IRT from SSI: BILOG-MG MULTILOG PARSCALE TESTFACT
Edited by Mathilda du Toit
BILOG-MG, MULTILOG, PARSCALE, and TESTFACT are trademarks of Scientific Software International, Inc.
General notice: Other product names mentioned herein are used for identification purposes only and may be trademarks of their respective companies.
IRT from SSI: BILOG-MG, MULTILOG, PARSCALE, TESTFACT. Copyright © 2003 by Scientific Software International, Inc. All rights reserved. Printed in the United States of America.
No part of this publication may be reproduced or distributed, or stored in a database or retrieval system, or transmitted, in any form or by any means, without the prior written permission of the publisher. Edited by Mathilda du Toit. Cover by Clint Smith of Esse Group. Based on a design by Louis Sullivan for elevator grillwork in the Chicago Stock Exchange (1893).
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Published by: Scientific Software International. Inc. 7383 North Lincoln Avenue, Suite 100 Lincolnwood, IL 60712—1704 Tel: + 1.847.675.0720 Fax: +1.847.675.2140 URL: http://www.ssicentral.com ISBN: 0-89498-053-X
Preface Software for item analysis and test scoring has long been an important subset of the products published by SSI. In this new volume, four of the IRT programs that have previously been published separately have been brought together for the first time. The four programs—BILOGMG, MULTILOG, PARSCALE, and TESTFACT—have been ported to the Windows platform. In the case of BILOG-MG and MULTILOG, analyses can be set up and performed interactively via dialog boxes from within the program. Interfaces for TESTFACT and PARSCALE do not presently include dialog boxes to build syntax interactively. All programs offer extensive on-line help, and BILOG-MG, MULTILOG, and PARSCALE also include an IRT graphing program, capable of producing quality graphics.
The programs BILOG-MG, an extension of the BILOG program for the analysis of dichotomous data, was written by Michele Zimowski (National Opinion Research Center, Chicago), Eiji Muraki (Tohoku University, Japan), Robert Mislevy (Educational Testing Service), and Darrell Bock (University of Chicago). This program can also perform multiple group analysis, allowing the user to study both DIF and DRIFT. The documentation for the program, which has been incorporated into Chapters 2, 7, 8, and 10 of this volume, was written by Darrell Bock and Michele Zimowski, while Eiji Muraki and Robert Mislevy made major contributions in terms of programming. MULTILOG, written by David Thissen (University of North Carolina, Chapel Hill), is designed to facilitate the analysis and scoring of items with multiple alternatives and makes use of logistic response models, such as Samejima’s (1969) model for graded responses, Bock’s (1972) model for nominal (non-ordered) responses, and Steinberg’s (1984) model for multiple-choice items. Documentation by David Thissen has been included in Chapters 4, 7, 8, and 12 of this volume. Eiji Muraki and Darrell Bock wrote PARSCALE, a program for the analysis and scoring of rating-scale data. The program, which has proven to be a very flexible tool over the years, can also perform multiple-group and DIF analysis. Documentation for PARSCALE, provided by Eiji Muraki, is included in Chapters 3, 7, and 8. The fourth program, TESTFACT, was written by Robert Wood (Pearn Kandola Downs, Oxford, England). Other contributors to the program are Darrell Bock, Robert Gibbons (University of Illinois, Chicago), Steven Schilling (University of Michigan), Eiji Muraki, and Douglas Wilson (London, England). TESTFACT performs classical test scoring, item analysis, and item factor analysis. Documentation provided by Robert Wood has been included in Chapters 4, 7, and 8.
About this book This volume can be divided into two sections: a setup and reference guide, and an applications guide. The first section contains a description of data preparation and reference guides for each of the four programs. It also provides descriptions of the user's interfaces (where applicable) and the IRT graphing program. Chapter 1, dealing with the preparation of data for use in the programs, was written by Leo Stam, SSI’s president and IRT consultant. Chapters 2, 3, 4, and 5 provide reference guides to both syntax and interface for BILOG-MG, PARSCALE, MULTILOG and TESTFACT respectively.
Chapter 6 deals with a new feature common to BILOG-MG, MULTILOG, and PARSCALE: the new graphics module. The item characteristic curves, item and test information curves, and a matrix plot of all item characteristic curves simultaneously, can all be plotted with this module. The option to obtain a histogram of the estimated abilities has also been included. The final two chapters in the first section of this volume provide information on the various models that may be fitted in each program (See Chapter 7), while Chapter 8 discusses the methods of estimation and the implementation of these in each of the applications. The applications guide, covering chapters 9 to 13, starts with an overview of item response theory and current applications thereof given by Professor Darrell Bock, the cofounder and former president of SSI and one of the main authors of the IRT software. Chapters 10-13 provides annotated examples for the four programs. These chapters are meant as an aid both to setting up command files and the interpretation of results obtained from IRT analyses. In each example a description of the research problem as well as the program keywords used in the syntax file for the analysis are given. I have also revised and, in a number of cases, added to the annotation of key sections of the output files produced by each program. Appendix A contains a paper by Darrell Bock, A brief history of item response theory. This paper, which first appeared in Educational Measurement: Issues and Practice, has been reprinted here with the kind permission of the journal editors and provides a fascinating overview of the development of IRT to date.
Using the CD / Installing the programs The software CD contains four IRT programs. Each one can be installed separately, and in each case complete on-line help is provided. SSI provides technical support for all registered users of its software and it is recommended that the registration card, included in each shipment, be returned to SSI for this purpose. If the installation process does not begin automatically, locate and run setup.exe from the root directory of your computer’s CD drive. Each of the IRT programs has a unique serial number that appears on the CD jacket and/or shipment invoice; these should be retained for your records. Although provision is made for a custom installation, the typical installation is recommended. This installation includes the program files, the online help and a subfolder with all the examples discussed in the help file and in this volume. The default installation folder can be changed to suit the user’s needs. The readme.txt and/or readme.wri files contain instructions on how to create a desktop icon for and shortcut to each program. In addition to the IRT programs, the CD contains the most recent student editions of the LISREL and HLM programs that are also published by SSI. Other extra resources include this volume (in .PDF format) and a copy of Adobe Systems’ Acrobat® Reader®.
