, Wang, & Thissen, 1991). The use of item response theory to score the test, or to equate forms comprising chosen questions, explicitly requires that the test (or forms) be essentially unidimensional – that all the items measure more or less the same thing. Thus, we must answer the dimensionality question to be able to score the test in a meaningful way. This is explicitly true when using IRT but also must be true when a test score is calculated in many other, less principled rubrics. Are hybrid tests unidimensional? The literature on this subject is equivocal. Bennett, Rock, Braun, Frye, Spohrer, and Soloway (1991) fitted different factor structures to two relatively similar combinations of multiple-choice, constructed response, and constrained constructed response items; a one-factor model was sufficient for one set of data, but a two-factor model was required for another similar set of data. Bennett, Rock, and Wang (1991) examined a particular two-factor model for the combined multiple-choice and constructed response items on the College Board’s Advanced Placement (AP)

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Test in Computer Science and concluded that the one-factor model provided a more parsimonious fit. We reanalyzed (Thissen, Wainer, & Wang, 1993) the Computer Science AP data reported by Bennett et al. (1991) and showed that significant, albeit relatively small, factors explain some of the observed local dependence among the constructed response items. We replicated this finding using data from the AP test in chemistry. There was clear evidence that the constructed response problems on both of these tests measure something different than the multiple-choice sections of those tests: There were statistically significant factors for the constructed response items, orthogonal to the general factor. However, there was also clear evidence that the constructed response problems predominantly measure the same thing as the multiple-choice sections: The factor loadings for the constructed response items were almost always larger on the general (multiple-choice) factor than on the constructed response factor(s). The loadings of the constructed response items on the specifically constructed response factors were small, indicating that the constructed response items do not measure something different very well. Given the small size of the constructed response factor loadings, it is clear that it would take many constructed response items to produce a reliable score on the factor underlying the constructed response items alone – many more items than are currently used. When we asked the practical question, “Is it meaningful to combine the scores on the constructed response sections with the multiple-choice score to yield a single reported score?” we were driven to conclude that it probably is; indeed, given the small size of the loadings of the constructed response items on their own specific factors, it would probably not be meaningful to attempt to report a constructed response score separately, because it would not be reliably distinct from the multiple-choice score. Our investigation, and hence the above conclusions, utilized much of the same factor analytic technology, founded on complete data, that has become the standard in dimensionality studies (Jöreskog & Sörbom, 1986,1988). The procedure assumes that estimates of the covariances were obtained from what is essentially a random sample from the examinee population. However, when there are choice items, assuming a noninformative sampling process5 is not credible. What is analogous to Assumption 2 that will allow us to factor analyze the observed covariances and treat the results as if they came from the unselected population? Obviously, missingcompletely-at-random would suffice, but this is usually patently false in a choice situation. Can we weaken it? Unfortunately, not much. Suppose we make the obvious assumption that the covariances that we observe are the same as those we do not. Does this allow us to analyze what are observed as if they were the unconditioned covariances? It does not, even with this strong an assumption. To understand why, it is best if we trace the logic mathematically.

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What must we assume to allow us to treat Cov( yi, yj | R i × R j = 1) as if they were Cov( yi, yj )? There are many possible assumptions. One, parallel to Assumption 2, would be to assume that the covariance involving a choice item is the same among those examinees who did not choose that item as it was among those that did – that is, Condition 1: Cov( yi, yj | R i × R j = 1) = Cov( yi, yj | R i × R j = 0). But this is not enough. We must also assume that the means for at least one of the two items in the covariance must be the same for those who chose it as it would have been for those who did not. Condition 2: E( yi | Ri = 1) = E( yi | Ri = 0) or E( yj | Rj = 1) = E( yj | R j = 0). A little algebra will confirm that these conditions will yield the desired result.6 How plausible is it that these two conditions will be upheld in practice? Clearly, if one thought that they were likely to be true, what would be the point of providing choice to examinees? Yet, to be able to justify the typical analyses used to answer the crucial dimensionality question, one must posit performance for examinees on the choice items that is essentially the same regardless of whether or not the items were chosen. We find this compelling evidence to look elsewhere for methodologies to answer dimensionality questions when there is choice. The missing data theory described above presents a convincing argument for the necessity of a special data gathering effort to estimate the covariances associated with choice items. We have demonstrated that there is no easy and obvious model that would allow the credible use of the observed covariances as a proxy for the covariances of interest. To obtain these, we need a special data gathering effort analogous to the ones described earlier. Both kinds of designs require a sample from the unselected population. Design 1 is exactly the same as described earlier. Design 2 is slightly different. Design 1: Within subjects. In a randomly chosen subset of the examinee population, examinees must be required to indicate their choice but then required to answer all items. As before, this provides estimates of the covariances involving the choice items that are uncontaminated by selfselection. They might suffer the same shortcoming as before; that is, examinees might not be particularly judicious in their choices when they know that they will have to answer all the questions anyway. This will affect any measured relations of yi and Ri but will probably be satisfactory for estimates of the covariances between the items. We have no data to shed light on these conjectures. Design 2: Between subjects. In a randomly chosen subset of the examinee population, examinees must be randomly assigned to all pairs of the choice items. This will provide us with unbiased estimates of Cov( yi, yj) for all pairs of the choice items. It will provide more stable estimates of the covariances between each choice item and all of the required items as well. It will thus

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allow us to do dimensionality studies. Obviously, because this design does not gather any choice information, it cannot provide estimates of Cov( yi, Ri).

What Can We Learn from Choice Behavior? Thus far, our proposed requirements prior to implementing examinee choice fairly require a good deal of work on the part of both the examinee and the examiner. We are aware that extra work and expense are not part of the plan for many choice tests. Often, choice is allowed because there are too many plausible items to be asked and too little time to answer them. Is all of this work really necessary? Almost surely. At a minimum, one cannot know whether it is necessary unless it is done. To paraphrase Derek Bok’s comment on the cost of education, if you think doing it right is expensive, try doing it wrong. Yet many well-meaning and otherwise clear-thinking individuals ardently support choice in exams. Why? The answer to this question must, perforce, be impressionistic. We have heard a variety of reasons. Some are nonscientific; an example is “To show the examinees that we care.” The implication is that, by allowing choice, we are giving examinees the opportunity to do their best. We find this justification difficult to accept, because there is overwhelming evidence to indicate that this goal is unlikely to be accomplished. Which is more important – fairness or the appearance of fairness? Ordinarily, the two go together, but, when they do not, we must be fair and do our best to explain why. A second justification (W. B. Schrader, personal communication, March 7th, 1993) is that outstanding individuals are usually outstanding on a small number of things. If the purpose of the exam is to find outstanding individuals, we ought to allow them to have the option to show their maximum performance. We find this argument more convincing, but it is moot in a measurement task that is essentially unidimensional. A third justification might be termed instructional driven measurement (IDM). The argument is that because, in the classroom, students are often provided with choice options evaluation instruments ought to as well. This argument can be compelling, especially if one thinks of the choice options being those that teachers make: which topics to cover, in what order, from what perspective. Why should students suffer the consequences of unfortunate choice that were made on their behalf? The central question is: Can these issues be fairly addressed through the mechanism of allowing choice on exams? Let us consider more narrowly what we can learn from the choice behavior. Suppose we administer a test that is constructed of two sections. One section is mandatory, and everyone is required to answer all items. A second section contains choice. Equating of different test forms constructed by choice behavior can be done, if we make the usual assumptions required for IRT as well

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as an assumption about the shape of the choice items’ trace lines among those who opted for other items. Suppose, instead, we examine the estimates of proficiency obtained from the mandatory section of the test. How well is proficiency predicted from the choices that examinees make? An illustration of such a test uses data drawn from the 1989 Advanced Placement Examination in Chemistry (Wainer & Thissen, 1993b). A full description of this test, the examinee population, and the scoring model is found in Wainer, Wang, and Thissen (1991). For the purposes of this illustration, we consider only the five constructed response items in Part II, Section D. Section D has five problems (Problems 5, 6, 7, 8, and 9), of which the examinee must answer three. This section accounts for 19% of the total grade. Because examinees had to answer three out of the five questions, a total of 10 choice groups was formed, with each group taking a somewhat different test form than the others. Each group had at least one problem in common with every other group; this overlap can be used to place all examinee selected forms on a common scale. The common items serve the role of the mandatory section described earlier. The fitting of a polytomous IRT model to all 10 forms simultaneously was described in Wainer, Wang, and Thissen (1991). As part of this procedure, we obtained estimates of the mean value of each choice group’s proficiency (μi) as well as the marginal reliability of this section of the test. Our findings are summarized in Table 6. The proficiency scale had a standard deviation of one; those examinees who chose the first three items (5, 6, and 7) were considerably less proficient, on the average, than any other group. The groups labeled 2 through 7 were essentially indistinguishable in performance from one another. Groups 8, 9, and 10 were the best performing groups. If we think of Section D as a single item with an examinee falling into one of 10 possible categories, then the estimated proficiency of each examinee is the mean score of everyone in that category. How reliable is this one-item test? We can derive an analog of reliability (see the appendix for a derivation), the Table 6: Summary statistics for the 10 groups formed by examinee choice on Problems 5–9 Problems chosen

Mean group proficiency (mi)

1

5,6,7

–1.02

2,555

0.63

2 3 4 5 6 7

6,7,9 5,6,8 5,7,9 5,7,8 6,7,8 5,6,9

–0.04 0.00* 0.04 0.08 0.08 0.09

121 5,227 753 4,918 1,392 457

0.65 0.57 0.64 0.51 0.54 0.67

8 9 10

6,8,9 7,8,9 5,8,9

0.40 0.43 0.47

407 898 1,707

0.57 0.59 0.59

Group

n

Cronbach’s α

*The mean for Group 3, the largest group, is fixed at 0.0 to set the location of the proficiency scale.

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), from the squared correlation of proficiency (θ) with estimated proficiency (θ between-group variance [var(μi)] and the within-group variance (unity). This index of reliability, , θ) = var(μ )/[var(μ ) + 1], r2(θ i

i

is easily calculated. The variance of the μi is .17, and so r 2 ( θ, θ) is .15(= .17/1.17). It is informative to consider how close .15 is to .57, the reliability of these items when actually scored. Suppose we think of the task of selecting three out of five questions to answer as a single testlet. We can calculate the reliability of a test made up of any number of such testlets using the Spearman-Brown prophesy formula. Thus, if we ask the examinee to pick three from five on one set of topics and then three from five on another, we have effectively doubled the test’s length, and its reliability rises from .15 to .26. The estimated reliabilities for tests built of various numbers of such choice testlets are shown in Table 7. How much information is obtained by requiring examinees to actually answer questions and then grading them? The marginal gain for the AP Chemistry Test is very small; see Figure 6, which shows that at all of the important choice points the error of measurement is virtually the same whether the questions chosen are scored for the content of the answers or scored by noting which choices were made. Thus, we have seen that, for one test, the marginal gain in information by merely noting the choice is almost the same as that which is available from scoring the items. Interestingly, we did not need to make any assumptions about choice behavior, as we did when we scored the item content in the presence of choice-induced missing data, because there is no missing data if the data are the choices. There is no doubt that more information is available from scoring the constructed response items of the chemistry test than from merely observing which items were chosen to answer. This is reflected in the difference in the size of the reliabilities of the choice test versus the traditionally scored Table 7: Spearman-Brown extrapolation for building a test of specified reliability Number of testlets* 1 2 3 4 5 10 20

Reliability 0.15 0.26 0.35 0.41 0.47 0.64 0.78

*Here, each testlet comprises the task of selecting three questions out of five.

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There is little gain in accuracy from scoring the choice items except at the highest levels of proficiency

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version. This advantage may be diminished considerably on tests based on constructed response items that are holistically scored. Such tests typically have much lower reliability than analytically scored tests. The reliabilities for the constructed response sections of 20 Advanced Placement Tests are shown in Table 8. Note that there is very little overlap between the distributions of reliability for analytically and holistically scored tests, the latter being considerably less reliable. Chemistry is a little better than average, among analytically scored tests, with a reliability of .78 for its constructed response sections. It is sobering to consider how well a test that uses only the information about which options are chosen would compare to one of the less reliably scored tests (i.e., any of the holistically scored tests). The structure of such a choice test might be to offer three or four sets of, say, five candidate essay topics, ask the examinees to choose three of those topics in each set that they would write on, and then stop. Perhaps a more informative analysis of the information available in choices compares it to other sorts of categorical information. Figure 7 shows that more Fisherian information is obtained from examinee choice than is obtained from knowledge of examinee sex and ethnicity but that it is still less than the information obtained from just two (good) multiple-choice items. It is not our intention to suggest that it is better to have examinees choose questions to answer than it is to actually have them answer them.7 We observe

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Table 8: Reliabilities of constructed response sections of AP tests Analytically scored

Score reliability

Calculus AB Physics B Computer Science Calculus BC French Language Chemistry Latin – Virgil Latin – Catullus-Horace Physics C – Electricity Music Theory, Biology Spanish Language Physics C – Mechanics

Holistically scored

0.85 0.84 0.82 0.80 0.79 0.78 0.77 0.76 0.74 0.73 0.72 0.70 0.69 0.63 0.60 0.56 0.49 0.48 0.29

History of Art French Literature Spanish Literature English Language & Composition English Literature & Composition American History European History Music: Listening & Literature

While there is more information in choice patterns than in sex & ethnicity, there is much more information still in item responses 0.8

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Figure 7: On the AP Chemistry exam, the information about chemistry knowledge provided by just two multiple-choice items dwarfs that available from sex and ethnicity or even choice behavior

only that, if the purpose is accurate measurement, some information can be obtained from the choices and that we can obtain this information without relying on untestable (and perhaps unlikely) assumptions about unobservable choice behavior. Moreover, one should feel cautioned if the test administration

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and scoring scheme yield a measuring instrument little different in accuracy than would have been obtained by ignoring the performance of the examinee entirely.

Discussion We have painted a bleak psychometric picture for the use of examinee choice within fair tests. To make tests with choice fair requires equating the test forms generated by the choice for their differential difficulty. Accomplishing this requires either some special data gathering effort or trust in assumptions about the unobserved responses that, if true, obviate the need for choice. If we can successfully equate choice items, we have thus removed the value of choice in any but the most superficial sense. To extend these considerations, we need to be explicit about the goals of the test. There are many possible goals of a testing program. In this exposition, we will consider only three: contest, measurement, and device to induce social change. When a test is a contest, we are using it to determine a winner. We might wish to choose a subset of examinees for admission, for an award, or for a promotion. In a contest, we are principally concerned with fairness. All competitors must be judged under the same rules and under the same conditions. We are not concerned with accuracy, except to require that the test is sufficiently accurate to tell us the order of finish unambiguously. When a test is used for measurement, we wish to make the most accurate possible determination of some characteristic of an examinee. Usually measurement has some action associated with it; we measure blood pressure and then consider exercise and diet; we measure a child’s reading proficiency and then choose suitable books; we measure mathematical proficiency and then choose the next step of instruction. Similarly, we employ measurement to determine the efficacy of various interventions. How much did the diet lower blood pressure? How much better was one reading program than another? When measuring, we are primarily concerned with accuracy. Anything that reduces error may fairly be included on the test. When a test is a device to induce social change, we are using the test to influence behavior (Torrance, 1993). Sometimes the test is used as a carrot or a stick to influence the behavior of students; we give the test to get students to study more assiduously. Sometimes the test is used to influence the behavior of teachers; we construct the test to influence teachers’ choice of material to be covered. The recent literature (Popham, 1987) has characterized this goal as measurement driven instruction (MDI). MDI has engendered rich and contentious discussions, and we will not add to them here. The interested reader can begin with Cizek (1993) and work backward through the references provided by him. At first, it might appear that, when

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a test is being used in this way, issues of fairness and measurement precision are not important, although the appearance of fairness may be. However, that is false. When a test is used to induce change, the obvious next question must be, “How well did it work?” If we used the test to get students to study more assiduously, or to study certain specific material, or to study in a different way, how much did they do so? How much more have the students learned than they would have under some other condition? The other condition might be no announced test, or it might be with a test of a different format. There are obvious experimental designs that would allow us to investigate such questions – but all require measurement.8 Thus, even when the purpose of the test is to influence behavior, that test still ought to satisfy the canons of good measurement practice. Thus far, we have confined our discussion to situations in which it is reasonable to assign any of the choice items to any examinee. Such an assumption underlies the notions of equating, which as we have used the term requires essential unidimensionality (using Stout’s, 1990, useful terminology), and also of the experiments we have described to ascertain the difficulty of the choice items in the unselected population. Situations in which examinees are given such a choice we call small choice. Small choice is used most commonly because it is felt that measurement of the underlying construct may be contaminated by the particular context in which the material is embedded. It is sometimes thought that by allowing examinee choice from among several different contexts a purer estimate of the underlying construct may be obtained. Consider, for example, the following two math problems that are intended to test the same conceptual knowledge: 1. The distance between the Earth and the Sun is 93 million miles. If a rocket ship took 40 days to make the trip, what was its average speed? 2. The Kentucky Derby is one and one-fourth miles in length. When Northern Dancer won the race with a time of 2 minutes, what was his average speed? The answer to both problems may be expressed in miles/hour. Both problems are formally identical, except for differences in the difficulty of the arithmetic. Allowing an examinee to choose between these items might allow us to test the construct of interest (Does the student know the relation Rate × Time = Distance?), while at the same time letting the examinees pick the context within which they feel more comfortable. Big choice. In contrast to small choice is a situation in which it makes no sense to insist that all individuals attempt all tasks (e.g., it is of no interest or value to ask the editor of the school yearbook to quarterback the football team for a series of plays in order to gauge proficiency in that context). We call this sort of situation big choice. Using more precise language, we would characterize situations involving big choice as multidimensional. Making

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comparisons among individuals after those individuals have made a big choice is quite common. College admissions officers compare students who have chosen to take the French Achievement Test against those who opted for one in physics, even though their scores are on completely different scales. Companies that reward employees with merit raises usually have a limited pool of money available for raises and, in the quest for an equitable distribution of that pool, must confront such imponderable questions as “is person A a more worthy carpenter than person B is a statistician?” At the beginning of this account, we set aside big choice while we attempted to deal with the easier problems associated with small choice. Most of what we have discussed so far leans heavily on sampling responses in an unselected population and thus applies primarily to the small choice situation. Can we make useful comparisons in the context of big choice? Yes, but only for tests as contests, at least for the moment. When there is big choice, we can set out rules that will make the contest fair. We are not able to make the inferences that are usually desirable for measurement. To illustrate, let us consider the scoring rules for the decathlon as an illustration of scoring a multidimensional test without choice. Building on this example, we will expand to the situation of multidimensionality and choice. The decathlon is a 10-part track event that is clearly multidimensional. There are strength events like discus, speed events like the 100 m dash, endurance events like the 1,500 m run, and events that stress agility, like the pole vault. Of course, underlying all of these events is some notion of generalized athletic ability, which may predict performance in all events reasonably accurately.9 How is the decathlon scored? In a word, arbitrarily. Each event is counted “equally” in that an equal number of points is allocated for someone who equaled the world record that existed in that event at the time that the scoring rules were specified.10 How closely one approaches the world record determines the number of points received (i.e., if one is within 90% of the world record, one gets 90% of the points). As the world record in separate events changes, so too does the number of points allocated. If the world record got 10% better, then 10% more points would be allocated to that event. Let us examine the two relevant questions: Is this accurate measurement? Is this a fair contest? To judge the accuracy of the procedure as measurement, we need to know the qualities of the scale so defined. Can we consider decathlon scores to be on a ratio scale? Is an athlete who scores 8,000 points twice as good as someone who scores 4,000? Most experts would agree that such statements are nonsensical. Can we consider decathlon scores to be on an interval scale? Is the difference between an athlete who scores 8,000 and one who scores 7,000 in any way the same as the difference between one who scores 2,000 and another who scores 1,000? Again, experts agree that this is not true in any meaningful sense.

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Can we consider decathlon scores to be ordinally scaled? Yes. A demonstration uses standard mathematical notation and is virtually identical to the description given in Krantz, Luce, Suppes, and Tversky (1971, p. 14): Definition: Let A be a set and ≥ be a binary relation on A, i.e. ≥ is a subset of A × A. The relational structure (A, ≥) is a weak order if and only if, for all a, b, c ∈ A, the following two axioms are satisfied: 1. Connectedness: Either a ≥ b or b ≥ a. 2. Transitivity: If a ≥ b and b ≥ c, then a ≥ c. If such a definition holds, it can be proved that If A is a finite nonempty set and if (A, ≥) is a weak order, then there exists a real-valued function φ on A such that for all a, b ∈ A, a ≥ b if and only if φ(a) ≥ φ(b). φ is then an ordinal scale.

Translating this into the current context, A might represent the collection of performances on one of the various decathlon events, scaled in seconds or meters or whatever, φ is the scoring function that translates all of those performances into points. It is straightforward to examine any particular scoring function to see if it satisfies these conditions. Obviously, any function that is monotonic will satisfy them. We conclude that decathlon scoring satisfies the conditions for an ordinal scale. A fair contest must. This raises an important and interesting issue: If we are using a test as a contest and we wish it to be fair, we must gather data that would allow us to test the viability of the assumptions stated in the definition above. The most interesting condition is that of transitivity. The condition suggests two possible outcomes in a situation involving multidimensional comparisons: 1. There may exist instances in which Person A is preferred to Person B and Person B to Person C, and, last, Person C is preferred to Person A. This happens sufficiently often so that we cannot always attribute it to random error. It means that, in some multidimensional situations, no ordinal scale exists. 2. Data that allow the occurrence of an intransitive triad are not gathered. This means that while the scaling scheme may fail to satisfy the requirements of an ordinal scale, which are crucial for a fair contest, we will never know. In a situation involving big choice, we do not know if the connectedness axiom is satisfied. How can we test the viability of this axiom if we can observe only a on one person and only b on another?

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To get a better sense of the quality of measurement represented by the decathlon, let us consider what noncontest uses might be made of the scores. The most obvious use would be as a measure of the relative advantage of different training methods. Suppose we had two competing training methods – for example, one emphasizing strength and the other endurance. We could then conduct an experiment in which we randomly assigned athletes to one or the other of these two methods. In a pretest, we could get a decathlon score for each competitor and then another after the training period had ended. We could then rate each method’s efficacy as a function of the mean improvement in total decathlon score. While one might find this an acceptable scheme, it may be less than desirable. Unless all events showed the same direction of effect, some athletes might profit more from a training regime that emphasizes strength; others might need more endurance. It seems that it would be far better not to combine scores but, instead, to treat the 10 component scores as a vector. Of course, each competitor would almost surely want to combine scores to see how much his total had increased, but that is later in the process. The measurement task, from which we are trying to understand the relation between training and performance, is better done at the disaggregated level. It is only for the contest portion that the combination takes place. We conclude that scoring methods that resemble those used in the decathlon can only be characterized as measurement in an ordinal sense. And thus, the measures obtained are only suitable for crude sorts of inferences.

When Is a Contest Fair? In addition to the requirement of an ordinal scale, fair measurement also requires that all competitors know the rules in advance, that the same rules must apply to all competitors equally, and that there is nothing in the rules that gives one competitor an advantage over another because of some characteristic unrelated to the competition. How well do the decathlon rules satisfy these criteria? Certainly the scoring rules, arcane as they might be, are well known to all competitors, and they apply evenhandedly to everyone. Moreover, the measurements in each event are equally accurate for every competitor. Thus, if two competitors both throw the shot the same distance, they will get the same number of points. Last, is a competitor placed at a disadvantage because of unrelated characteristics? No; each competitor’s score is determined solely by his performance in the events. We conclude that the decathlon’s scoring rules comprise a fair contest even though they comprise a somewhat limited measuring instrument. The decathlon represents a good illustration of what can be done with multidimensional tests. Sensible scoring can yield a fair contest, but it is not

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good measurement. There has been an attempt to somehow count all events equally, balancing the relative value of an extra inch in the long jump against an extra second in the 1,500 meter run. But no one would contend that they are matched in any formal way. Such formal matching is possible, but it requires agreement on the metric. The decathlon is a multidimensional test, but it is not big choice as we have previously defined it. Every competitor provides a score in each event (on every item). How much deterioration would result if we add big choice into this mix? Big choice makes the situation worse. One may be able to invent scoring rules that yield a fair contest but do not give an accurate measurement. As one example, consider ABC’s “Super Star’s Competition,” a popular TV pseudosport in which athletes from various sports are gathered together to compete in a series of seven different events. The athletes each select five events from among the seven. The winner of each event is awarded 10 points, second place 7, third place 5, and so on. The overall winner is the one who accumulates the most points. Some events are “easier” than others because fewer and /or lesser athletes elected to compete in that event; nevertheless, the same number of points are awarded. This is big choice by our definition, in that there are events that some athletes could not compete in (i.e., Joe Frazier, a former world champion boxer, chose not to compete in swimming because he could not swim). Are the scores in such a competition measurement? No. Is the contest fair? By the rules of fairness described above, yes, although the missingness of some of the data makes checking key underlying assumptions problematic. The current state of the art allows us to use big choice in a multidimensional context and, under limited circumstances, to have fair contests. We cannot yet have measurement in this context at a level of accuracy that can be called anything other than crude. As such, we do not believe that inferences based on such procedures should depend on any characteristic other than their fairness. This being the case, users of big choice should work hard to assure that their scoring schemes are indeed as fair as they can make them. Wainer (1993) and Wainer and Deveaux (1994) provide two detailed case studies describing how this might be accomplished. When is it not fair? Paul Holland (Allen, Holland, & Thayer, 1993, p. 5) calls big choice “easy choice,” because often big choice is really no choice at all. Consider a choice item in which an examinee is asked to discuss the plot of either (a) The Pickwick Papers or (b) Crime and Punishment from a Marxist perspective. If the student’s teacher chose The Pickwick Papers, there really is no choice. At least, the student had no choice. Because many times in a big choice situation the examinee really has no choice, in that it is not plausible to answer any but a single option, fairness requires the various options to be of equal difficulty. This returns us to the primary point of this account. How are we to ascertain the relative difficulty of big choice items?

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Is Big Choice Useful When the Test’s Goal Is to Induce Social Change? If we wish to use the test to influence instruction, we might evaluate the success of the enterprise by surveying the field before and after the test became widespread. But this is surely only a superficial goal. The primary goal is not the structure of instruction but rather the effects of that instruction on the students. Thus, any attempt to measure the efficacy of an intervention (in this case a particular kind of test structure) must eventually use some sort of measuring instrument. We must also pay careful attention that the use of a test to induce change does not compromise its fairness. We know of one standardized science test that introduced a very easy item on a new topic as a possible choice. The goal was to influence teachers to cover this new area. Examinees whose teachers covered this topic had a distinct advantage over examinees whose teachers had not. Since the choice was really made months before the test, and by the teacher, not the student, is this fair?

Conclusions This summary of research is far from conclusive; many questions remain. It would be good to know how far away from unidimensionality a test can be and still yield acceptable measurement when choices are allowed. How far from ignorable can nonresponse be and still be acceptably adjusted for statistically? What kinds of conditioning variables are helpful in such adjustments? What are the most efficient kinds of data-gathering designs? Such questions lend themselves to solutions through careful experimentation and computer simulation. Can the uncritical use of choice lead us seriously astray? While there are several sources of evidence summarized in this article about the size of choice effects, we focused on just one series of exams. Summaries from other sources, albeit analyzed in several different ways, lead us to believe that the Advanced Placement Tests, often referred to because they currently involve choice, are not unusual. In fact, they may be considerably better than average. A recent experience with allowing choice in an experimental SAT is instructive (Lawrence, 1992). It has long been felt by math teachers that it would be better if examinees were allowed to use calculators on the mathematics portion of the SAT. An experiment was performed in which examinees were allowed to use a calculator, if they wished. The hope was that it would have no effect on the scores. Calculators did improve scores. The experiment also showed that examinees who used more elaborate calculators got higher scores than those who used more rudimentary ones. Sadly, a preliminary announcement had already been made indicating that the future SAT-M would allow examinees the option of using whatever calculator they wished, or not using one at all.

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A testing situation corresponds to measuring people’s heights by having them stand with their backs to a wall. Allowing examinees to bring a calculator to the testing situation, or not, but not knowing for sure whether they had one, or what kind, corresponds to having some persons to be measured for height, unbeknownst to you, bring a stool of unknown and varying height on which to stand. Accurate and fair measurement is no longer possible in either case. Our discussion has concentrated on explicitly defined choice in tests, or alternative questions in the language of the first half of this century. However, in the case of portfolio assessment, the element of choice is implicit and not amenable to many of the kinds of analysis that have been described here. Portfolio assessment may be more or less structured in its demands on the examinee – that is, it may specify the elements of the portfolio more or less specifically. However, to the extent that the elements of the portfolio are left to the choice of the examinee, portfolio assessment more closely resembles ABC’s “Super Star’s Competition” than even the decathlon. In portfolio assessment, how many forms of the test are created by examinee choice? Often, as many as there are examinees! If that is the case, can those forms be statistically equated? No. This fact has clear consequences in the results obtained with portfolio assessment; for instance, Koretz, McCaffrey, Klein, Bell, and Stecher (1992) report that the reliability of the 1992 Vermont portfolio program measures was substantially less than is expected for useful measurement. Can it be otherwise, when the examinees (effectively) construct their own tests?11 Is building examinee choice into a test possible? Yes, but it requires extra work. Approaches that ignore the empirical possibility that different items do not have the same difficulty will not satisfy the canons of good testing practice, nor will they yield fair tests. But, to assess the difficulty of choice items, one must have responses from an unselected sample of fully motivated examinees. This requires a special sort of data gathering effort. What are we estimating when we use examinee selected items? If we are interested in θMax, then we need to choose the items for the examinees. The belief that the estimate of θMax obtained from examinee selected items is accurate has been disconfirmed by the data gathered so far. Although these data are of modest scope, they indicate what sorts of data need to be gathered to examine this question more fully. What can we do if the assumptions required for equating are not satisfied across the choice items? If test forms are built that cannot be equated (made comparable), scores comparing individuals on incomparable forms have their validity compromised by the portion of the test that is not comparable. Thus, we cannot fairly allow choice if the process of choosing cannot be adjusted away. Choice is anathema to standardized testing unless those aspects that characterize the choice are irrelevant to what is being tested.

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Notes 1. Section II of the 1921 exam asked the examinee to answer 5 of 26 questions. This alone yielded more than 65 thousand different possible “forms.” When coupled with Section III (pick one essay topic from among 15) and Section I (“Answer 1 of the following 3”), we arrive at the unlikely figure shown in Table 1. 2. We will use both the language and notation of item response theory (IRT). This is not necessary; our argument could be phrased in traditional true score theory terms. We chose to place this argument within an IRT framework because it allows greater precision of explanation. This is especially important in later sections where being explicit about the estimand and the assumptions is critical. 3. We are oversimplifying IRT scoring here; for most IRT models, the score associated with each item response actually depends on the other item responses, and so it may be different for each examinee. 4. This varies a little from Little and Rubin’s (1987) conception. They would require the independence of choice given some observed conditioning variable. In our construction, the conditioning variable, θ, is latent. In any operational test, this difference is only a technical one for, when the test is longish, raw score (observable) and θ can be transformed from one to the other easily. This does not apply in situations like adaptive testing in which raw score is unrelated to θ. 5. A sampling process is noninformative in this case if, by knowing an individual’s choice, we learn nothing about how well they will do on the item. 6. Our thanks to Nick Longford for pointing this out to us. 7. Our colleague Nick Longford commented that this “suits perfectly the current American culture in which no one ever actually does anything but is concerned instead with management.” 8. It is not uncommon in education for innovations to be tried without an explicit design to aid in determining the efficacy of the intervention. Harold Gulliksen (personal communication, October 26, 1965) was fond of recounting the response he received when he asked what the control condition was against which the particular education innovation was to be measured. The response was “We didn’t have a control because it was only an experiment.” 9. Actually, it only predicts accurately for top-ranked competitors, who tend to perform “equally” well in all events. There are some athletes who are very much better in one event or another, but they tend to have much lower overall performance than generalists who appear more evenly talented. 10. The Olympic Decathlon scoring rules were first established in 1912 and allocated 1,000 points in each event for a world record performance. These scoring rules have been revised in 1936, 1950, 1964, and 1985. It is interesting to note (Mislevy, 1992) that the 1932 gold medal winner would have finished second under the current (1985) rules. 11. The idea of portfolio assessment includes two components, one of which is examinee choice of material to submit, and the other is that the material is collected over some longer period of time than in a conventional test. The latter idea, collecting responses over a long period of time, is certainly a useful one. However, the former idea, letting the examinees choose their test, leads to noncomparable (and unreliable) scores. Long-term data collection is certainly possible with well-specified prompts, questions, or items that leave the examinee no choice. Portfolio assessment would provide better measurement to the extent that the element of choice was removed. 12. We are grateful to Charles Lewis who suggested this analog for reliability, provided a derivation, and cautioned against its too broad usage.

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Appendix12 How can we calculate a reliability coefficient from the classification of examinees by their choice of items? Let us assume that we know the mean proficiency of all examinees in each choice group. We will index examinees by j and choice groups by i, and the model we use is θij = μi + zij

(A1)

where the proficiency of person j in group i is θíj and is distributed normally with mean μi and variance 1. We represent the deviation of each person j within group i from that group’s mean as zij. θij with μi, the mean of group i, that is If we estimate   θij = μ i .