Acknowledgements Invaluable contributions from Darrell Bock and David Thissen made this project possible. The daunting task of porting the IRT programs to Windows and designing the new dialog boxes was undertaken by Shyan Lam. All data sets and examples were carefully revised by Leo Stam. Debugging the programs and writing the graphics module were the responsibilities of Stephen du Toit, whose untiring work and support went a long way toward making this volume a reality. Bola King and Gerhard Mels spent weeks patiently working through all the documentation, proofreading and offering suggestions on how this volume could be made more consistent in
style and more useful to the user of IRT programs. Without the assistance of all of these people, this volume would never have been anything more than a good idea. Lastly, I must mention that a venture of this magnitude is bound to be imperfect; I accept responsibility for any errors or omissions in this volume and look forward to constructive criticism that will make the next version even better. – Mathilda du Toit
Table of Contents 1 DATA PREPARATION ..........................................................................................................16 2 BILOG-MG...............................................................................................................................24 2.1 NEW FEATURES IN BILOG-MG ...........................................................................................24 2.2 PHASES OF THE ANALYSIS: INPUT, CALIBRATION AND SCORING ...........................................26 2.3 THE BILOG-MG INTERFACE ...............................................................................................37 2.3.1 File menu................................................................................................................................................... 38 2.3.2 Edit menu .................................................................................................................................................. 40 2.3.3 Setup menu............................................................................................................................................... 40 2.3.4 Data menu ................................................................................................................................................. 54 2.3.5 Technical menu ........................................................................................................................................ 64 2.3.6 Save menu ................................................................................................................................................ 80 2.3.7 Run menu.................................................................................................................................................. 81 2.3.8 Output menu ............................................................................................................................................. 82 2.3.9 View menu................................................................................................................................................. 82 2.3.10 Options menu ......................................................................................................................................... 83 2.3.11 Window menu ......................................................................................................................................... 85 2.3.12 Help menu ............................................................................................................................................... 85 2.3.13 Location of keywords in interface ........................................................................................................ 85
2.4 GETTING STARTED WITH BILOG-MG..................................................................................91 2.4.1 A first model: 2PL model for spelling data ............................................................................................ 92 2.4.2 A second model: DIF model for spelling data ..................................................................................... 100
2.5 SYNTAX..............................................................................................................................108 2.5.1 Data structures: ITEMS, TEST, GROUP and FORM commands ........................................................ 108
2.6 USING THE COMMAND LANGUAGE ......................................................................................113
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2.6.1 Overview of syntax................................................................................................................................. 113 2.6.2 Order of commands ............................................................................................................................... 114 2.6.3 CALIB command .................................................................................................................................... 116 2.6.4 COMMENT command............................................................................................................................. 141 2.6.5 DRIFT command..................................................................................................................................... 142 2.6.6 FORM command..................................................................................................................................... 144 2.6.7 GLOBAL command ................................................................................................................................ 147 2.6.8 GROUP command .................................................................................................................................. 159 2.6.9 INPUT command..................................................................................................................................... 163 2.6.10 ITEMS command .................................................................................................................................. 182 2.6.11 LENGTH command............................................................................................................................... 185 2.6.12 PRIORS command ............................................................................................................................... 187 2.6.13 QUAD command................................................................................................................................... 193 2.6.14 QUADS command ................................................................................................................................ 196 2.6.15 SAVE command.................................................................................................................................... 199 2.6.16 SCORE command................................................................................................................................. 208 2.6.17 TEST command .................................................................................................................................... 224 2.6.18 TITLE command ................................................................................................................................... 234 2.6.19 Variable format statement ................................................................................................................... 235 2.6.20 Input and output files........................................................................................................................... 241
3 PARSCALE ............................................................................................................................257 3.1 THE PARSCALE INTERFACE .............................................................................................258 3.1.1 Main menu............................................................................................................................................... 258 3.1.2 Workspace .............................................................................................................................................. 259 3.1.3 Run menu................................................................................................................................................ 259 3.1.4 Output menu ........................................................................................................................................... 260
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3.1.5 Font option.............................................................................................................................................. 260 3.1.6 Window menu ......................................................................................................................................... 261
3.2 COMMAND SYNTAX ............................................................................................................261 3.2.1 Order of commands ............................................................................................................................... 262 3.2.2 BLOCK command................................................................................................................................... 265 3.2.3 CALIB command .................................................................................................................................... 274 3.2.4 COMBINE command .............................................................................................................................. 285 3.2.5 COMMENT command............................................................................................................................. 287 3.2.6 FILES command ..................................................................................................................................... 288 3.2.7 INPUT command..................................................................................................................................... 292 3.2.8 MGROUP command ............................................................................................................................... 300 3.2.9 MRATER command ................................................................................................................................ 303 3.2.10 PRIORS command ............................................................................................................................... 305 3.2.11 QUADP command ................................................................................................................................ 308 3.2.12 QUADS command ................................................................................................................................ 310 3.2.13 SAVE command.................................................................................................................................... 312 3.2.14 SCORE command................................................................................................................................. 316 3.2.15 TEST/SCALE command....................................................................................................................... 325 3.2.16 TITLE command ................................................................................................................................... 330 3.2.17 Variable format statements ................................................................................................................. 331
3.3 INPUT FILES ........................................................................................................................333 3.3.1 Specification of input files .................................................................................................................... 333 3.3.2 Individual level data ............................................................................................................................... 333 3.3.3 Group-level data ..................................................................................................................................... 335 3.3.4 Key files................................................................................................................................................... 336
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3.4 OUTPUT FILES.....................................................................................................................337 3.4.1 Format of output files ............................................................................................................................ 337 3.4.2 Combined score file ............................................................................................................................... 337 3.4.3 Fit statistics file ...................................................................................................................................... 338 3.4.4 Item parameter file ................................................................................................................................. 340 3.4.5 Item information file ............................................................................................................................... 342 3.4.6 Subject scores file.................................................................................................................................. 343
4 MULTILOG ...........................................................................................................................345 4.1 THE MULTILOG USER’S INTERFACE ................................................................................345 4.1.1 Main menu............................................................................................................................................... 346 4.1.2 Run menu................................................................................................................................................ 346 4.1.3 Output menu ........................................................................................................................................... 347 4.1.4 Window menu ......................................................................................................................................... 347 4.1.5 Font option.............................................................................................................................................. 348
4.2 CREATING SYNTAX USING THE MULTILOG SYNTAX WIZARD ..........................................349 4.2.1 New Analysis dialog box ....................................................................................................................... 349 4.2.2 Fixed Theta dialog box .......................................................................................................................... 350 4.2.3 Input Data dialog box............................................................................................................................. 351 4.2.4 Input Parameters dialog box................................................................................................................. 352 4.2.5 Test Model dialog box ........................................................................................................................... 354 4.2.6 Response Codes (Binary Data) dialog box.......................................................................................... 355 4.2.7 Response Codes (Non-Binary Data) dialog box ................................................................................. 356
4.3 GETTING STARTED WITH MULTILOG...............................................................................357 4.3.1 Two-parameter model for the skeletal maturity data.......................................................................... 357 4.3.2 Three-parameter (and guessing) model for the LSAT6 data.............................................................. 364
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4.3.3 Generating syntax for a fixed- θ model............................................................................................... 370
4.4 COMMAND SYNTAX ............................................................................................................375 4.4.1 Overview of syntax................................................................................................................................. 375 4.4.2 END command........................................................................................................................................ 378 4.4.3 EQUAL command................................................................................................................................... 379 4.4.4 ESTIMATE command ............................................................................................................................. 382 4.4.5 FIX command.......................................................................................................................................... 385 4.4.6 LABELS command ................................................................................................................................. 387 4.4.7 PROBLEM command ............................................................................................................................. 388 4.4.8 PRIORS command.................................................................................................................................. 393 4.4.9 SAVE command...................................................................................................................................... 395 4.4.10 START command ................................................................................................................................. 396 4.4.11 TEST command .................................................................................................................................... 398 4.4.12 TGROUPS command ........................................................................................................................... 401 4.4.13 TMATRIX command ............................................................................................................................. 403 4.4.14 Variable format statement ................................................................................................................... 405