(A2)

The correlation between θij and  θij is analogous to validity if we think of θíj as the analog of true score. The square of this correlation can be thought of as a measure of reliability. Keeping this in mind, we can derive a computational formula for r 2 ( θij , θij) by noting that r 2 ( θij , θij) = [cov( θij , θij)]2 / [Var (θij) × Var(( θij )] .

(A3)

In the numerator, cov ( θij , θij ) = cov [E(θij | i), E( θij | i )] + E[cov ( θij , θij | i)] = cov(μi, μi) + E[Cov(θij, μi |i)] = var(μi).

θij , θij | i)]} is zero, and hence The rightmost term in the initial expression {E[Cov( the expression reduces to the covariance of μi with itself or the variance of μi. This is the expression in the numerator that we need to compute (A3). The denominaθij and θij. These are easily computed from tor requires the variance of both  Var(θij) = Var[E(θij | i)] + E[Var(θij | i)] = var(μi) + 1,

(A4)

and Var(θij) = var(μi).

(A5)

Substituting these results into (A3) yields r 2 ( θij, θij ) = var(μ i ) / [var(μ i ) + 1].

(A6)

The estimate of var(μi ) we obtained from Section D of AP Chemistry is .17, and hence the estimated reliability [from (A6)] is .15.

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References Allen, N. L., & Holland, P. W. (1993). A model for missing information about the group membership of examinees in DIF studies. In P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 241–252). Hillsdale, NJ: Erlbaum. Allen, N. L., Holland, P. W., & Thayer, D. T. (1993). The optional essay problem and the hypothesis of equal difficulty (ETS Tech. Rep. No. 93–94). Princeton, NJ: Educational Testing Service. Bennett, R. E., Rock, D. A., Braun, H. I., Frye, D., Spohrer, J. C., & Soloway, E. (1991). The relationship of expert-system scored constrained free-response items to multiplechoice and open-ended items. Applied Psychological Measurement, 14, 151–162. Bennett, R. E., Rock, D. A., & Wang, M. (1991). Equivalence of free-response and multiplechoice items. Journal of Educational Measurement, 28, 77–92. Brigham, C. C. (1934). The reading of the comprehensive examination in English. Princeton, NJ: Princeton University Press. Cizek, G. J. (1993). Rethinking psychometricians’ beliefs about learning. Educational Researcher, 22(4), 4 – 9. College Entrance Examination Board. (1905). Questions set at the examinations held June 19–24, 1905. New York: Ginn. College Entrance Examination Board. (1990). The 1989 Advanced Placement Examinations in Chemistry and their grading. Princeton, NJ: Advanced Placement Programs. DeMauro, G. E. (1991). The effects of the availability of alternatives and the use of multiple choice or essay anchor tests on constructed response constructs (Draft Report). Princeton, NJ: Educational Testing Service. Dorans, N. J. (1990). Scaling and equating. In H. Wainer with N. J. Dorans, R. Flaugher, B. F. Green, R. J. Mislevy, L. Steinberg, and D. Thissen, Computerized adaptive testing: A primer (pp. 137–160). Hillsdale, NJ: Erlbaum. Fitzpatrick, A. R., & Yen, W. M. (1993, April). The psychometric characteristics of choice items. Paper presented at the Annual Meeting of the National Council on Measurement in Education, Atlanta. Fremer, J., Jackson, R., & McPeek, M. (1968). Review of the psychometric characteristics of the Advanced Placement Tests in Chemistry, American History, and French (Internal Memorandum). Princeton, NJ: Educational Testing Service. Glynn, R. J., Laird, N. M., & Rubin, D. B. (1986). Selection modeling versus mixture modeling with nonignorable nonresponse. In H. Wainer (Ed.), Drawing inferences from self-selected samples (pp. 115–142). New York: Springer-Verlag. Gulliksen, H. O. (1950). A theory of mental tests. New York: Wiley. (Reprinted,1987, Hillsdale, NJ: Erlbaum). Holland, P. W., & Wainer, H. (1993). Differential item functioning. Hillsdale, NJ: Erlbaum. Jöreskog, K. J., & Sörbom, D. (1986). PRELIS: A program for multivariate data screening and data summarization. Chicago, IL: Scientific Software. Jöreskog, K. J., & Sörbom, D. (1988). LISREL 7: A guide to the program and applications. Chicago, IL: SPSS. Kierkegaard, S. (1986). Either/or. New York: Harper & Row. Koretz, D., McCaffrey, D., Klein, S., Bell, R., & Stecher, B. (1992). The reliability of scores from the 1992 Vermont Portfolio Assessment Program (Interim Report). Santa Monica, CA: RAND Institute on Education and Training. Krantz, D. H., Luce, R. D., Suppes, P., & Tversky, A. (1971). Foundations of measurement, Vol. 1. New York: Academic. Lawrence, I. (1992). Effect of calculator use on SAT-M score conversions and equating (Draft Report). Princeton, NJ: Educational Testing Service.

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Little, R. J. A., & Rubin, D. B. (1987). Statistical analysis with missing data. New York: Wiley. Lukhele, R., Thissen, D., & Wainer, H. (1993). On the relative value of multiple-choice, free-response, and examinee-selected items in two achievement tests (ETS Tech. Rep. No. 93 - 28). Princeton, NJ: Educational Testing Service. (Also in press, Journal of Educational Measurement, 31.) Mislevy, R. J. (1992). Linking educational assessments: Concepts, issues, methods, and prospects (Draft Report). Princeton, NJ: Educational Testing Service. Pomplun, M., Morgan, R., & Nellikunnel, A. (1992). Choice in Advanced Placement Tests (Unpublished Statistical Report No. SR-92 - 51). Princeton, NJ: Educational Testing Service. Popham, W. J. (1987). The merits of measurement-driven instruction. Phi Delta Kappan, 68, 679–682. Powers, D. E., Fowles, M. E., Farnum, M., & Gerritz, K. (1992). Giving a choice of topics on a test of basic writing skills: Does it make any difference (Research Report No. 92 - 19)? Princeton, NJ: Educational Testing Service. Stout, W. F. (1990). A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation. Psychometrika, 55, 293–325. Thissen, D., Steinberg, L., & Wainer, H. (1988). Use of item response theory in the study of group differences in trace lines. In H. Wainer & H. Braun (Eds.), Test validity (pp. 147 – 169). Hillsdale, NJ: Erlbaum. Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response models. In P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 67–113). Hillsdale, NJ: Erlbaum. Thissen, D., Wainer, H., & Wang, X. B. (1993). How unidimensional are tests comprising both multiple-choice and free-response items? An analysis of two tests (ETS Tech. Rep. No. 93-32). Princeton, NJ: Educational Testing Service. (Also in press, Journal of Educational Measurement, 31.) Torrance, H. (1993). Combining measurement-driven instruction with authentic assessment: Some initial observations of the national assessment in England and Wales. Educational Evaluation and Policy Analysis, 15, 81–90. Wainer, H. (1993). How much more efficiently can humans run than swim? Chance, 6, 17–21. Wainer, H., & Deveaux, R. (1994). Resizing triathlons for fairness. Chance 7(1): xxx–xxx. Wainer, H., Sireci, S. G., & Thissen, D. (1991). Differential testlet functioning: Definitions and detection. Journal of Educational Measurement, 28, 197–219. Wainer, H., & Thissen, D. (1993b). Choosing: A test (ETS Tech. Rep. No. 92–25). Princeton, NJ: Educational Testing Service. Wainer, H., Wang, X. B., & Thissen, D. (1991). How well can we equate test forms that are constructed by examinees (Tech. Rep. No. 91–15)? Princeton, NJ: Educational Testing Service. (Also in press, Journal of Educational Measurement, 31.) Wainer, H., & Wright, B. D. (1980). Robust estimation of ability in the Rasch model. Psychometrika, 45, 373–391. Wang, X. B. (1992). Achieving equity in self-selected subsets of test items. Unpublished doctoral dissertation, University of Hawaii at Manoa, Honolulu. Wang, X. B., Wainer, H., & Thissen, D. (1993). On the viability of some untestable assumptions in equating exams that allow examinee choice (ETS Tech. Rep. No. 93–31). Princeton, NJ: Educational Testing Service.

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76 Historical Views of Invariance: Evidence from the Measurement Theories of Thorndike, Thurstone, and Rasch George Engelhard, Jr

The history of science is the history of measurement (Cattell, 1893, p. 316) The scientist is usually looking for invariance whether he knows it or not. (Stevens, 1951, p. 20)

S

tevens (1951) has presented a strong case for the general importance of the concept of invariance within the behavioral sciences. Invariance has also been identified as a fundamental aspect of measurement (Andrich, 1988a; Bock and Jones, 1968; Jones, 1960; Stevens, 1951). In essence, the goal of invariant measurement has been succinctly stated by Stevens: “the scientist seeks measures that will stay put while his back is turned” (1951, p. 21). The concept of invariance has implications for both item calibration and the measurement of individuals. As pointed out by Jones and Appelbaum (1989), developments in item response theory have led to constructive changes in psychological testing and the “primary advantage of IRT over classical test theory resides in properties of invariance” (p. 24). In a chapter on “Mathematics, Measurement and Psychophysics” which appeared in the Handbook of Experimental Psychology, Stevens (1951) described the role of invariance in mathematics and physics, and he argued that “many psychological problems are already conceived as the deliberate search for invariances” (p. 20). In fact, Stevens denned the whole field of science in terms of a quest for invariance and the concomitant generalizability of results. In his words, Source: Educational and Psychological Measurement, 52 (1992): 275–291.

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The scientist is usually looking for invariance whether he knows it or not. Whenever he discovers a functional relationship his next question follows naturally: under what conditions does it hold? … The quest for invariant relations is essentially the aspiration toward generality, and in psychology, as in physics, the principles that have wide applications are those we prize. (Stevens, 1951, p. 20)

Applying this view of invariance more specifically to measurement issues, Stevens used the concept of invariance to define his familiar scales of measurement – nominal, ordinal, interval, and ratio scales (Stevens, 1946). In his words, Each of the four classes of scales is best characterized by its range of invariance – by the kinds of transformations that leave the “structure” of the scale undistorted. And the nature of invariance sets limits to the kinds of statistical manipulations that can be legitimately applied to the scaled data. (Stevens, 1951, p. 23)

Influenced by the insightful work of Mosier (1940, 1941), Stevens pointed out the symmetry between the fields of psychophysics and psychometrics as related to the concept of invariance: Psychophysics sees the response as an indicator of an attribute of the individual – an attribute that varies with the stimulus and is relatively invariant from person to person. Psychometrics regards the response as indicative of an attribute that varies from person to person but is relatively invariant for different stimuli. Both psychophysics and psychometrics make it their business to display the conditions and limits of these invariances. (Stevens, 1951, p. 31)

The first sentence in this quotation illustrates the idea of sample-invariant item calibration, whereas the second sentence points to the idea of iteminvariant measurement of individuals. This duality between psychophysics and psychometrics, which was clearly described by Mosier (1940, 1941) and pointed out even earlier by Guilford (1936), represents one of the five major ideas underlying test theory identified by Lumsden (1976). Measurement problems related to invariance can be meaningfully viewed in terms of these two broad classes – sample-invariant item calibration and item-invariant measurement of individuals. Within each of these two classes, invariance over methods and conditions can be examined. Methods refer to the statistical procedures and models, including the method used to collect the data, employed within the measurement theory. For example, paired comparison and successive interval scaling not only would represent different methods of data collection, but also would also require different statistical models. Conditions can refer to either subgroupings of items and/or examinees. For example, test equating is concerned

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with the development of procedures which yield comparable estimates of an individual’s ability. These estimates are invariant over the subgroups of items (tests) which are used to obtain these ability estimates. As another example, the research on item bias or differential item functioning, as it has come to be labelled, reflects a concern with whether or not the meaning of an individual’s responses on a particular test item varies as a function of irrelevant factors related to membership in various social categories, such as gender, race, and social class.

Sample-Invariant Item Calibration The basic measurement problem underlying sample-invariant item calibration is how to minimize the influence of arbitrary samples of individuals on the estimation of item scale values. For example, Engelhard (1984) described how Thorndike provided a single adjustment (location) for differences in group characteristics, whereas Thurstone provided for two adjustments (location and scale). Rasch’s (1961) approach to sample-invariant calibration can be viewed as providing three adjustments (location, scale, and an individual level response model). Andrich (1978) has also provided an important comparison between Thurstone and Rasch approaches to item scaling by using paired comparison responses which can also lead to sample-invariant item calibrations. The overall goal of sample-invariant calibration of items is to estimate the location of items on a latent variable of interest which will remain unchanged across subgroups of individuals and also across various subgroups of items. For example, if the goal of sample-invariant calibration is achieved, then the item scale values will not be a function of subgroup characteristics, such as ability level, gender, race, or social class. Further, the calibration of the items should also be invariant over subsets of items, so that if a calibrated item bank (Wright and Bell, 1984) is being developed, the scale values of the items are not affected by the inclusion or exclusion of other items in the bank.

Item-Invariant Measurement of Individuals In the case of item-invariant measurement, the basic measurement problem involves minimizing the influence of the particular items which happen to be used to estimate an individual’s ability. This problem is also related to the scaling and equating of test scores, as well as to the scoring of each individual’s performance. Solutions to this problem usually include adjustments for item characteristics (item difficulty) and test characteristics (location, dispersion, and shape of item distributions on the latent variable scale). The overall objective is to obtain comparable estimates of individual ability regardless of which items are included in the test. This objective is essentially the problem of

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equating person measurements obtained on tests composed of different items (Engelhard and Osberg, 1983). Invariance over scoring method also requires attention. In addition to considering invariance over methods, it is important to examine invariance over conditions within this context; an individual’s score should not depend on the scores of other individuals being tested at the same time. In summary, invariance can be viewed as an important general concept in the physical and behavioral sciences, as well as a key aspect of successful measurement in the behavioral sciences. As pointed out by Bock and Jones (1968), “in a well-developed science, measurement can be made to yield invariant results over a variety of measurement methods and over a range of experimental conditions for any one method” (p. 9).

Three Measurement Theories and Invariant Measurement The purposes of this section are to describe and to illustrate how the concept of invariance emerged within the measurement theories of Thorndike, Thurstone, and Rasch. As the most cogent statement of the conditions necessary to accomplish invariance is presented in the measurement theory of Rasch, this section begins with his research and then traces the adumbrations of these ideas within the work of Thurstone and Thorndike. It also should be pointed out that all three of these theorists wrote extensively on various measurement problems, and for Thorndike especially it was sometimes difficult to point to one consistent set of principles that defined his definitive “theory of measurement.” In order to address this issue, certain texts are explicitly cited. It should be understood that these texts are being used to define a particular individual’s “measurement theory.” This endeavor was not much of a problem for Rasch because he was very consistent in his views related to invariance; Thurstone was fairly consistent, whereas Thorndike was the least consistent of the three.

Rasch Based on psychometric research conducted during the 1950s, Rasch (1980/ 1960, 1961, 1966a, 1966b) presented a set of ideas and methods which were described by Loevinger (1965) as a “truly new approach to psychometric problems” (p. 151), which can lead to “nonarbitrary measures” (p. 151). One of the major characteristics of this new approach was Rasch’s explicit concern with the development of individual-centered techniques as opposed to the group-based measurement models used by measurement theorists such as Thorndike and Thurstone. In Rasch’s words, “individualcentered statistical techniques require models in which each individual is

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characterized separately and from which, given adequate data, the individual parameters can be estimated” (1980/1960, p. xx). Problems related to invariance played an important role in motivating the measurement theory of Rasch. As pointed out by Andrich (1988a), Rasch presented “two principles of invariance for making comparisons that in an important sense precede, though inevitably lead to, measurement” (p. 18). The Rasch concept of specific objectivity which he formulated in terms of his principles of comparison form his version of the goals of invariant measurement (Rasch, 1977). In Rasch’s words, The comparison between two stimuli should be independent of which particular individuals were instrumental for the comparison; and it should also be independent of which stimuli within the considered class were or might also have been compared. Symmetrically, a comparison between two individuals should be independent of which particular stimuli within the class considered were instrumental for the comparison; and it should also be independent of which other individuals were also compared, on the same or on some other occasion (Rasch, 1961, pp. 331–332).

It is clear in this quotation that Rasch recognized the importance of both sample-invariant item calibration and item-invariant measurement of individuals. In fact, he made them the cornerstones of his quest for specific objectivity. In order to address problems related to invariance, Rasch laid the foundation for the development of a family of measurement models which are characterized by separability of item and person parameters (Masters and Wright, 1984). Rasch’s approach to sample-invariant item calibration involved the comparison of item difficulties obtained in separate groups. In this words, In relation to attainment tests all the school grades for which the tests are in practice applicable may be considered as forming a total collection of persons, that may be divided into subpopulations, such as single grades, sex groups, and age groups within a grade, social strata, etc. Between the test results in such more or less extensive groups the same fundamental relationship must hold, and if so we shall use the term that the relationship is “relatively independent of population,” the qualification “relatively” pointing to the degree of breakdown that has been applied to the data. (Rasch, 1980/1960, p. 9)

In his book, he used ability groups formed on the basis of raw scores. In essence, Rasch was “looking for trouble in a more or less definite direction; namely, for the possibility that the relative difficulties of the tests may vary with [raw score] that is, with the reading inability of the children” (Rasch, 1961, p. 323). This test of fit (or what Rasch referred to as control of the model) was presented graphically. Essentially, Rasch calibrated items separately in different score groups and then plotted them against the average calibrations

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across score groups. If these plots are linear and parallel with slopes close to one, then a sample-invariant item calibration was approximated. If these plots were non-linear and non-parallel, then invariance did not hold over these scores groups. Because of the formal symmetry in the model proposed by Rasch between items and individuals, he used a similar graphic approach to examine whether or not item-invariant measurement of individuals had been achieved. In this case, ability estimates are obtained separately for item groups, and then plotted against the average ability estimates obtained from different item groups. If these plots are linear and parallel with slopes close to one, then successful item-invariant measurement of individuals has been approximated. If these conditions are not met, then item-invariant measurement of individuals is not possible with these items. Even though there are more sophisticated methods for examining invariance using statistical tests of item and person fit (Wright, 1988; Wright and Stone, 1979), the graphical methods clearly show whether or not invariance has been achieved. As will be seen in the next section, Thurstone used a similar graphical method to examine whether or not his method of absolute scaling was appropriate for a particular set of test data. By focusing on the individual as the level of analysis, Rasch was able to examine test data and to identify when invariance was exhibited. When the data fit the Rasch model, then the types of invariance which eluded research workers in the test theory tradition can be obtained. To quote Loevinger, Rasch is concerned with a different and more rigorous kind of generalization than Cronbach, Rajaratnam and Gleser. When his model fits, the results are independent of the sample of persons and of the particular items with some broad limits. Within these limits, generality is, one might say, complete. (Loevinger, 1965, p. 151)

Detailed descriptions of Rasch measurement are presented in Andrich (1988a), Wright and Stone (1979), Wright and Masters (1982), and Wright (1988).

Thurstone Thurstone also recognized the importance of invariant measurement. In fact, as pointed out by Bock and Jones (1968), “in the system of psychological measurement based on the Thurstonian models, we achieved some of the invariance in measurement which is characteristic of the other sciences” (p. 9). In developing his method of absolute scaling for calibrating test items, Thurstone (1925, 1927, 1928a, 1928b) was specifically motivated by the lack of sample-invariance he observed in Thorndike’s scaling method. In his words,

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The probable error, or PE [used in Thorndike’s method], is not valid as a unit of measurement for educational scales. Its defect consists in that it does not possess the one requirement of a unit of measurement, namely constancy [emphasis added]. It fluctuates from one age to another. (Thurstone, 1927, p. 505)

The concept of constancy proposed by Thurstone is his version of an invariance condition, and it is an explicit consequence of measurement situations that yield objective measurements. Thorndike’s PE values fluctuate because the item scale values are not sample-invariant. A condition which violates Thurstone’s insight that the “scale value of an item should be the same no matter which age group is used in the standardization” (Thurstone, 1928a, p. 119). As did Rasch, Thurstone used the idea of a continuum to represent the latent variable of interest and assumed that items can be placed at points on this linear scale which would have a fixed position regardless of the group being tested. According to Thurstone, “if any particular test item or particular raw score is to be allocated on the absolute scale, its scale value should be ideally the same whether determined by group one or group two” (1925, p. 438). Thurstone also presented his ideas about invariance graphically. For example in several of his articles (1925, 1927), he presented overlapping ability distributions and pointed out that the location of the items on the latent variable scale should be invariant over different ability distributions. In order to adjust for differences in the location and variability of two or more distributions, Thurstone assumed a normal distribution of ability for each group and adjusted statistically for differences in locations (means) and scales (standard deviations). In order for these adjustments proposed by Thurstone to lead successfully to sample-invariant item calibration, Thurstone proposed a graphical test of fit that is essentially the same as Rasch’s approach. According to Thurstone, If the plot of Fig. 4 should be distinctly non-linear, the present scaling method is not applicable. Non-linearity here shows that the two distributions cannot both be normal on the same scale. If the plot is linear, it proves that both distributions may be assumed to be normal on the same scale or base line. (Thurstone, 1927, p. 513).

This test of fit can also be presented in the style of the graphical displays used by Rasch as shown by Engelhard (1984). The effects of using Thurstone’s method of absolute scaling, which provides adjustments for differences in the locations and variations of the ability distributions, as compared to Thorndike’s scaling method which simply adjusts for location differences, can be dramatic. Thurstone (1927) presented the results of using Thorndike’s method to calibrate a language scale developed by Trabue (1916). Trabue’s analysis based on Thorndike’s method indicates that

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the average language ability increases as a function of grade level, whereas the variances remain constant. The results obtained by using Thurstone’s method also indicate that average ability increases with grade level, but the variances of the scores are not constant, as they tend to increase as a function of grade level. These results seem theoretically plausible. Thurstone’s method of absolute scaling is described and illustrated in detail in Engelhard (1984). An “experimental” adjustment for sample effects which occurs with Thurstone’s model for paired comparisons is described in Andrich (1978). Thurstone’s method of absolute scaling can also be used to scale test scores (Gulliksen, 1950), but a more interesting discussion of issues related to iteminvariant measurement is presented by Thurstone (1926) in an article on the scoring of individual performance. In this article, Thurstone presented a set of conditions as follows: 1. It should not be required to have the same number of test elements at each step of the scale. 2. It should be possible to omit several test questions at different levels of the scale without affecting the individual score. 3. It should be possible to include in the same scale two forms of test. 4. It should not be required to submit every subject to the whole range of the scale. The starting point and terminal point, being selected by the examiner, should not directly affect the individual score. 5. It should be possible to use the scale so that a rational score may be determined for each individual subject and so that the performance of groups of subjects may be compared. 6. The arithmetical labor in determining individual scores should be a minimum. 7. The procedure should be as far as possible consistent with psychophysical methods so that it will be free from the logical errors involved in the Binet scales and its variants. Conditions one to five clearly show Thurstone’s concern with item-invariant measurement. In his 1926 paper, he went on to propose a scoring method which meets these conditions. Thurstone’s approach is presented in detail by Engelhard (1991). In essence, Thurstone proposed what would be recognized today as person characteristic curves. Many of Thurstone’s articles on scaling are included in The Measurement of Values (1959), although his work on absolute scaling is not included in that volume. The technical details and elaborations of Thurstonian models are presented in Bock and Jones (1968). Andrich (1988c) provided a useful overview of Thurstone’s contributions to measurement theory. Although it is not directly relevant for this paper, it is interesting to note that Thurstone (1947), as did Rasch (1953), also used the concept of invariance as an important aspect of his approach to factor analysis.

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Thorndike In 1904, Thorndike published the first edition of his highly influential book entitled An Introduction to the Theory of Mental and Social Measurements. Thorndike’s major aim in writing this book was to “introduce students to the theory of mental measurements and to provide them with such knowledge and practice as may assist them to follow critically quantitative evidence and argument and to make their own researches exact and logical (1904, p. v). Thorndike’s book was the standard reference on statistics and quantitative methods in the mental and social sciences for the first two decades of this century (Clifford, 1984; Engelhard, 1988; Travers, 1983). Much of this influence can be attributed to Thorndike’s (1904) clear and expository writing style. Thorndike explicitly acknowledged that contemporary work in measurement theory had not been presented in a manner suitable for students without fairly advanced mathematical skills. He set out to present a less mathematical introduction to measurement theory based on the belief that “there is, happily, nothing in the general principles of modern statistical theory but refined common sense, and little in the techniques resulting from them that general intelligence can not readily master” (p. 2). Thorndike, who wrote extensively on educational and psychological measurement, covered topics which ranged from the general statement of his theory (Thorndike, 1904) to the measurement of a variety of educational outcomes (Thorndike, 1910, 1914, 1918, 1921), as well as intelligence (Thorndike, Bregman, Cobb, and Woodyard, 1926). What were the basic measurement problems identified by Thorndike? Thorndike clearly stated that the “special difficulties” of measurement in the behavioral sciences are as follows: 1. Absence or imperfection of units in which to measure 2. Lack of constancy in the facts measured 3. Extreme complexity of the measurements to be made. In order to illustrate the problems related to the absence of an accepted unit of measurement, Thorndike (1904) pointed out that the spelling tests developed by Joseph Mayer Rice did not have equal units. Rice assumed that all his spelling words were of equal difficulty, whereas Thorndike argued that the correct spelling of an easy versus a hard word did not reflect equal amounts of spelling ability. Because the units of measurement are unequal, Thorndike asserted that Rice’s results were inaccurate. Without general agreement on units, the meaning of test scores becomes more subjective. Within the framework of this paper, Thorndike was illustrating that obtained scores may not be invariant over subsets of items which vary in difficulty. Inconstancy is the second major measurement problem identified by Thorndike (1904). Many of the measurement problems encountered in the

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behavioral sciences are related to random variation inherent in human characteristics. These variations are due not only to the unreliability of tests, but also to within subject fluctuations. For example, if a person’s motivation is measured repeatedly, these values tend to vary. Thorndike’s concept of “constancy” is also related to the idea of invariance as developed in this paper. The final measurement problem or “special difficulty” identified by Thorndike pertains to the extreme complexity of the variables and constructs the social and behavioral scientists wish to measure. This problem reflects a concern with dimensionality. Most of the variables worth measuring in the behavioral sciences do not readily translate into unidimensional tests which permit the reporting of a single score to represent the individual’s location on the latent variable or construct of interest. As pointed out by Jones and Applebaum (1989), if unidimensionality is obtained for all items and over all groups of examinees, then item parameters will be invariant across groups, and ability parameters will be invariant across items. Methods for conducting item factor analyses designed to explore this issue have been summarized by Mislevy (1986), and an approach to this problem has been illustrated by Muraki and Engelhard (1985). Thorndike’s method for obtaining sample-invariant item calibration is very similar to Thurstone’s method of absolute scaling. As described by Thurstone, Thorndike’s scaling method consists in first determining the scale value of each item for each grade separately with the mean of each grade as an origin. The difficulty of a test item for Grade V children, for example, is determined by the proportion of right answers to the test item in that grade. When a test item has been scaled in several grades, the scale values so obtained will, of course, be different because of the fact that they are expressed as deviations from different grade means as origins. Thorndike then reduces all these measurements to a common origin in the construction of an educational scale by adding by each scale value the scale value of the mean of the grade (Thurstone, 1927, p. 508).

The major difference between Thorndike’s method of item scaling and Thurstone’s method of absolute scaling is that Thorndike assumed that the variances of the groups are equal. Thurstone criticized this assumption, ... it is clear that in order to reduce the overlapping sentences or test items to a common base line or scale it is necessary to make not one but two adjustments. One of these adjustments concerns the means of the several grade groups, and this adjustment is made by the Thorndike scaling methods. The second adjustment which is not made by Thorndike concerns the variation in dispersion of the several groups when they are referred to a common scale (Thurstone, 1927, p. 509).

In his later work, Thorndike did include an adjustment for the range of scores (Thomson, 1940).

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Thorndike’s views of item-invariant measurement of individuals are presented in several places (Thorndike, 1914; Thorndike, Bregman, Cook, and Woodyard, 1926). Engelhard (1991) has presented a detailed description of Thomdike’s approach as applied to the measurement of reading ability (Thorndike, 1914). Essentially, Thorndike recommended using a set of procedures that are very similar to the methods of scoring individual performance used by Thurstone and Rasch. Thorndike also suggested examining person fit and proposed adjusting reading ability estimates when an individual responded in an inconsistent manner to the test items.

Comparison and Discussion of Three Measurement Theories The major similarities and differences among the measurement theories of Thorndike, Thurstone, and Rasch are summarized in this section. In general terms, it is clear that Thorndike, Thurstone, and Rasch were all working within a common scaling tradition. They based many of their proposed methods for calibrating test items and measuring individuals on statistical advances made within the field of psychophysics. One of the differences between psychophysics and psychometrics is that the independent variable is usually an observable variable in psychophysics, whereas in psychometrics the construct is usually unobservable. As this construct is not directly observable, these three psychometricians used the idea of a latent continuum to represent this unobservable variable. Although they all held similar positions on many measurement issues, there were also several important differences between the conceptualizations of Thorndike and Thurstone as compared to the views of Rasch. One of the major differences was the recognition by Rasch that measurement models can and should be developed based on the responses of individuals to single test items. This focus on the individual, rather than on groups, allowed Rasch to avoid making unnecessary assumptions regarding the distribution of abilities which were needed by both Thorndike and Thurstone. As pointed out earlier, Thorndike’s method of scaling test items and Thurstone’s method of absolute scaling were both based on the assumption that abilities were normally distributed. By using the individual and not the group, as the level of analysis, Rasch invented measurement models which are capable of providing estimates of the location of both items and individuals on a latent variable continuum simultaneously. This approach also allowed Rasch to develop probabilistic models rather than deterministic ones for modelling the probability of each individual succeeding on a particular test item as a function of his or her ability and the item difficulties. This probabilistic relationship is clearly shown in the familiar S-shaped item characteristic curves. Further, by simultaneously including item calibration and individual measurement within one model, he was able to derive “conditional” estimates

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of these parameters which provide a framework for determining whether or not invariance has been achieved. In summary, many of the measurement problems that confront researchers in psychology and education today, such as those related to invariance, are not new. By taking a historical perspective on these measurement problems, one may find it possible to increase the understanding of the measurement problems themselves, to assess the adequacy of solutions proposed by major measurement theorists, and to identify promising areas for future research. Progress, and in some cases lack of progress, towards the solution of basic measurement problems can also be meaningfully documented. Progress is as difficult to define within the field of measurement as in any other field of study (Donovan, Laudan, and Laudan, 1988; Laudan, 1977). The analysis presented in this paper suggests that Rasch’s work provides a theoretical and statistical framework for the practical realization of invariant measurement that was sought by both Thorndike and Thurstone. The simultaneous inclusion of both ability and item difficulty within a probabilistic model defined at the individual level of analysis has provided a general framework in which item and person parameters can be estimated separately. Rasch was able to use recent advances in statistics, such as the concept of sufficiency developed by Fisher (1925), to propose an approach to measurement which provides practical solutions to many testing problems related to invariance. Measurement problems related to invariance are of fundamental importance for the development of meaningful measures in education and psychology. Item-invariant estimates of individual abilities and sample-invariant estimates of item difficulties are essential in order to realize the advantages of objective measurement. The conditions for objective measurement correspond to the concept of invariance as developed in this paper. The conditions for objective measurement are as follows: First, the calibration of measuring instruments must be independent of those objects that happen to be used for the calibration. Second, the measurement of objects must be independent of the instrument that happens to be used for the measuring (Wright, 1968, p. 87).

This paper provides a historical and substantive review of the problems related to invariant measurement as well as illustrates the progress which has been made toward solving measurement problems related to invariance. Further, this paper contributes to an appreciation of Rasch’s accomplishments and of the elegance of Rasch’s approach to problems related to invariant measurement. As pointed out by Andrich (1988b), Rasch’s achievements did not occur in a “historical vacuum” (p. 13). This paper illustrates the continuity and progress that is evident within the measurement theories of Thorndike, Thurstone, and Rasch.

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Note This research was supported in part by the University Research Committee of Emory University. Support for this research was also provided through a Spencer Fellowship from the National Academy of Education. Earlier versions of this paper were presented at the Fifth International Objective Measurement Workshop at the University of California, Berkeley (March, 1989), and at the Sixth International Objective Measurement Workshop at the University of Chicago (April, 1991). Judith A. Monsaas and Larry Ludlow provided helpful comments on earlier drafts of this paper.