5 TESTFACT.............................................................................................................................410 5.1 INTRODUCTION ...................................................................................................................410 5.2 THE TESTFACT INTERFACE .............................................................................................411 5.2.1 Main menu............................................................................................................................................... 411 5.2.2 Run menu................................................................................................................................................ 412 5.2.3 Output menu ........................................................................................................................................... 412 5.2.4 Window menu ......................................................................................................................................... 412 5.2.5 Font option.............................................................................................................................................. 412
5.3 COMMAND SYNTAX ............................................................................................................413 5.3.1 Order of commands ............................................................................................................................... 414
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5.3.2 Overview of syntax................................................................................................................................. 415 5.3.3 BIFACTOR command............................................................................................................................. 418 5.3.4 CLASS command ................................................................................................................................... 424 5.3.5 COMMENT command............................................................................................................................. 426 5.3.6 CONTINUE command............................................................................................................................. 427 5.3.7 CRITERION command............................................................................................................................ 428 5.3.8 EXTERNAL command ............................................................................................................................ 430 5.3.9 FACTOR command ................................................................................................................................ 431 5.3.10 FRACTILES command ......................................................................................................................... 435 5.3.11 FULL command .................................................................................................................................... 437 5.3.12 INPUT command................................................................................................................................... 441 5.3.13 KEY command ...................................................................................................................................... 448 5.3.14 NAMES command ................................................................................................................................ 449 5.3.15 PLOT command.................................................................................................................................... 450 5.3.16 PRIOR command .................................................................................................................................. 452 5.3.17 PROBLEM command ........................................................................................................................... 454 5.3.18 RELIABILITY command ....................................................................................................................... 459 5.3.19 RESPONSE command ......................................................................................................................... 460 5.3.20 SAVE command.................................................................................................................................... 461 5.3.21 SCORE command................................................................................................................................. 474 5.3.22 SELECT command ............................................................................................................................... 480 5.3.23 SIMULATE command ........................................................................................................................... 482 5.3.24 STOP command.................................................................................................................................... 488 5.3.25 SUBTEST command............................................................................................................................. 489 5.3.26 TECHNICAL command......................................................................................................................... 491 5.3.27 TETRACHORIC command ................................................................................................................... 499
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5.3.28 TITLE command ................................................................................................................................... 502 5.3.29 Variable format statement ................................................................................................................... 502
6 IRT GRAPHICS.....................................................................................................................505 6.1 INTRODUCTION ...................................................................................................................505 6.2 MAIN MENU ........................................................................................................................505 6.2.1 The ICC option........................................................................................................................................ 506 6.2.2 The Information option .......................................................................................................................... 507 6.2.3 The ICC and Info option......................................................................................................................... 508 6.2.4 The Total Info option.............................................................................................................................. 509 6.2.5 Matrix Plot option ................................................................................................................................... 510 6.2.6 The Histogram option ............................................................................................................................ 512 6.2.7 The Bivariate Plot option ....................................................................................................................... 513 6.2.8 The Exit option ....................................................................................................................................... 514
6.3 MANIPULATING AND MODIFYING GRAPHS ..........................................................................514 6.3.1 File menu................................................................................................................................................. 514 6.3.2 Edit menu ................................................................................................................................................ 515 6.3.3 Options menu ......................................................................................................................................... 515 6.3.4 Graphs menu .......................................................................................................................................... 516 6.3.5 Axis Labels dialog box .......................................................................................................................... 517 6.3.6 Bar Graph Parameters dialog box ........................................................................................................ 518 6.3.7 Legend Parameters dialog box............................................................................................................. 520 6.3.8 Line Parameters dialog box .................................................................................................................. 521 6.3.9 Plot Parameters dialog box ................................................................................................................... 522 6.3.10 Text Parameters dialog box ................................................................................................................ 522
6.4 ITEM CHARACTERISTIC CURVES ..........................................................................................523 6.5 ITEM INFORMATION CURVES ...............................................................................................524 7
6.6 TEST INFORMATION CURVES ...............................................................................................526 7 OVERVIEW AND MODELS ...............................................................................................528 7.1 OVERVIEW OF IRT PROGRAMS ...........................................................................................528 7.1.1 BILOG-MG ............................................................................................................................................... 528 7.1.2 PARSCALE.............................................................................................................................................. 528 7.1.3 MULTILOG............................................................................................................................................... 529 7.1.4 TESTFACT............................................................................................................................................... 529
7.2 MODELS IN BILOG-MG ....................................................................................................530 7.2.1 Introduction ............................................................................................................................................ 530 7.2.2 Multiple-group analyses ........................................................................................................................ 531 7.2.3 Technical details .................................................................................................................................... 538 7.2.4 Statistical tests ....................................................................................................................................... 543
7.3 MODELS IN PARSCALE....................................................................................................544 7.3.1 Introduction ............................................................................................................................................ 544 7.3.2 Samejima’s graded response model.................................................................................................... 546 7.3.3 Masters’ partial credit model ................................................................................................................ 550 7.3.4 Scoring function of generalized partial credit model ......................................................................... 557 7.3.5 Multiple-group polytomous item response models............................................................................ 560 7.3.6 Constraints for group parameters........................................................................................................ 560 7.3.7 Test of goodness-of-fit .......................................................................................................................... 561 7.3.8 Initial parameter estimates.................................................................................................................... 562
7.4 MODELS IN MULTILOG ...................................................................................................567 7.4.1 Introduction ............................................................................................................................................ 567 7.4.2 The graded model .................................................................................................................................. 567 7.4.3 The one- and two-parameter logistic models...................................................................................... 567
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7.4.4 The multiple response model ............................................................................................................... 568 7.4.5 The multiple-choice model .................................................................................................................... 569 7.4.6 The three-parameter logistic model ..................................................................................................... 569 7.4.7 The nominal model................................................................................................................................. 570 7.4.8 Contrasts................................................................................................................................................. 570 7.4.9 Equality constraints and fixed parameters.......................................................................................... 575
7.5 OPTIONS AND STATISTICS IN TESTFACT ..........................................................................575 7.5.1 Introduction ............................................................................................................................................ 575 7.5.2 Classical item analysis and test scoring ............................................................................................. 575 7.5.3 Classical descriptive statistics ............................................................................................................. 576 7.5.4 Item statistics ......................................................................................................................................... 577 7.5.5 Fractile tables ......................................................................................................................................... 580 7.5.6 Plots......................................................................................................................................................... 582 7.5.7 Correction for guessing ........................................................................................................................ 582 7.5.8 Internal consistency .............................................................................................................................. 582 7.5.9 Tetrachoric correlations and factor analysis ...................................................................................... 583 7.5.10 IRT based item factor analysis ........................................................................................................... 584 7.5.11 Full information factor analysis.......................................................................................................... 585 7.5.12 Bifactor analysis................................................................................................................................... 586 7.5.13 Not-reached items in factor analysis ................................................................................................. 586 7.5.14 Constraints on item parameter estimates ......................................................................................... 586 7.5.15 Statistical test of the number of factors ............................................................................................ 587 7.5.16 Factor scores........................................................................................................................................ 588 7.5.17 Number of quadrature points.............................................................................................................. 589 7.5.18 Monte Carlo integration ....................................................................................................................... 591 7.5.19 Applications.......................................................................................................................................... 591
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8 ESTIMATION ........................................................................................................................592 8.1 INTRODUCTION ...................................................................................................................592 8.1.1 Trait estimation with Item Response Theory....................................................................................... 593 8.1.2 Information.............................................................................................................................................. 597