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Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports, 3, 635–694. Loevinger, J. (1965). Person and population as psychometric concepts. Psychological Review, 72, 143 –155. Lumsden, J. (1976). Test theory. Annual review of psychology, 27, 251–280. Master, G. N. and Wright, B. D. (1984). The essential process in a family of measurement models. Psychometrika, 49, 529 – 544. Mislevy, R. J. (1986). Recent developments in the factor analysis of categorical variables. Journal of Educational Statistics, 11, 3 – 31. Mosier, C. I. (1940). Psychophysics and mental test theory: Fundamental postulates and elementary theorems. Psychological Review, 47, 355 – 366. Mosier, C. I. (1941). Psychophysics and mental test theory II: The constant process. Psychological Review, 48, 235 – 249. Muraki, E. and Engelhard, G. (1985). Full-information item factor analysis: Applications of EAP scores. Applied Psychological Measurement, 9, 417– 430. Rasch, G. (1953). On simultaneous factor analysis in several populations. Uppsala Symposium on Psychological Factor Analysis. Nordisk Psykologi’s Monograph Series, 3. Rasch, G. (1961). On general laws and the meaning of measurement in psychology. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, (pp. 321– 333). Berkeley, CA: University of California Press. Rasch, G. (1966a). An individualistic approach to item analysis. In P. F. Lazarsfeld and N. Henry (Eds.), Readings in Mathematical Social Science (pp. 89–107). Chicago: Science Research Associates. Rasch, G. (1966b). An item analysis which takes individual differences into account. British Journal of Mathematical and Statistical Psychology, 19, 49 – 57. Rasch, G. (1977). On specific objectivity: An attempt at formalizing the request for generality and validity of scientific statements. Danish Yearbook of Philosophy, 14, 58 – 94. Rasch, G. (1980/1960). Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press. [Originally published in 1960 by the Danish Institute for Educational Research]. Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677– 680. Stevens, S. S. (1951). Mathematics, measurement, and psychophysics. In S.S. Stevens (Ed.), Handbook of experimental psychology, (pp. 1– 49). New York: Wiley. Thomson, G. H. (1940). The nature and measurement of the intellect. Teachers College Record, 41, 726 –750. Thorndike, E. L. (1904). An introduction to the theory of mental and social measurements. New York: Teachers College, Columbia University. Thorndike, E. L. (1910). Handwriting. Teachers College Record, 11, 83 –175. Thorndike, E. L. (1914). The measurement of ability in reading. Teachers College Record, 15, 207– 277. Thorndike, E. L. (1918). The nature, purposes, and general methods of measurements of educational products. In Whipple, G. M. (Ed.), The seventeenth yearbook of the national society for the study of education. Part II, The measurement of educational products. Bloomington, IL: Public School Publishing Company. Thorndike, E. L. (1921). Measurement in education: Teachers College Record, 22, 371– 379. Thorndike, E. L., Bregman, E. O., Cobb, M. V., and Woodyard, E. (1926). The measurement of intelligence. New York: Bureau of Publications, Teachers College, Columbia University. Thurstone, L. L. (1925). A method of scaling psychological and educational tests. Journal of Educational Psychology, 15, 433 – 451. Thurstone, L. L. (1926). The scoring of individual performance. Journal of Educational Psychology, 17, 446 – 457.

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Thurstone, L. L. (1927). The unit of measurement in educational scales. Journal of Educational Psychology, 18, 505 –524. Thurstone, L. L. (1928a). II. Comment of Professor L. L. Thurstone. Journal of Educational Psychology, 19, 117–124. Thurstone, L. L. (1928b). Scale construction with weighted observations. Journal of Educational Psychology, 19, 441– 453. Thurstone, L. L. (1947). Multiple-factor analysis: A development and expansion of the vectors of mind. Chicago: The University of Chicago Press. Thurstone, L. L. (1959). The measurement of values. Chicago: The University of Chicago Press. Trabue, M. R. (1916). Completion-test language scales. Contributions to Education, No. 77. New York: Columbia University, Teachers College. Travers, R. M. W. (1983). How research has changed American schools: A history from 1840 to the present. Kalamazoo, MI: Mythos Press. Wright, B. D. (1968). Sample-free test calibration and person measurement. In Proceedings of the 1967 invitational conference on testing problems. Princeton, NJ: Educational Testing Service. Wright, B. D. (1988). Rasch measurement models. In J. P. Keeves (Ed.), Educational Research, Methodology, and Measurement: An International Handbook. Oxford, England: Pergamon Press. Wright, B. D. and Bell, S. R. (1984). Item banks: What, why and how. Journal of Educational Measurement, 21, 331– 345. Wright, B. D. and Masters, G. (1982). Rating scale analysis: Rasch measurement. Chicago: MESA Press. Wright, B. D. and Stone, M. H. (1979). Best test design: Rasch measurement. Chicago: MESA Press.

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77 If Statistical Significance Tests Are Broken/Misused, What Practices Should Supplement or Replace Them? Bruce Thompson

A

few years ago Pedhazur and Schmelkin (1991) asserted that ‘probably very few methodological issues have generated as much controversy’ (p. 198) as have the use and interpretation of statistical significance tests. These tests have certainly proven surprisingly resistant to repeated efforts ‘to exorcise the null hypothesis’ (Cronbach, 1975, p. 124). Particularly noteworthy among the historical efforts to accomplish the exorcism have been works by Rozeboom (1960), Morrison and Henkel (1970), Carver (1978), Meehl (1978), Shaver (1985) and Oakes (1986). The entire Volume 61, Number 4 issue of the Journal of Experimental Education (1993) was devoted to these themes. Yet, notwithstanding the long-term availability of these publications, even today some psychologists still do not understand what statistical significance tests do and do not do. In a public-domain brief digest disseminated as a class handout by the US Department of Education Educational Resources Information Center, the present author (Thompson, 1994a) provided some simple tests of understanding of what pCALCULATED actually evaluates: In which one of each of the following [three] pairs of studies will the pCALCULATED be smaller? • In two studies each involving three groups of subjects each of size 30, in one study the means were 100, 100, and 90, and in the second study the means were 100, 100, and 100. Source: Theory & Psychology, 9(2) (1999): 165–181.

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•

•

In two studies each comparing the standard deviations (SD) of scores on the dependent variable of two groups of subjects, in both studies SD1 = 4 and SD2 = 3, but in study one the sample sizes were 100 and 100, while in study two the samples sizes were 50 and 50. In two studies involving a multiple regression prediction of Y using predictors X1, X2, and X3, and both with samples sizes of 75, in study one R2 = .49 and in study two R2 = .25 (p. 5).

These judgments do not require calculations or additional information. However, making such judgments does require a genuine understanding of what statistical significance tests are all about.1 It is not clear how well most authors of journal articles would do on the previous three-item evaluation (cf. Falk & Greenbaum, 1995; Nelson, Rosenthal, & Rosnow, 1986; Oakes, 1986; Zuckerman, Hodgins, Zuckerman, & Rosenthal, 1993). Many of us continue to prefer ‘investing ... [these tests] with what appear to be magical powers’ (Pedhazur & Schmelkin, 1991, p. 198). And some of us try to use p values to cling to a mantle of unattainable objectivity. The use of statistical tests has recently stimulated yet more controversy. Harlow, Mulaik and Steiger (1997) provide a compendium of views on these issues (for a review, see Thompson, 1998a). Contemporary commentaries include those provided by Hunter (1997), Kirk (1996), Schmidt (1996) and the present author (Thompson, 1996, 1997). The less positive treatments of statistical significance tests have also provoked reactions from test advocates (cf. Chow, 1988; Frick, 1996; Greenwald, Gonzalez, Harris, & Guthrie, 1996; Hagen, 1997; Robinson & Levin, 1997). Yet even Frick (1996) acknowledged that critics of conventional practices ‘usefully point out the limitations of null hypothesis testing’ (p. 388). Given growing consciousness regarding these limitations, the APA Board of Scientific Affairs recently named a Task Force on Statistical Inference (Shea, 1996). The APA Task Force is charged with recommending policies and practices leading to more informed and thoughtful statistical analyses, including those involving the use of statistical significance tests. Articles within the American Psychologist, published on a seemingly periodic basis, have especially informed the movement of the field as regards statistical significance testing. Table 1 lists some of these articles, and also reports citation frequencies for the articles as of 1996. These American Psychologist articles, and the related comments published within the journal, have considerably influenced psychology and the social sciences more generally. For example, Roger Kirk (1996) characterized the two American Psychologist articles by Cohen as ‘classics’, and argued that ‘the one individual most responsible for bringing the shortcomings of hypothesis testing to the attention of behavioral and educational researchers is Jacob Cohen’ (p. 747). The present paper briefly reviews some of the consensus that has arisen or seems to be occurring as regards the use and limits of statistical significance tests. However, the present treatment also explores both (a) recommendations

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Table 1: Citations of selected American psychologist articles Number of citations Year

Author(s)

Pre-1991

1991

1992

1993

1994

1995

1996*

Total

1994 1991

Cohen

_

_

_

_

_

17

42

59

Rosenthal

_

_

6

2

2

2

1

13

1990

Cohen

_

18

36

38

23

28

23

166

1989

Rosnow & Rosenthal

4

18

23

20

14

12

13

104

1988

Kupfersmid

19

3

6

6

2

4

1

41

1987

Dar

10

2

6

2

2

1

1

24

*The most current Index at the time of this compilation only covered 1996 through September of that year.

involving changes in research practices and editorial policies and (b) related issues that the field has yet to resolve. Given some consensus that statistical significance tests are broken, misused or at least have somewhat limited utility, the focus of discussion within the field ought to move beyond additional bashing of statistical significance tests, and toward more constructive suggestions for improved practice.

Emerging Consensus The field appears to have achieved or is approaching consensus regarding certain limitations of statistical significance tests, notwithstanding some psychological resistance (Schmidt & Hunter, 1997; Thompson, 1998b). At least three noteworthy realizations can be briefly cited.

Result Effect Size First, researchers have recognized that p values are not useful as indices of study effect sizes (although some researchers still may implicitly deem more important those studies reporting smaller p values – cf. Rosenthal & Gaito, 1963; Zuckerman et al., 1993). The calculated p values in a given study are a function of several study features, but are particularly influenced by the confounded, joint influence of study sample size and study effect sizes. Because p values are confounded indices, in theory 100 studies with varying sample sizes and 100 different effect sizes could each have the same single PCALCULATED, and 100 studies with the same single effect size could each have 100 different values for PCALCULATED. This realization led to an important change in the fourth edition of the American Psychological Association Publication Manual (APA, 1994). The manual noted that

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Neither of the two types of probability values [statistical significance tests] reflects the importance or magnitude of an effect because both depend on sample size. . . . You are [therefore] encouraged to provide effect size information. (APA, 1994, p. 18; emphasis added)

Result Importance Second, more and more researchers and editors have come to recognize that p values do not evaluate result importance. Therefore, p values cannot be used as an effective vehicle for escaping disagreement and confrontation regarding our subjective judgments of the worth of our results. As Thompson (1993) noted, … importance is a question of human values, and math cannot be employed as an atavistic escape (à la Fromm’s Escape from Freedom) from the existential human responsibility for making value judgments. If the computer package did not ask you your values prior to its analysis, it could not have considered your value system in calculating p’s, and so p’s cannot be blithely used to infer the value of research results. (p. 365)

Result Replicability Third, researchers have recognized that pCALCULATED values are not informative regarding the likelihood of result replication in future samples (Thompson, 1996). As Cohen (1994) made so clear, these calculations presume that the null hypothesis exactly describes the population, and then indicate the probability of the sample results (or of sample results even more disparate from the null than those in the actual sample), given the sample size. But what we want to know is the population parameters, given the statistics in the sample and the sample size. This interest in true population values stems from a desire to avoid the discovery of cold fusion, which leads to a single jubilant conference experience, followed by a lifetime of being shunned at all remaining professional meetings. If we could infer the population parameters, given the sample statistics and sample size, then we might have some confidence that future research would yield sample statistics similar to those in our own sample. Unfortunately, the direction of the inference in inferential statistics is from the population and to the sample, and not from the sample to the population (Thompson, 1997). Thus Cohen (1994) concluded that the statistical significance test ‘does not tell us what we want to know, and we so much want to know what we want to know that, out of desperation, we nevertheless believe that it does!’ (p. 997).

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Recommended Changes in Practice A few scholars have called for the banning of statistical significance tests (cf. Carver, 1978, 1993). However, the fact that many psychologists misinterpret statistical significance tests is not a reasonable warrant for banning these tests. As Strike (1979) explained, ‘To deduce a proposition with an “ought” in it from premises containing only “is” assertions is to get something in the conclusion not contained in the premises, something impossible in a valid deductive argument’ (p. 13). In logic this fallacy is called a ‘should/ would’ or ‘is/ought’ error (Hudson, 1969). But more and more researchers also now realize that ‘virtually any study can be made to show [statistically] significant results if one uses enough subjects’ (Hays, 1981, p. 293). This means that Statistical significance testing can involve a tautological logic in which tired researchers, having collected data from hundreds of subjects, then conduct a statistical test to evaluate whether there were a lot of subjects, which the researchers already know, because they collected the data and know they’re tired. (Thompson, 1992b, p. 436)

Consequently, attention has now turned toward ways to improve practice. Five potential improvements in practice are suggested here.

Effect Size Reporting Empirical studies of articles published since 1994 in psychology, counseling, special education and general education suggest that merely ‘encouraging’ effect size reporting (APA, 1994) has not appreciably affected actual reporting practices (e.g. Kirk, 1996; Snyder & Thompson, 1998; Thompson & Snyder, 1997, 1998; Vacha-Haase & Nilsson, 1998). Apparently, when it comes to reporting and interpreting effect sizes, many are called but few choose to be chosen. Consequently, editorial policies at some journals now require authors to report and interpret effect sizes (Heldref Foundation, 1997; Thompson, 1994b; see also Loftus, 1993; Shrout, 1997). It is particularly noteworthy that editorial policies even at one APA journal now indicate that If an author decides not to present an effect size estimate along with the outcome of a significance test, I will ask the author to provide specific justification for why effect sizes are not reported. So far, I have not heard a good argument against presenting effect sizes. Therefore, unless there is a real impediment to doing so, you should routinely include effect size information in the papers you submit. (Murphy, 1997, p. 4)

Effect sizes are important to report and interpret for at least two reasons. First, these indices can help inform judgment regarding the practical

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or substantive significance of results. Statistical significance tests do not bear upon the noteworthiness of results, because improbable events are not necessarily important (see Shaver’s [1985] classic example), and because ‘if the null hypothesis is not rejected, it is usually [only] because the N is too small’ (Nunnally, 1960, p. 643). Second, reporting effect sizes facilitates the meta-analytic integration of findings across a given literature. People who incorrectly believe, either consciously or unconsciously, that statistical significance tests evaluate the probability of population parameters can exaggerate the importance of a single study, because the study then generalizes to the population. Persons who recognize the limits of these statistical tests realize that most single studies are important primarily only as building blocks within a cumulative body of evidence. As Schmidt (1996) noted: Meta-analysis ... has revealed how little information there typically is in any single study. It has shown that, contrary to widespread belief, a single primary study can rarely resolve an issue or answer a question. (p. 127)

Reporting effect sizes helps meta-analysts more easily and more accurately synthesize findings, because the analyst can then avoid using more approximate effects computed based on sometimes tenuous statistical assumptions. Of course, effect size is no more a panacea than is a statistical significance test, for two reasons noted by Zwick (1997). First, because human values are also not part of the calculation of an effect size, any more than values are part of the calculation of p, ‘largeness of effect does not guarantee practical importance any more than statistical significance does’ (p. 4). Second, some researchers seem to have adopted Cohen’s (1988) definitions of small, medium and large effects with the same rigidity that ‘α = .05’ has been adopted. Such rigidity is inappropriate. Cohen only intended these as impressionistic characterizations of result typicality across a diverse literature, and not as rigid universal criteria. However, some empirical studies suggest that the characterization is reasonably accurate (Glass, 1979; Olejnik, 1984), at least as regards a literature historically built with a bias against statistically non-significant results (Rosenthal, 1979). Notwithstanding these caveats, it is suggested that all authors of quantitative studies should report and interpret effect sizes. Because merely encouraging these practices has to date had little or no effect, at some point it may become necessary to require that effect sizes are reported. Of course, a requirement that effect sizes be reported does not inherently require that a whole new system of statistical analyses be invoked; all our classical analytic methods can be used to yield both pCALCULATED and effect size values, even though the methods have traditionally been used only for the first purpose.

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Effect Size Interpretability There are myriad effect sizes from which the researcher can choose. Useful reviews of the choices have been provided by Kirk (1996), Snyder and Lawson (1993) and Friedman (1968), among others. Effect sizes can be categorized into two broad classes: variance-accountedfor measures (e.g. R2, eta(η)2 ) and standardized differences (e.g. Cohen’s d, Hedges’ g) (Kirk [1996] identifies a third, ‘miscellaneous’ class. Varianceaccounted-for indices can be computed in all classical statistical analyses because all analyses are correlational, even though some designs are experimental and some are not (Knapp, 1978; Thompson, in press). Furthermore, effect sizes can be further subdivided as being either ‘uncorrected’ (e.g. R2, eta2) or ‘corrected’ (e.g. adjusted R2, omega(ω)2). Because all conventional analyses are least-squares correlational methods that capitalize on all sample variance, including the sampling error variance unique to the sample, all uncorrected variance-accounted-for statistics are positively biased and overestimate population effects. This bias can be statistically removed via the corrected effect size formulas which estimate the influence of the three major factors contributing to sampling error: 1. Samples with smaller sample sizes tend to have more sampling error. 2. Studies with more variables tend to have more sampling error. 3. Samples from populations with larger variance-accounted-for parameters tend to have less sampling error. Regarding this last influence, the case can be made clear at the extreme for a study involving the statistic r2. If the population parameter is 1.0, it is impossible to draw a sample that yields an inaccurate effect size, since from this population every sample involving any number of pairs of scores will yield an r2 of 1.0. The field has not yet established a single preferred effect size, a preference for variance-accounted-for as against standardized differences indices, or a preference for corrected as against uncorrected indices. It is doubtful that the field will ever settle on a single index to be used in all studies, given that so many choices exist and because the statistics can usually be translated into approximations across the two major classes. However, some pluses and minuses for both variance-accounted-for and standardized differences indices can be noted. On the one hand, variance-accounted-for indices do have the benefit of reinforcing the realization that all classical analyses are correlational (Knapp, 1978; Thompson, in press). This may minimize the autonomic choice of ANOVA as an analytic method based on an unconscious association of ANOVA with the ability to make causal inferences (cf. Humphreys & Fleishman, 1974).

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On the other hand, standardized difference effect sizes (e.g. the difference of the experimental group mean minus the control group mean divided by the control group standard deviation) may be more directly interpretable. For example, Saunders, Howard and Newman (1988) argued that a varianceaccounted-for effect is ‘still cast in a language that was foreign to (and unusable by) practitioners’ (pp. 207–208); a variance-accounted-for 2 percent effect usually must be expressed in the metric of an outcome variable to be meaningful. However, not all studies involve experiments or a focus on means, and the use of standardized differences can seem stilted in such contexts. Thus, there are no clear-cut choices of an optimal effect size, or even a class of effect indices. But it does seem reasonable to expect at a minimum that effect sizes should always be presented in an accessible metric (e.g. years added to longevity, on the average, from not smoking; median number of additional months due to an intervention that Alzheimer’s patients were able to live without institutionalization). Several clinical disciplines have explored innovative ways to meet these requirements (see, e.g., the half-dozen articles in a 1988 special issue of Behavioral Assessment, including the report by Saunders et al. [1988]). But continued development of more effective ways to communicate effects remains warranted.

Values Explication Cohen’s (1988) typicality characterizations are not suitable as rigid criteria for noteworthiness, nor were they meant to be so used. The only suitable criteria for evaluating result value (a) must be informed by the personal, idiosyncratic values of each researcher and (b) must take into account the particular context of a given study. Regarding the first point, Huberty and Morris (1988) noted that, ‘As in all of statistical inference, subjective judgment cannot be avoided. Neither can reasonableness!’ (p. 573). Regarding the context of a given study, a 2 percent variance-accounted-for effect size will not be noteworthy to most researchers (or to most readers) in the context of a study like one I once read titled ‘Smiling and Touching Behavior of Adolescents in Fast Food Restaurants’. However, Gage (1978) pointed out that the relationship between cigarette smoking and lung cancer involves roughly this same effect size, and noted that: Sometimes even very weak relationships can be important. . . . [O]n the basis of such correlations, important public health policy has been made and millions of people have changed strong habits. (p. 21)

Certainly a small variance-accounted-for effect size involving highly valued outcomes, such as longevity, can be noteworthy. But since the judgments of result noteworthiness are inherently value-driven, and are ‘on the average’,

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even here some may reach a seemingly reasoned decision that the effect is not noteworthy, or at least not noteworthy enough to merit changed behavior. Many scientists will probably feel uncomfortable declaring their effects in a meaningful metric and then explicating the associated personal or societal values that make these effects noteworthy. Declarations that ‘My results were [statistically] significant’ will have to be replaced with, ‘This intervention extends life expectancy, on the average, by 1.4 years, and given my valuing of life, I believe this result is noteworthy.’ Historically, social scientists have used p statistics as a way to finesse values differences, because conflicting values of different people are not readily reconcilable. Nevertheless, researchers should be expected to declare the values that make their effects noteworthy. Normative practices for evaluating such assertions will have to evolve. Research results should not be published merely because the individual researcher thinks the results are noteworthy. By the same token, editors should not quash research reports merely because they find explicated values unappealing. These resolutions will have to be formulated in a spirit of reasoned comity. But we also must realize that our historical reliance on p values as a way to avoid value assertions led only to feigned objectivity, and not to real objectivity. This feigned objectivity was built on the edifice of misinterpretation of what statistical significance tests really do.

Evidence of Replicability The cumulation of knowledge about relationships that recur under specified conditions is the sine qua non of science for those psychologists who believe that such laws can reasonably be formulated. For these psychologists evidence of result replicability is critical for creating a warrant that results are noteworthy. The required nature of this warrant has received too little attention in an era when statistical significance tests were thought to evaluate result replicability, when these tests were thought to evaluate (rather than merely to presume) selected population parameters. Several vehicles for establishing these warrants can be noted. One warrant involves an important contribution that Jacob Cohen made in his 1994 article; this very important contribution has not been as widely noticed as might be hoped (Hagen, 1997). Cohen (1994) carefully distinguished the general class of ‘null’ hypothesis tests from a subclass of null tests he labeled the ‘nil’ hypothesis test. (A related important distinction is what Meehl [1997] has described as ‘strong’ vs ‘weak’ null hypothesis refutation.) For Cohen, a nil null hypothesis always specifies zero difference or zero relationship (e.g. for the especially inappropriate test of a reliability statistic,

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H 0: rXX = 0; H A: rXX ≠ 0 ), while other non-nil null hypotheses may test an alternative hypothesis such as HA: rXX > .7). Cohen’s important distinction recognizes that a ‘null hypothesis means the hypothesis to be nullified, not necessarily a hypothesis of no difference’ (Chow, 1988, p. 105). Some specific null must be presumed true in the population, or otherwise infinitely many parameters are possible and the pCALCULATED for the sample results becomes indeterminate (Thompson, 1996). Most researchers use a nil hypothesis as the null partly because this is what most computer packages assume, and partly because methodology for invoking non-nil null hypotheses has some ‘complexity, and it is not yet readily applicable in many designs’ (Dar, Serlin, & Omer, 1994, p. 81). The mindless use of the nil hypothesis obviates the necessity prospectively to extrapolate thoughtful expected effect sizes from prior literature as part of study design. Furthermore, the interpretation of ‘[statistical] significance’ as indicating result value means that some researchers do not retrospectively interpret their study effects in the context of specific previous findings. These failures are most unfortunate, because the prospective and retrospective use of effects from prior studies is itself a check on the replicability of results in a given inquiry. Empirical evidence for result replicability can be either ‘external’ or ‘internal’ (Thompson, 1993, 1996). ‘External’ replication studies invoke a new sample measured at a different time and/or a different location. Such replications have unfortunately been undervalued (Robinson & Levin, 1997), perhaps because some researchers thought they were already testing replicability by conducting statistical significance tests. ‘Internal’ replicability analyses use the sample in hand to combine the participants in different ways to try to estimate how much the idiosyncracies of individuality within the sample have compromised sample results. The major ‘internal’ replicability analyses are cross-validation, the jackknife and the bootstrap (Diaconis & Efron, 1983); the logics are reviewed in more detail elsewhere (cf. Thompson, 1993, 1994c). ‘Internal’ evidence for replicability is never as good as an actual replication (Robinson & Levin, 1997; Thompson, 1997), but is certainly better than presuming that a statistical significance test assures result replicability. And such ‘internal’ replicability evidence is useful for researchers who for practical reasons cannot externally replicate all results prior to graduation or tenure review. It is important that, when used to evaluate result replicability, these logics are not confused with other uses of the same logics (Thompson, 1993). For example, the inferential use of the bootstrap involves using the bootstrap to estimate a sampling distribution when the sampling distribution is not known or assumptions for the use of a known sampling distribution cannot be met. The descriptive use of the bootstrap looks primarily at the variance in parameter estimates across many different combinations of the participants.

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The inferential application requires considerably more ‘re-samples’ (see Thompson, 1994c) than the descriptive application recommended here. This is because the inferential focus is on the tails of the estimated sampling distribution (e.g. the 95th percentile of the distribution, for a one-tailed statistical significance test), rather than the descriptive focus on the standard deviation (i.e. the ‘standard error’) of the sampling distribution. Participants in the tails of the sampling distribution are rarer, and therefore many more bootstrap re-samples are required to estimate these very small or large percentiles. The field has not yet resolved all the issues involved in establishing a sufficient warrant for result replicability, again, perhaps, because some authors incorrectly assumed that statistical tests evaluated the population. The relevant software to conduct ‘internal’ bootstrap analyses is already available (e.g. Lunneborg, 1987, for univariate applications, and Thompson, 1992a, 1995, for multivariate applications). Because replicability evidence is critical to the cumulation of knowledge, more authors should be expected to provide some evidence of result replicability.

Reporting Confidence Intervals Various scholars have recommended that confidence intervals should be used to replace or supplement statistical significance tests (e.g. Dar, Serlin, & Omer, 1994; Meehl, 1997; Schmidt, 1996; Serlin, 1993). However, researchers using confidence intervals must remember that ‘the interval endpoints are themselves random variables’ (Zwick, 1997, p. 5) also estimated using sample data. That is, the confidence interval does not indicate that, given the endpoints, the chances are X percent that the interval will include the parameter (Falk & Greenbaum, 1995; Howson & Urbach, 1994). Furthermore, researchers who mindlessly interpret confidence intervals only against the standard of whether the interval subsumes zero are doing nothing more than a mindless ‘nil’ hypothesis test (Cortina & Dunlap, 1997). However, confidence intervals do have one very appealing feature, as Schmidt (1996) made clear. Even if all the research in an area of inquiry was based on radically erroneous estimates of parameters (and even if these a priori estimates were used in specifying non-nil null hypotheses), the parameter would still emerge across studies as a series of overlapping confidence intervals converging on the same parameter. The use of confidence intervals might also mitigate against the current bias in the literature (a) first favoring the publication of Type I errors and (b) then disfavoring publication of replication studies revealing the previously published Type I error. Setting alpha at a small level does not prevent any Type I errors; rather, the percentage of such errors is capped at a small proportion. But some such errors will unavoidably occur. Because the literature has been biased in favor of statistically significant results (Rosenthal, 1979), such

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Type I errors are afforded priority for publication, but the replications with statistically non-significant results will compete at a disadvantage for journal space, and so the self-correction of science through replication will be impeded. Greenwald (1975) cited relevant actual examples. A focus on consistency of findings across studies can be achieved with confidence intervals interpreted in relation to each other, rather than against the nil standard of a zero value. Therefore, it is suggested that more authors should report confidence intervals as part of their results.

Summary Kirk (1996) recently noted that, ‘Our science has paid a high price for its ritualistic adherence to null hypothesis significance testing’ (p. 756). The overuse and misinterpretation of statistical tests has been frequently decried as well in literatures other than psychology, including medicine (Kraemer, 1992; Pocock, Hughes, & Lee, 1987), business (Sawyer & Peter, 1983), occupational therapy (Ottenbacher, 1984) and speech and hearing (Young, 1993). Nevertheless, the use of statistical significance tests remains common, and some empirical studies reflect even an increased use of these methods (Parker, 1990)! Many have marveled at the robustness of the statistical significance logic against the application of the wooden stake through the heart. For example, Falk and Greenbaum (1995) noted: We have shown the compelling nature and the robustness of that illusion [that statistical significance tests give us the information we need]. A massive educational effort is required to eradicate the misconception and extinguish the mindless use of a procedure that dies hard. (p. 94)

And Harris (1991) observed, ‘it is surprising that the dragon will not stay dead’ (p. 375). Frick (1996) cited an anonymous reviewer of his defense of statistical significance testing who argued that, ‘A way of thinking that has survived decades of ferocious attacks is likely to have some value’ (p. 379). Of course, this view presumes a completely rational model of science in which scientists are objective, dispassionate logicians never acting merely out of habit; the view also presumes that scientists are always anxious to admit past errors publicly made in the articles they themselves published over the courses of their careers. Five specific suggestions for improved analytic practice have been presented here. It should be noted that these suggestions can be followed even by those psychologists still employing conventional statistical significance tests. But social science will proceed most rapidly when research becomes the search for replicable effects noteworthy in magnitude in the context of both the inquiry and personal or social values.

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Note 1. For each of the three pairs of studies, the first study within each pair has a smaller PCALCULATED value, if conventional nil null hypotheses (i.e. H0: M1 = M2 = M3; H0: SD1 = SD2; and R2 = 0) are used.

References American Psychological Association. (1994). Publication manual of the American Psychological Association (4th ed.). Washington, D.C.: Author. Carver, R. (1978). The case against statistical significance testing. Harvard Educational Review, 48, 378 – 399. Carver, R. (1993). The case against statistical significance testing, revisited. Journal of Experimental Education, 61, 287 – 292. Chow, S.L. (1988). Significance test or effect size? Psychological Bulletin, 103, 105 – 110. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum. Cohen, J. (1990). Things I have learned (so far). American Psychologist, 45, 1304 – 1312. Cohen, J. (1994). The earth is round ( p < .05). American Psychologist, 49, 997 – 1003. Cortina, J.M., & Dunlap, W.P. (1997). Logic and purpose of significance testing. Psychological Methods, 2, 161–172. Cronbach, L.J. (1975). Beyond the two disciplines of psychology. American Psychologist, 30, 116 –127. Dar, R. (1987). Another look at Meehl, Lakatos, and the scientific practices of psychologists. American Psychologist, 42, 145 –151. Dar, R., Serlin, R.C., & Omer, H. (1994). Misuse of statistical tests in three decades of psychotherapy research. Journal of Consulting and Clinical Psychology, 62, 75 – 82. Diaconis, P., & Efron, B. (1983). Computer-intensive methods in statistics. Scientific American, 248(5), 116 – 130. Falk, R., & Greenbaum, C.W. (1995). Significance tests die hard: The amazing persistence of a probabilistic misconception. Theory & Psychology, 5, 75 – 98. Frick, R.W. (1996). The appropriate use of null hypothesis testing. Psychological Methods, 1, 379 – 390. Friedman, H. (1968). Magnitude of experimental effect and a table for its rapid estimation. Psychological Bulletin, 70, 245 – 251. Gage, N.L. (1978). The scientific basis of the art of teaching. New York: Teachers College Press. Glass, G.V. (1979). Policy for the unpredictable (uncertainty research and policy). Educational Researcher, 8(9), 12 – 14. Greenwald, A.G. (1975). Consequences of prejudice against the null hypothesis. Psychological Bulletin, 82, 1– 20. Greenwald, A.G., Gonzalez, R., Harris, R.J., & Guthrie, D. (1996). Effect size and p-values: What should be reported and what should be replicated? Psychophysiology, 33, 175 –183. Hagen, R.L. (1997). In praise of the null hypothesis statistical test. American Psychologist, 52, 15 – 24. Harlow, L.L., Mulaik, S.A., & Steiger, J.H. (Eds.). (1997). What if there were no significance tests? Mahwah, NJ: Erlbaum. Harris, M.J. (1991). Significance tests are not enough: The role of effect size estimation in theory corroboration. Theory & Psychology, 1, 375 – 382. Hays, W.L. (1981). Statistics (3rd ed.). New York: Holt, Rinehart & Winston.