8.2 ESTIMATION IN BILOG-MG ..............................................................................................599 8.2.1 Item calibration....................................................................................................................................... 599 8.2.2 Test scoring ............................................................................................................................................ 605 8.2.3 Test and item information ..................................................................................................................... 608 8.2.4 Effects of guessing ................................................................................................................................ 610 8.2.5 Aggregate-level IRT models .................................................................................................................. 610
8.3 ESTIMATION IN PARSCALE..............................................................................................611 8.3.1 Prior densities for item parameters...................................................................................................... 612 8.3.2 Rescaling the parameters ..................................................................................................................... 612 8.3.3 The information function ....................................................................................................................... 613 8.3.4 Warm’s weighted ML estimation of ability parameters ...................................................................... 615
8.4 ESTIMATION IN MULTILOG .............................................................................................616 8.4.1 Item parameter estimation .................................................................................................................... 616
9 USES OF ITEM RESPONSE THEORY .............................................................................618 9.1 INTRODUCTION ...................................................................................................................618 9.2 SELECTION TESTING ...........................................................................................................618 9.3 QUALIFICATION TESTING ....................................................................................................619 9.4 PROGRAM EVALUATION AND ASSESSMENT TESTING ...........................................................619 9.5 CLINICAL TESTING ..............................................................................................................619 9.6 MEASUREMENT METHODS AND RESEARCH .........................................................................620
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9.7 APPROACHES TO ANALYSIS OF ITEM RESPONSE DATA .........................................................620 9.7.1 Test scoring ............................................................................................................................................ 621 9.7.2 Test generalizability ............................................................................................................................... 622 9.7.3 Item analysis........................................................................................................................................... 623 9.7.4 Estimating the population distribution ................................................................................................ 625 9.7.5 Differential item functioning ................................................................................................................. 626 9.7.6 Forms equating ...................................................................................................................................... 626 9.7.7 Vertical equating .................................................................................................................................... 627 9.7.8 Construct definition ............................................................................................................................... 629 9.7.9 Analysis and scoring of rated responses............................................................................................ 629 9.7.10 Matrix sampling .................................................................................................................................... 630 9.7.11 Estimating domain scores .................................................................................................................. 631 9.7.12 Adaptive testing ................................................................................................................................... 632
10 BILOG-MG EXAMPLES ...................................................................................................634 10.1 CONVENTIONAL SINGLE-GROUP IRT ANALYSIS ...............................................................634 10.2 DIFFERENTIAL ITEM FUNCTIONING ...................................................................................638 10.3 DIFFERENTIAL ITEM FUNCTIONING ...................................................................................650 10.4 EQUIVALENT GROUPS EQUATING ......................................................................................652 10.5 VERTICAL EQUATING........................................................................................................658 10.6 MULTIPLE MATRIX SAMPLING DATA .................................................................................666 10.7 ANALYSIS OF VARIANT ITEMS...........................................................................................670 10.8 GROUP-WISE ADAPTIVE TESTING ......................................................................................674 10.9 TWO-STAGE SPELLING TEST..............................................................................................679 10.10 ESTIMATING AND SCORING TESTS OF INCREASING LENGTH ............................................685 10.11 COMMANDS FOR PARALLEL-FORM CORRELATIONS .........................................................685 11
10.12 EAP SCORING OF THE NAEP FORMS AND STATE MAIN AND VARIANT TESTS..................686 10.13 DOMAIN SCORES.............................................................................................................688 11 PARSCALE EXAMPLES ...................................................................................................692 11.1 ITEM CALIBRATION AND EXAMINEE BAYES SCORING WITH THE RATING-SCALE GRADED MODEL ......................................................................................................................................692 11.2 EXAMINEE MAXIMUM LIKELIHOOD SCORING FROM EXISTING PARAMETERS .....................708 11.3 CALIBRATION AND SCORING WITH THE GENERALIZED PARTIAL CREDIT RATING-SCALE MODEL: COLLAPSING OF CATEGORIES.......................................................................................709 11.4 TWO-GROUP DIFFERENTIAL ITEM FUNCTIONING (DIF) ANALYSIS WITH THE PARTIAL CREDIT MODEL ......................................................................................................................................710 11.5 A TEST WITH 26 MULTIPLE-CHOICE ITEMS AND ONE 4-CATEGORY ITEM: THREE-PARAMETER LOGISTIC AND GENERALIZED PARTIAL CREDIT MODEL ..............................................................720 11.6 ANALYSIS OF THREE TESTS CONTAINING ITEMS WITH TWO AND THREE CATEGORIES: CALCULATION OF COMBINED SCORES .......................................................................................722 11.7 RATER-EFFECT MODEL: MULTI-RECORD INPUT FORMAT WITH VARYING NUMBERS OF RATERS PER EXAMINEE .............................................................................................................723 11.8 RATER-EFFECT MODEL: ONE-RECORD INPUT FORMAT WITH SAME NUMBER OF RATERS PER EXAMINEE ................................................................................................................................727 11.9 RATERS-EFFECT MODEL: ONE-RECORD INPUT FORMAT WITH VARYING NUMBERS OF RECORDS PER EXAMINEE ..........................................................................................................728 12 MULTILOG EXAMPLES ..................................................................................................730 12.1 ONE-PARAMETER LOGISTIC MODEL FOR A FIVE-ITEM BINARY-SCORED TEST (LSAT6).....730 12.2 TWO-PARAMETER MODEL FOR THE FIVE-ITEM TEST..........................................................732 12.3 THREE-PARAMETER (AND GUESSING) MODEL FOR THE FIVE-ITEM TEST ............................733 12.4 THREE-CATEGORY GRADED LOGISTIC MODEL FOR A TWO-ITEM QUESTIONNAIRE .............735 12.5 THREE-CATEGORY PARTIAL CREDIT MODEL FOR THE TWO-ITEM QUESTIONNAIRE ............738 12.6 FOUR-CATEGORY GRADED MODEL FOR A TWO-ITEM INTERVIEW SCALE ...........................740
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12.7 A GRADED MODEL ANALYSIS OF ITEM-WORDING EFFECT ON RESPONSES TO AN OPINION SURVEY ....................................................................................................................................741 12.8 GRADED-MODEL SCORES FOR INDIVIDUAL RESPONDENTS ................................................748 12.9 FIVE-CATEGORY RATINGS OF AUDIOGENIC SEIZURES IN MICE IN FOUR EXPERIMENTAL CONDITIONS..............................................................................................................................749 12.10 A NOMINAL MODEL FOR RESPONSES TO MULTIPLE-CHOICE ALTERNATIVES ....................751 12.11 A CONSTRAINED NONLINEAR MODEL FOR MULTIPLE-CHOICE ALTERNATIVES.................757 12.12 A NOMINAL MODEL FOR TESTLETS .................................................................................759 12.13 A CONSTRAINED NOMINAL MODEL FOR QUESTIONNAIRE ITEMS .....................................761 12.14 A CONSTRAINED GENERALIZED PARTIAL CREDIT MODEL................................................762 12.15 A MIXED NOMINAL AND GRADED MODEL FOR SELF-REPORT INVENTORY ITEMS .............765 12.16 A MIXED THREE-PARAMETER LOGISTIC AND PARTIAL CREDIT MODEL FOR A 26-ITEM TEST .................................................................................................................................................767 12.17 EQUIVALENT GROUPS EQUATING OF TWO FORMS OF A FOUR-ITEM PERSONALITY INVENTORY ..............................................................................................................................768 12.18 DIFFERENTIAL ITEM FUNCTIONING (DIF) ANALYSIS OF EIGHT ITEMS FROM THE 100-ITEM SPELLING TEST .........................................................................................................................770 12.19 INDIVIDUAL SCORES FOR A SKELETAL MATURITY SCALE BASED ON GRADED RATINGS OF OSSIFICATION SITES IN THE KNEE ..............................................................................................772 13 TESTFACT EXAMPLES....................................................................................................775 13.1 CLASSICAL ITEM ANALYSIS AND SCORING ON A GEOGRAPHY TEST WITH AN EXTERNAL CRITERION ................................................................................................................................775 13.2 TWO-FACTOR NON-ADAPTIVE FULL INFORMATION FACTOR ANALYSIS ON A FIVE-ITEM TEST (LSAT7) ..................................................................................................................................778 13.3 ONE-FACTOR NON-ADAPTIVE FULL INFORMATION ITEM FACTOR ANALYSIS OF THE FIVEITEM TEST .................................................................................................................................780
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13.4 A THREE-FACTOR ADAPTIVE ITEM FACTOR ANALYSIS WITH BAYES (EAP) ESTIMATION OF FACTOR SCORES: 32 ITEMS FROM AN ACTIVITY SURVEY ...........................................................780 13.4.1 Discussion of output............................................................................................................................ 782
13.5 ADAPTIVE ITEM FACTOR ANALYSIS AND BAYES MODAL (MAP) FACTOR SCORE ESTIMATION FOR THE ACTIVITY SURVEY .......................................................................................................802 13.6 SIX-FACTOR ANALYSIS OF THE ACTIVITY SURVEY BY MONTE CARLO FULL INFORMATION ANALYSIS .................................................................................................................................803 13.7 ITEM BIFACTOR ANALYSIS OF A 12TH-GRADE SCIENCE ASSESSMENT TEST........................804 13.7.1 Discussion of bifactor analysis output .............................................................................................. 805
13.8 CONVENTIONAL THREE-FACTOR ANALYSIS OF THE 12TH-GRADE SCIENCE ASSESSMENT TEST .................................................................................................................................................814 13.9 COMPUTING EXAMINEE GENERAL FACTOR SCORES FROM PARAMETERS OF A PREVIOUS BIFACTOR ANALYSIS .................................................................................................................815 13.10 ONE-FACTOR ANALYSIS OF THE 12TH-GRADE SCIENCE ASSESSMENT TEST .....................817 13.11 ITEM FACTOR ANALYSIS OF A USER-SUPPLIED CORRELATION MATRIX ............................818 13.12 SIMULATING EXAMINEE RESPONSES TO A THREE-FACTOR TEST WITH USER-SUPPLIED PARAMETERS ............................................................................................................................819 13.13 SIMULATING EXAMINEE RESPONSES IN THE PRESENCE OF GUESSING AND NON-ZERO FACTOR MEANS.........................................................................................................................820 13.14 THREE-FACTOR ANALYSIS WITH PROMAX ROTATION: 32 ITEMS FROM THE SCIENCE ASSESSMENT TEST ....................................................................................................................823 13.15 PRINCIPAL FACTOR SOLUTION OF A FACTOR ANALYSIS ON SIMULATED DATA: NO GUESSING .................................................................................................................................................825 13.16 NON-ADAPTIVE FACTOR ANALYSIS OF SIMULATED DATA: PRINCIPAL FACTOR SOLUTION, NO GUESSING ............................................................................................................................826 13.17 ADAPTIVE ITEM FACTOR ANALYSIS OF 25 SPELLING ITEMS FROM THE 100-ITEM SPELLING TEST .........................................................................................................................................827 13.18 CLASSICAL ITEM FACTOR ANALYSIS OF SPELLING DATA FROM A TETRACHORIC CORRELATION MATRIX .............................................................................................................828
14
14 APPENDIX A: A BRIEF HISTORY OF ITEM RESPONSE THEORY .......................830 14.1 ANTECEDENTS ..................................................................................................................830 14.2 CONNECTIONS ..................................................................................................................833 14.3 IRT TEST SCORING ...........................................................................................................836 14.4 IRT ITEM ANALYSIS .........................................................................................................840 14.5 CURRENT TRENDS.............................................................................................................844 15 REFERENCES .....................................................................................................................848
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1 DATA PREPARATION
1 Data preparation1 1.1 Data characteristics: What kind of data can I use? The only type of data that the IRT programs currently can handle is fixed format with one or more lines per record (case) and one-character response codes. Fixed format means that the variables occupy the same column positions throughout the data file. The only acceptable values in such a data file are the upper- and lowercase characters a through z, the digits 0 through 9, and any of the special characters like +-.*&. Tab characters (^t) and other control characters that are usually embedded in files from word processing (e.g., doc), database (e.g., dbf), spreadsheet (e.g., xls), and statistical applications (e.g., sav) are not acceptable and data files with such extraneous characters will produce unexpected program behavior that may be difficult to trace. Section 1.5 illustrates the conversion of an Excel2 file to a fixed format file. In its simplest form the data file contains individual response data. Such a flat file usually has one line per record, starting with a subject ID (identification field) and followed by a number of one-character response codes for the items in the test. Spaces in between fields and/or items are permitted, as long as those blanks maintain the column positions of the item responses throughout the file. Example: John abbac aaacc Mary-Ann bcabb bbcaa
Mary-Ann selected response category a for items 3, 9, and 10, while John answered b, c, and c, respectively. The item response codes may represent right/wrong answers, selected response categories, nominal category codes, ordinal variable values, ratings, etc. The maximum number of different codes per item is dependent on the program used for analysis. BILOG-MG and TESTFACT analyze binary (dichotomous) responses only. The data may be multiple-category (1,2,3,4 or a,b,c,d,e, etc.), but the program reduces that to right/wrong data with the correct response code key that the user provides. MULTILOG and PARSCALE can handle both binary and multiplecategory items or mixtures of those types.
1
This section was contributed by Leo Stam.
2
Excel 2000 was used in the examples.
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1 DATA PREPARATION
Besides a subject ID with up to 30 characters and the single-character item response codes, other fields that may be present in the records are:
A case weight or a frequency A subtest number A group identifier A form number A rater code
The specific requirements for these fields can be found in the Command Reference section for the different programs. For example, the group identifier in BILOG-MG has to be a single digit (integer) , starting with 1, while in TESTFACT it can be any single character (M,F, etc.), and in PARSCALE it can be a name up to eight characters. Including the single-subject data described above, the programs allow the following data types:
Single-subject data with or without case weights Number tried/number right data with or without case weights Response patterns with frequencies
1.2 Format statement: How do I tell the program about my data? The IRT programs are command-driven and are run in batch mode. That is to say that the user prepares a command file (either directly in an editor or through a dialog-box user interface, if present) and submits this command file to the program for execution (Run). While it is true that command-driven programs were the standard before the “point-and-click” user interfaces (“GUI”) entered the computing scene, maintaining this standard for the current programs was done deliberately. The dialog-box interfaces that have been added are merely a socalled front-end for the convenience of the user in building such a command file. Despite the progress that has been made with the graphical user interfaces, in our experience users who use a program routinely still prefer the ease of use of the command file. Moreover, such a file stores the particulars of an analysis in a very succinct way, such that making small changes to an analysis, retrieving an old analysis, or sharing the analysis with other users of the program (also: technical support) is a straightforward task. It is like giving somebody a map of how to get from A to B instead of having to describe the route with “take the first street to the right, then a left at the third traffic light”, etc. Granted, learning and remembering the commands, keywords, and options used in a program requires a considerable effort (like learning how to read maps), while the click-and-point interface can lay claim to being intuitive for the user. The dialog-box user interface is especially helpful in that learning process or as a means to refresh the memory of the occasional user of the programs.
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1 DATA PREPARATION Besides the particular analysis specifications, the command file informs the program where the data file can be found and how it should be read. The location of the data file to be analyzed is simply a matter of specifying that location with a keyword. For example: >FILES … DFNAME=’C:\PARSCALE\DATA\EXAMPL01.DAT’;
or >GLOBAL … DFNAME=’F:\BILOGMG\EXAMPLES\EXAMPL06.RAW’;
or >INPUT … FILE=’D:\TESTFACT\DATAFILES\TEST01.DAT’;
or >PROBLEM … DATA=’G:\MULTILOG\DATA\TEST04.RAW’;
This shows that each program has its own flavor of command file syntax but also that those specifications are essentially the same and that it is fairly easy to tell a program where it can find the data input. Note that the name of the data file must be enclosed in single quotes. The drive and directory path should be included if the data file is not in the same folder as the command file. It is also good practice to keep all the files, including the command file, for a particular analysis together in a separate folder. In that case, all that is needed is the filename. Now that the program knows where to find the data, it needs to be told how to read those data. What part of a record has the subject ID, in which column is the response code for the first item to be read, where is the group code, if any, etc. To that end, the user includes a format statement in the command file. Format statements are enclosed in parentheses. They are entered on a separate line in the command file and usually one line is all that is needed. However, if more lines are needed, the user can indicate that with a keyword (e.g., NFMT=2 tells the program that the format statement occupies two lines). The format statement for the simple example above is: (8A1,1X,5A1,1X,5A1). Here is the file again, with a column counter added above for convenience: 12345678901234567890 John abbac aaacc Mary-Ann bcabb bbcaa
As can be seen, the total length of each record in the file is 20 columns. The first eight columns contain the ID field. This is specified in the format statement with “8A1.” That stands for “eight 18
1 DATA PREPARATION alphanumeric characters of length one.” The “A” is a format code and stands for alphanumeric. The 1 indicates the width and the 8 is a repeat count. Other possible format codes are “F” (for floating point, used to read real numbers) and “I” (for integer). The next element in the format statement is an example of an operator, in this case “X”. The “X” is used to tell the program to skip one or more columns. The example specifies “1X” or skip one column. Next follows a block of five item responses to be read as “5A1”. Then, we instruct the program to skip another column and to read a second set of five alphanumeric characters: items 6 through 10. Thus, the complete format statement, (8A1,1X,5A1,1X,5A1), describes how to read each of the twenty columns in a record. Because the format statement describes one data record and that description is applied to the whole data file, all the records in the data file should look identical: the essence of a fixed format. Instead of the “X” operator, the “T” (tab) operator can be used with the same result. The tab operator specifies the column position to tab to. Thus, the format statement (8A1,1X,5A1,1X,5A1) becomes (8A1,T10,5A1,T16,5A1) when using the tab operator. Tabbing backward is also possible. That is often used when the examinee records have the examinee ID at the end of each line, while the program wants it as the first thing being read. Here is our example in that format. The first line is a column counter added for your convenience. It is not part of the actual data file. 12345678901234567890 abbac aaacc John bcabb bbcaa Mary-Ann
With the format statement (T13,8A1,T1,5A1,1X,5A1) we instruct the program to read the eightcharacter ID starting at column 13, then go back to column 1 and read two blocks of five items, skipping a blank column in the middle. This examples also illustrates that the “X” and “T” operators can be used within the same format statement. Obviously, the “T” operator can also be used to read the items in an order that is different from the order in the data file. For example, with (T13,8A1,T7,5A1,T1,5A1) we read the second block of 5 items before the first block of 5 items. The final operator that the user of our IRT programs should know about is the “/” (slash) operator. It instructs the program to go to the next line of the data file. Oftentimes, users have data where the record for each examinee spans more than one line in the data file. A simple example is as follows (again, with the column counter added for convenience). 1234567890123456 John 1 abbac John 2 aaacc Mary-Ann 1 bcabb Mary-Ann 2 bbcaa
Here, each block of five items is given on a separate line. This could easily result from two different data files (each with an examinee ID and five items) that were concatenated into one file, then sorted on examinee ID. To keep the order of the item blocks the same for each examinee, a block number was added to the original data files.
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1 DATA PREPARATION The format statement (8A1,T12,5A1,/,11X,5A1) will read the examinee ID from the first line of the record (8A1), tab to column 12 and read the first five items (T12,5A1), then go to the next line of the record (/), skip the first 11 columns and read columns 12—16 as the responses to the second set of five items. Note that the examinee ID in the second line of each record is not needed. A special use of the forward slash operator is to read every first, second, third, etc. record of a large data file. For example, (8A1,1X,20A1,/) reads every odd record of a data file, starting with the first one, while (/,8A1,1X,20A1) reads every even record of a data file, starting with the second one. The examples that come with the programs use a variety of format statements and it is a good idea to look for an example that resembles your data when in doubt about the right format statement. The chapters in this book that describe the examples also offer further details on the use of the format statement.