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Heldref Foundation (1997). Guidelines for contributors. Journal of Experimental Education, 65, 287– 288. Howson, C., & Urbach, P. (1994). Probability, uncertainty and the practice of statistics. In G. Wright & P. Ayton (Eds.), Subjective probability (pp. 39 – 51). Chichester: Wiley. Huberty, C.J., & Morris, J.D. (1988). A single contrast test procedure. Educational and Psychological Measurement, 48, 567– 578. Hudson, W.D. (1969). The is/ought question. London: Macmillan. Humphreys, L.G., & Fleishman, A. (1974). Pseudo-orthogonal and other analysis of variance designs involving individual-differences variables. Journal of Educational Psychology, 66, 464 – 472. Hunter, J.E. (1997). Needed: A ban on the significance test. Psychological Science, 8, 3 – 7. Kirk, R. (1996). Practical significance: A concept whose time has come. Educational and Psychological Measurement, 56, 746 – 759. Knapp, T.R. (1978). Canonical correlation analysis: A general parametric significance testing system. Psychological Bulletin, 85, 410 – 416. Kraemer, H.C. (1992). Reporting the size of effects in research studies to facilitate assessment of practical or clinical significance. Psychoendocrinology, 17, 527– 536. Kupfersmid, J. (1988). Improving what is published: A model in search of an editor. American Psychologist, 43, 635 – 642. Loftus, G.R. (1993). Editorial comment. Memory & Cognition, 21, 1 – 3. Lunneborg, C.E. (1987). Bootstrap applications for the behavioral sciences. Seattle: University of Washington Press. Meehl, P.E. (1978). Theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology. Journal of Consulting and Clinical Psychology, 46, 806 – 834. Meehl, P.E. (1997). The problem is epistemology, not statistics: Replace significance tests by confidence intervals and quantify accuracy of risky numerical predictions. In L.L. Harlow, S.A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 391– 423). Mahwah, NJ: Erlbaum. Morrison, D.E., & Henkel, R.E. (Eds.). (1970). The significance test controversy. Chicago, IL: Aldine. Murphy, K.R. (1997). Editorial. Journal of Applied Psychology, 82, 3 – 5. Nelson, N., Rosenthal, R., & Rosnow, R.L. (1986). Interpretation of significance levels and effect sizes by psychological researchers. American Psychologist, 41, 1299 –1301. Nunnally, J. (1960). The place of statistics in psychology. Educational and Psychological Measurement, 20, 641– 650. Oakes, M. (1986). Statistical inference: A commentary for the social and behavioral sciences. New York: Wiley. Olejnik, S.F. (1984). Planning educational research: Determining the necessary sample size. Journal of Experimental Education, 53, 40 – 48. Ottenbacher, K. (1984). Measures of relationship strength in occupational therapy research. The Occupational Therapy Journal of Research, 4, 271– 285. Parker, S. (1990). A note on the growth of the use of statistical tests in Perception & Psychophysics. Bulletin of the Psychonomic Society, 28, 565 – 566. Pedhazur, E.J., & Schmelkin, L.P. (1991). Measurement, design, and analysis: An integrated approach. Hillsdale, NJ: Erlbaum. Pocock, S.J., Hughes, M.D., & Lee, R.J. (1987). Statistical problems in the reporting of clinical trials. The New England Journal of Medicine, 317, 426 – 432. Robinson, D., & Levin, J. (1997). Reflections on statistical and substantive significance, with a slice of replication. Educational Researcher, 26(5), 21– 26. Rosenthal, R. (1979). The ‘file drawer problem’ and tolerance for null results. Psychological Bulletin, 86, 638 – 641.

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Rosenthal, R. (1991). Effect sizes: Pearson’s correlation, its display via the BESD, and alternative indices. American Psychologist, 46, 1086 –1087. Rosenthal, R., & Gaito, J. (1963). The interpretation of levels of significance by psychological researchers. Journal of Psychology, 55, 33 – 38. Rosnow, R.L., & Rosenthal, R. (1989). Statistical procedures and the justification of knowledge in psychological science. American Psychologist, 44, 1276 –1284. Rozeboom, W.W. (1960). The fallacy of the null hypothesis significance test. Psychological Bulletin, 57, 416 – 428. Saunders, S.M., Howard, K.I., & Newman, F.L. (1988). Evaluating the clinical significance of treatment effects: Norms and normality. Behavioral Assessment, 10, 207– 218. Sawyer, A.G., & Peter, J.P. (1983). The significance of statistical significance tests in marketing research. Journal of Marketing Research, 20, 122 –123. Schmidt, F.L. (1996). Statistical significance testing and cumulative knowledge in psychology: Implications for the training of researchers. Psychological Methods, 1, 115 –129. Schmidt, F.L., & Hunter, J.E. (1997). Eight common but false objections to the discontinuation of significance testing in the analysis of research data. In L.L. Harlow, S.A. Mulaik, & J.H. Steiger (Eds.), What if there were no significance tests? (pp. 37– 64). Mahwah, NJ: Erlbaum. Serlin, R.C. (1993). Confidence intervals and the scientific method: A case for Holm on the range. Journal of Experimental Education, 61, 350 – 360. Shaver, J. (1985). Chance and nonsense. Phi Delta Kappan, 67, 57 – 60. Shea, C. (1996). Psychologists debate accuracy of ‘significance test’. Chronicle of Higher Education, 42, A12, A16. Shrout, P. E. (1997). Should significance tests be banned? Introduction to a special section exploring the pros and cons. Psychological Science, 8, 1– 2. Snyder, P.A., & Lawson, S. (1993). Evaluating results using corrected and uncorrected effect size estimates. Journal of Experimental Education, 61, 334 – 349. Snyder, P.A., & Thompson, B. (1998). Use of tests of statistical significance and other analytic choices in a school psychology journal: Review of practices and suggested alternatives. School Psychology Quarterly, 13, 335 – 348. Strike, K.A. (1979). An epistemology of practical research. Educational Researcher, 8(1), 10 – 16. Thompson, B. (1992a). DISCSTRA: A computer program that computes bootstrap resampling estimates of descriptive discriminant analysis function and structure coefficients and group centroids. Educational and Psychological Measurement, 52, 905 – 911. Thompson, B. (1992b). Two and one-half decades of leadership in measurement and evaluation. Journal of Counseling and Development, 70, 434 – 438. Thompson, B. (1993). The use of statistical significance tests in research: Bootstrap and other alternatives. Journal of Experimental Education, 61, 361– 377. Thompson, B. (1994a). The concept of statistical significance testing (An ERIC/AE Clearinghouse Digest #EDO-TM-94 - 1). Measurement Update, 4, 5 – 6. (ERIC Document Reproduction Service No. ED 366 654). Thompson, B. (1994b). Guidelines for authors. Educational and Psychological Measurement, 54, 837– 847. Thompson, B. (1994c). The pivotal role of replication in psychological research: Empirically evaluating the replicability of sample results. Journal of Personality, 62, 157–176. Thompson, B. (1995). Exploring the replicability of a study’s results: Bootstrap statistics for the multivariate case. Educational and Psychological Measurement, 55, 84 –94. Thompson, B. (1996). AERA editorial policies regarding statistical significance testing: Three suggested reforms. Educational Researcher, 25(2), 26 –30. Thompson, B. (1997). Editorial policies regarding statistical significance tests: Further comments. Educational Researcher, 26(5), 29 – 32.

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Thompson, B. (1998a). Review of What if there were no significance tests? Educational and Psychological Measurement, 58. Thompson, B. (1998b, January). Why ‘encouraging’ effect size reporting isn’t working: The etiology of researcher resistance to changing practices. Paper presented at the annual meeting of the Southwest Educational Research Association, Houston, TX. (ERIC Document Reproduction Service No. ED 416 214). Thompson, B. (in press). Canonical correlation analysis. In L. Grimm & P. Yarnold (Eds.), Reading and understanding multivariate statistics, Vol. 2. Washington, D.C.: American Psychological Association. Thompson, B., & Snyder, P.A. (1997). Statistical significance testing practices in the Journal of Experimental Education. Journal of Experimental Education, 66, 75 – 83. Thompson, B., & Snyder, P.A. (1998). Statistical significance and reliability analyses in recent JCD research articles. Journal of Counseling and Development, 76, 436 – 441. Vacha-Haase, T., & Nilsson, J.E. (1998). Statistical significance reporting: Current trends and usages within MECD. Measurement and Evaluation in Counseling and Development, 31, 46 – 57. Young, M.A. (1993). Supplementing tests of statistical significance: Variation accounted for. Journal of Speech and Hearing Research, 36, 644 – 656. Zuckerman, M., Hodgins, H.S., Zuckerman, A., & Rosenthal, R. (1993). Contemporary issues in the analysis of data: A survey of 551 psychologists. Psychological Science, 4, 49–53. Zwick, R. (1997, March). Would the abolition of significance testing lead to better science? Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL.

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78 Musical Aptitude Testing: From James McKeen Cattell to Carl Emil Seashore Jere T. Humphreys

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hen psychologist Carl Emil Seashore (1866–1949) began the two decades of research that led to the development of his famous tests of musical aptitude,1 he drew upon beliefs and research methods then prevalent in the field of psychology. Many of those beliefs and methods were examined in a previous article.2 The purpose of this article is to describe the remaining major links between late nineteenth-century musicrelated psychological research and Seashore’s early work: the music-related research of James McKeen Cattell (1860–1944), the leader of the mental testing movement during the 1890s.3 In the 1890s, European and American researchers in the new field of scientific, empirical, laboratory-based psychology (as opposed philosophical “armchair” psychology) focused their research on sensory perception, the first of the new psychology’s three “great topics.”4 At about the same time, American psychologists assumed the leadership in mental testing research, which was part of the new psychology. Carl Seashore undertook his doctoral studies in the first half of the 1890s, a period that coincided with the birth of sensory psychology and mental testing research in the United States. Not long thereafter, he applied the methods of scientific psychology and mental testing to his research on musical aptitude.

Source: Research Studies in Music Education, 10 (1998): 42–53.

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Perception Research Speculation about sensory perception began in Ancient Greece. Empirical research on the same began during the Renaissance, including studies of the least discernible differences in musical pitch. Theoretical and empirical perception research by Ernst Heinrich Weber (1795–1878), Gustav Theodor Fechner (1801–1887), Hermann Ludwig Ferdinand von Helmholtz (1821–1894), and other physicists led to a fusion of philosophical speculation and physiological research on sensation, which in turn contributed significantly to the emergence of the field of modem psychology. Wilhelm Wundt (1832–1920) of the University of Leipzig, the world’s first famous psychologist, borrowed testing ideas and research methods from these early researchers and helped develop the subfields of experimental psychology and psychophysics.5 Cambridge University researcher Francis Galton (1822–1911) borrowed sensory perception research methods from Wundt and others. An adherent of the long-standing belief that all knowledge is obtained through the five senses, Galton was also influenced by Charles Darwin’s (1809–1882) theory of evolution, the natural selection properties of which implied individual differences between people;6 the widespread belief in faculty psychology, whose adherents held that sensory faculties correspond to faculties of the brain; the emergence of atomistic chemistry, which encouraged psychologists to study the “psychological elements,” or senses; and the development of the concept of the normal, or random, distribution curve.7 Eventually, Galton hypothesized that “a measure of sensory acuity would provide a crude measure of a person’s level of intelligence,” and that mental ability is normally, or randomly, distributed.8 He also came to believe that mental abilities are related to each other, which led him to develop the rudiments of statistical correlation.9 Unlike Wundt, who attempted to identify traits common to all (or most) people, Galton used Wundt’s methods to measure individual differences in mental ability. Galton’s research, which began in the 1870s, included tests of musical discrimination and perceptions.10

James Cattell Leadership of the mental testing movement passed from Galton in the 1880s to James Cattell in the 1890s. Cattell graduated from Lafayette College in Easton, Pennsylvania, where his father was president, in 1880. For the next several years, he divided his time between completing a master’s degree at Lafayette, working on a Ph.D. in psychology under Wundt (granted in 1886), studying with Galton at Cambridge, and several other activities. One of those activities was a graduate fellowship at Johns Hopkins University (1882–83), where he and fellow graduate students (two of whom were John Dewey and Joseph Jastrow) helped G. Stanley Hall (1844 –1924)

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establish one of the first American psychological laboratories.11 He went to the University of Pennsylvania in the late 1880s, where he opened a psychological laboratory and held the first university faculty position in psychology in the United States. After moving to Columbia University in 1891, he provided leadership to the new experimental psychology movement for the next twenty-six years.12 Cattell seems to have begun his sensory perception studies while at Johns Hopkins in 1883.13 He continued at least one of those experiments in Wundt’s laboratory.14 He also seems to have developed his keen interest in experimental apparatus at Leipzig, including those for music research: We have in the [Wundt’s] laboratory two excellent pieces of apparatus for testing the power of distinguishing notes. The one is an organ arrangement, which gives the notes at intervals of four vibrations from 32 to 1024 [Hz.] … The other apparatus is a set of tuning forks made up by König [sic], in Paris. Pairs of tuning forks are taken, one always gives the same note, the other (by means of weights) can be so regulated as to give a note a little lower or higher. Experiments on this subject are being made by three groups of students … In one case, memory of notes is being especially investigated.15

Similarly, in 1888, Cattell described “[c]areful experiments, not yet published,” that had “been carried on for several years past in the Leipsic [sic] laboratory” on the least perceptible differences in loudness and pitch, and on the perception of musical intervals.16 Cattell also seems to have first become interested in individual differences during his time with Hall at Johns Hopkins.17 He took that interest with him to Leipzig, where Wundt, himself uninterested in individual differences,18 allowed Cattell to write a paper on the subject as early as 1885.19 Cattell’s interest in individual differences intensified during his intermittent work with Galton at Cambridge over several years. For example, his letters from Cambridge tell of his “association experiments,”20 which he employed in his mental testing efforts. Cattell studied extensively with Wundt, the early leader in the psychological measurement of sensory perception, and Galton, the pioneering mental tester and the early leader in the measurement of individual differences in sensory perception. The fact that both men incorporated tests of musical perception in their research appears to have influenced Cattell to do the same.

Cattell the Mental Tester At the University of Pennsylvania, his first full-time position, Cattell gathered for the laboratory “a valuable collection of Koenig’s [sic] apparatus for the study of hearing and the elements of music …”21 Soon thereafter, in 1890,

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he published an article in a British journal that scholars believe was the first time the term “mental test” appeared in print.22 In this article, a watershed in the history of mental measurement,23 Cattell described a series of ten tests then in use at Pennsylvania. None of the ten tests involved music, although one measured “Reaction-time for Sound.” However, Cattell listed an additional fifty tests still under development, “which I look on as the more important in order that attention may be drawn to them, and co-operation secured in choosing the best series of tests and the most accurate and convenient methods.” Some of these were music tests.24 Upon his arrival at Columbia, he established the department of psychology and developed what became known as the “Freshman Tests,” which he administered to at least fifty volunteer freshmen each year beginning in 1893. Cattell held great hope for these tests, which he predicted would correlate with each other and with academic grades. In an 1896 article, Cattell and a collaborator described their research methods in some detail and provided preliminary results from what may have been the first predictive study of academic success. Only two tests related to music. For one, a test of hearing (of tones), the researchers simply divided subjects from each year into “normal,” “subnormal,” and “abnormal” categories. The other music test measured the “accuracy of the perception of pitch.” After subjects heard a pitch (F below middle C) played on a monochord, they attempted to match the pitch by adjusting the instrument’s bridge.25 After several more years of data collection, one of Cattell’s graduate students, Clark Wissler, reported more results from the study, including data collected from a small number of female students from Barnard College. Wissler correlated the test scores with each other and with senior grade-point averages using the technique of statistical correlation that had been discovered by Galton and developed by Galton’s young associate, Karl Pearson.26 Most of the instruments in the battery were tests of sensory discrimination. In addition to the pitch perception test described above, Wissler discussed a music-related test of “Rhythm and Perception of Time” that measured subjects’ abilities to continue tapping a steady beat on a telegraph key fifty times after hearing a stimulus of ten tapped beats, and two “Imagery” questions that required written responses.27 On the pitch perception test, the “average error” (monochord bridge distance from the “correct” placement) was 7.2 centimeters for freshmen and 3.7 centimeters for seniors. Wissler concluded that women were superior to men and seniors were superior to freshmen on that test, with a “certainty of results” of p < .01 in each case.28 He found no statistically significant differences in pitch perception between freshmen from different years.29 Unfortunately, with one exception, Wissler did not report correlation coefficients between the pitch perception test and the other variables. The exception was a coefficient of r = .01 between pitch perception and reaction time (N = 100). In general, he found only chance intercorrelations between

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the physical and mental tests, and moderate intercorrelations between grades for specific courses. Most disappointing of all, he found only chance correlations between individual tests taken as freshmen and overall grades as seniors. Among other things, Wissler complained about the inadequacy of undergraduate grades as a representative measure of students’ abilities to handle “life tasks,” which he deemed “exceedingly complex.” He concluded that Cattell’s physical and mental tests promised little “from a practical point of view.”30

Other Mental Testers European researchers were beginning to conduct similar studies of mental functions in the 1890s.31 In the United States, where most of the work occurred, Frank Boas (1858–1942) related school children’s test scores to their mental alertness as estimated by teachers; Joseph Jastrow (1863–1944) developed fifteen tests for college students; and James A. Gilbert (b. ?-d. ?) studied the mental and physical development of school children. Like Cattell and Wissler, Boas and Gilbert found only chance relationships between test scores and teacher ratings.32 More important than any of these mental testing efforts was the work of Alfred Binet (1857–1911) and Victor Henri (1872–1940) in France and Hugo Münsterberg (1863–1916) in the United States. These researchers experimented with a radically different type of mental test based on cognitive functioning rather than sensory perception.33 Cattell seems to have recognized as early as 1896 the importance of these new tests: of a strictly psychological character. For the psychologist these are, of course, the most interesting and important. But we are at present concerned with anthropometric work, and measurements of the body and of the senses come as completely within our scope as the higher mental processes.34

Indeed, the mental testing movement soon followed Binet and Henri’s lead. Probably for that reason, Cattell, like Galton before him, turned to other interests.35 He eventually became embittered, in part because “his major contribution to experimental psychology … [was] thoroughly discredited and replaced by the … tests of Alfred Binet.”36

Cattell and Seashore Several pieces of evidence suggest that Cattell influenced Carl Seashore’s work on musical aptitude testing. First, as a founding member and fourth president of the American Psychological Association, founding editor of the

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American Journal of Psychology, founding head of the psychology department at a leading university (Columbia), and leader of the mental testing movement during the 1890s, James Cattell was an extremely prominent psychologist. Second, Cattell was a professional friend of Edward Wheeler Scripture (1864–1945), Seashore’s doctoral mentor at Yale University who himself had taken his doctorate under Wundt in 1891. Scripture was a highly productive researcher, but because of his disagreeable personality, he “was largely estranged from his generation of American psychologists,” except for Cattell, his “best friend among the American psychologists.”37 In addition to the personal relationship between Cattell and Scripture, both Scripture and Seashore adopted “an approach like Cattell’s” to the study of sensation.38 A final set of clues to the link between Cattell and Seashore resides in the James McKeen Cattell Collection held by the Library of Congress. The author located more than seventy pieces of personal correspondence between the two men, the earliest dating from 1899.39 Seashore’s doctoral dissertation, which he completed in 1895, was about neither mental testing nor music. Instead, his interest in mental testing may have come indirectly from the prominent Cattell, whose article on the Columbia “Freshman Tests” appeared after Seashore completed his dissertation but before he published his first article on a music-related study.40 Cattell’s work with music tests probably appealed to Seashore, a former singing school student, church organist and choir director, and college glee club director from an amateur musical family.41

Conclusions Cattell learned from Wundt about the long German tradition of perception research, with its “precision, accuracy, order, and reproducibility of data and findings.”42 He may have become interested in individual differences under Hall. Under Galton, he developed his interest in the measurement of sensory perception differences between individuals. In addition, Galton’s concepts about statistical correlation undoubtedly formed the basis for Cattell’s hypotheses about relationships between mental ability and academic grades (and other “life tasks”). Despite the failure of Cattell’s tests, his work “was of great importance as it was the first attempt to apply the ‘new psychology’ to problems of individual differences.”43 Nevertheless, he experienced difficulty in selecting valid, measurable dependent variables, a problem that continues to plague today’s music education researchers. His main problem, however, was his presumably false hypothesis about strong relationships between mental ability and sensory perception ability.44 It is not surprising that Seashore and many other American psychologists followed Cattell and not Binet, because the latter’s most important work

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appeared a few years later. However, most American researchers eventually joined Binet in defining intelligence as cognitive functioning ability, not as sensory perception ability. By contrast, Seashore’s tests remained largely in the sensory perception realm, and he appears to have followed several other early mental testing researchers when he added (tonal) memory to his list of important “psychological processes,” something that Cattell did not do.45 However, Binet and other mainstream mental testing researchers dropped “sensation, attention, perception, association, and memory” from their test batteries around 1904.46 Seashore retained his 1890s belief about yet another issue: that a series of mental tests could not yield a single score that represents general musical (or intellectual) ability. That was Cattell’s position and Binet’s, but subsequent American testing researchers went on to develop the concept of the intelligence quotient and other unitary measures of mental ability. On that issue, at least, Seashore’s conservatism aligned his work with current thinking, which has now returned to that position.

Implications for Music Education Cattell, the leading sensory mental tester of the 1890s, formally tested his first complete battery in the 1890s. Seashore, the leading sensory musical tester, formally tested his first complete battery in the 1910s, some twenty years later. Binet and Henri published their first battery of cognitive-type tests in 1904, only a few years after Cattell’s sensory tests failed to predict academic achievement. Other researchers further developed the French tests in the first decade of the twentieth century, and have continued to develop them to this day. By contrast, the field of music waited until 1965 for the appearance of a well-constructed test that corresponded to the second generation of intelligence tests.47 Researchers now question the validity of this second generation of tests, both of intelligence and of musical aptitude. Both types of tests predict performance on school-related tasks,48 but not necessarily on “life tasks.” Two central historical questions remain. First, why did Seashore, unlike Cattell, not abandon the effort after his tests failed to demonstrate predictive validity? In other words, why did Seashore remain committed to sensory measures when his (mostly American) counterparts in other fields shifted from tests of sensory perception to tests of reasoning and judgment? It was partly a matter timing, Seashore having received his doctoral training and begun his research program during the crucial few years between Cattell’s bold hypothesis about relationships between sensory skills and mental ability and the failure of his statistical correlations to support that hypothesis. By the time failure was reported, Seashore may have already committed himself to his life-long agenda. After all, Binet and Henri’s

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“key article” – in which they “argued for mental testing based not on sensory and motor functions but on the psychological processes thought to be involved in intelligence …”49 – appeared in 1895, the year Seashore received this doctorate. It is also probable that Seashore believed that musical ability really is based largely on sensory ability, and therefore is somehow different from other mental abilities. Regardless, years later, Seashore explained that he had “drifted gradually into the field of psychology of music primarily for two reasons: first, my love of music and realization of great possibilities in an unworked field; and, secondly,” because his first research interest, vision, plagued its research subjects with eye fatigue, a problem that did not trouble aural researchers.50 The second question is: Since Seashore did not turn to other types of tests, why did other music researchers not do so either? Almost from the beginning, critics charged that Seashore’s battery was sensory and atomistic, but no one, including his most prominent critic, Columbia University psychologist James Mursell (1893–1963),51 conducted extensive, rigorous research on the tests or developed alternative measures. The lack of researchoriented graduate training in music education undoubtedly hampered the profession’s efforts to test the validity of Seashore’s battery thoroughly and to keep pace with new developments in the mental testing and research worlds generally.52 In addition, Seashore himself – with his Yale Ph.D. in the “scientific” field of psychology, his deanship at the University of Iowa and presidency of the American Psychological Association, and his tireless research efforts and prolific publication record – brought considerable prestige to the field of musical aptitude testing. Indeed, unlike most other earlier sensory mental testing researchers, Seashore never changed his mind about the sensory nature of mental (musical) aptitude, although eventually he tacitly acknowledged the possibility of other types of “musical capacity” when he wrote, in 1930, that his six published measures “furnish a fairly good index to music capacity on the sensory side.”53 Given his professional prestige and that of the field of psychology, and in the absence of strong evidence against his claims or of alternative approaches to musical aptitude testing, Seashore’s tests stood nearly alone for a long time. An implication of this historical research is that the field of music education can benefit from the research of prominent individuals from outside the field. Wundt, Galton, Cattell, and Seashore developed some of the concepts and research methods and tools still in use today. Each made large intellectual “leaps of faith” and each worked diligently to test his hypotheses. Each rendered the field a great service, directly or indirectly. In particular, this story of James Cattell and Carl Seashore suggests that music educators should consider carefully the contributions and prestige brought by those from outside the field. Seashore himself mentioned that he had been “more

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or less justly the butt of criticism from the musical profession.” However, he also wrote that: In the field of diagnosing musical talent, I have had a rather extraordinary following, but unfortunately much of it a gullible and non-critical type on the part of people who would take an isolated element in my procedure and handle it as if it covered the whole situation.54

Clearly, it is incumbent upon music educators to decide which contributions to embrace, which to disregard, and which to use as building blocks for the next generation of ideas.

Notes 1. Carl E. Seashore, Seashore Measures of Musical Talent (New York: The Psychological Corporation, 1919). 2. Jere T. Humphreys, “Precursors of Musical Aptitude Testing: From the Greeks through the Work of Francis Galton,” Journal of Research in Music Education 41 (Winter 1993): 315–27. 3. Edwin G. Boring, A History of Experimental Psychology, 2d ed. (New York: Appleton Century-Crofts, 1950), 569. 4. The other two “great topics,” learning and motivation, did not emerge until a few years later. Humphreys, “Precursors,” 323. 5. Ibid., 316–17. 6. Charles Darwin, in his The Descent of Man and Selection in Relation to Sex, 2d ed. (New York: American Publishers Corporation, 1874), had argued that sensitivity to pitch is important in the natural selection process because “the vocal organs were primarily used and perfected in relation to the propagation of the species” (589). 7. Humphreys, “Precursors,” 318–19. 8. Ibid., 319. Attempts to measure mental ability had occurred at least since the early nineteenth century. According to Florence Goodenough, Mental Testing: Its History, Principles, and Applications (New York: Rinehart and Company, 1949), 3, the first major work along those lines was produced in France and differentiated between mental deficiency and mental disease. Jean-Etienne Dominique Esquirol, Des maladies mentales considé rées sous les rapports médical, hygienique, et médico-légal, vols. I, II and atlas (Paris: J. B. Bailliére, 1838). Galton knew from this research that idiots and imbeciles frequently exhibit inferior sensory acuity, so he hypothesized a relationship between sensory acuity and intelligence. Richard Herrnstein, I.Q. in the Meritocracy (Boston: Little, Brown, 1973), 63. 9. Humphreys, “Precursors,” 321. Galton developed his first regression line from the size of “mother” and “daughter” peas. Karl Pearson, The Life, Letters and Labours of Francis Galton, vol. IIIA (Cambridge, England: Cambridge University Press, 1930), 3–5, 69. 10. Humphreys, “Precursors,” 319–20. 11. Controversy remains over whom should be credited with establishing the first psychological laboratory in the United States: Hall at Johns Hopkins in early 1883 or William James at Harvard University around 1876. The controversy centers on whether “James’ room for demonstrational experiments at Harvard” was really a laboratory. J. McKeen Cattell, “Early Psychological Laboratories,” Science 67 (May 1928): 546. 12. Most biographical accounts state that Cattell went to Pennsylvania in 1888, but he himself wrote later that he founded the laboratory there in 1887. Ibid., 546. Published

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13. 14.

15.

16. 17.

18. 19.

20. 21. 22. 23.

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accounts of Cattell’s life differ in many details as to his exact whereabouts at specific times before his extended stays abroad ended in 1894. Numerous personal letters, primarily to his parents, suggest that he traveled frequently between Leipzig, Cambridge, and various other places in the United States and Europe. E.g., Jim [James McKeen Cattell], Leipzig, letters to “Mama and Papa” [William and Elizabeth Cattell], Philadelphia, 25 July 1894 and 5 December 1894, in James McKeen Cattell Collection, Container #55, “Family Correspondence,” December 1888-March 1903, Manuscript Division, Library of Congress, Washington, DC. The most complete account of Cattell’s activities from 1880–1888 appears in Michael M. Sokal, ed., An Education in Psychology: James McKeen Cattell’s Journal and Letters from Germany and England, 1880–1888 (Cambridge, MA: The MIT Press, 1981). Ibid., 70, note 3. J. McKeen Cattell, “Ueber die Trägheit der Netzhaut und des Sehcentrums,” Philosophische Studien 3 (1885): [94 – 127]; reprinted as “The Inertia of the Eye and Brain,” (no trans.) in James McKeen Cattell, James McKeen Cattell, 1860–1944: Man of Science, vol. I (Lancaster, PA: The Science Press, 1947), 27. (Page citation is to the reprint edition.) [James McKeen Cattell], Leipzig, letter to Francis Galton, Cambridge, England, [n.d. given], quoted in Francis Galton, “On Recent Designs for Anthropometric Instruments,” The Journal of the Anthropological Institute of Great Britain and Ireland 16 (1887): 8. Several of Cattell’s journal entries and letters to his parents beginning in 1884 also contain references to his involvement with apparatus in Wundt’s Leipzig laboratory. E.g., Sokal, Education, 98 –105. James McKeen Cattell, “The Psychological Laboratory at Leipsic [sic],” Mind 13 (January 1888): 43. Sokal, Education, 70, note 3. Although Galton is usually regarded as the first to study individual differences and was the undisputedly leader of the movement, Hall was eclectic and progressive and, like other psychologists in the child-study movement, eventually became interested in the characteristics of individual children. Jere T. Humphreys, “The Child-Study Movement and Public School Music Education,” Journal of Research in Music Education 33 (Summer 1985): 82. Humphreys, “Precursors,” 319. J. McKeen Cattell, “Ueber die Zeit der Erkennung und Benennung von Schriftzeichen, Bildern und Farben.” Philosophische Studien 2 (1885): 635–50; translated by R. S. Woodworth as “On the Time Required for Recognizing and Naming Letters and Words, Pictures and Colors,” in Cattell, Cattell, vol. I, 13 – 25. Years later, Cattell gave this revealing account of Wundt’s reactions to his early work on individual differences: . . . in my second interview with Wundt [probably in 1883] I presented an outline of the work I wanted to undertake, which was the objective measurement of the time of reactions with special reference to individual differences. Wundt said that . . . only psychologists could be the subjects in psychological experiments. I later bought and made the apparatus needed and did the work in my own room, without, however, any interruption in relations that were then becoming friendly. J. McKeen Cattell, “In Memory of Wilhelm Wundt,” Psychological Review 28 (May 1921): 156. E.g., Jim [James McKeen Cattell], Cambridge, letter to “Mama and Papa,” Philadelphia, 23 November 1893, James McKeen Cattell Collection, Container #55. James McKeen Cattell, “Psychology at the University of Pennsylvania,” American Journal of Psychology 3 (April 1890): 282. James McKeen Cattell, “Mental Tests and Measurements, Mind 15 (January 1890): 373–80. Katherine W. Linden and James D. Linden, Modern Mental Measurement: A Historical Perspective (Boston: Houghton Mifflin Company, 1968), 9–10.

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24. Cattell, “Mental Tests,” 378. Cattell acknowledged his indebtedness to Galton in that article, when he wrote that Galton had “already used some of these tests, and I hope the series here suggested will meet with his approval” (373, note 1). 25. J. McKeen Cattell and Livingston Farrand, “Physical and Mental Measurements of the Students of Columbia University,” Psychological Review 3 (1896): 636. 26. Clark Wissler, “The Correlation of Mental and Physical Tests,” Psychological Review Monograph Supplements 3 (Whole No. 16) (June 1901): 1– 62. 27. Ibid., 9. 28. Ibid., 6, 15 –17. The present author translated Wissler’s archaic statistical terms and symbols to modem usage. 29. Ibid., 21– 22. Wissler did not report exact significance levels for these and most of his other statistical tests. 30. Ibid., 54, 61– 62. 31. For more information see Linden and Linden, 10. 32. Frank Boas, “Anthropological Investigations in Schools,” Pedagogical Seminary 1 (June 1891): 225 – 28; Joseph Jastrow, “Some Anthropometric and Psychologic [sic] Tests on College Students: A Preliminary Survey,” American Journal of Psychology 4 (April 1892): 420–28; J. Allen Gilbert, “Researches on Mental and Physical Development of School-Children,” in Studies from the Yale Psychological Laboratory, vol. II, ed. E. W. Scripture (New Haven, CT: Yale University Press, 1894); and J. Allen Gilbert, “Researches upon School Children and College Students,” in University of Iowa Studies in Psychology, vol. I, ed. George T. W. Patrick and J. Allen Gilbert (Iowa City: State University of Iowa, 1897). One of Gilbert’s testing experiments involved music: J. A. Gilbert, “Experiments on the Musical Sensitiveness of School Children,” in Studies from the Yale Psychological Laboratory, vol. I, ed. E. W. Scripture (New Haven, CT: Yale University Press, 1893). 33. A. Binet and V. Henri, “La Psychologie individuelle,” L’année psychologique 2 (1895): 411–65; excerpts translated by Mollie D. Boring in A Source Book in the History of Psychology, ed. Richard J. Herrnstein and Edwin G. Boring (Cambridge, MA: Harvard University Press, 1965), 428 – 33; and Hugo Münsterberg, Zur individual Psychologie,” Centralblatt f. nervenkeilkunde und psychiatrie 14 (1891): 196 – 98. 34. Cattell and Farrand, 623. 35. Humphreys, “Precursors,” 322. 36. Michael M. Sokal, “The Unpublished Autobiography of James McKeen Cattell,” American Psychologist 26 (July 1971): 629. Evidence that Cattell never completely gave up on his “Freshman Tests” can be found in a report written in 1922. J. M. Cattell, “The First Year of the Psychological Corporation,” unpublished report, 1 December 1922, in James McKeen Cattell Collection, Container #178, “Subject File,” 1890 –1936, Manuscript Division, Library of Congress, Washington, DC. 37. Michael M. Sokal, “Biographical Approach: The Psychological Career of Edward Wheeler Scripture,” in Historiography of Modern Psychology: Aims, Resources, Approaches, ed. Josef Brozek and Ludwig J. Pongratz (Toronto: C. J. Hogrefe, Inc., 1980), 268. Cattell, for example, praised one of Scripture’s books, Edward Wheeler Scripture, The New Psychology (New York: Scribner’s, 1897), when other psychologists criticized it harshly. Sokal, “Biographical,” 266 – 67. In addition, Cattell apparently helped Scripture become a fellow of the American Association for the Advancement of Science. [Edward Wheeler] Scripture, New Haven, CT, letter to [James McKeen] Cattell, New York, NY, 7 August 1901, in James McKeen Cattell Collection, Container #38, “General Correspondence,” 1884 –1944, Manuscript Division, Library of Congress, Washington, DC. In 1902, Scripture visited Cattell about his future plans, just before he was released by Yale later that year. After earning a medical degree in his native Germany and teaching briefly at Johns Hopkins, both Scripture and his wife

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38. 39.