1.3 Telling right from wrong with the response key When you are analyzing multiple-choice items that are either answered correctly or incorrectly, the program needs to know the item response code for each item that represents a correct answer. The user provides that information with a response key. MULTILOG and TESTFACT require the response key in the command file as a string of item codes for correct responses, while users of BILOG-MG should specify in the command file where the response key can be found (unless the data are already coded as 1 for a right and 0 for a wrong answer). Because it is slightly more complicated, let us look at a BILOG-MG example. The response key is a record with the exact same format as the data records. It can be in its own file, or it can be part of the data file. The latter option makes it easier to check that the format is indeed identical. key acaab baaba John abbac aaacc Mary-Ann bcabb bbcaa
The file has the response key as the first record. The word key is used in the ID field for convenience. It is not needed and will not be read by the program. BILOG-MG will apply the response key to the data records and it will convert John’s responses to 1001001100 and Mary-Ann’s responses to 0110110001.
1.4 What about missing data? In educational assessment, the reason for an item response in a data file to be coded as missing is generally limited to two possibilities. The specific item was not presented to the examinee or the examinee did not respond to the specific item. The former occurs when examinees answer different forms (selection of items) of the same test and all the items of the test are included in the data 20
1 DATA PREPARATION file. The importance of the differentiation in missing codes lies in the fact that omitted items can be treated as a wrong response, a fractionally correct response, or the same as a not-presented item, i.e., excluded from the calculations. Using the simple example again, the data file with not-presented items could look like: John abbac xxxxx Mary-Ann xxxxx bbcaa
John took a different form of the test than Mary-Ann. They both responded to the five items in their form and all ten items of the two forms are included in the data file. Although the example uses the same not-presented code for all items, note that with BILOG-MG and PARSCALE the not-presented (or omitted item code) may vary among items. The four programs approach handling missing codes differently; details can be found in the chapters describing the programs. BILOG-MG and PARSCALE are similar and accommodate both omitted and not-presented codes. TESTFACT allows only one value for all items to represent an omitted item and another value for not-presented items. TESTFACT is the only program that allows omitted items to be differentiated into skipped items and not-reached items. The latter are defined as all the omitted items after the last item the examinee responded to. This situation occurs when tests are administered under a time restriction (speed tests) and such tests are not considered appropriate ability measurements under the assumptions underlying the power test models used in the other programs. MULTILOG does not distinguish between omitted and notpresented items, the user can only assign one missing code per item. The format of not-presented and omitted keys is as described in Section 1.3. Note that, if more than one key is used as part of the data file, the keys should follow the order as described in the Command Reference sections for the respective programs.
1.5 Data import: What if my data are different? The IRT programs from SSI expect plain text (ascii) data files with a fixed format. Because the programs do not include an import facility to handle various file formats, the user with data in such a format faces the task of converting the dataset to the plain text, fixed format. Spreadsheet, database, and statistical applications generally offer the user some form of data export (or Save As) that includes the plain text format. In this section we will illustrate such a conversion with an Excel dataset as starting point. We selected Excel, because it has a format that other applications include in their export formats, and it is a widely used program. This way, users that are unclear about how to convert a specific data format to plain text format may convert to Excel, then follow one of the two methods described below. The user is advised always to use copies of the original dataset. With Excel, for example, the Save As operation uses a format that can only save the active worksheet, so some of your work may get lost.
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1 DATA PREPARATION 1.5.1 Using the print format Using this simple format can only be done with files up to 240 columns (after conversion). In other words, if your Excel worksheet has more than 240 (minus maximum ID length minus possible form and/or group indicators) items, this method will not work. In Excel, highlight all the columns with the item response codes and set the column width of the highlighted columns to 1. This assumes that your response codes are already one-character codes. If not, you should use the recode capabilities of Excel. For example, if a twelve-category item is coded as 1 through 12, recode it as 1,2,3,4,5,6,7,8,9,A,B,C or as A through L. The column with the ID field should be set to the maximum length of the values appearing in that column. Form or group indicators are best coded as numbers, starting with one. Now, save the data file as a “*.prn” file. Excel calls that a Formatted text (Space-delimited) file. If you want your filename to have the extension dat (instead of the automatic prn extension), use double quotation marks (") around the name of the file you want to save it to. Answer Yes to the question about loosing special formatting features. The resulting file should look as shown below, where the first 8 columns are the ID field, followed by 17 item responses. Note that the leading blanks in the first ID field are automatically included because the column width in Excel was set to 8 and the ID itself has only 4 characters. The alignment of the item responses is preserved. John0101010101010101 Mary-Ann1010101010101010 ....
1.5.2 Using the tab-delimited format Another option in Excel is to Save As txt format, which produces a tab delimited file. This method has no limitations on the maximum record length. However, the IRT programs stumble on tab characters (do not know how to handle that) and they have to be removed. You can do that in MS Word, for example, by reading in the file as a plain text file, then do a global replace of the “^t” with either a blank or nothing. Then save the file. This works well if your ID field has the same number of characters. Otherwise, you can move the column to the end of the worksheet before you do the Save As operation. A second problem occurs when your worksheet has cells with no entries at all (missing response). When exporting (Save As) this as a tab-delimited text file, a global replacement of the tab character with a blank will throw off the column alignment. In that case, you should replace all instances of tab-tab with tab-space-tab. To accommodate the user, SSI has included a NOTAB utility on the program CD that can filter out unwanted tab characters correctly. This utility as well as a worked example can be found in the dataprep folder on the “IRT from SSI” CD.
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1 DATA PREPARATION
1.6 Data export: What if my data needs editing? Going the other way, from a plain text, formatted data file to an Excel file has a number of usages. Foremost is data editing. The first attempt at analysis may reveal several difficulties in the data. Values that are out of range, negative item-test correlations, group codes that are coded with characters instead of numbers, etc. Importing the plain text data file into Excel or a similar application provides the user with powerful tools for data editing and data cleaning. From within Excel, select Get External Data from the Data menu, then Import Text File. Select the data file to import. The Text Import Wizard opens with a preview of the data file. Select Fixed width as the type that best describes the data, then click the Next button. In the Data Preview box, use the mouse to set break lines separating the data into columns. Once satisfied, click Next. The last step allows you to skip columns, if needed. Click Finish.
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2 BILOG-MG REFERENCE
2 BILOG-MG BILOG-MG is an extension of the BILOG program that is designed for the efficient analysis of binary items, including multiple-choice or short-answer items scored right, wrong, omitted, or not-presented. BILOG-MG is capable of large-scale production applications with unlimited numbers of items or respondents. It can perform item analysis and scoring of any number of subtests or subscales in a single program run. All the program output may be directed to text files for purposes of selecting items or preparing reports of test scores. The BILOG-MG program implements an extension of Item Response Theory (IRT) to multiple groups of respondents. It has many applications in test development and maintenance. Applications of multiple-group item response theory in education assessment and other large-scale testing programs include:
Nonequivalent groups equating for maintaining the comparability of scale scores as new forms of the test are developed. Vertical equating of test forms across school grades or age groups. Analysis of Differential Item Functioning (DIF) associated with demographic or other group differences. Detecting and correcting for item parameter trends over time (DRIFT). Calibrating and scoring tests in two-stage testing procedures designed to reduce total testing time. Estimating latent ability or proficiency distributions of students in schools, communities, or other aggregations.
In addition, the BILOG-MG program provides for “variant items” that are inserted in tests for purpose of estimating item statistics, but that are not included in the scores of the examinees.
2.1 New features in BILOG-MG The most important change is that BILOG-MG is now a Windows application. Syntax can be generated or adapted using menus and dialog boxes or, as before, with command files in text format. The interface has menu options in the order the user would most generally use: model specification is followed by data specification and technical specifications, etc. Each of the menu options provides access to a number of dialog boxes in which the user can make specifications. For an overview of the required and optional commands in BILOG-MG syntax, please see Section 2.6.1. For more information on which dialog box to use to specify a specific keyword or option, please see the location of keywords in the interface discussed in Section 2.3.13.
Filename length: All filenames with path may now extend to 128 characters. The filename must be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1.