40.

41.

42. 43. 44.

45. 46. 47.

48.

49. 50.

51. 52.

53.

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obtained positions at Columbia, “possibly with Cattell’s Hopkins, help.” Sokal, “Biographical,” 269–70. Ibid., 256. James McKeen Cattell Collection, Container #38. Years later, Cattell and others collaborated with Seashore to found the nonprofit Psychological Corporation. Their correspondence continued through the 1930s. James McKeen Cattell, New York, NY, various letters, memoranda, and undated manuscripts, James McKeen Cattell Collection, Container #178. One of Seashore’s first music-related publications was his “Hearing-ability and Discriminative Sensibility for Pitch,” in University of Iowa Studies in Psychology, vol. II, ed. G. T. W. Patrick (Iowa City: State University of Iowa, 1899). His doctoral dissertation was “Measurements of Illusions and Hallucinations in Normal Life” (Ph.D. diss., Yale University, 1895); published in Studies from the Yale Psychological Laboratory, vol. III, ed. E. W. Scripture (New Haven, CT: Yale University Press, 1895). Interestingly, Scripture began to study pitch perception, but not mental testing per se, during Seashore’s student years at Yale. E. W. Scripture, “The Method of Regular Variation,” in “Psychological Notes,” American Journal of Psychology 4 (August 1892): 577– 84. Carl Emil Seashore, “Carl Emil Seashore,” in A History of Psychology in Autobiography, vol. I, ed. Carl Murchison (Worcester, MA: Clark University Press, 1930; reprint, New York: Russell & Russell, 1961), 236 – 38, 245. (Page citations are to the reprint edition.) Linden and Linden, 5. Sokal, “Unpublished,” 629. Cattell may yet be proven correct. Recently, Deary reported a strong link between intelligence and auditory ability. Ian J. Deary, “Intelligence and Auditory Discrimination: Separating Processing Speed and Fidelity of Stimulus Representation,” Intelligence 18 (March 1994): 189 – 213. Philip H. DuBois, A History of Psychological Testing (Boston: Allyn and Bacon, 1970), 28. Ibid., 46. Edwin Gordon, Musical Aptitude Profile (Boston: Houghton Mifflin Company, 1965). The “Musical Sensitivity” portion of this test clearly represents a move away from sensory measurement. Various aptitude tests, including those of musical aptitude, predict achievement in school music. See Jere T. Humphreys, William V. May, and David J. Nelson, “Research on Music Ensembles,” in Handbook of Research on Music Teaching and Learning, ed. Richard Colwell (New York: Schirmer Books, 1992), 651– 53. Herrnstein, 65. The “key article” was Binet and Henri, “La psychologie individuelle.” Seashore, “Seashore,” 272. Interestingly, neither Cattell nor Galton investigated the relationships between sensory ability and musical aptitude per se. Rather, Galton gathered antecdoctal data about artistic ability and compared them to expected statistical values. Humphreys, “Precursors,” 322. Among his many writings about the Seashore tests is James L. Mursell, The Psychology of School Music Teaching (New York: Silver, Burdett and Company, 1931), 333 – 35. Jere T. Humphreys, “Applications of Science: The Age of Standardization and Efficiency in Music Education,” The Bulletin of Historical Research in Music Educafion 9 (January 1988): 17–18. Seashore, “Seashore,” 273 – 74. Seashore’s statement stands in contrast to the reflections of one of his contemporaries published in the same year, the prominent mental tester Joseph Jastrow of the University of Wisconsin: My interest in the subject goes back to 1893 and before. In that early period Cattell had emphasized the importance of tests of indices of individual differences. . . . But it remained for Binet . . . to recognize in ordinary achievements (not merely in specially arranged sensory, motor, memory,

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and intelligence functions, such as I had used) an available means of grading natural aptitudes. Joseph Jastrow, “Joseph Jastrow,” in A History of Psychology in Autobiography, vol. I, ed. Carl Murchison (Worcester, MA: Clark University Press, 1930; reprint, New York: Russell & Russell, 1961), 156. (Page citation is to the reprint edition.) 54. Seashore, “Seashore,” 272.

Bibliography Binet, A., and V. Henri. “La psychologie individuelle.” L’année psychologique 2 (1895): 411– 65. Excerpts translated by Mollie D. Boring. In A Source Book in the History of Psychology, ed. Richard J. Herrnstein and Edwin G. Boring, 428–33. Cambridge, MA: Harvard University Press, 1965. Boas, Frank. “Anthropological Investigations in Schools.” Pedagogical Seminary 1 (June 1891): 225 –28. Boring, Edwin G. A History of Experimental Psychology. 2d ed. New York: Appleton Century-Crofts, 1950. Cattell, J. McKeen. “Early Psychological Laboratories.” Science 67 (May 1928): 543 – 48. ———. “The First Year of the Psychological Corporation.” Unpublished report to the Board of Directors, 1 December 1922. James McKeen Cattell Collection, Container #178, “Subject File, 1890–1936. Manuscript Division, Library of Congress, Washington, D.C. ———. James McKeen Cattell, 1860 –1944: Man of Science. Vol. I (Lancaster, PA: The Science Press, 1947). ———. Cambridge [England], to “Mama and Papa” [William and Elizabeth Cattell], Philadelphia, 23 November 1893, 25 July 1894, 5 December 1894. Letters in the hand of James McKeen Cattell. James McKeen Cattell Collection, Container #55, “Family Correspondence,” December 1888-March 1903. Manuscript Division, Library of Congress, Washington, D.C. (Two of these letters are misfiled in the Collection.) ———. Leipzig [Germany], to Francis Galton, Cambridge [England], [n.d. given]. Quoted in Francis Galton. “On Recent Designs for Anthropometric Instruments.” The Journal of the Anthropological Institute of Great Britain and Ireland 16 (1887): 2–9. ———. “In Memory of Wilhelm Wundt.” Psychological Review 28 (May 1921): 155 – 59. ———. “Mental Tests and Measurements.” Mind 15 (January 1890): 373 – 80. ———. “The Psychological Laboratory at Leipsic [sic].” Mind 13 (January 1888): 37– 51. ———. “Psychology at the University of Pennsylvania.” American Journal of Psychology 3 (April 1890): 281– 83. ———. “Ueber die Trägheit der Netzhaut und des Sehcentrums.” Philosophische Studien 3 (1885): 94–127. Reprinted as “The Inertia of the Eye and Brain.” In (no translator) James McKeen Cattell. James McKeen Cattell, 1860 –1944: Man of Science. Vol. I, 26–40. Lancaster, PA: The Science Press, 1947. ———. “Ueber die Zeit der Erkennung und Benennung von Schriftzeichen, Bildern und Farben.” Philosophische Studien 2 (1885): 635 – 50. Excerpts translated by R. S. Woodworth in “On the Time Required for Recognizing and Naming Letters and Words, Pictures and Colors.” In James McKeen Cattell. James McKeen Cattell, 1860 –1944: Man of Science. Vol. I, 13 – 25. Lancaster, PA: The Science Press, 1947. Cattell, J. McKeen, and Livingston Farrand. “Physical and Mental Measurements of the Students of Columbia University. Psychological Review 3 (1896): 618 – 48. Darwin, Charles. The Descent of Man and Selection in Relation to Sex, 2d ed. New York: American Publishers Corporation, 1874. Deary, Ian J. “Intelligence and Auditory Discrimination: Separating Processing Speed and Fidelity of Stimulus Representation.” Intelligence 18 (March 1994): 189 – 213. DuBois, Philip H. A History of Psychological Testing. Boston: Allyn and Bacon, 1970.

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Esquirol, Jean-Etienne Dominique. Des maladies mentales considé rées sous les rapports médical, hygienique, et médico-légal. Vols. I, II and Atlas. Paris: J. B. Bailliére, 1838. Galton, Francis. “On Recent Designs for Anthropometric Instruments.” The Journal of the Anthropological Institute of Great Britain and Ireland 16 (1887): 2 – 9. Gilbert, J. A. “Experiments on the Musical Sensitiveness of School Children.” In Studies from the Yale Psychological Laboratory. Vol. I, ed. E. W. Scripture, 80 – 87. New Haven, CT: Yale University Press, 1893. ———. “Researches on Mental and Physical Development of School-Children.” Studies from the Yale Psychological Laboratory. Vol. II, ed. E. W. Scripture, 40 –100. New Haven, CT: Yale University Press, 1894. ———.“Researches upon School Children and College Students.” University of Iowa Studies in Psychology. Vol. I, ed. G. T. W. Patrick and J. Allen Gilbert, 1– 39. Iowa City: State University of Iowa, 1897. Goodenough, Florence L. Mental Testing: Its History, Principles, and Applications. New York: Rinehart and Company, 1949. Gordon, Edwin. Musical Aptitude Profile. Boston: Houghton Mifflin Company, 1965. Herrnstein, Richard J. I. Q. in the Meritocracy. Boston: Little, Brown, 1973. Herrnstein, Richard J., and Edwin G. Boring, eds. A Source Book in the History of Psychology. Cambridge, MA: Harvard University Press, 1965. Humphreys, Jere T. “Applications of Science: The Age of Standardization and Efficiency in Music Education.” The Bulletin of Historical Research in Music Education 9 (January 1988): 1– 21. ———. “The Child-Study Movement and Public School Music Education.” Journal of Research in Music Education 33 (Summer 1985): 79 – 86. ———. “Precursors of Musical Aptitude Testing: From the Greeks through the Work of Francis Galton.” Journal of Research in Music Education 41 (Winter 1993): 315 – 27. Humphreys, Jere T., William V. May, and David J. Nelson. “Research on Music Ensembles.” In Handbook of Research on Music Teaching and Learning, ed. Richard Colwell, 651– 68. New York: Schirmer Books, 1992. Jastrow, Joseph. “Joseph Jastrow.” In A History of Psychology in Autobiography. Vol. I, ed. Carl Murchison, 135 – 62. Worcester, MA: Clark University Press, 1930; reprint, New York: Russell & Russell, 1961. ———. “Some Anthropometric and Psychologic [sic] Tests on College Students.” American Journal of Psychology 4 (April 1892): 420 – 28. Linden, Katherine W., and James D. Linden. Modern Mental Measurement: A Historical Perspective. Boston: Houghton Mifflin Company, 1968. Münsterberg, Hugo. “Zur individual Psychologie,” Centralblatt f. Nervenheilkunde und Psychiatrie 14 (1891): 196 – 98. Mursell, James L. The Psychology of School Music Teaching. New York: Silver, Burdett and Company, 1931. Pearson, Karl. The Life, Letters and Labours of Francis Galton. Vol. IIIA. Cambridge, England: Cambridge University Press, 1930. Scripture, [Edward Wheeler], New Haven, CT, to [James McKeen] Cattell, New York, NY, 7 August 1901. Letter in the hand of Edward Wheeler Scripture. James McKeen Cattell Collection, Container #38, “General Correspondence,” 1884 –1944. Manuscript Division, Library of Congress, Washington, D.C. ———. “The Method of Regular Variation,” in “Psychological Notes.” American Journal of Psychology 4 (August 1892): 577– 84. ———. The New Psychology. New York: Scribner’s, 1897. Seashore, Carl Emil. “Carl Emil Seashore.” In A History of Psychology in Autobiography. Vol. I, ed. Carl Murchison, 225 – 97. Worcester, MA: Clark University Press, 1930; reprint, New York: Russell & Russell, 1961.

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Seashore, Carl Emil. “Hearing-ability and Discriminative Sensibility for Pitch.” University of Iowa Studies in Psychology. Vol. II, ed. G. T. W. Patrick, 163 –78. Iowa City: State University of Iowa, 1899. ———. “Measurements of Illusions and Hallucinations in Normal Life.” Ph.D. diss., Yale University, 1895; published in Studies from the Yale Psychological Laboratory. Vol. III, ed. E. W. Scripture, 1– 67. New Haven, CT: Yale University Press, 1895. Seashore, Carl E. Seashore Measures of Musical Talent. New York: The Psychological Corporation, 1919. Sokal, Michael M. “Biographical Approach: The Psychological Career of Edward Wheeler Scripture.” In Historiography of Modern Psychology: Aims, Resources, Approaches, ed. Josef Brozek and Ludwig J. Pongratz, 255 – 78. Toronto: C.J. Hogrefe, Inc., 1980. ———. ed. An Education in Psychology: James McKeen Cattell’s Journal and Letters from Germany and England, 1880–1888. Cambridge, MA: The MIT Press, 1981. ———. “The Unpublished Autobiography of James McKeen Cattell.” American Psychologist 26 (July 1971): 626 – 35. Wissler, Clark. “The Correlation of Mental and Physical Tests.” Psychological Review Monograph Supplements 3 (Whole No. 16) (June 1901): 1– 62.

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79 The Life and Labors of Francis Galton: A Review of Four Recent Books about the Father of Behavioral Statistics Brian E. Clauser

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f one individual can be credited as the founder of the field of behavioral and educational statistics, that individual is Francis Galton. Galton was not a great mathematical statistician; he made no important contributions to that field. In fact, his efforts to earn an honors degree in mathematics at Cambridge resulted in a physical and mental breakdown (Gillham, 2001). The contributions that justify Galton’s status as father – or grandfather – of the field are based on his rediscovery of statistical methods and his application of those methods to the measurement of the mental and physical characteristics of humans. Galton deserves credit for our use of such basic analytic frameworks as percentile rank, correlation, and regression. He was not the first to describe the mathematical relationship represented by the correlation coefficient, but he rediscovered this relationship and demonstrated its application in the study of heredity, anthropology, and psychology. He is responsible for the term correlation (from co-relation), he discovered the phenomenon of regression to the mean, and he is responsible for the choice of r (for reversion or regression) to represent the correlation coefficient. Galton developed statistical applications for the behavioral sciences. He demonstrated the importance of the normal distribution and the normal cumulative frequency distribution in understanding human characteristics. Through this research and his influence on Karl Pearson (who provided a mathematically superior alternative to Galton’s formulation of the correlation Source: Journal of Educational and Behavioral Statistics, 32(4) (2007): 440 – 444.

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coefficient), Galton influenced “Student” (William Gosset), R. A. Fisher, and the applied statisticians that have followed. Galton also pioneered the use of surveys in the behavioral sciences. In one study, he asked his fellow members of the Royal Society of London to describe mental images that they experienced. In another, he collected in-depth surveys from eminent scientists for a work examining the effects of nature and nurture on the propensity toward scientific thinking. Galton’s activities did not stop there. Francis Galton was the quintessential Victorian polymath. He was an explorer in Africa years before Stanley uttered the phrase “Dr. Livingstone, I presume.” (In fact, later in life he was involved in an unpleasant and very public controversy with Stanley.) When he returned from Africa, he wrote a manual for travelers with advice on topics as diverse as how to cross a river with a horse, protect provisions from foraging animals, and prepare for medical emergencies in the wild. To the explorer in need of medical assistance, he offered the consolation, “Though there is a great difference between a good physician and a bad one, there is very little between a good one and none at all” (Galton, 1883, p. 14). He collaborated with his cousin, Charles Darwin, providing statistical analysis for results Darwin (1876) presented in his volume on the effects of cross-fertilization. (R. A. Fisher, 1935, later used this as an example of how not to do statistical analysis, but he did so with the advantage of six decades of hindsight.) And he conducted studies that refuted Darwin’s hypothesis of pangenesis, a Lamarckian description of how acquired characteristics could be passed on to offspring. Galton developed weather maps and discovered the existence of the anticyclone. He wrote three monographs on the use of fingerprints and stands as the major influence in the adoption of this technology in criminology. He studied and wrote papers on the visions of sane people, statistical evidence for the efficacy of prayer (the results were not supportive), and the mechanism of heredity (Mendel’s work was unknown at the time; Galton conducted his own experiments with peas). Galton’s fascination with and admiration of Darwin’s work and his obsession for measurement of human characteristics led to an interest in inheritance in humans. He was the first to make the case that intelligence and other mental characteristics could be inherited, and he published several books of evidence to support his views (e.g., Hereditary Genius, 1869; Natural Inheritance, 1889). Ultimately, this line of work led to the conclusion that society had control and responsibility for improvement of the human stock. He coined the term eugenics (to describe the science that would support such improvement through the control of human mating) and wrote essays and a novel in support of this science. Among Galton’s final works was an autobiography, and shortly after Galton’s death in 1911 Karl Pearson (1914, 1924, 1930a, 1930b) wrote a monumental four-volume biography on Galton’s life and works. But for the

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seven decades following the publication of Pearson’s opus, Galton received relatively little attention from biographers (Forrest’s [1974] volume is a noteworthy exception, although like Pearson’s biography it is now out of print). In recent years, however, there has been a renewed interest in Galton; four volumes have appeared that describe Galton’s life. The interested reader can choose from an array of writing styles and perspectives. The most literary of these efforts comes from the pen of A. S. Byatt in the form of a novel. The Biographer’s Tale tells a story within a story within a story; actually, at the center of the tale are three stories. The narrator/protagonist is attempting to write the biography of a fictional biographer who apparently died while researching three historical figures: Linnaeus, Ibsen, and Galton. The notes for this research are discovered by the protagonist and provide an opportunity to present fascinating (and mostly factual) information about all three of these individuals. Readers who are already convinced that they wish to know more about Galton will likely not be satisfied by the intriguing but all too brief presentation provided by Byatt. The reader who starts with Byatt’s novel likely will decide that he or she wishes to know more, but time spent reading the novel will not have been wasted. Of the three recent volumes taking a more traditional biographical approach, Brookes’s Extreme Measures: The Dark Visions and Bright Ideas of Francis Galton is the most accessible. It has been written with a broad audience in mind and is the least scholarly of the three. Brookes inserts descriptions of his personal experiences at locations visited in the process of researching the book and so establishes a relaxed, narrative style that is pleasantly readable. Brookes’s biography is also the briefest of the three; as such, it lacks detail about some aspects of Galton’s life. Although there is discussion of Galton’s obsession for measurement, there is little attention given to his statistical innovation. The volume is also limited by Brookes’s tendency to see every aspect of Galton’s life in relation to his views on eugenics. Brookes fails to place Galton’s views in the context of the times in which he lived. In the process, he makes too little of a distinction between Galton’s views and the final solution practiced by the Nazis decades after Galton’s death. Although Galton’s views of the indigenous populations that he encountered in Africa might well be seen as enlightened by Victorian standards, Brookes views them with a 21st-century perspective and finds evidence of Galton’s intolerance. This intolerance is then used as a basis for interpreting Galton’s eugenic interests. Little attention is given to Galton’s sensitivity about the importance of developing a plan within the constraints of social acceptability. Similarly, Brookes makes no effort to place Galton’s views within the social and historical context of the times; for example, it should be remembered that both Karl Pearson and R. A. Fisher actively participated in the eugenics movement. Gillham’s A Life of Sir Francis Galton: From African Exploration to the Birth of Eugenics differs from Brookes’s effort in several important respects. The Gillham book is less a narrative and although not exhaustive, provides a more

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detailed academic account of Galton’s work. It is also much more substantial (at more than 400 pages) and concludes with nearly 40 pages of notes and references. Gillham’s effort differs from that of Brookes’s in that although nearly half of his volume falls in the section titled The Triumph of Pedigree, particular emphasis is not placed on eugenics. Eugenics is considered in the context of his life and work rather than the other way around. Although not written in the conversational tone of Brookes’s biography, Gillham’s style is pleasant and readable. The notes and references will be valuable to the serious reader, and the book also has numerous illustrations. One expects a biography to contain at least a few photographs, and Gillham obliges; more interesting are the many tables and figures from Galton’s own papers and monographs. Finally, Michael Bulmer’s Francis Galton: Pioneer of Heredity and Biometry provides a highly focused review of Galton’s contributions to genetics and applied statistics. Bulmer begins with an introductory chapter that gives an overview of Galton’s life; the reader progresses from Galton’s birth to old age in 41 pages. Following this overview are eight chapters that focus on Galton’s ideas about statistics, hereditary ability, the laws and mechanisms of heredity, eugenics, evolution, and biometry. Bulmer offers a well-balanced description of these areas. He describes Galton’s innovations and contributions, but he is also willing to point out where and when Galton got it wrong. Galton discovered regression to the mean, but his mathematical understanding of regression was limited; Galton carefully collected and studied data to understand the mechanisms and laws of heredity, but he never produced an accurate model to explain those data. Although Bulmer provides a reasonably extensive reference list, he does not include footnotes and does not follow the academic writing practice of providing references in support of his assertions. This is somewhat surprising considering that this is far and away the most detailed of the discussions of Galton’s intellectual efforts. Although this lack of referencing will be a disappointment for some readers, Bulmer does provide considerable background and framework for interpreting Galton’s work within the context of the times. For example, in discussing Galton’s statistical theory of heredity he shows how Galton’s views changed over time and contrasts those views with Mendel’s and Pearson’s, among others. Bulmer’s work provides the most detail on Galton’s use of statistics. Although the statistical presentations (occasionally including matrix formulation) will not present a challenge to readers of this journal, much of the discussion is in the context of genetic theories; the relatively naive reader can make his or her way through the text, but Bulmer clearly expects his reader to have some background in this area. Together, these four books provide a range of options for becoming familiar with the contributions that Francis Galton made to statistical and biological science. In addition, any readers who are left wanting more can seek out

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Galton’s original works or Pearson’s encyclopedic biography. This said, the best introduction to Galton’s life and work may well be his autobiographical writing on the subject, Memories of My Life (1908). Whatever choices one makes, it is clear that with the range of current works on Galton there is no excuse for ignorance about this foundational figure in the field of applied statistics.

References Darwin, C. (1876). The effects of cross and self fertilization in the vegetable kingdom. London: John Murray. Fisher, R. A. (1935). The design of experiments. Edinburgh, UK: Oliver & Boyd. Forrest, D. W. (1974). Francis Galton: The life and work of a Victorian genius. New York: Taplinger. Galton, F. (1869). Hereditary genius. London: Macmillan. Galton, F. (1883). The art of travel. London: John Murray. Galton, F. (1889). Natural inheritance. London: Macmillan. Galton, F. (1908). Memories of my life. London: Methuen. Pearson, K. (1914). The life letters and labours of Francis Galton. Vo1. 1, birth 1822 to marriage 1853. Cambridge, UK: Cambridge University Press. Pearson, K. (1924). The life letters and labours of Francis Galton. Vol. 2, researches and middle life. Cambridge, UK: Cambridge University Press. Pearson, K. (1930a). The life letters and labours of Francis Galton. Vol. 3A, correlation, personal identification and eugenics. Cambridge. UK: Cambridge University Press. Pearson, K. (1930b). The life letters and labours of Francis Galton. Vol. 3B, characterization, especially by letters; index. Cambridge, UK: Cambridge University Press.

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80 Regression towards the Mean, Historically Considered Stephen M. Stigler

1 Introduction

R

egression towards the mean is an elementary concept in statistics. When properly understood, it is transparent to the point of being obvious. Yet despite its simplicity, it has been consistently misunderstood and it has repeatedly been the source of major errors in analysis, some with significant policy implications, attracting such names as ‘the regression paradox’, ‘the regression fallacy’ and ‘the regression trap’. Milton Friedman has written ‘I suspect that the regression fallacy is the most common fallacy in the statistical analysis of economic data’,1 a sentiment that could with justice be carried over to any other field where multivariate data are employed for the analysis and formulation of policies. To understand the nature of this phenomenon, of how a simple idea could cause so much difficulty, it will be useful to examine the history of the idea, because the historical origins reveal a number of ways of interpreting it that could, if more widely known, alleviate much confusion. That history is remarkably short, a fact that itself may seem paradoxical. Modern texts on ‘regression analysis’ or ‘applied linear regression’ or ‘multiple regression analysis’ are almost entirely occupied with examining the use of the method of least squares to fit linear relationships to multivariate data, often for predictive purposes. These texts are based on a statistical methodology that dates back to at least 1805 and the work of Legendre and

Source: Statistical Methods in Medical Research, 6 (1997): 103 –114.

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Gauss and Laplace, methods that were in part foreshadowed by developments a half-century before that.2 Yet the name ‘regression’ itself and the concept I discuss here only date from the period 1877–85, and those same texts on ‘regression analysis’ discuss that concept only sparsely, if at all.

2 The Concept of Regression Regression can be viewed as a purely mathematical phenomenon or as an intrinsically statistical concept; to begin with, let us consider how it can be expressed verbally, mathematically, and geometrically, since all of these can be traced to the early days of the concept. Verbally, we may consider a stochastic time-varying phenomenon, where two correlated measurements are taken of the same person or object at two different times. For example, we might consider the scores recorded on two examinations taken by the same individual at two separated times. Suppose the first score is exceptionally high – near the top of the class. How well do we expect the individual to do on the second test? The answer, regression teaches us, is ‘less well’, relative to the class’s performance. And the reasoning is clear: there is a selection effect. The high score on the first occasion is surely due to some mixture of successes in two components, to a high degree of skill (a permanent component) and to a high degree of luck (a transient component). The relative bearings of the two components of skill and luck on the first-time score would require measurement to pin down, but the fact that we expect both to have, on average, contributed to the exceptional first outcome is intuitively plausible, even obvious. And on the second occasion we expect the permanent component of skill to persist (for that is the meaning of permanent) while the transient component of luck will, on average, not be present (for that is the meaning of transient). We would not expect that the ‘luck’ on the second occasion will be bad luck; it may even be good luck – possibly on rare occasions even better that the first time. But it cannot be counted on to persist, and on average there will be no luck at all, neither good nor bad. And so we will have gone from ‘high skill plus good luck’ to ‘high skill alone’, a net decrease; still better than average, but less so than before. We expect (with of course no guarantee) regression towards the average. If the first score were exceptionally low, the situation would be reversed, with regression towards the average from below. Geometrically, the phenomenon can be seen in terms of one simple picture. Figure 1 shows a bivariate normal density; both variables are standardized and the correlation is 0.5. The solid object pictured, if complete, would have a total volume of 1.0 contained in the space between the surface and the X–Y plane. It has, however, been sliced apart. First, a cross-sectional slice is taken perpendicular to the X–Y plane and parallel to the Y-axis, intersecting the X-axis at X = x > 0, which might be taken as the exceptionally high first-occasion score. Next, the surface is decapitated parallel to the X–Y plane, such that the level

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Figure 1: The bivariate normal surface: A geometric illustration of the concept of regression

curve of intersection (an ellipse) is exactly tangent to the curve of intersection of the first slice (which is a curve proportional to a normal density, the conditional density of Y given X = x). The major and minor axes of the ellipse are shown (they are the lines Y = X and Y = –X), as is the line from the origin through the point of tangency of the two curves. This latter line is the line of the conditional expectation of Y given X = x (this is clear since it must pass through the mode of the conditional density of Y given X, and for the symmetrical normal distributions, the mode, the median, and the mean must all agree). Then in terms of this diagram the regression phenomenon consists of the obvious observation that the line of conditional expectations must be closer to the X-axis than is the major axis of the ellipse – for it would be clearly impossible for the first slice to touch the ellipse at the point the major axis crosses it, unless the ellipse were collapsed to a line segment, as would only be true if the correlation were 1.0. And so, unless there is perfect correlation between X and Y there must be regression towards the average. Mathematically, there are several different, equivalent ways of deriving the regression phenomenon. 1. You may begin with two standard normal random variables X and Y with correlation r and bivariate density f( x, y) =

⎛ ⎞ 1 exp − ⎜⎜⎜ ( x 2 − 2ρ xy + y 2 )⎟⎟⎟ 2 ⎟⎠ ⎝ 2(1 − ρ ) 2π 1 − ρ 1

2

Then after some algebra the conditional density of Y given X = x is found to be f( y | x) = =

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f( x, y) fx ( x ) 2⎞ ⎛ ⎜⎜ 1 ⎛⎜ y − ρ x ⎞⎟ ⎟⎟ ⎟ exp ⎜⎜− ⎜⎜ ⎟ ⎟⎟⎟ 2 2 ⎟ ⎜ ⎜ 2 ⎜ 2π 1 − ρ ⎜⎝ ⎝ 1 − ρ ⎟⎠ ⎟⎟⎠

1

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which we recognize as the density of a N(rx, 1 −r2) random variable. Hence the conditional expectation of Y given X = x is rx, representing regression from x towards the mean of 0. 2. The verbal description given earlier can be expressed mathematically. We may represent X = S + E1 Y = S + E2 where S, E1, and E2 are independent, S is the ‘persistent’ trait and the Ei are the ‘transient’ traits. For the simplest form of the argument, suppose that S and the Ei all have the same distribution, with E(S) = 0 and E(Ei) = 0. Then E(X|Y = y) = E(S + E1|S + E2 = y) = E(S|S + E2 = y)+E(E1|S + E2 = y) = E(S|S + E2 = y) + E(E1) (by independence) = E(S|S + E2 = y) But y = E(S + E2|S + E2 = y) =E(S|S + E2 = y) + E(E2|S + E2 = y) = 2E(S|S + E2 = y) and so E(X|Y = y) = 0.5y. Note that this argument does not require normality or even the existence of second moments, although if the correlation exists we would clearly have r = 0.5, in agreement with (1). 3. A different approach is not in terms of standardized variables, but rather is framed sequentially, in terms of a conditional distribution. Let X have a normal distribution N(0, c2), and let Y = X + Z, where Z is N(0, b2), independent of X. Then Y is N(0, b2 + c2) and the correlation of X and Y is

ρ = ρ XY =

c2 2

2

2

c (b + c )

=

c 2

b + c2

Clearly the conditional expectation of Y given X = x is simply x; what is the conditional expectation of X given Y = y? Finding the bivariate distribution of X and Y and employing a derivation such as that in (1) above tells us that E(X|Y = y) is not y, but rather it is [(c2)/(b2 + c2)]y, clearly closer to the mean of 0 than is y. The fact that E(Y|X = x) is equal to x (rather than being itself closer to the mean of 0) is a reminder that ‘regression towards the mean’ need literally be true only when the variables are

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standardized to have the same variances. If we rescale Y to have the same variance as X, by Y′= rY, then E(Y ′|X = x) = rx and E(X|Y ′ = y) = r y

3

Galton and Regression

Francis Galton discovered the phenomenon of regression. Few conceptual advances in statistics can be as unequivocally associated with a single individual. Least squares, the central limit theorem, the chi-squared test – all of these were realized as the culmination of many years of exploration by many people. Regression too came as the culmination of many years’ work, but in this case it was the repeated efforts of one individual. The first glimmers of the idea can be found already in Galton’s 1869 book Hereditary genius. In that work he studied the way talent ran in families, and most of the book consists of lists of eminent people and their eminent relatives – great scientists and their kin with known scientific accomplishments (e.g. the Bernoullis), musicians and their musical kin (e.g. the Bachs), and so forth. But despite the inevitable arbitrariness in his classifications and evaluation of eminence, Galton noted that there was a marked tendency for a steady decrease in eminence the further down or up the family tree one went from the great man (e.g. Jacob Bernoulli or Johann Sebastian Bach) whose fame led to the family’s inclusion in the study. Even with dogs this was true: ‘If a man breeds from strong, well-shaped dogs, but of mixed pedigree, the puppies will be sometimes, but rarely, the equals of their parents. They will commonly be of a mongrel, nondescript type, because ancestral peculiarities are apt to crop out in the offspring.’3 In 1869 Galton only vaguely approached the concept in its verbal form, but he was unable to formulate in a precise way how the accidental ‘cropping out’ of ‘ancestral peculiarities’ might be encompassed in a theory. Still the question kept gnawing at him; over the years 1874 – 88 he revisited this problem repeatedly, and, bit by bit, he overcame it in one of the grand triumphs of the history of science. The story is an exciting one, involving science, experiment, mathematics, simulation, and one of the great mental experiments of all time. But it is a long story, one I have examined in detail in my book,2 and so I shall only relate it in outline here. In the years 1874 – 77, Galton launched his first assault upon this conundrum: how and why was it that talent or quality once it occurred tended to dissipate rather than grow. He never lost interest in the study of the inheritance of human genius, but he realized early on that intellectual quality was not an area that permitted either easy measurement on a wide scale or active experimentation. And so he fell back on studies of other measurable qualities, particularly stature – height – in humans, and he began a series of experiments

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involving the measurement in successive generations of the diameter of sweet peas. And while considering these experiments, he invented a wonderful machine, the Quincunx, that was to serve as an analogue for hereditary processes and provide the key insight to the solution. Galton had been puzzled by how to reconcile the standard theory of errors with what he observed and knew to be true from experiments. The theory of errors held that a normal population distribution would be produced through the accumulation of a large number of small accidental deviations, and there seemed to be no other way to account for the ubiquitous appearance of that normal outline. Galton’s experiments with sweet peas and his studies of human stature agreed with earlier work by the Belgian statistician Adolphe Quetelet: the world, by and large, was normally distributed. Yet, as Galton realized, this did not square with the fact that in heredity there were large and important causes of deviations at work: inheritance of talent, height, or diameter was not perfect, but these qualities did run in families. The normal distribution he and others found was not the exclusive result of small accidental causes; it had somehow to be reconciled with the influence of the large and invariable causes of heredity. In 1873 Galton had a tradesman make for him a machine he called the Quincunx. It consisted of a board with a funnel at the top through which lead shot could be released to fall through a succession of offset rows of pins, collecting at the bottom in vertical compartments (for a photograph of the original machine see Stigler,2 p. 277). The left panel of Figure 2 shows a schematic rendition. The name ‘Quincunx’ was derived from the similarity of the pattern of pins to the arrangement of cultivated fruit trees in English agriculture, a pattern that was known as quincunxial because it was based on a square of four trees with a fifth in the centre. Galton’s Quincunx was initially intended