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2 BILOG-MG REFERENCE
Factor loadings: The item dispersion (reciprocal of the item slope) previously listed among the parameter estimates has been replaced by the one-factor item factor loading given by the expression Slope / 1 + Slope 2 . Average measurement error and empirical reliability for each subtest: The meansquare error and root-mean-square error for the sample cases are listed for each test. In addition, the empirical reliability computed from the IRT scale score variance and the mean-square error is listed. Note that for EAP and MAP estimated ability the formula for this reliability differs from the formula for ML estimated ability (to account for the regression effect in EAP and MAP estimation). If there are multiple test forms, these test statistics are averages over the forms. If there are multiple groups, the statistics are listed for both the combined groups and the separate groups. Reliabilities in connection with information plots: The reliabilities given by the program in connection with the information plots of Phase 3 differ from empirical reliabilities in that they assume a normal distribution of ability in the population. They depend only on the parameters of the items and not on the estimated abilities in the sample. The program now computes and lists these theoretical reliabilities for both combined and separated test forms and sample groups. (For a discussion of empirical and theoretical reliability see Bock & Zimowski (1999).) Information curves and reliabilities for putative test forms: It may be useful in test development to preview the information and theoretical reliability of test forms that might be constructed from items drawn from a calibrated item bank. (For a discussion of this procedure, see Section 2.2.) GLOBAL command—PRNAME keyword: This keyword instructs the program to read the provisional values of parameters of selected items in the test forms from the specified file. SAVE command—PDISTRIB keyword: This keyword allows the user to save the points and weights of the posterior latent distribution at the end of Phase 2. These quantities can be included as prior values following the SCORE command for later EAP estimation of ability from previously estimated item parameters. TEST command—FIX keyword: This keyword allows the user to keep selected item parameters fixed at their starting values. Starting values may be entered on the SLOPE, THRESHLD, and GUESSING keywords on the same command or read from an existing item parameter file. CALIB command—NOADJUST option: BILOG-MG routinely rescales the origin and scale of the latent distribution, even in the one-group case. This option may be used to suppress this adjustment. CALIB command—CHI keyword: This keyword determines the number of items required and the number of intervals used for χ 2 computations. CALIB command—FIXED option: If this option is present, the prior distributions of ability in the population of respondents are kept fixed at the values specified in the IDIST keyword and/or the QUAD commands. It suppressed the updating of the means and standard deviations of the prior distribution at each EM cycle in the multiple-group case. CALIB command—GROUP-PLOTS option: By default, the program item plots show observed proportions of correct responses in the data combined for all groups. The GROUP25
2 BILOG-MG REFERENCE PLOTS option provides plots for each separate group, along with the combined plot.
CALIB command—RASCH option: If this option is specified, the parameter estimates will be rescaled according to Rasch model conventions: that is, all the slopes will be rescaled so that their geometric mean equals 1.0, and the thresholds will be rescaled so that their arithmetic mean equals 0.0. If the 1-parameter model has been specified, all slope parameters will therefore equal 1.0. PRIORS command—SMU and SSIGMA keywords: Prior values for slope parameter means and sigma are now entered in arithmetic units rather than natural log units. The means for both forms are printed in the Phase 2 output, however. The default for SMU is 1.0 (log SMU = 0.0) and for SSIGMA the default is 1.64872127 (log SSIGMA = 0.5). SCORE command—MOMENTS option: Inserting the MOMENTS option in the SCORE command causes the program to compute and list the coefficients of skewness and kurtosis of the ability estimates and of the latent distribution. SCORE command—DOMAIN keyword: BILOG-MG now includes a procedure for converting the Phase 3 estimates of ability into domain scores if the user supplies a file containing the item parameters for a sample of previously calibrated items from the domain. Weights can be applied to the items to improve the representation of the domain specifications. SCORE command—FILE keyword: This keyword is used to supply the external file used to calculate the domain scores (see above).
2.2 Phases of the analysis: input, calibration and scoring Phase 1: INPUT
The input routine reads formatted data records. Data for each observation consist of subject identification, optional form number, optional group number, optional case weight, and item response data. Item responses of individual examinees comprise one character for each of n items. The answer key, not-presented, and omit codes are read in exactly the same format as the observations. For aggregate-level data, the “responses” consist of number of attempts and number correct for each item. If data are for the aggregate-level model, vectors of numbers of attempts and correct responses to the items are read in decimal format. Omits and attempts
Omits may be scored “wrong”, treated as fractionally correct, or omitted from calculations. Items and forms
The INPUT routine accepts a list of numbers and corresponding names for all items to be read from the data records. The order in which the items appear in the data records is specified in a form key(s). If the data are collected with a multiple-form test, the program accepts a key for each form. Each respondent’s data record is identified by its form number.
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2 BILOG-MG REFERENCE Multiple groups
When multiple-group IRT analysis is requested, the INPUT routine accepts a list of item numbers or names identifying the items administered to each group. Each respondent’s data record is identified by its group number. The Phase 1 program computes classical item statistics separately for each group. Subtests
The INPUT routine also accepts lists of item numbers or names, not necessarily mutually exclusive, describing i subtests. It scores each subtest and creates a file containing the item scores, item attempts, subtest scores, and other input information for each respondent. Each subtest is calibrated separately in Phase 2. Each respondent is scored on all subtests in Phase 3. Case weights
If there are case weights for respondents (because they were drawn in an allocation sample), the item responses and item attempts are multiplied by the weight. If the data consist of response patterns, the case weights are the frequencies of the patterns. Samples
If there are a large number of respondents or aggregate-level records, the INPUT routine can be instructed to select a random sample of a specified size to be passed to CALIBRATE (Phase 2). The complete master file of cases will nevertheless be passed to Phase 3 for scoring. Classical item statistics
While preparing the item-score file, the INPUT routine also accumulates, subtest by subtest, certain item and test statistics (accumulated from the sample file when the number of cases exceeds the user-specified sampling level). These statistics consist of
item facilities (percent correct), item-subscore correlations, and the number of respondents attempting each item.
These quantities are listed and passed to the Phase 2 and Phase 3 routines to provide starting values for item parameter and respondent scale-score estimation. Phase 2: CALIBRATE
The CALIBRATE routine fits a logistic item-response function to each item of each subscale. There are many options available to the user in this section of the program.
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2 BILOG-MG REFERENCE Item-response model
The response model may be the 1-, 2- or 3-parameter logistic response function. The scaling factor D = 1.7, employed to scale estimates in the normal metric, may be included or omitted at the user’s option. Information that assists the user in model selection is provided in the marginal log likelihood and goodness of fit indices and statistics for individual items. The user may request plots of the observed and expected item-response curves. Individual data or aggregate data
Item parameters may be estimated from either binary (right/wrong/omit) data or aggregate-level frequency data (number of correct responses, number of attempts) input from Phase 1. If aggregate-level data are used, it is assumed that each respondent in each group responds to only one item per subscale, as required in matrix-sampling applications (see Mislevy, 1983). The aggregate-level option can also be applied to individual data if weights are used and the binary responses take on fractional values. In this use of the aggregate-level option, each respondent responds to more than one item. Marginal maximum likelihood (MML) estimation of item parameters
Estimation of item parameters by the method of marginal maximum likelihood is applicable to tests of three or more items. The solution assumes the respondents are drawn randomly from a population or populations of abilities, which may be assumed to have either a normal distribution, an arbitrary distribution specified by the user, or an arbitrary distribution to be estimated jointly with item parameters. The empirical distributions of ability are represented as discrete distributions on a finite number of points (histogram). In the case of multiple groups, the CALIBRATE routine also provides estimates of the means and standard deviations of the posterior distributions of ability. The MML solution employs two methods of solving the marginal likelihood equations: the socalled EM method and Newton-Gauss (Fisher scoring) iterations. The default number of cycles for the EM algorithm is 10; the default for Newton steps is 2. Convergence in the EM steps is hastened by the accelerator described in Ramsay, 1975. Results of each cycle are displayed so that the extent of convergence can be judged. The information matrix for all item parameters is approximated during each Newton step and then used at convergence to provide large-sample standard errors of estimation for the item parameter estimates. Item statistics supplied by CALIBRATE
Phase 2 provides the item parameters in the form of the lower asymptote and the item intercept (equal to minus the product of the slope and threshold) and so-called “slope” or “discrimination” parameter, and the item threshold (location) and loading (one-factor item factor loading = Slope / 1 + Slope 2 ).