A

A

B

B

Figure 2: A schematic drawing of Galton’s Quincunx4

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to illustrate the workings of a large number of small accidental causes to produce a normal-like distribution. It might be likened to a dynamic version of Pascal’s triangle: As shot pass from top to bottom they are randomly deflected at each row, and if the machine is well made and in balance the shot will produce an outline at the bottom where the number of shot in each compartment is proportional to the number of paths to that compartment. That is, the number of shot in a compartment will be proportional to the binomial coefficients – a nearly normal distribution if the number of rows of pins is at all large. The Quincunx illustrated the manner in which a large number of small accidents could produce a normal distribution. But what of the large and notso-accidental causes that Galton found inherited to one degree or another in his studies? The evolutionary progress of the shot through the Quincunx led Galton to his fundamental first insight through one of the great mental experiments in the history of science. I term this a mental experiment because, while Galton clearly in several places described the variant of the Quincunx that performed the experiment, there is no indication that he actually built the apparatus. And having tried to build such a machine, I can testify that it is exceedingly difficult to make one that will accomplish the task in a satisfactory manner. Galton first imagined taking the Quincunx apart in the middle and stretching it out, but to ensure that the stretching does not alter the final distribution of the shot he would add vertical barriers to keep them from straying while they traversed the gap. Galton’s printed diagram from 1889 is shown in the second panel of Figure 1; he illustrated the idea in correspondence as early as 1877 (see Stigler,2 pp. 278–79). Clearly with these barriers the introduction of the gap would have no effect on the distribution of shot among the compartments at the bottom. Galton then conceived of introducing a barrier at the bottom of the gap, turning the barriers into a second set of compartments like those at the bottom. What effect would that have? Again it is clear that all this would do is to foreshorten the Quincunx; with fewer rows of pins to traverse, the shot would still come to rest in a normal-like distribution, but one that was less disperse than if they had been allowed to finish the course. Galton would then release the shot from this midlevel, but only from one compartment: this would be expected to produce a small normal distribution immediately below the compartment from which they were released. Proceed then to release the remaining compartments, one at a time. Each will produce its own little normal curve; those near the centre being larger than those more extreme, because more shot will have been deposited in the central compartments by the first stage of the Quincunx. And when all have been released, the result – the sum of all the little normal curves – will be as if no interruption at all had taken place! Galton’s imagination had shown how the normal world could be dissected into components, components which could be traced back to the location of the shot at the end of a first stage. The machine was a beautiful match to his

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investigations of inheritance. The seeming homogeneity of the final outline could be seen now as a mixture derived from previous generations. Indeed, Galton’s mental experiment can be interpreted as an analogue proof of the mathematical theorem, that a normal mixture of normal distributions is itself normal (or, in the discrete version, that a convolution of binomial distributions with the same p is binomial). You can even see the phenomenon of regression: the expected final position of a shot released from the mid-level is immediately below it, but what is the expected origin of a shot on the bottom level? Clearly towards the centre from its position, since there are more shot originating towards the centre than further away. With the Quincunx in mind. Galton’s later correlation tables take on a whole new meaning. For example, in Table 1 the right-hand column ‘Total no. of adult children’ is seen as the distribution of the shot at the mid-level, the rows of counts as the corresponding little normal curves, and the ‘Totals’ of the bottom row as the final outline of the Quincunx. Even by 1877 Galton had begun to assemble these insights mathematically. He had empirically noted the tendency for ‘reversion’ towards the mean and labelled this ‘r’. In his notation, if c = the dispersion (essentially, standard deviation) of the first generation, d = the dispersion of the second generation and υ = the dispersion of the offspring (the little normal curves), then since the position of a second generation individual was the sum of its ‘reverted’ average displacement from its parent (say rz, where z was the first generation position) and its random deviation from that position, these dispersions would be related by d2 = u2 + r2c2. But why did the reversion take the linear form rz? And why would the population dispersion remain stable; that is, what mechanism produced d = c? The answer to this (that d = c was a necessary consequence of population stability) did not come to Galton until 1885, when, inspired by tables such as Table 1, and with a slight assist from the Cambridge mathematician JH Dickson, he produced a full formulation in terms of the bivariate normal distribution. He summarized and elaborated upon this formulation in his 1889 Natural inheritance.4 His discussion there included the geometric interpretation of regression and the mathematical formulation given earlier as (3) (which we can recognize now as a description of the working of the Quincunx, with X = the reverted first generation position and Z is the displacement of offspring from parent), and much more. He was aware that there were two regression lines. He even described a variance components model for fraternal relationships, and he discussed how to estimate the components of variance. By the time Natural inheritance appeared, he had, while considering problems in physical anthropology and forensic science, noticed that when two variables were expressed in standardized units, the two regression lines had the same slope, and he suggested using that slope, which he termed the ‘index of co-relation,’ as a measure of the strength of the relationship. He interpreted the correlation coefficient both as a regression coefficient and as what we would now term an intraclass correlation coefficient.5

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1 1 1

5

..

Totals ..

Medians ..

.. .. .. 1 .. 1 ..

Below

Above .. 72.5 71.5 70.5 69.5 68.5 67.5 66.5 65.5 64.5 Below ..

Heights of the mid-parents in inches

..

7

.. .. .. .. .. .. 3 3 .. 1 ..

62.2

66.3

32

.. .. .. 1 1 7 5 3 9 4 2

63.2

67.8

59

.. .. .. .. 16 11 14 5 5 4 4

64.2

67.9

48

.. .. 1 1 4 16 15 2 7 1 1

65.2

67.7

117

.. .. 3 1 17 25 36 17 11 5 2

66.2

67.9

138

.. .. 4 3 27 31 38 17 11 5 2

67.2

68.3

120

.. 1 3 12 20 34 28 14 7 .. 1

68.2

68.5

167

.. 2 5 18 33 48 38 13 7 2 1

69.2

Heights of the adult children

69.0

99

.. 1 10 14 25 21 19 4 5 .. ..

70.2

69.0

64

.. 2 4 7 20 18 11 .. 2 .. ..

71.2

70.0

41

1 7 9 4 11 4 4 .. 1 .. ..

72.2

..

17

3 2 2 3 4 3 .. .. .. .. ..

73.2

..

14

.. 4 2 3 5 .. .. .. .. .. ..

Above

..

928

4 19 43 68 183 219 211 78 66 23 14

Adult children

..

205

5 6 11 22 41 49 33 20 12 5 1

Mid-parents

Total number of

..

..

.. 72.2 60.9 69.5 68.9 68.2 67.6 67.2 66.7 65.8 ..

Medians

Table 1: One of Galton’s correlation tables (from Francis Galton, family likeness in stature, Proceedings of the Royal Society of London 1886; 40: 42–73). Galton’s 1885 crosstabulation of 928 ‘adult children’ born of 205 mid-parents, by their height and their mid-parent’s height

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4

The Understanding of the Regression Phenomenon

It is fair to say that by 1889 Francis Galton had a clear understanding of the concept of regression. He did not have the command of all the mathematical apparatus I used in the discussion of the concept early in this essay, but his written discussion captured the essence of all of the different formulations given, and his mathematics reflected at least that of (2) and (3). Regression was no longer simply an empirical observation, it was a mathematical deduction. He wrote (p. 95) However paradoxical it may appear at first sight, it is theoretically a necessary fact, and one that is clearly confirmed by observation, that the Stature of the adult offspring must on the whole be more mediocre than the stature of their Parents.4

Questions about how to best estimate the coefficients of the problem, the correlation coefficient and the parameters of the bivariate normal distribution, would be addressed later by Francis Edgeworth and Karl Pearson, but Galton’s grasp of the concepts was as firm as any you are likely to encounter even today. Galton himself was naive in assuming that if data were recorded on a sequence of occasions (not only two) that regression necessarily continued, even at the same rate. Karl Pearson named this ‘Galton’s Law of Ancestral Heredity,’ and even Pearson did not seem to appreciate that the continuation of the phenomenon after the first generation requires rather special assumptions.6 How well did Galton do in communicating that understanding? If judged by the way he is received by a reader a century after he wrote, the answer would have to be, very well indeed. He wrote in clear and direct prose, in terms that we can understand, with the penetration and clarity that are characteristic of only some of the greatest minds. But that is not the standard that is called for. How well did his contemporaries understand his message? Statisticians generally grasped the concept quite well at one level. Edgeworth and Pearson set to work developing the mathematics of regression further, moving towards multiple dimensions and exploring optimum procedures for estimating correlation. In 1901 Bowley wrote the earliest English text to include the new statistical methods, and he included a chapter on the mathematics of the bivariate normal distribution, including both lines of conditional expectation.7 A reader who came away from Bowley’s discussion with the impression that the primary importance of regression was for the study of evolution should have been excused, however, Udny Yule incorporated a full appreciation of the idea of two regression lines into his highly influential text from the first edition.8 At least one perceptive early reviewer of Galton, the philosopher John Dewey, called specific attention to the phenomenon of regression, even noting in effect its dependence upon a stationary population, when

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he wrote that it might not hold in the inheritance of wealth: ‘The tendency of wealth to breed wealth, as illustrated by any interest table, and the tendency of extreme poverty to induce conditions which plunge children still deeper into poverty, would probably prevent the operation of the law of regression toward mediocrity.’9 Of course Galton could have replied that even then the law would hold in standardized units. Still, there were clearly limitations to the general understanding of regression as a phenomenon capable of dangerously misleading. The biometrician Frank Weldon, who himself had a very good grasp of Galton’s message, wrote in a 1905 lecture that [T]his phenomenon of regression ... is not generally understood [V]ery few of those biologists who have tried to use [Galton’s] methods have taken the trouble to understand the process by which he was led to adopt them, and we constantly find regression spoken of as a peculiar property of living things, by virtue of which variations are diminished in intensity during their transmission from parent to child, and the species is kept true to type. This view may seem plausible to those who simply consider that the mean deviation of children is less than that of their fathers: but if such persons would remember the equally obvious fact that there is also a regression of fathers on children, so that the fathers of abnormal children are on the whole less abnormal than their children, they would either have to attribute this feature of regression to a vital property by which children are able to reduce the abnormality of their parents, or else to recognize the real nature of the phenomenon they are trying to discuss.10

In the decades after Weldon wrote, the situation did not change. Following Yule, regression was a staple of textbooks. Its mathematics could be said to be well understood by mathematical statisticians, while applied statisticians, if they were aware of it at all, thought of it as either the use of the method of least squares or as only a biological process. The term ‘regression’ soon came to be regarded as archaic, often accompanied by a brief explanation of its roots in biology but with no indication of the relevance of those roots to other applications. In 1924 the economic statistician Frederick C Mills could write, ‘The term is now used generally, as indicated above, though the original meaning has no significance in most of its applications.’11 It was therefore a trap waiting for the unwary, who were legion. The most spectacular instance of a statistician falling into the trap was in 1933, when a Northwestern University professor named Horace Secrist unwittingly wrote a whole book on the subject, The triumph of mediocrity in business.12 In over 200 charts and tables, Secrist ‘demonstrated’ what he took to be an important economic phenomenon, one that likely lay at the root of the great depression: a tendency for firms to grow more mediocre over time. Secrist was aware of Galton’s work; he cited it and used Galton’s terminology. The preface even acknowledged ‘helpful criticism’ from such statistical luminaries

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as HC Carver (the editor of the Annals of Mathematical Statistics), Raymond Pearl, EB Wilson, AL Bowley, John Wishart and Udny Yule. How thoroughly these statisticians were informed of Secrist’s work is unclear, but there is no evidence that they were successful in alerting him to the magnitude of his folly (or even if they noticed it). Most of the reviews of the book applauded it.13–15 But there was one dramatic exception: in late 1933 Harold Hotelling wrote a devastating review, noting among other things that ‘The seeming convergence is a statistical fallacy, resulting from the method of grouping. These diagrams really prove nothing more than that the ratios in question have a tendency to wander about.’16 Secrist did not understand the criticism, leading Hotelling to reiterate the lesson in a subsequent letter in even plainer language: ‘When in different parts of a book there are passages from which the casual reader may obtain two different ideas of what the book is proving, and when one version of the thesis is interesting but false and the other is true but trivial, it becomes the duty of the reviewer to give warning at least against the false version.’17 One would think that so public a flogging as Secrist received for his blunder would wake up a generation of social scientists to the dangers implicit in this phenomenon, but that did not happen. Textbooks did not change their treatment of the topic, and if there was any increased awareness of it, the signs are hard to find. In the more than two decades between the SecristHotelling exchange in 1933 and the publication in 1956 of a perceptively clear exposition in a textbook by W Allen Wallis and Harry Roberts, I have only encountered the briefest acknowledgements.18 A paper in Psychometrika by RL Thorndike19 is an exception; like Hotelling’s review, Thorndike’s paper was a reaction to blunders in the literature. Thorndike disclaimed originality (‘It is not the purpose of this paper to present any scintillating new statistical ideas’), but he clearly expected that his tutorial would be news to many readers. He mentioned only one offender, a psychologist from the University of Iowa named Crissey, but stated, ‘I select this example without malice – I might have selected any of a number of others’. The more common rule over this two-decade period was that textbooks such as the successive revisions of Yule’s book by MG Kendall kept repeating the earlier material with more recent references and enhanced mathematics. Even after 1956, when (perhaps influenced by Wallis and Roberts) the topic attracted increasing attention, blunders persisted. In 1970 a political economist AO Hirschman (who had presumably not read Hotelling’s review, and was evidently innocent of any awareness of the regression phenomenon) cited Secrist’s book, writing, ‘An early, completely forgotten empirical work with a related theme has the significant title The triumph of mediocrity in business, by Horace Secrist, ... The book contains an elaborate statistical demonstration that, over a period of time, initially high-performing firms will on the average show deterioration while the initial low performers will exhibit improvement.’20 Some writers have known of the problem but still

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fallen in the trap (see, for example, Friedman21 for a discussion of two of these). Other researchers who have known of the phenomenon but not understood it have been frightened by the spectre of one type of error into making another: in at least one instance,22 researchers were so worried about the possibility of committing the fallacy that they introduced a correction for ‘regression effects’ where, not only was none needed, the ‘correction’ produced an erroneous result! The recurrence of regression fallacies is testimony to its subtlety, deceptive simplicity, and, I speculate, to the wide use of the word regression to describe least squares fitting of curves, lines, and surfaces. Researchers may err because they believe they know about regression, yet in truth have never fully appreciated how Galton’s concept works. History suggests that this will not change soon. Galton’s achievement remains one of the most attractive triumphs in the history of statistics, but it is one that each generation must learn to appreciate anew, one that seemingly never loses its power to surprise.

References 1. Friedman M. Do old fallacies ever die? Journal of Economic Literature 1992; 30: 2129–32. 2. Stigler SM. The history of statistics. Cambridge, MA: Harvard University Press, 1986. 3 Galton F. Hereditary genius. London: Macmillan 1869: 64. 4. Galton F. Natural inheritance. London: Macmillan, 1889. 5 Stigler SM. Francis Galton’s account of the invention of correlation. Statistical Science 1989; 4: 73 – 86. 6. Nesselroade J, Stigler SM, Baltes P. Regression toward the mean and the study of change. Psychological Bulletin 1980; 87: 622 – 37. 7. Bowley AL. Elements of statistics. London: PS King, 1901: 316 –26, and later editions. 8. Yule GU. An introduction to the theory of statistics. London: Charles Griffin, 1911 (and many later editions). 9. Dewey J. Galton’s statistical methods. Publications of the American Statistical Association 1889; 7: 331– 34. [Quoted in Stigler, The history of statistics. 1986: 301, and at more length in Stigler, A look backward on the occasion of the centenary of JASA, Journal of the American Statistical Association 1988; 83: 583– 87.] 10. Strong TB ed. Lectures on the method of science. Oxford: Clarendon Press, 1906: 106 –107. 11. Mills FC. Statistical methods. Applied to economics and business. New York: Henry Holt, 1924: 394. 12. Secrist H. The triumph of mediocrity in business. Evanston, IL: Bureau of Business Research, Northwestern University 1933. 13. Elder RF. Review of The triumph of mediocrity in business by Secrist H. American Economic Review 1934: 24: 121– 22. 14. King WI. Review of The triumph of mediocrity in business by Secrist H. Journal of Political Economy 1934; 42: 398 – 400. 15. Riegel R. Review of The triumph of mediocrity in business by Secrist H. Annals of the American Academy of Political and Social Science 1933; 170: 178 –79. 16. Hotelling H. Review of The triumph of mediocrity in business by Secrist H. Journal of the American Statistical Association 1933; 28: 463 – 65.

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17. Secrist H, Hotelling H, Rorty MC. Open letters I. Journal of the American Statistical Association 1934; 29: 196 –200; see Stigler SM., The history of statistics in 1933. Statistical Science 1996, 11: 244 –52, for a full account of Secrist and Hotelling. 18. Wallis WA, and Roberts H. Statistics: a new approach. Glencoe IL: Free Press, 1956: 258 – 63. 19. Thorndike RL. Regression fallacies in the matched groups experiment. Psychometrika 1942; 7: 85–102. 20. Hirschman AO. Exit, voice, and loyalty: responses to decline in firms, organizations, and states. Cambridge, MA: Harvard University Press, 1970. 21. Friedman M. Do old fallacies ever die? Journal of Economic Literature 1992; 30: 2129 –32. 22. Stigler SM. Psychological functions and regression effect. Science 1979; 206: 1430.

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81 Karl Pearson and Statistics: The Social Origins of Scientific Innovation Bernard J. Norton

K

arl Pearson (1857–1936) is widely regarded as the founder of the modern discipline of statistics, and is also famous as a philosopher of science, as a writer on social Darwinism and as a leading mover to install eugenics as the key social science.1 He offers the prospect of a profitable study of the relations which may hold between a man’s scientific work on the one hand and his social and philosophical views on the other – and between both of these and the historical ‘forces’ of his time. It is good to begin by recalling some leading aspects of Pearson’s life and career. He was the son of William and Fanny Pearson. William was a self-made man who had risen from a rural background to become a successful London barrister: Fanny was the daughter of a ship’s captain and owner. In his youth Pearson moved steadily through the educational channels then available to the professional middle classes, going from University College School, via a crammers, to King’s College Cambridge where, in 1879, he was third wrangler in the mathematics tripos. In the following year he was awarded a college fellowship, which gave him six years of financial independence. Pearson undertook postgraduate studies in the universities of Heidelberg and Berlin, and later, whilst ostensibly preparing for a legal career, wrote and lectured on German history and on the ‘advanced’ topics of his day – anarchy, socialism, sex, womens’ rights, and so on. This radical scholarship was not staunched by his appointment to the chair of applied mathematics and mechanics at University College London in 1884, being in fact supplemented by work in the history and philosophy of science.2 ‘Non-scientific’ writing, interestingly, ceased only Source: Social Studies of Science, 8 (1978): 3–34.

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after Pearson’s meeting with W.F. R. Weldon (1860–1906), University College’s professor of zoology, who, on his appointment in 1890, was seeking to inject the then new statistical techniques of Francis Galton into what he (Weldon) had come to regard as the moribund field of evolutionary biology.3 Weldon needed mathematical assistance if he was to succeed, and it was perhaps natural that he should turn to Pearson, as they were colleagues in the cause of university reform.4 Pearson gave more than a little assistance, and from 1893 onwards, began to produce memoir after memoir on the ‘mathematical theory of evolution’, published at first in the mathematical volumes of the Philosophical Transactions of the Royal Society. These memoirs were, in fact, exemplars of a new discipline of biometry, and Pearson’s contributions to biometry over the next fifteen years were to yield developments in statistical theory which Churchill Eisenhart sees as having ‘firmly established statistics as a discipline in its own right’.5 These developments in theory were sustained by institutional moves: in 1901 Pearson and Weldon founded Biometrika, and, on Galton’s demise in 1911, Pearson became the first Galton professor of eugenics at University College London, taking a chair established in that year with funds left by Galton in his will. By 1911, Pearson was already director of a ‘Biometric Laboratory’ within the applied mathematics department at University College, and also director of the ‘Galton Laboratory for National Eugenics’, which had been set up, with Galton’s assistance, in 1906. Now he could combine the two into a Department of Applied Statistics – the first such department.6 The Biometric Laboratory developed statistical methods in a biological context, and the Eugenics Laboratory applied these in work held to show the high dominance of nature over nurture in human affairs. The two put out a range of publications: Biometrika itself, a range of biometric and eugenic memoirs, tracts on issues of the ‘Day and Fray’, several ‘Studies in National Deterioration’, and, from 1926 onwards, the Annals of Eugenics, now reborn as the Annals of Human Genetics. For many years Pearson’s department was England’s premier source of statistical tuition, attracting students later to achieve fame and posts of importance, and producing publications that were to affect significantly the thought of biologists, psychologists, sociologists and statisticians. Both G. Udny Yule and (looking to a later period) Jerzy Neyman were intimately associated with the department at various times.7 Certainly, in Pearson’s time, statistics was always associated with eugenics, and, more generally, was strongly promoted as a mathematical methodology that was capable of elevating several disciplines – for instance, psychology, anthropology, sociology and craniometry – into truly scientific ones. To the end of his tenure in 1930, Pearson emphasized the need to construct a research institute where a ‘novel calculus could be applied to problems concerning living forms’.8 On retirement, Pearson saw his department divided into a statistics department under E.S. Pearson, and a department of eugenics under R.A. Fisher.

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Interestingly, in 1937 there was set up a Weldon chair of biometry, funded by money bequeathed by Weldon’s widow: the first incumbent was to be J.B.S. Haldane. Putting aside the fascinating issues of funding and personnel involved in Pearson’s development of the discipline of statistics, we should now be able to discern a number of clear and important historical problems. One wonders why it is that Pearson should take to evolutionary biology, to biometry, some fifteen years after his graduation as a mathematician. Similarly, one wonders why this biological work, this biometry, should have led to major developments in statistical theory. Then, one wonders how Pearson’s statistics related to his work in the philosophy of science and eugenics – and, indeed, why he should have promoted statistics as a universal methodology for the human sciences. In this paper I will attempt to develop a thesis of the following sort. Pearson entered willingly into biometry when presented with the opportunity by Weldon, not because of Weldon’s exceptional charm or because Pearson was short of problems of his own, but because by the time that he met with Weldon, Pearson had independently developed a pattern of social, philosophical and political thought which disposed him to find Weldon’s programme of mathematical biology one of the greatest possible significance. Before meeting with Weldon, I shall argue, Pearson had grown into a social Darwinist anxious to provide his particular form of Darwinism with a proper scientific basis, and to show that Darwin’s ideas and socialism were complementary, and not opposed, as had been maintained by several leading thinkers of the nineteenth century. Biometry offered him the chance of pursuing these ends. Moreover, I shall argue, Pearson’s conception of ‘properly scientific’ (as articulated in his philosophical writings) was one that made it probable that the development of biometry, should it be at all forthcoming, would yield a harvest of statistical methods. Statistics, thus formed, embodied the central tenets of Pearson’s philosophy of science, and, as such, was to be universally recommended. It was to be applied to eugenics in particular, for eugenic thought was a component of Pearson’s social Darwinism before his meeting with Weldon. Pearson’s Darwinism and his philosophy of science, I shall argue, were integrated components in a world view constructed by Pearson in early manhood, when he was attempting to come to terms with the social and intellectual problems posed to him by his life within late-Victorian society. Thus, I shall argue, we must see Pearson’s work in statistics as the outcome of his attempts to deal with his social and intellectual milieu. The thesis is here developed in several sections, and it will perhaps be useful to give a preliminary account of the ordering of these sections and of their contents. I commence with a section entitled ‘Biometry and Statistics’. Here, after providing social and intellectual background to the biometric movement, I attempt to show something of the way in which biometric problems led to

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the creation of the statistical ideas for which Pearson is famous and which were to form the core of the tuition offered within his biometric laboratory and his department of applied statistics. At this stage, something of the relationship between the distinctive philosophy of science developed by Pearson before his meeting with Weldon and his subsequent biometric and statistical endeavours should start to become apparent. We should be able to see by the end of this section that the form taken by biometry, and its role as the midwife of statistics may largely be understood via its relations with the philosophical views formed by Pearson before he took to biometry. At this stage too, Pearson’s espousal of statistics as a universal methodology should become comprehensible. The second section, ‘Science, Socialism and Social Darwinism’, addresses the further topic of why it was that Pearson was prepared to be interested in biology when approached by Weldon. It is one thing, after all, to explain (in the manner of Section 1) the particular form taken by biometry, and to exhibit this form as a cause of biometry’s having led to statistics. It is another, distinct task to explain why Pearson should have been prepared to enter into biological work. At the time it was not a recognized or honoured path for the mathematician and seems to have done little for Pearson’s career prospects – as, for example, when he applied without success for the Savilian Chair at Oxford in 1897. The line I take in this second section is that of denying that Pearson was ever primarily interested in biology in its own right. I shall suggest rather that by the time of his meeting with Weldon, Pearson was already an established social Darwinian – that is to say, one who supposed that a scientific guide to human affairs could be obtained from the philosophy of Darwin, suitably interpreted. Pearson, I will show, entered into biometry, into evolutionary biology, not only with a view to giving an exemplar of a truly scientific biology, but also with the aim of providing his social Darwinism with suitable underpinnings; he also hoped to show that Darwinism enjoined a move to state socialism, rather than to the laissez-faire capitalism recommended by earlier writers on social Darwinism. At this stage too, we shall see that before meeting Weldon Pearson’s thought already had a significant eugenic component. In a third section, entitled ‘Scenes from a Victorian Life’, I attempt to trace the development of the patterns of thought which, I claim, predisposed Pearson to take to biometry. Here I will discuss his early days in Cambridge, Heidelberg and London, tracing the incidents and problems thrust upon him by the conditions of his life; I will show how his responses to these led him to the ‘primed’ condition that disposed him to respond so favourably when approached by Weldon, and thus started the major enterprise of his life – the building up of a biometric school of statistics and social biology. Naturally, the explanations I offer have their difficulties, and, perhaps, foremost amongst these is that of explaining the particular pattern of Pearson’s response to the stimuli of his early life. After all, in human affairs, the same set of stimuli do not always call forth the same response: here I explore the

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possibility of explaining Pearson’s making the sort of response that he did in terms of the natural ‘interests’ of persons occupying his sort of social role in later Victorian society. Such a strategy has severe difficulties and these are finally made very clear.

1. Biometry and Statistics (a) General Background Biometry was a construct of England of the late 1890s, and to a degree to be determined, reflected its circumstances, some of which were as follows. In ‘scientific’ England, in the home of Darwin, relatively little work had been done on the mechanism of evolution – on the physiology of heredity and variation and the action of natural selection, for example.9 Academic biologists, by and large, had tended to devote their energies to the establishment of the historical evolutionary relationships connecting different groups in the plant and animal kingdoms. Statistics, insofar as it was an institutionalized concern, was basically non-mathematical, despite the existence of good work by Venn, Marshall, Edgeworth and others.10 British social thought of the period contained several streams which we shall see to have been relevant to the development of Pearson’s statistical work. The 1880s saw the onset of various types of socialist thought.11 In 1881 Henry George came to England: in the following year Hyndman set up the Social Democratic Federation, and, in 1883, the Fabian Society was inaugurated. All of this was played out against a growing recognition of the rottenness of urban England. 1883 saw the publication of The Bitter Outcry of Outcast London, revealing the conditions of the sub-proletariat, who were to feature in Charles Booth’s Life and Labours of the People in London as the ‘very poor’. 1890 saw the appearance of William Booth’s In Darkest England and the Way Out. 1884, 1886 and 1887 saw large civil disturbances, deeply worrying to the English middle classes. At about the same period we find Bradlaugh making a reputation on the strength of atheism, Besant facing prosecution for issuing a tract on birth control, and good popular audiences for the lay sermons of scientific populists like Tyndall, Clifford and Huxley.12 Social Darwinism was a popular genre of thought, with Darwin’s ideas being adapted in many directions to suit the preference of the adaptor.13 Some thinkers still followed Spencer in seeing Darwin’s work as underpinning a social philosophy of individualism and competition, but others (as we shall see) now read a more collectivist message from the pages of the Origin of Species. T.H. Huxley, typically, threw doubt on the value of any such process of extrapolation from nature to man.14 In the 1890s, Francis Galton was one of Britain’s leading ‘men of science’. As several authors have pointed out, he was a man motivated by strong

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eugenic views, a man whose attempts to understand human heredity were inspired by the hope of showing the dominance of nature over nurture; and this, in turn, led him to uncover certain crucial statistical notions – notably those of a distribution of variations, of correlation and of regression. Before 1900, Galton was able to attract only a small following for eugenics, which remained more of a catalyst to research than a social movement. But, as several authors have noted, the events of the Boer war, coming as they did in a period occupied with a ‘quest for national efficiency’, were to pave the way for a strong popular interest in eugenics in the first decade of the twentieth century.15 As early as 1913, the Daily Sketch was splashing the birth of Eugenette Bolce, Britain’s – indeed, Hampstead’s – first ‘eugenic baby’.16

(b) Intellectual Structures Let us now pass from the background to biometry to the subject itself. Statements of its aims were common in the literature, but it may conveniently be regarded as a discipline which applied mathematics to the study of the variations found among the members of large populations, including human populations. Perhaps the standard statement of biometric problems is one due to Weldon, published first in 1893: The problem of animal evolution is essentially a statistical problem: that before we can properly estimate the changes at present going on in a race or species we must know accurately (a) the percentage of animals which exhibit a given amount of abnormality with regard to a particular character; (b) the degree of abnormality of other organs that accompanies a given abnormality of one; (c) the difference between the death rate per cent in animals of different degrees of abnormality with respect to any organ; (d) the abnormality of offspring in terms of the abnormality of parents and vice-versa. These are all questions of arithmetic; and when we know the numerical answers to these questions for a number of species, we shall know the direction and rate of change in these species at the present day — a knowledge which is the only legitimate basis for speculations as to their past history and future fate.17

The statistical developments which the pursuit of these and related biometric problems led Pearson to were nicely summarized by the sociologist S.A. Stouffer in a paper which conveys something of Pearson’s personal magnetism – one, it should be said, that could attract or repel, but was a strong force in either case.18 I wish I could communicate to you, and especially to those of you who are just now beginning your professional careers in a world of statistics incredibly more sophisticated than that of Karl Pearson’s day, something of the thrill in meeting in person and studying under a man of Pearson’s immense reputation. Author of the Grammar of Science; perfector of simple linear

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correlations; inventor of multiple and partial correlation, of curvilinear correlation, of tetrachoric and biserial correlation; discoverer of the χ2 function for summarizing multinomial data with magnificent simplicity; builder of a beautiful system of frequency curves derived from a single differential equation which in turn harked back to the hypergeometric series; founder of Biometrika and author or co-author of a prolific literature applying thse new statistics to biological and sociological data — Karl Pearson was a hero of Asgard to an American boy vouchsafed a visit to the home of the gods. Indeed, Pearson was Thor himself — for the thunderbolts with which he attacked unsparingly those who dared oppose him were echoing and reechoing.

Why, one asks, did the study of biology, albeit of mathematical biology, lead to such results? Certainly, they are not the inexorable consequence of the successful application of mathematics to evolutionary biology, as readers of D’Arcy Thomson’s On Growth and Form will appreciate.19 The answer, I wish to suggest, resides in the circumstance that, for Pearson, biometry was a branch of biology which stressed very heavily the importance of exact measurement and exact description, without theory, of the observable phenomena of evolutionary biology. To see this point it is useful to consider a particular example, namely that of Pearson’s study of heredity which led to the massive developments in the theory of correlation itemized by Stouffer above. As such, heredity is a particularly good choice, for, as Stouffer’s passage indicates, Pearson’s work in statistical theory was focused very strongly upon the theory of correlation; and it would appear that this was no accident, as Pearson’s statement of the aims and goals of statistics ran as follows: The purpose of the mathematical theory of statistics is to deal with the relationship between 2 or more variable quantities without assuming that one is a single-valued mathematical function of the rest. The statistician does not think a certain x will produce a single-valued y; not a causative relation but a correlation. The relationship between x and y will be somewhere within a zone and we have to work out the probability that the point (x,y) will lie in different parts of that zone. The physicist is limited and shrinks the zone into a line. Our treatment will fit all the vagueness of biology, sociology, etc. A very wide science.20

Galton had developed the notions of correlation and regression whilst studying heredity in man, but in doing so, he always linked his statistical investigations with exercises in theorizing about the physiology of heredity – about the underlying biological mechanisms that might be responsible for the patterns of correlation and regression which he observed.21 Pearson had absolutely no time for such a combined approach. Science, for him, was the stern business of observation and measurement, and he stressed heavily what is now termed ‘operational definition’. The thrust of his approach may be gauged from the following Pearsonian definition of the problem of heredity.

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Heredity. Given any organ in a parent and the same or any other organ in its offspring, the mathematical measure of heredity is the correlation of these organs for pairs of parent and offspring ... The word organ here must be taken to include any characteristic which can be quantitatively measured.22

Pearson’s goal was a phenomenal theory of heredity lacking any theoretical mediation (such as Galton’s ideas on hereditary particles). Given his chosen mathematical measure of heredity, it is unsurprising that biometry should have led to the developments in theory mentioned above. Let us take a particular example – namely, Pearson’s development of the theory of multivariate normal correlation. This was first presented in a memoir of 1896 in which he investigated contemporary claims that a relaxation of natural selection would put evolution into reverse.23 This, of course, was a view that could be supported by citing Galton’s observation that sons regressed linearly upon fathers in respect of stature with a coefficient of regression of about one third. This suggested that if an ‘improved’ population deviating from an original population mean stature by z inches was allowed to reproduce without the operation of selection, then successive generations of posterity would show z/3, z/9, z/27 inches of deviation, and so on. Pearson was anxious to combat this view, and while I prefer to discuss his motivation for so doing at a later point in the paper, it is worth pointing out that even at this early stage the social and eugenic side of biometry was present in Pearson’s published works.24 For, while he treats this problem of regression quite generally, he does make it clear that the human situation is of most concern. Galton, of course, was familiar with the bivariate normal distribution – for that, in good approximation, is the distribution followed by parental and filial statures taken jointly.25 Pearson now, in an attempt to construct a model allowing for the influence of ancestry more distant than the immediate parentage, developed an expression for the joint distribution of n normal variates – an expression, that is, for the multivariate normal correlation surface. He hoped that it would transpire that the values of the various correlation coefficients connecting different degrees of ancestry would be such as to yield multiple regression equations which indicate that when a line of ancestry had been long selected (that is, if the grandfather and the great grandfather and so on had been exceptional as well as the immediate parentage), then regression of the sort observed by Galton among the general population would no longer occur. This, indeed, was the start of Pearson’s work on the ‘law of ancestral heredity ’, which deserves separate treatment.26 All that matters for the moment is that the very significant step of developing the theory of multivariate normal correlation arose from a concern with a biological problem and from a determination to treat the problem in a particular way. Interestingly, in the same paper Pearson showed that the best value of correlation coefficient (ρ) of a bivariate normal distribution is given by the formula now said to give the ‘sample product moment coefficient of correlation.’

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We can see therefore that Pearson’s massive developments of the statistical theory of correlation, the branch of his work that he invested with the highest significance, orginated in his theory-free approach to heredity. He wished to make probabilistic predictions about the outcome of a line of ancestry without the necessity of discussing underlying mechanisms of heredity. This was quite out of step with contemporary biological practice, which was, if anything, a great deal more interested in getting to grips with the underlying physiology of heredity than in the sheer business of prediction. But, said Pearson, on the eve of the rediscovery of Mendel’s ideas, the would-be physiologists were like planetary theorists rushing to prescribe a law of attraction for planets, the very orbital forms of which they have not first ascertained.27

It was in this way that the advantage of biometry led to developments in statistical theory – a circumstance, of course, that it is quite consistent with the mathematics, once embarked upon, ‘taking up a life of its own’: issues like those of the sampling distribution of the correlation coefficient then ‘arose naturally’ and had to be dealt with. But the point remains that the search for a new mathematical science of heredity, for a science of a particularly austere sort, led to developments in statistical theory. Correlation looms large in Pearson’s work, and this should not surprise us, having seen his definition of the purposes of statistics. But, as Stouffer showed, Pearson’s work was not exhausted by his labours in the field of correlation. Other aspects of his work also arose in a biometric context, and it is not too much to say that they reflect an approach to science with a massive emphasis on the production of mathematical ways of describing observable phenomena, and on ways of checking up on the goodness of the description. Thus, for example, Pearson’s first biometric paper was devoted to developing a method for deciding whether a particular assymetrical frequency curve found by Weldon when sampling crabs could be resolved as the sum of two normal distributions.28 His second paper developed the series of Pearson curves as a way of describing non-symmetrical and unresolvable distributions of (biological) data.29 And, generally, if the correlational part of Pearson’s work stemmed from a desire to find theory-free connections between different sets of data, then the aim in this other part of his work seems to have been to find ways of accurately describing any given set of data – notably by fitting a curve to it. Not all of Pearson’s early statistical developments can be seen as the direct outcome of attempts to deal with specific biological problems, but they can, I think, be reasonably seen as more general developments jibing with the aims for biometry (and, more generally, for science) noted already in Pearson’s approach. The chi-squared goodness of fit test, for example, developed in I900, is surely a good instance.30 It is not that if we know Pearson’s aims for science, his insistence on mathematical representation of the phenomena as the major goal, then

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we are led to the test. That is where his genius came into play. Rather, it is that if we understand these aims and goals we can see the attraction, for him, in pursuing such a mathematical investigation.

(c) Questions of Method The remarks just made about the methodological style of biometry may be supported by going to texts, to Pearson’s methodological writings which were largely completed before his entry into biometry. They were most widely publicized in his Grammar of Science, first published in 1892.31 Given the aims and goals of biometry at the level of methodology we can, I hope, see why and how biometry led on to statistics. What I wish to suggest now is that it is no surprise that biometry had these aims and goals, for they came directly out of Pearson’s already formed methodological ideas. These, interestingly, were ones that he could develop and enhance as he developed his statistical thought. In the three editions of the Grammar (1892, 1900, 1911) we find a philosophy of science which resembles some of the views of the later Logical Positivist school of philosophy. In a doctoral thesis Chauncey Riddle has discerned three main components to Pearson’s epistemological writings, namely ‘empiricism, a Kantian emphasis on the role of the mind in organising and interpreting sensation, and a Cartesian faith in mathematics as the key to organised scientific thought’.32 The Grammar, Riddle notes, is ‘largely an attempt to impress the ideas of Mach upon the English speaking world’. This seems entirely correct; Pearson was an instrumentalist and a sensationalist, a man who denied the possibility of getting to grips with the Ding an sich and who expressly ruled out the possibility of a fruitful metaphysics. Metaphysical speculation, he in effect said, was meaningless. Objects, in this philosophy, were mental constructs out of sense data, and what so fascinates one about this aspect of Pearson’s thought is his Kantian emphasis on the possibly active power of the mind in creating experience. For he wrote that it may be the perceptive faculty itself, which, without being directly conscious of it, contributes the ordered sequence in time and space to our sense impressions. The routine of perceptions may be due to the recipient and not characteristic of the material.33

Any connection, through experience, between the self and the real world was therefore highly tenuous, and the only goal for science that made sense was an instrumental one. One could not learn about underlying realities, and the postulation of a realist ontology of atoms, molecules and so on was, in this philosophy, rendered incoherent or redundant. All that science could do was to uncover laws that summarized the flow of phenomena and functioned as instruments of prediction, whose ultimate rationale lay in the enhanced

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potential for survival that they offered in the evolutionary struggle. This they did best when they partook of the economy and precision granted by expression in mathematical form. Pearson, clearly, saw biometry as an exemplar of his philosophy put into operation. He saw himself as finally ridding biology of its traditional metaphysical integuements, and took pains to introduce two new chapters on biometry in the second edition of the Grammar. Biometry, clearly, was a natural Pearsonian research programme and, it should also be clear, the statistical methods emanating from it must be seen as the mathematical encapsulation of a philosophy of science Pearson had developed before taking up biometry. Good Cartesian that he was, statistics offered a mathematical way of economically describing the flow of appearances in the non-physical sciences. But, good Kantian that he was in other respects, statistics offered the makings of a philosophical revolution which could be carried forward as his work in biometry and statistics grew. As his contributions to the theory of correlation became more refined, Pearson took to suggesting that this work was philosophically profound. For it showed that the great Kant had been wrong in asserting that determinism was a precondition for human experience.34 What was needed, Pearson wrote, was the kind of semi-determinism that the statistical methods of correlation were adapted to handling. The category under which experience fell was not deterministic causation, but, rather, the looser framework now describable via the mathematical theory of correlation. All scientists, he thundered, should desist from trying to conceptualize the world under the category of causation. Instead, they should adopt the new category implicit in his own work, namely that of correlation, under which all our experience whatever of the links between phenomena can be classified.35

All of the foregoing, I hope, lends support to the thesis that biometry begat statistics on account of its peculiar methodological form. This, by turn, was due to the circumstance that before meeting Weldon, Pearson had worked out a distinctive epistemology and methodology for science. In particular, the Kantian tinge of this philosophy made it possible for Pearson to see his work in correlation as being philosophically significant – a feature which undoubtedly sustained his interest in correlation and all its possible ramifications.

2. Science, Socialism and Social Darwinism I now come to the problem of why it was that fifteen years after graduation, after a period in which he had done no biological work at all, Pearson should have been prepared to embark upon a new career in biometry when tackled

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by Weldon in the early 90s. One response, seemingly that of J.B.S. Haldane, is that Pearson’s decision to move in a biological direction rather than some other, and his founding Biometrika rather than, say, Technometrika, were largely accidents of fate: it just happened to be Weldon, a biologist, who wished for assistance.36 It seems to me that such an approach is implausible, for it undervalues the magnitude of Pearson’s response. This may be gauged from the following bibliographical statistics.37 In the period up to 1894 (that is, Pearson’s ‘pre-biometric’ phase), Pearson published 55 items listed as ‘Literary and Historical’ in the official bibliography of his works; thereafter he published only a further 10 items so classified. The period after 1894 contained 405 items listed as ‘Statistical’. Moreover, the section headed ‘Pure and Applied Mathematics and Physical Science’ contains 4 items in the period to 1894, and 32 thereafter, suggesting a more or less uniform rate of productivity in this area. In short, there does seem to have been an amazing turn-about in Pearson’s pattern of work, as if biometry had the power to absorb the interests that were previously being discharged in the production of literary and historical work. We must ask why this change occurred. It is this turn-about by Pearson that I now address, but not before stressing that it would be wrong to see Weldon’s role as an overly simple one. Weldon may have led Pearson to use and develop methods pioneered by Galton, but we have to explain why it was that Galton’s works did not speak to Pearson unmediated by Weldon. Indeed, things are more difficult even than this, for Pearson had encountered Galton’s Natural Inheritance at the date of its publication in 1889, and had given a talk upon it to a Men and Women’s Club of which he was then a member. (I shall return to this club in the next section of the paper.) In his talk, Pearson gave a less than fulsome account of Galton’s methods: Personally I ought to say that there is, in my own opinion, considerable danger in applying the methods of the exact sciences to problems in descriptive science, whether they be problems of heredity or of political economy: the grace and logical economy of the mathematical processes are apt to so fascinate the descriptive scientist that he seeks for sociological hypotheses which fit his mathematical reasoning and this without first ascertaining whether the basis of his hypothesis is as broad as that human life to which the theory is to be applied. I write therefore as a very partial sympathiser with Galton’s methods.38

And, in his copy of Galton’s book, Pearson pencilled in his exasperation with Galton’s style of argument. On page 30, for example, he wrote, testily, that It is merely an analogy without any scientific value as to the how still less to the why.39

Yet, later on, Pearson recalled that he had interpreted the introduction to Natural Inheritance to mean that

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there was a category broader than causation, namely correlation, of which causation was only the limit, and that this new conception of correlation brought psychology, anthropology, medicine and sociology in large parts into the field of mathematical treatment. It was Galton who first freed me from the prejudice that sound mathematics could only by applied to natural phenomena under the category of causation.40

Clearly, Weldon acted as a middleman, able to reinterpret Galton’s statistical approach to biological matters in a manner that harmonized with Pearson’s stern methodological criteria. Certainly, in the statement of problems due to Weldon, and in Weldon’s early work, we find none of the analogical reasoning and physiological theorizing that Pearson so disliked in Galton’s work. But, if we accept that some methodological refining was necessary if Pearson was to take the biostatistical bait, so to speak, there remains the issue of explaining his subsequent total devotion to biostatistical inquiry, his new devotion to biological inquiry. One still wishes to know why Pearson was so prepared to dive into biological and evolutionary issues fifteen years after graduating as a mathematician. In the remainder of this section, I shall try to show that by the time of his meeting with Weldon, Pearson was intellectually primed to take up just the investigations that he did. In the final section I shall address the issue of how he came to be so primed. It should be remembered that Pearson’s philosophy of science was also a philosophy of life. It is no surprise to learn this when one recalls that Pearson’s ideal was the freethinker, the abider by the ‘ethic of freethought’. This person would have ‘assimilated the results of the highest scientific and philosophical knowledge of the day’, he would be a ‘sound citizen’, trained in the ‘impersonal judgement’ criteria of the scientific intellectual: he would be able to assess, for example, the views of Weismann on the continuity of the germ plasm and to employ this judgement when considering the right conduct of society towards its ‘anti-social members’. This, Pearson averred, would remain an open question until one knew ‘what science has to tell us on the fundamental problems of inheritance’. Quite generally, Pearson wrote, in the Grammar, each one of us is now called upon to give a judgement upon an immense variety of problems, crucial for our social existence. If that judgement confirms measures and conduct tending to the increased welfare of society, then it may be termed a moral, or better, a social judgement. It follows then that to ensure a judgement’s being moral, method and knowledge are essential to its formation. It cannot be too often insisted upon that the formation of a moral judgement — that is one which the individual is reasonably certain will lead to social welfare — does not depend solely on the readiness to sacrifice individual gain or comfort, or on the impulse to act unselfishly: it depends in the first place upon knowledge and method. The first demand of the state upon the individual is not for self sacrifice, but for self improvement.41

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And, as one reads further into the pages of the Grammar, it becomes clear that what Pearson means by ‘increased welfare of society’ is not some Benthamite entity, but, rather, something crucially related to ideas like those of ‘national survival and supremacy in the inevitable international competition for existence’. Pearson, indeed, is known to social historians as a key promoter of ‘external’ social Darwinism, of the doctrine that the correct way of envisaging the struggle for existence in human affairs is not at the level of man against man, but at that of nation or race against nation or race, with success going to the best organized group. ‘ The growth of national and social life’, Pearson wrote, can give us the most wonderful insight into natural selection, and into the elimination of the unstable on the widest and most impressive scale.42

So, for Pearson, morality was dictated by considerations of what would be of avail to a society in its necessary struggle with other societies, and it is in this context that the defence of socialism appears in Pearson’s work – though, as we shall see, his style of socialism was distinctive. Socialism, by which he meant the ‘tendency for social organisation, always prominent in political communities’, could be justified by its power to bestow success in the ‘intense struggle which is ever waging between society and society’. The lesson of history was the lesson of socialism, and science would ultimately balance ‘the individualistic and socialistic tendencies better than Haeckel and Spencer seem to have done’. Certainly, in the face of the severe struggle, physical and commercial, this fight for land, for food and for mineral wealth between existing nations, we have every need to strengthen by training the partially dormant socialist spirit, if we as a nation are to be among the surviving fit.43

This new pattern of organization, said Pearson, must ‘largely proceed from the state’. Here it is that science relentlessly proclaims: a nation needs not only a few prize individuals; it needs a finely regulated social system — of which the members as a whole respond to each external stress by organized reaction — if it is to survive in the struggle for existence.44

And, quite generally, if we look at his writings produced by the time of his meeting with Weldon, we can see that Pearson’s social and ethical thought had a thoroughgoing Darwinian form. It certainly included commitments to the following propositions.45 (i) History must be understood in terms of the principles of Darwinian evolution. At this stage it may become a science, a biological determinism to rival historical materialism.

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(ii) In important practice, the Darwinian struggle for existence in history goes on between group and group, with different social mores waxing and waning in influence according to their power to assist the group in its inexorable struggles. (iii) The ultimate legitimation of morality has to be sought in the biological standard of group survival. Only with a people attuned in their outlook, showing Clifford’s ‘tribal conscience’, could there be built up a society with ‘permanent stability’. (iv) On scientific grounds, therefore, the proper goal for the members of a society is the production of ‘a finely regulated social system’ enabling it best to survive in the struggle and to emerge ‘among the surviving fit’. The best way to achieve this was a move to a form of state socialism, run by talented experts. By now, I suggest, we should be able to see why work in evolutionary biology could so attract Pearson; why he was, so to speak, ‘primed’ to respond to Weldon. We can see too, at least in outline (an outline to be filled in in the next section), why eugenics could so attract him – for eugenics was just the branch of evolutionary biology that could be deployed to maximize the fitness of the socialist state envisaged by Pearson. No wonder we find that, in 1894, Pearson could write that it would only be when mathematical work on the ‘relative numerical importance of the several factors of natural selection’ had been completed that it would be time to talk about ‘the antagonism of socialist theory to biological laws’.46 Clearly, he was anticipating the results of work that, he would hold, showed that laissez-faire in reproduction led, not as Spencer had predicted, to sociobiological advance, but, in fact, to the proliferation of the unfit at the expense of the professional middle classes.47 Certainly, this general perspective – namely that Pearson was prepared to work in a biological field when approached by Weldon because his thought was already steeped in Darwinian notions needing, given his philosophy, mathematical development – may be supported powerfully by autobiographical evidence. This takes the form of a letter which Pearson wrote to the Manchester Guardian in 1901, replying to its review of his recent work, National Life from the Standpoint of Science. The latter was a gloomy and aggressive jeremiad which had presented a ‘scientific’ view of the nation as that of an organised whole, kept up to a high pitch of internal efficiency by insuring that its numbers are substantially recruited from the better stocks, and kept up to a high pitch of external efficiency by contest, chiefly by way of war with inferior races, and with equal races by the struggle for trade routes and for the sources of raw material and food supply. This is the natural history view of mankind, and I do not think you can in its main features subvert it.48

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In his letter, Pearson took great pains to rebut the Guardian’s charge that he was just another politically ignorant biologist turning his microscope to the world of affairs with the usual disastrous consequences. What grounds, he inquired, did the reviewer have for supposing that I may not have spent more years of my life in historical work than in the study of heredity; that I may not possibly have laboured more carefully at history than at biology; that more of my published work may not deal with the former rather than the latter; nay that even my endeavour to understand something of inheritance and of racial struggle may not have arisen from my attempts to read history aright? May it not be that I am convinced that through the principle of evolution by natural selection combined with inheritance, light alone can be thrown on that maze of wars, movements, national survivals and national decays which passes for history in our current textbooks? Is it not just possible that a man who has thought and worked in the historical field may have turned to the biological field because he has been driven by the force of facts to see that the keynote to the history of man lies in the struggle for food and in the struggle to reproduce, which are the great factors at the base of all biological reasoning with regard to the development of animal life? I ask what reason you have for supposing my history an outgrowth of ‘biological consciousness’ rather than that my interest in heredity has arisen from my conviction of its bearing on historical studies.49

Here, it seems plain, we have the source of Pearson’s preparedness to enter the field of evolutionary biology.

3. Scenes from a Victorian Life If the foregoing analysis is approximately correct, and it is accepted that Pearson’s readiness to enter into biometry and the power of biometry to produce statistics linked to eugenics can be understood in terms of the social, ethical and epistemological ideas which Pearson had developed prior to his meeting with Weldon, then there remains the task of explaining how it is that he came to have this intellectual disposition. It is to this task that I now turn, and I shall proceed by discussing Pearson’s development during his ‘pre-biometric’ phase – that is, the period in which he was an undergraduate, a fellow of King’s and a London-based intellectual. As the section develops it should be possible to clarify the exact nature of Pearson’s ‘non-scientific’ thought.

(a) Cambridge The roots of Pearson’s philosophy of science and social Darwinism may first be sought in his undergraduate years at King’s College Cambridge.

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Here he met Robert Parker the future law lord,50 Henry Bradshaw the librarian, Macaulay the mathematician and Oscar Browning the historian. Then, as ever, he looked for a few close friends, and was especially close with Parker. Like many undergraduates, Pearson did not enjoy a carefree life. His ‘Commonplace Book’ for 1877, for example, suggests a state of mental turmoil which led him to a piece of self-analysis in which he attempted to clarify his views on religion ‘till I was left with some definite idea of what religious belief I have or whether I have any at all’. His answer was vague and rambling, but showed clearly enough a growing contempt for laissez-faire society and for Christianity.51 At times, he wrote, he could believe in a God, but not when he encountered the poverty of Victorian Britain. Pearson, in short, was a candidate for philosophy (as had been Clifford, Marshall and others before him at similar periods in their development),52 and his writings portray him as searching for a creed, for some secular religion upon which he could focus the religious feelings so common among Victorians. This comes out more clearly in a letter to Parker, where Pearson wrote that since all my religious dogmatic faith fell to the ground, I feel that I can only be happy by adding a mystic ideality to everything, and looking at everything from a religious point of view ... It is this spirit of the ideal which Carlyle tries to cast over everything and which delights me so.53

At this time, Pearson’s non-mathematical reading was chiefly in British empiricist philosophy and in German literature – in Goethe, Herder, Schelling and others. Like Carlyle he was an enthusiast for Wilhelm Meister. In February 1879 he read Berkeley’s works, and at about the same time decided to go to Heidelberg to study philosophy and physics.

(b) Heidelberg In Heidelberg, doubtlessly, Pearson hoped to find a new philosophy, a new creed that would satisfy his need for something in which to believe. We can garner something of his mood and thoughts from his letters, but also from a book, the New Werther, which Pearson published under the pen-name ‘Loki’. The Werther, Pearson was to claim, was written in a deliberately ‘gush style’, but nevertheless it tells a great deal about Pearson’s time in Germany – for, judging from Pearson’s other attempts at fiction, it seems improbable that he had the skill to create a character whose thoughts strayed too far from his own. In the pages of the Werther we learn a great deal about his unhappiness in Cambridge, his decision to turn to Germany – the ‘country of ideas’ – and his love of things German, which was to be reflected in his changing his name from Carl to Karl. In Germany he seems to have developed a mild nature-mysticism and to have kept the company of Raphael Wertheimer, a Jewish law student and radical who

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features prominently in the Werther; there he is depicted as introducing Arthur (the autobiographical tragic hero) to socialism, saying of the English that they do not recognise the difference between a French communist, a Russian nihilist, and a German social democrat, but brand them with a common stigma as subverters of society.55

Wertheimer, a social democrat, insisted that We do not wish a revoutionary change in all old laws and customs; we recognise the truths which history has taught, that real change is gradual, and yet also that change is necessary to life. The violence of some persons claiming to be members of the party is due to the ignorant and vicious whom the leaders cannot prevent from joining their banner.56

Clearly, Wertheimer found a convert of sorts in Pearson, who thereafter proclaimed himself a socialist – though, as will become ever more apparent, an elitist state-socialist. This comes out rather clearly in one of the first papers which he wrote after his return from Germany, a short work entitled ‘Anarchy ’. In this he wrote with genuine horror of the state of London’s sub-proletariat: Those weak and emaciated beings, weak and feeble as they look, have the power in their millions to throw down the few feet of bricks which guard the arsenals. Those three million could sweep a few thousand police and soldiers before them as the wind blows a handful of chaff.57

He was fearsome lest there be an uncontrolled anarchic revolution from below, something he took to be the natural outcome of existing conditions. In its place, Pearson recommended a gradual ‘revolution’ from above, leading to a form of society with ‘forms and grades’ and with power based not on a financial hierarchy but on a hierarchy of ‘power intellectual’ which alone would determine whether the life-calling of a man is to scavenge the streets, or to guide a nation.58

How the transfer was to be effected was unstated, but, Pearson insisted, the new order would need a new religion which would form a real bond ‘between class and class, between man and man solely on the score of their manhood’. Some indication of what this might mean was given in a further paper of the same year, on ‘Political Economy for the Proletariat’, which attacked traditional political economy and compared the ‘individualism of Bentham’ unfavourably with the ‘socialism of Fichte’. Pearson, clearly, was attached to some of Fichte’s ideas, and wrote that in the new order, for which he (Pearson) hoped, the state would be charged with the duty of ‘the improvement of mankind’, and that in the science that would treat of the organization of the state.

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All the ordinary categories of political economy — capital, labour, land, trade and so forth — must be judged from this new standpoint, and I fear not a few of the results attained will be found to differ from the mammon-worshipping doctrines of Ricardo and his disciples.59

The nearest extant approach to what he had in mind, wrote Pearson, again reflecting his German experience, was to be found in the work of the KathederSocialisten who, under Schmoller, helped frame Bismarck’s social policies. In particular, Pearson singled out the ideas of Held and his school, citing their claims and demands with approval: They demand that the economic man must also be considered as a member of a state organism, they reject the suggestion of an unusually valid natural law, and demand that each existing judicial system must in whole and part be considered critically as a factor of the greatest importance in the formation of economic relations ...60

It seems therefore that in Germany Pearson picked up what might, somewhat anachronistically, be described as a Spenglerian view of the state, one stressing the desirability of an organic unity with hierarchical ranks and grades bound by feelings of common purpose. Shortly we shall see how this political line of thought developed whilst in London in the period prior to his meeting with Weldon. But, for the present, I would like to pause briefly to trace the early development of Pearson’s epistemology and philosophy of science at this period, thinking particularly of his interesting neo-Kantian and instrumentalist perspective upon knowledge. Returning to Heidelberg, we find that Pearson studied philosophy under Kuno Fischer, but read more widely than was required. By May 1879 he was reading Kant’s Metaphysics of Ethics as a follow up to the Critique of Pure Reason, which he had meticulously studied whilst in Cambridge. By 25 May, Pearson felt able to write to Parker, saying more about his work and rejecting the possibility of a metaphysical foundation for ethical judgement. You are certainly right about the foundation of religion not being the pure reason, this Kant I think has conclusively prove in the Kritik der reinen Vernunft. In the Metaphysics of Ethics and the Practical Reason, he attempts to base religion on morality, or a belief in God follows from the necessity of moral order in the Universe. They seem both to me thoroughly unsatisfactory. He even contradicts himself by founding his moral system on a moral sense (conscience, which is innate and universal), which he asserts dogmatically to exist. Is this innate sense the same in the cannibal and the educated man? It is not empirical, according to Kant, and there is no question of its development. If then we can’t found religion on morality we are left alone with the emotions, the feeling of want, religiosity, and quite enough too.61

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Perhaps the sequel to this was not surprising. By 20 June Pearson was writing to Parker, telling of a dinner at which he had told Fischer that philosophy was a vain pursuit, and that he (Pearson) ‘felt at a lower ebb of despair with regard to the truth than I have ever felt before in my life’. And, as for truth, it was a dubious affair. Let us consider whether it can be a law of nature. Does anybody know what we mean by this expression? The more I have studied science and physics, the more I see that we know nothing of what we call nature — of electricity, light and attraction we know nothing. What is the sense of calling light a vibration? Or that gravity is a force between particles of matter varying as the inverse square of the distance? ... The term was invented some hundred years ago to describe a phenomenon which it attempts to explain . ... Besides, the whole tendency of modern philosophy since Kant is to assure us that the so-called laws of nature exist in our minds, are a logical necessity of our minds which impress them on the things themselves for they can only observe things in such relations. Fancy truth a function of that absurd humbug man’s mind!62

Faced with such difficulties, Pearson decided temporarily to abandon the study of philosophy, his reason having been shattered ‘by the purely negative results’ found in the works of the philosophers. Briefly thereafter he toyed with the idea of going to Berlin, to work in natural science with Kirchoff or Helmholtz; but, by October, Pearson had decided to throw over both physics and philosophy and reluctantly to submit to a career at the bar.63 As we have seen, he was to return to philosophy, and would build upon the base, small that it was, that was constructed in Heidelberg – namely his conviction that science described but did not explain; his views on the impossibility of knowing the thing in itself; and his addiction to some of Kant’s ideas. Unsurprisingly, Pearson did not favour Kant’s metaphysical approach to ethics. We have seen this above, but the full force of his distaste came out in a review of 1883, of one of Fischer’s books. In the review Pearson wrote kindly of Kant’s Critique of Pure Reason but harshly of his ethics. And, thinking doubtlessly of the Hegelian revival in Oxford, he noted that there was in the ethical writings an entire change of front, the door is to be thrown open to the whole body of emotionalists, mystics and metaphysical idealists.64

Clearly, Pearson was open to a non-metaphysical account of ethics, and, as we have seen, he was to find – or, more accurately, to suppose that he had found – such an account in his Darwinian explorations. Thus, it might be said that once we understand Pearson’s intellectual development in Heidelberg we are well on the way to understanding how he came to that intellectual state which made him a candidate for the sort of work in biology that would produce statistics and would ally itself with eugenics. It remains now to

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consider the remainder of the 1880s, which Pearson spent in London, at first as a lawyer, and later as professor of applied mathematics at University College London.

(c) London and the Men and Women’s Club Back in London, Pearson’s thought developed steadily. On the philosophical side we find that in October 1884 publishers asked him to edit and complete the late W.K. Clifford’s Commonsense of the Exact Sciences, which he was able to publish in 1885. On the social and ethical side he was able to publish a book of collected essays, the Ethic of Freethought, in 1887. In these writings two trends may be discerned. In the Commonsense, Pearson developed the epistemological ideas which had begun to crystallize whilst in Germany, ideas bringing him closer to the Grammar of Science. While preparing the Commonsense Pearson read the works of Ernst Mach, and when contributing his own ideas on the laws of motion was delighted to be able to record that these views seemed to have ‘the weighty authority of Professor Mach of Prag’. By 1885, it would seem, the creation of his philosophy of science was almost complete.65 Pearson’s social, political and ethical thought underwent a more significant development, for we find an increasing introduction of ‘Darwinian’ ideas when discussing social organization and moral principles. This, perhaps, is unsurprising, for Darwin’s ideas were then on everyone’s tongues. It is hard to say precisely where Pearson’s own style of Darwinism came from, but we do know him to have been a keen student of the writings of Clifford and there is much in Clifford’s essay on ‘The Scientific Basis of Morals’ that found its way into Pearson’s thought. Certainly, he deployed Clifford’s idea of a ‘tribal conscience’.66 The drift to Darwinism is clear enough in the essays that make up the Ethic of Freethought. By 1885, in fact, most of his Darwinian ideas seem to have been formed, and may be discerned in his essay of that year on ‘The Woman’s Question’. Here, when discussing womens’ rights, he insisted that a decision about the woman’s proper social role should be consequent upon an analysis of the effects of any proposed role on national fitness. We have first to settle what is the physical capacity of woman, what would be the effect of her emancipation on her function of racereproduction, before we can talk about her ‘rights’, which are, after all, only a vague description of what may be the fittest position for her, the sphere of her maximum usefulness in the developed society of the future. The higher education of women may connote a general intellectual progress for the community, or, on the other hand, a physical degradation of the race, owing to prolonged study having ill effects on woman’s child-bearing efficiency.67

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And, by 1887, judging from a paper on ‘Socialism and Sex’, the Darwinian perspective seems to have become total. In this essay we find Pearson outlining all of the theses discussed in Section 2 above, insisting, for example, that the moral or good action is that which tends in the direction of growth of a particular society at a particular time.

that Herder attempted a philosophy of history on the basis of metaphysics and naturally failed. The philosophy of history is only possible since Darwin, and the rationalisation of history by the ‘future Darwin’ will consist in the explanation of human growth by the action of physical and sexualogical laws in varying human institutions.

and that we are students of history, not because we are socialists, but socialists because we have studied history.68

The style of socialism which he advocated was taking clearer shape, but along the lines outlined in the paper on ‘Anarchy’ discussed above. In Pearson’s socialist state, in the state whose structures he increasingly supported by Darwinian rhetoric, persons like himself, ‘labourers with the head’ as he called them, would play a preeminent role. This was made quite clear at several points.69 Pearson’s growing interest in and commitment to sociobiological studies was reflected in his formation, along with Parker, of a ‘Men and Women’s Club’. The secretary of the club was Maria Sharpe, his future wife. By looking at some of the activities of the club we shall, I think, see finally and clearly why and how, by the early 1890s, Pearson was able to plunge into biometry and to link it with eugenics. The club was established in 1885, by Pearson, Parker, Elizabeth Cobb (wife of Cobb the MP) and her sisters Maria and Laetitia Sharpe, for the purpose of frank discussion of the relations between men and women. It was a select middle class group, anxious to avoid scandal, whose members were, by and large, just the sort of people one might expect to find joining the new Fabian Society.70 Members, proposed members and guests included Annie Besant, Havelock Ellis, Olive Schreiner, Eleanor Marx and Mrs Wilson the Hampstead anarchist. Mrs Wilson, interestingly, had written to Pearson in the previous year asking him whether he would care to join her, Sidney Webb and others in a reading of Marx’s Capital.71 The thirty-six meetings of the club covered a wide range of topics: prostitution, then an outrageous scandal; the relative sex drives of men and women; and, above all, patterns of sexual relations in contemporary and

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defunct societies. In these surroundings Pearson’s interest in the biological basis of national fitness increased, and we find for example that in contemporary writings he referred to the right to bear children as a sacred one, and inquired if, in ‘a better organized society than the present’, it would not be fitting that either the state should have a voice in the matter, or else that a strong public opinion should often intervene? Shall those who are diseased, shall those who are nighest to the brute have the right to reproduce their like? Shall the reckless, the idle, be they poor or wealthy, those who follow mere instinct without reason, be the parents of future generations? ... Out of the law of inherited characteristics spring problems which strike very deeply into the roots of our present social habits.72

By 1889, the Club was coming apart from flagging interest, but Pearson introduced Galton’s Natural Inheritance to a final meeting, criticizing (as we have seen) its methodological structure. But what, perhaps, is of the greatest interest is his conviction, mentioned in Section l, that the regression observed by Galton in the general population would not hold for long-selected lines. And said Pearson, in one of the Club’s closing meetings, I am not advocating a return to group or even to close intermarrying, but a far more careful sexual selection on the part of those members of the community who have a large deviation physically or mentally from mediocrity.73

Here, it seems, is laid bare the basis of Pearson’s preparedness to enter biological work. By 1890 several ideas were converging. Pearson had adopted a Darwinian historicism to justify his state socialism, and, as we can see, his interest in national fitness was moving on from issues of organization to issues of biological efficiency: already he was concerned with eugenic problems, as well as the more general issues of evolution. In the period up to 1890, therefore, we can see the emergence of a framework of thought that would make biometry an attractive proposition, which would make it a science likely to produce statistical results which could be prized for their philosophical significance, and which could be used in eugenic investigations. This should be seen as another phase of Pearson’s socialism, with its emphasis on national fitness and the production of a socialist élite class of administrators of the highest quality.

Conclusion I have depicted a pattern of intellectual growth and change on Pearson’s part, reflecting in various ways the late-Victorian tide of secularism and religious doubt after the advent of Darwin, and concerns for the urban proletariat.

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Pearson, one might say, responded in various ways to the conditions of his life. But to say this is only to invite the further question of why he responded in the manner that he did. Why, one wonders, did he not perhaps become a Christian socialist, or, like the respectably born Hyndman, a revolutionary? Why, in philosophy, did he tread the Machian path when others did not? Why should he have become a Darwinian in ethics when Huxley was inveighing against such moves? Possibly some answers may be obtained by studying Pearson’s social position and the natural interests arising from it.74 He was a brilliant intellectual with no investment in land or capital, with friends similarly located in the ‘nouvelle couche sociale’ which Hobsbawm has seen the Fabians as inhabiting.75 Up to a point, therefore, it may be possible to see Pearson’s élitist socialism as a reflection of this position – for, certainly, it was a form of social organization in which he and his circle would play esteemed roles. His sensationalist philosophy might perhaps be similarly interpreted, as one that eliminated the clergy from the sphere of rational influence and entrenched a new class of scientifically trained persons, again like Pearson. The eugenics concerns may perhaps be seen as jibing with the natural interests of such persons, for it gave a biological foundation to their supremacy. In short, we can see that many of Pearson’s ideas appear to be enhancing the esteem of the group with whom he identified. Whether or not such a harmonization can be seen as explaining his espousal of these ideas is, it seems to me, a question that brings us hard against the philosophical difficulties inherent in explaining an individual’s thought in terms of the interests of a group to which he has attached himself. Perhaps it is unwise to take this issue on at this point. It needs separate treatment. Possibly the case of Pearson and statistics could serve as a useful reference in such discussions.

Notes I would like to acknowledge gratefully financial assistance from the UK SSRC whilst preparing this paper. I would also like to thank Professor E.S. Pearson for permission to use the Pearson papers. 1. For the best biography of Pearson, see E.S. Pearson, Karl Pearson: An Appreciation of Some Aspects of his Life and Work (Cambridge: Cambridge University Press, 1938). For an account of Pearson’s social Darwinism, see Bernard Semmel, ‘Karl Pearson: Socialist and Darwinist’, British Journal of Sociology, Vol. 9 (1958), 111–25. The best account of secondary literature on Pearson is contained in Churchill Eisenhart’s article on Pearson in the Dictionary of Scientific Biography, Vol. 10 (New York: Charles Scribner’s Sons, 1974). 2. The pattern of development of Pearson’s writings may be discerned in G.M. Morant, A Bibliography of the Statistical and Other Writings of Karl Pearson (Cambridge: Cambridge University Press, 1938). 3. The fullest biography of Weldon is Pearson’s paper ‘W.F.R. Weldon, 1860–1906’, Biometrika, Vol. 5 (1906), 1–50.

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4. For an account of Pearson’s involvement, see his biography of Weldon, ibid. note 3. See also K. Pearson, The New University for London: A Guide to its History and a Criticism of its Defects (London: T. Fisher Unwin, 1892). 5. Eisenhart, op. cit. note 1, 450. 6. An excellent account of some of the stages involved in the setting up of the department may be had in Lyndsay Farrall, ‘The Origin and Growth of the English Eugenics Movement 1865–1925’ (unpublished PhD thesis, Indiana University, Bloomington, 1970), available from University Microfilms. 7. Good discussions of the students of Pearson’s department are to be found in Farrall, ibid. 8. The impact of Pearson’s methods on psychology, for example, was significant especially in the area of the study of individual differences. See B. Norton, ‘Charles Spearman and the Doctrine of ‘g’: Genesis and Interpretation’, forthcoming in the Journal of the History of the Behavioural Sciences. For the citation, see E.S. Pearson, op. cit. note 1, 119. 9. It should be recalled that, before the 1870s, there was very little biological research done in the English Universities, and that, at Cambridge, for example, experimental work was seriously introduced only after the appointment of Michael Foster to a praelectorship of physiology at Trinity College in 1870. His protégé F.M. Balfour started England’s leading school of evolutionary biology, and as may be seen by inspecting Balfour’s masterful Treatise on Comparative Embryology, 2 Vols. (London: Macmillan, 1880–81), the paradigm of this school was one of phylogenetic morphology. 10. The best general study of the history of statistics is, perhaps, H.M. Waller, Studies in the History of Statistical Method (Baltimore, Md.: Williams and Wilkins, 1929). The generally non-mathematical tenor of institutionalized statistics prior to 1900 may be seen by inspecting the volumes of the Journal of the Royal Statistical Society for that period. 11. For details put in a way relevant to this paper see Bernard Semmel, Imperialism and Social Reform: English Social-Imperial Thought, 1895–1914 (London: Allen and Unwin, 1960). 12. Perhaps the best account of the radical London intelligentsia of the period is to be had in W.S. Smith, The London Heretics 1870–1914 (New York: Dodd Mead and Co., 1968). See also G. Stedman-Jones, Outcast London (Oxford: The Clarendon Press, 1971). 13. For a good discussion of social Darwinism, see G. Himmelfarb, Victorian Minds (London: Weidenfeld and Nicolson, 1968). See, in particular, Chapter 12, ‘Varieties of Social Darwinism’. 14. See T.H. Huxley, ‘Evolution and Ethics’, in Evolution and Ethics (London: Macmillan, 1911), 46–116. This chapter was based on Huxley’s Romanes Lecture for 1893. 15. For an account, see D. MacKenzie, ‘Eugenics in Britain’, Social Studies of Science, Vol. 6 (1976), 499–532. 16. See the Daily Sketch (3 October 1913). 17. W.F.R. Weldon, ‘On Certain Correlated Variations in Carcinus Moenas’, Proceedings of the Royal Society, Series A, Vol. 54 (1893), 329. 18. S.A. Stouffer, ‘Karl Pearson—An Appreciation on the 100th Anniversary of His Birth’, Journal of the American Statistical Association, Vol. 53 (1958), 23–27, esp. 23. 19. See Ruth D’Arcy Thompson, D’Arcy Wentworth Thompson (London: Oxford University Press, 1968), particularly the postscript by P.B. Medawar, ‘D’Arcy Thompson and “Growth and Form”.’ 20. E.S. Pearson, op. cit. note 1, 97. 21. See, for example, Galton’s early mathematical speculations on Darwin’s theory of pangenesis, in F. Galton, Hereditary Genius (London: Macmillan, 1869), especially the closing section, ‘General Considerations’. 22. K. Pearson, ‘Mathematical Contributions to the Theory of Evolution III: Regression, Heredity and Panmixia’, Philosophical Transactions of the Royal Society, Series A, Vol. 187 (1896), 253–318; quotation at 259.

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23. Ibid. Donald MacKenzie’s accompanying paper, in the same issue of this journal, gives another vivid illustration of the way in which the study of heredity led Pearson into work in correlation: see D. MacKenzie, ‘Statistical Theory and Social Interests: A CaseStudy’, Social Studies of Science, Vol. 8 (1978), 35–83. 24. We shall see this point with increasing force as the paper proceeds. But, for the present, note that Pearson’s 1896 paper (op. cit. note 22) is clearly written with the human condition in mind: see, particularly, 306–08. 25. For accounts of Galton’s involvements in statistics, see V. Hilts, ‘Statistics and Social Science’, in R. Giere and R. Westfall (eds), Foundations of Statistical Method in the 19th Century (Bloomington, Ind.: Indiana University Press, 1973), 243–58. See also R.S. Cowan, ‘Francis Galton’s Statistical Ideas: the Influence of Eugenics’, Isis, Vol. 63 (1972), 509–28. 26. For a discussion, see W. Provine, The Origins of Theoretical Population Genetics (Chicago: The University of Chicago Press, 1971), 179–87. 27. K. Pearson, ‘Mathematical Contributions to the Theory of Evolution VIII’, Philosophical Transactions of the Royal Society, Series A, Vol. 195 (1901), 121. 28. K. Pearson, ‘Contributions to the Mathematical Theory of Evolution’, Philosophical Transactions of the Royal Society, Series A, Vol. 185 (1894), 71–110. 29. K. Pearson, ‘Contributions to the Mathematical Theory of Evolution II: Skew Variations in Homogeneous Material’, Philosophical Transactions of the Royal Society, Series A, Vol. 186 (1895), 343–414. 30. K. Pearson, ‘On the Criterion that a Given System of Deviations from the Probable in the Case of a Correlated System of Variables is Such that it can be Reasonably Supposed to have Arisen from Random Sampling’, Philosophical Magazine, Series 5, Vol. 50 (1900), 157–75. 31. K. Pearson, The Grammar of Science (London: Walter Scott, 1892). 32. C. Riddle, ‘Karl Pearson’s Philosophy of Science’ (unpublished PhD dissertation, Columbia University, New York, 1958). Available from University Microfilms. See Abstract. 33. K. Pearson, op. cit. note 31, 128. 34. See Pearson’s chapter on ‘Contingency and Correlation’, in the third edition of his Grammar of Science (London: Adam and Charles Black, 1911). 35. Ibid., 170. 36. See J.B.S. Haldane, Karl Pearson 1857–1957 (London: Biometrika Trustees, 1958), 10. 37. Taken from Morant, op. cit. note 2. 38. Pearson papers: the text of Pearson’s talk ‘On the Laws of Inheritance according to Galton’ is in Cabinet 5, drawer 6. The Pearson papers are kept at the archive room, University College London. 39. This book is kept in the Pearson archive, University College London. 40. K. Pearson, in Speeches at a Dinner held in University College London, in Honour of Professor Karl Pearson (Cambridge: privately printed, 1934). 41. K. Pearson, op. cit. note 31, 34. 42. Ibid., 425. 43. Ibid., 435. 44. Ibid., 436. 45. These views are gathered from Chapter 9, ‘Life’, of the Grammar of Science (op cit. note 31), and from the various essays making up Pearson’s The Ethic of Freethought (London: T. Fisher Unwin, 1888). A very brief statement of Pearson’s position is given in the Grammar, 438, where, after asserting that it was ‘a false view of human solidarity’ that would regret ‘that a capable and stalwart race of white men should replace a dark skinned tribe’, he claimed again that the ‘principle of the survival of the fittest ... is from

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47.

48. 49. 50. 51. 52.

53. 54.

55. 56. 57. 58. 59. 60. 61. 62. 63. 64. 65. 66.

67. 68. 69.

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the standpoint of science the sole account we can give of those purely human faculties of healthy activity, of sympathy, of love, and of social action which men value as their chief heritage.’ K. Pearson, ‘Socialism and Natural Selection’. This essay was reprinted in Vol. 1 of Pearson’s Chances of Death and Other Studies in Evolution (London: Edward Arnold, 1897). See p. 138. From quite an early stage in his work Pearson floated the idea that the British nation was declining due to the growth in fertility of the lower classes and the diminution of fertility among the professional classes, whom he took to be genetically superior. The most politically effective work that was done in this area was performed, under Pearson’s instruction, by David Heron, whose memoir on ‘The Relation of Fertility in Man to Social Status and on the Changes in this Relation that have taken Place in the Last Fifty Years’, was published in 1906 as one of the Biometric Laboratory’s ‘Studies in National Deterioration’. See K. Pearson, National Life from the Standpoint of Science (London: Adam and Charles Black, 1901), 43–44. K. Pearson, letter to the Manchester Guardian (15 February 1901). Robert Parker (1857–1918) rose to a Baronetcy, and was to be a leading law lord. Pearson papers: the Commonplace Book is in Cabinet 2, drawer 1. A detailed account of some of the tensions felt on religious issues, most useful for comparative purposes, by some leading Victorian intellectuals is contained in F.M. Turner, Between Science and Religion (London and New Haven, Conn.: Yale University Press, 1974). Pearson papers: Pearson to Parker, 18 September 1878. See K. Pearson (‘Loki’), The New Werther (London: C. Kegan Paul and Co., 1880). At the beginning of the book, interestingly, Pearson wrote that its contents ‘truly image the mind of him who has written them, and therefore necessarily to some extent the minds of the children of his generation, who are passing through a like struggle’. Ibid., 33. Ibid., 34. K. Pearson, ‘Anarchy’, The Cambridge Review, Vol. 2 (1881), 268–70. Ibid., 270. K. Pearson, ‘Political Economy for the Proletariat’, The Cambridge Review, Vol. 3 (1881), 123–26. Ibid., 124. Pearson papers: Parker to Pearson, 25 May 1879. Ibid., Pearson to Parker, 20 June 1879. Ibid., Pearson to Parker, October 1879. K. Pearson, ‘Kuno Fischer’s New Critique of Kant’, The Cambridge Review, Vol. 5 (1883), 109–11, esp. 111. See the prefatory remarks by Pearson to W.K. Clifford, The Common Sense of the Exact Sciences (London: Kegan Paul Trench and Co., 1885). Clifford’s ideas were developed in his famous 1875 essay ‘On the Scientific Basis of Morals’, reproduced in W.K. Clifford, Lectures and Essays (London: Macmillan, 1879), Vol. 2, 74–95. Pearson, The Ethic of Freethought, op. cit. note 45, 371. The original essay was written in 1885. Ibid., 428. The essay dates from 1887. See, for example, Pearson’s ‘Socialism in Theory and Practice: being a lecture delivered to a working class audience’, dating from February 1884 and reproduced in The Ethic of Freethought, op. cit. note 45. In this lecture Pearson made it quite clear that while all forms of labour were equally honourable, some forms of labour, namely that done with the brain, were far the most important.

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70. For an analysis of the Fabians, see Chapter 14, ‘The Fabians Reconsidered’, of E.J. Hobsbawm’s Labouring Men (London: Weidenfeld and Nicolson, 1968). 71. Pearson papers: Mrs Wilson to Pearson, 22 October 1884. 72. Pearson, The Ethic of Freethought, op. cit. note 45, 391. 73. Pearson papers: op. cit. note 38. 74. This possibility was interestingly discussed by Donald MacKenzie at a colloquim on Eugenics in England held at the University of Leeds, July 1977. See also the papers by MacKenzie cited in notes 15 and 23. 75. See Hobsbawm, op. cit. note 70.

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82 A History of Effect Size Indices Carl J. Huberty

D

uring the past several decades, there has been an exponential increase in the frequency of publications criticizing uses of statistical testing; this pattern has occurred across disciplines as diverse as psychology and wildlife studies (Anderson, Burnham, & Thompson, 2000). Concomitantly, there has been an increased emphasis placed on the reporting and interpretation of effect sizes. For example, the American Psychological Association (APA) Task Force on Statistical Inference recently emphasized, “Always provide some effect-size estimate when reporting a p value” (Wilkinson & APA Task Force on Statistical Inference, 1999, p. 599). The Task Force also wrote, Always present effect sizes for primary outcomes . . . . It helps to add brief comments that place these effect sizes in a practical and theoretical context. . . . We must stress again that reporting and interpreting effect sizes in the context of previously reported effects is essential to good research. (p. 599)

The editorial policies of the following 19 journals now require effect size reporting: • • • • •

Career Development Quarterly Contemporary Educational Psychology Early Childhood Research Quarterly Educational and Psychological Measurement Exceptional Children

Source: Educational and Psychological Measurement, 62(2) (2002): 227–240.

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Journal of Agricultural Education Journal of Applied Psychology Journal of Community Psychology Journal of Consulting & Clinical Psychology Journal of Counseling and Development Journal of Early Intervention Journal of Educational and Psychological Consultation Journal of Experimental Education Journal of Learning Disabilities Language Learning Measurement and Evaluation in Counseling and Development The Professional Educator Reading and Writing Research in the Schools

It is noteworthy that two of these journals are the flagship journals of the American Counseling Association and the Council of Exceptional Children. The fifth edition of the APA (2001) Publication Manual also emphasizes the importance of effect size reporting: For the reader to fully understand the importance of your findings, it is almost always necessary to include some index of effect size or strength of relationship in your Results section. You can estimate the magnitude of the effect or the strength of the relationship with a number of common effect size estimates. . . . The general principle to be followed ... is to provide the reader not only with information about statistical significance but also with enough information to assess the magnitude of the observed effect or relationship. (pp. 25–26)

For the past two decades or so, the notion of effect size has been fairly common across introductory statistical methods textbooks, particularly in the behavioral sciences. (An exception to this is the book by Moore, 2000, who is, by background and current position, a bona fide statistician; as good as this book is, it does not include the notion of effect size.) The commonality has not, however, carried over to behavioral science journals that typically report results of quantitative studies, although an increased emphasis on the reporting of effect size index values has taken place very recently. For the past three decades or so, the notion of effect size very commonly pertained to differences between (and sometimes among) means of scores on a single outcome variable. Even to this day, if the expression effect size is heard or read, it is estimated that a large percent (>95%?) of empirical researchers and methodologists will think of the univariate (two-group?) mean-comparison context. I would maintain that the effect size notion applies to contexts in addition to that involving univariate mean comparisons. These other contexts include, but are not limited to, multiple regression or prediction, multiple correlation,

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multivariate analysis of variance, and univariate proportion comparisons. No consistent historical pattern was found across all types of effect size indices. In fact, some indices that are currently considered effect size indices were not originally proposed as effect size indices. Bases for univariate mean-comparison effect size indices may be categorized according to three interpretations: relationship, group difference, and group overlap. The historical development of these three interpretations of effect size will now be addressed. (As will be evidenced below, these three interpretations may not be considered in any way to reflect “new” thinking.) Some effect size indices in a multiple-response-variable context will follow in a separate section. The article concludes with a Comments section.

Relationship Indices In the context of data analysis, relationship typically refers to the correlation between two characteristics or attributes for a set of analysis units. According to some documentation (e.g., Cowles, 1989, pp. 123, 132; Hald, 1998, p. 164; Johnson & Kotz, 1997, p. 109; Stigler, 1986, p. 298), Francis Galton (1822–1912) originated the concept of correlation in 1889, although a year earlier he used the word co-relation (Galton, 1888). Stigler (1999, p. 89) maintained, however, that the concept of correlation was reported some 30 years earlier by Charles Darwin (1809–1882), who was a cousin of Galton. Cowles (1989, p. 141) and Stigler (1986, p. 353) disagreed on whether Auguste Bravis (1811–1863) used the idea of correlation in a 1846 paper. (Stigler, 1986, chap. 9, provides an excellent discussion of the history of [simple] correlation.) It was in 1892 that Francis Y. Edgeworth (1845–1926) used the expression coefficient of correlation for the symbol ρ (parameter and statistic were not then commonly differentiated). A disciple of Galton, Karl Pearson (1857–1936), began to popularize the correlation coefficient – he used r – around 1896. Some years later, Pearson (1905) defined and labeled η the correlation ratio. This coefficient was developed in the context of multiple data arrays (like groups in analysis of variance [ANOVA]) that typically suggested a nonlinear relationship between the grouping variable and the outcome variable. In 1924, Ronald A. Fisher (1890 – 1962) derived the probability distribution of η in the context of ANOVA. But the explicit analysis connection between the ANOVA F test and the correlation ratio was not made until 1935 by Truman L. Kelley (1884 –1961). (In an ANOVA context, the η value reflects the correlation between the grouping variable and the outcome variable.) In making the connection, Kelley (1935) proposed an adjustment of the statistic η2 (to reduce the estimation bias) that he labeled ε2. (For a more detailed historical discussion of ε2 and ω2, along with the respective estimators, see Glass & Hakstian, 1969.) It may be pointed out that the bias in η2 as an estimator for its population counterpart was recognized by Pearson (1923). (Relationships

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among η2, ε2, and ω2 and how the latter two reduce but do not remove the bias related to η2 are discussed in some detail by Richardson, 1996, pp. 18–19.) What may be the first textbook that connected η2 and ε2 to ANOVA is that by Peters and Van Voorhis (1940, p. 319). It is interesting that the many editions of the two Fisher (1925, 1935) books (Statistical Methods for Research Workers and The Design of Experiments) did not make the connection. Fisher’s lack of attention to the connection was emphasized by Yates (1951) when he mentioned that “research workers ... pay undue attention to the results of ... tests of significance ... and too little to the estimates of the magnitude of the effects they are investigating” (p. 32). (This appears contrary to Kirk’s 1996, p. 748, statement that Fisher did make the connection in his 1925 book, Statistical Methods for Research Workers.) In a textbook aimed at the behavioral sciences, Diamond (1959) proposed another expression, differentiation ratio, for η2 because “it tells us with what success the groups ... have been differentiated by the principle which underlies their classification” (p. 55). A second alternative to η2 was proposed in a textbook by William L. Hays (1926 –1995) in 1963. The Hays (p. 325) index, denoted “est. ω2,” is interpreted as an estimator for the strength of association between a grouping variable and an outcome variable. (Later in his book, Hays, p. 547, used η2 to denote a correlation ratio.) In sum, then, three different strength-ofrelationship estimators have been proposed from 1935 to 1963: η, ε, and ω. The proposals for the latter two were made with the idea of reducing estimation bias associated with the first one. (Over the years, notation has been a little bit of an issue in that the three Greek letters are often used to represent sample values.) When levels of the grouping variable in an ANOVA context are random (rather than fixed), some methodologists suggest that an intraclass correlation coefficient, ρ1, be used as an effect size index. The random-fixed issue was recognized by Hays (1963, p. 424) and further discussed by Vaughan and Corballis (1969). Richardson (1996, p. 19) provided a more detailed discussion of this issue. It was alluded to earlier in a two-group research situation that the strength of the relationship pertains to the relationship between the (continuous) outcome variable and the dichotomous grouping variable. If the dichotomy is imposed, then the index of relationship is the biserial correlation coefficient. The biserial r idea was suggested by Pearson (1910). According to Stigler (1999, p. 18), Pearson later used the expressions “biserial r” and “biserial η.” The use of biserial η implies (to me, at least) that the biserial correlation coefficient is simply a special case of η. If the dichotomy is natural (e.g., with gender or with experimental versus control), then the square of the pointbiserial correlation coefficient, which is a special case of η2, could be considered an effect size index, although it was not so considered 90 years ago. Although indices of relationship have been and are currently considered to assess effect size, it is common to square such an index and consider percent

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of shared variance to assess the magnitude of an effect. A little history on such a perspective of effect is mentioned earlier in this section. Standard cutoffs for some such indices have been suggested (see, for example, Cohen, 1969, pp. 277–281). Rosenthal and Rubin (1979) pointed out a problem of assessing an r2 value of, say, .14, as being “small” in some specific research situations. A little later, Rosenthal and Rubin (1982) proposed the binomial effect size display (BESD) as an aid in assessing the “practical importance of any effect indexed by a correlation coefficient” (p. 242). Finally, an index of relationship that was used with two dichotomous variables was originally proposed by George Udney Yule (1871–1951). Three variations of the Yule (1900) index, Q, have been suggested: Pearson coefficient of mean square contingency, Pearson tetrachoric coefficient of correlation, and Tschuprow coefficient (see Cowles, 1989, pp. 142–143, and Mac-Kenzie, 1981, chap. 7, “The Politics of the Contingency Table”). In the current context of comparing two groups using a dichotomous outcome variable, a Q-derived value could be used as an effect size value. A popular index that could be used, but seldom is in research practice, is the so-called C coefficient named after Harald Cramér (1893–1985), which Cramér (1946, p. 282) actually attributed to Karl Pearson. The Cramér C may also be used as an effect size index in the context of comparing multiple proportions. (Use of effect size indices for categorical data was fairly recently discussed by Fleiss, 1994.)

Group Difference Indices In the two-group mean-comparison situation, the typical effect size index considered is a standardized mean difference. Such an index was proposed by Jacob Cohen (1923–1998) in 1962 when he used the letter d. A standardized difference was also included in a discussion by Hays (1963, p. 329), which involved a fairly direct relationship between the population counterpart of d, δ, and ω2. During the 1970s and 1980s, there were some discussions as to which standard deviation should be used as the denominator in d. Two suggestions made were the standard deviation pooled across the two groups proposed by Cohen (1969, p. 18) and the standard deviation of the control group – the definition of which is not always clear – proposed by Glass (1976). The letter d was used by both Cohen and Glass. (Hedges, 1981, took an exception to these two proposals because of bias in the estimators and suggested an adjusted d, denoted by g.) Cohen (1962, p. 148) also proposed a standardized-difference type of an index that might be used in a multiple group context. Here, as in Cohen (1969, p. 267), the letter f was used. This index reflects the variability of the group means relative to a standard deviation. (As pointed out by Cohen, 1969, p. 274, there is a relationship between f and η: f 2 = η2/(1 − η2).)

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About the same time, Winer (1962, p. 57) proposed an index for “the effect of treatment j” (p. 274): tj = μj − μ, where μj is the mean for population j and μ is the grand mean across all of the populations. A short time later, Cohen (1969, p. 269) suggested a standardized mean difference in the context of more than two groups; parameterwise, the index is δ = (μmax − μmin)/σ, where μmax is the largest mean, μmin is the smallest mean, and σ is the standard deviation common to all of the populations involved. (The use of a standardized difference in more complex ANOVA designs is discussed by Olejnik & Algina, 2000, pp. 248–258.) When the outcome variable is dichotomous, group differences are assessed by comparing two proportions. Cohen (1962) suggested the simple difference in proportions, |P1 − P2|, as an effect size index in the two-group context. For testing equality of multiple proportions, Cohen (1962) suggested the use of the ratio of the largest proportion to the smallest proportion as an effect size index.

Group Overlap Indices Building on the earlier work of Kelley (1920, 1923), in 1937, John W. Tilton (1891–1980) suggested that the amount of group overlap be considered – in two-group univariate mean comparisons – in determining whether two means are significantly different. Tilton (1937) proposed that “the comparison of means should be supplemented whenever possible by an explicit measure of overlapping, such as the percentage of area common to the two distributions” (p. 657) and that this calculation be based on “two perfectly normal distributions” (pp. 661–662). Tilton’s notion of group overlap as related to two-group statistical testing sat dormant for about 30 years, until it was revisited by Dunnette (1966) and Alf and Abrahams (1968) and a few years later by Elster and Dunnette (1971). Dunnette (1966) restated Tilton’s idea: “The greater the amount of overlap, the less effective is the predictor in separating the two distributions” (p. 142). (He used predictor for what we currently call an outcome variable.) Alf and Abrahams (1968) presented a fair bit of detail of calculating the percent of group overlap assuming two normal distributions for the outcome variable. Elster and Dunnette (1971) studied the robustness of Tilton’s (1937) measure of overlap when the two distributions of outcome variable scores are nonnormal. Oakes (1986, p. 54) mentioned that the misclassified proportion was considered by Eysenck (1971, p. 34) to distinguish the theoretical interest in differences in IQ scores between races. The concept of group overlap as an effect size basis was also considered by Cohen (1969, pp. 19–21) in the context of a two-group mean comparison. Group overlap was also considered in a two-group context by Kraemer and Andrews (1982) when they suggested using D as the standard normal deviate that corresponds to the proportion of

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analysis units in one group that are less than the median score of the other group. (For a recent discussion of some parametric and nonparametric effect size indices, see Hogarty & Kromrey, 2001.) It was Levy (1967) who may have been the first to relate the notion of group overlap to univariate predictive discriminant analysis (PDA). What he considered was the proportion of misclassified units of analysis into the two groups as a “simple matter to proceed from the usual test of statistical significance to a measure of the substantive significance” (p. 38). (It may be noted that the outcome variable in the original study design will play the role of a predictor variable in the PDA, a conceptual variable role reversal.) The relationship between Levy’s idea of group overlap was not explicitly connected to that of Tilton (1937). Some elaboration on the group overlap idea as applied to univariate two-group comparisons was given about 20 years ago by Huberty and Holmes (1983). More recently, Huberty and Lowman (2000) proposed the use of the better-than-chance notion in using group overlap assessed via a PDA as a basis for effect size in the multiple outcome variable context – here, the letter I is used (see also Hess, Olejnik, & Huberty, 2001).

Multivariable Indices In this section, multivariable refers to multiple response variables. A discussion of a design with one or more grouping variables and one response variable was given in the previous sections. The concept of multiple correlation was originated by Pearson and Lee (1897), and in 1914, Pearson proposed the expression coefficient of multiple correlation when he used the symbol R. The association of an effect size index with a multiple regression analysis (MRA) or a multiple correlation analysis (MCA) has been virtually ignored by applied researchers in the behavioral sciences. In relating MRA to ANOVA, Cohen (1977, p. 410) suggested an f-type index, f 2 = R 2/(1 − R 2); the statistical test of interest here is that the true multiple correlation coefficient is zero. (Cohen’s use of f here is consistent with what he used in an ANOVA context when f 2 = η2/(1 − η2).) This index reflects a single-to-noise ratio. A better-than-chance effect size index in an MRA or an MCA zero-correlation context was recently suggested by Huberty 2 2 (1994b): Radj – k /(N−1), where Radj is an adjusted R2 value (which depends on whether an MRA or an MCA is the focus), k is the number of X variables, and N is the sample size. The expression k /(N − 1) represents the chance 2 value of R2 under the null hypothesis that ρ2 = 0; thus, Radj − k /(N − 1) is a better-than-chance index of effect size. For testing that the true regression weight for Xj is zero, Maxwell (2000, p. 435) suggested using f 2 = (ρ2 − ρ(2− j )) / (1 − ρ2) as an effect size index, where ρ(−j) denotes the population multiple correlation coefficient involving all X variables except Xj. (It is not clear if adjusted R2 values are to be used to calculate Maxwell’s sample f 2 value.)

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The effect size concept is also applicable in the context of grouping variable effects with multiple outcome variables. This is the multivariate analysis of variance (MANOVA) context. The development and discussion of multivariate indices of strength of relationship appear to have started in the early 1970s. The relevant literature was pretty much summarized by Maurice M. Tatsuoka (1922–1996) in 1973. The use of a multivariate effect size index was first (at least in the behavioral sciences) proposed by Tatsuoka (1970). Tatsuoka (1973, p. 48) and Olejnik and Algina (2000, p. 272) provided other early 1970s references of MANOVA-related effect size indices. As in the univariate mean-comparison context, the proposed MANOVA effect size indices are simple transformations of statistical test criteria. For example, one effect size index is, simply, η2 = 1 − Λ, where Λ is the MANOVA criterion originated by Samuel S. Wilks (1906–1964) in 1932, which he described as a generalization of the univariate correlation ratio (Cooley & Lohnes, 1971, p. 225). Cramer and Nicewander (1979) proposed three additional indices that may be used as effect size indices in a MANOVA context, one of which is τ2 = 1−Λ 1/r, where r = min(p, q), p denotes the number of outcome variables, and q denotes the hypothesis degrees of freedom. A little later, Serlin (1982) 2 = U/r , where U denotes the Bartlett-Pillai test criterion. proposed ηPB (Transformations of other MANOVA criteria are discussed by Huberty, 1994a, pp. 194 –196.) Just as for the univariate relationship effect size indices, an adjustment was proposed for the multivariate counterparts by Tatsuoka (1973) (see also Huberty, 1994a, p. 195; Olejnik & Algina, 2000; Tatsuoka, 1993). There is a multivariate index based on group overlap that may be utilized in a group comparison context. Following a one-factor MANOVA, group overlap may be assessed using a PDA. It is recognized that there is a role reversal for the multiple response variables and the lone grouping variable. A PDA hit rate is determined, and then the hit rate is transformed to a better-than-chance index, I, which may serve as an effect size index (Huberty & Lowman, 2000). It should be noted that the I index is applicable under covariance heterogeneity as well as under covariance homogeneity – univariate or multivariate. An abbreviated time line depicting some originations related to effect size developments is given in Figure 1.

Comments The recent rise in the popularity of the effect size concept in the behavioral sciences was mentioned earlier in this article. Elmore (2001) recently counted 61 effect size choices. Summaries of many of the available choices have been provided by Cortina and Nouri (2000), Kirk (1996, in press), Rosenthal (1994), Snyder and Lawson (1993), and Thompson (2002).

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d (Glass) h2 (Tatsuoka) d (Cohen)

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Figure 1: Some approximate years in historical developments related to effect size

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One might hypothesize that this increased popularity of effect size use is a response to the critics of statistical testing. Such criticisms go back a number of decades. There was a large number of concerns pertaining to significance tests and substantive significance in the collection of writings in Morrison and Henkel (1970), but no one explicitly proposed an effect size index. (One of the 31 writings in this book is a critique of statistical testing by Joseph Berkson (1899–1982), M.D., which appeared in a 1942 issue of the American Statistical Association Journal.) How one considers the “effect” in the interpretation process of study results depends, of course, on the study purpose and on the study design. There are study purposes that pertain to intervariable rel