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2 BILOG-MG REFERENCE
In the one-parameter solution, all slopes are equal. In both the one- and two-parameter solutions, all lower asymptotes are zero. In the three-parameter solution with a common lower asymptote, all lower asymptote parameters are equal. Otherwise, they are estimated separately for each item. When an analysis of differential item functioning (DIF) is requested, the program provides estimates of the unadjusted and adjusted threshold parameters for each group along with their standard errors. Estimates of group differences in the adjusted threshold parameters are also provided. When an item parameter drift (DRIFT) analysis is selected, the program provides estimates of the coefficients of the linear or polynomial function. In Phase 2, when there is a single group, the unit and origin of the scale on which the parameters are expressed are based on the assumption that the latent ability distribution has zero mean and unit variance. This is referred to as the “0, 1” metric. When there are multiple groups, the program provides the option of setting the mean and standard deviation of the combined estimated distributions of the group to zero and one. The parameter estimates in Phase 3 can be rescaled according to scale conventions selected by the user. If the one-parameter model has been selected, the item slope estimates are uniformly 1.0. In other cases, the scores can be scaled to a specified mean and standard deviation in the sample. In both Phase 2 and Phase 3, the item parameter estimates can be saved before and after rescaling, respectively, in formatted external files. Maximum marginal a posteriori estimation of item parameters
When some items are extremely easy or extremely difficult, there may be insufficient information in the sample to estimate their parameters accurately. This will be especially true if the number of respondents is only moderate (250 or fewer). As an alternative to deleting these items, prior distributions can be placed on the item parameters. The user may specify normal priors for item thresholds, log-normal priors for slopes, and beta priors for lower asymptotes. Each item may have a different specification for its prior. Default specifications are for prior distributions on slopes under the two-parameter models, and on slopes and lower asymptotes under the three-parameter model. By specifying tight priors on selected item parameters, the user may hold these values essentially fixed while estimating other item parameters. This feature is useful in linking studies, where new test items are to be calibrated into an existing scale without changing parameter values for old items. Item fit statistics
Approximate χ 2 indices of fit are computed for each item following the final estimation cycle. For the purpose of computing these χ 2 , the scale score continuum is divided into a number of successive intervals convenient for displaying the response proportions (maximum of 20). Each respondent is assigned to the interval that includes the EAP estimate (based on the type of prior 29
2 BILOG-MG REFERENCE specified by the user) of his or her score. For the item in question, the expected response probabilities corresponding to the average EAP estimate of ability of cases that fall in the interval are used as the expected proportion for the interval. A likelihood ratio χ 2 is then computed after combining extreme intervals so that the expected frequency exceeds five. Degrees of freedom are equal to the number of combined intervals. There is no reduction in degrees of freedom due to estimating the item parameters because the marginal maximum likelihood method does not place linear constraints on the residuals. At the user’s request, observed and expected item-response curves are plotted for each item. Test of improved fit if the number of parameters is increased
When the expected frequencies of the individual response patterns are too small to justify the likelihood ratio test of goodness-of-fit, the change in likelihood ratio χ 2 between the 1- and 2parameter models, or between the 2- and 3-parameter models, is a valid large-sample test of the hypothesis that the added parameters are null. The degrees of freedom of each of these change χ 2 are equal to the number of items. Test of overall fit when the number of items is 10 or less
If the sample size is large and the number of items is small, the overall fit of the response functions of all items can be tested by comparing the observed frequencies of the patterns with the expected marginal frequencies computed from the fitted functions. The data must be in the form of response patterns and frequencies. The likelihood ratio χ 2 statistic for the test of fit is 2n
ri
i =1
N Pi
G = 2∑ ri log e 2
_
where 2n is the number of possible patterns of the n binary item scores, ri is the observed frequency of pattern i, N is the number of respondents, and Pi is the estimated marginal probability of pattern i. The number of degrees of freedom is 2n − kn − 1 , where k is the number of parameters in the response model. This test should be used only when the number of respondents is large relative to the number of patterns. If a few patterns have zero observed frequency, ½ should be substituted as the frequency for those patterns and corresponding ½s subtracted from the frequency of the most frequent pattern (or 1 could be used for this purpose).
30
2 BILOG-MG REFERENCE Phase 3: SCORE
The SCORE routine makes use of the master response file from Phase 1 and the item parameter estimate files from Phase 2 to compute estimated scale scores for respondents. The user may select one of the three methods described below for estimating scale scores. In each of these methods the user has the option of biweight robustification to protect the estimates from spurious responses due to guessing or inattention. Because effects of guessing are suppressed by the robustification, the lower asymptote is not incorporated in the response model in Phase 3 when the biweight option is selected. Scores and standard errors for all subscales are calculated simultaneously for each respondent. Results may be printed and/or saved on an external file. Maximum likelihood (ML)
ML estimates with or without robustification are computed by the Newton-Raphson method starting from a linear transformation of the logit of the percent-correct score for the subject. In those rare cases where the Newton iterations diverge, an interval-bisection method is substituted. Estimates for respondents with all correct or all incorrect responses are attributed by the halfitem rule. That is, respondents who score all incorrect are assigned one-half a correct response to the easiest item; respondents who score all correct are assigned one-half a correct response to the hardest item. The estimate is then computed from this modified response pattern. Standard errors are computed as the square root of the negative reciprocal of the expected second derivative of the log likelihood at the estimate, i.e., the square root of the reciprocal Fisher information. Bayes or expected a posteriori (EAP)
EAP estimates with or without robustification are computed by quadrature using a discrete distribution on a finite number of points as the prior. The user may select the number of points and has the choice of a normal, locally uniform, or empirical prior. For the latter, the user may supply the values of the points and the corresponding empirical weights or may use the empirical weights generated in Phase 2. The EAP estimate is the mean of the posterior distribution and the standard error is the standard deviation of the posterior distribution. Bayes modal or maximum a posteriori (MAP)
MAP estimates with or without robustification are also computed by the Newton-Gauss method. This procedure always converges and gives estimates for all possible response patterns. A normal prior distribution with user-specified mean and variance is assumed [the default is N(0, 1)]. The estimate corresponds to the maximum of the posterior density function (mode); the standard
31
2 BILOG-MG REFERENCE error is the square root of the negative reciprocal of the curvature of the density function at the mode. Estimated latent distribution
When EAP estimation is selected, the SCORE routine obtains an estimate of the population distribution of ability in the form of a discrete distribution on a finite number of points. This distribution is obtained by accumulating the posterior densities over the subjects at each quadrature point. These sums are then normalized to obtain the estimated probabilities at the points. The program also computes the mean and standard deviation for the estimated latent distribution. Sheppard’s correction for coarse grouping is used in calculating the standard deviation. Rescaling
The ability estimates are calculated initially in the scale of the item parameter estimates from Phase 2. In addition, however, rescaled estimates may be obtained by one of the following options:
the mean and standard deviation of the sample distribution of score estimates are set to arbitrary values specified by the user (default = 0, 1); a linear transformation of scale is provided by the user; if EAP estimation has been selected, the mean and standard deviation of the latent score distribution may be set to arbitrary values by the user (default = 0, 1). Any of these options may be applied to all subtests in the same computer run, or different rescaling parameters may be used for each subtest. Parameter estimates and standard errors for items from Phase 2 are rescaled for each subtest according to the selected option.
Marginal probabilities of response patterns
When EAP estimation is selected, the marginal probability of each response pattern in the sample is calculated and printed along with the corresponding number-right score and scale score. Item and test information tables and curves
BILOG-MG provides at the user’s request a number of indices and plots concerning item and test information:
Plots of test information and standard error curves for each subtest. Tables of item information indices, including the point and value of maximum information.
Classical reliability
The classical definition of reliability is simply the ratio of the true score variance to the observed score variance, which is the sum of the true scores variance and the error variance. In an IRT context, the true scores are the unobservable theta values that are estimated with a specified 32
2 BILOG-MG REFERENCE standard error from item response patterns, as for example in Phase 3 of the BILOG-MG program. Classical reliability is implemented in BILOG-MG in two different ways according to how the true score and error variances are estimated. To distinguish the two results, we refer to one as “theoretical” reliability and the other as “empirical” reliability. The result for theoretical reliability appears in connection with the test information plots in the Phase 3 output; the result for “empirical” reliability appears following the display of the means, standard deviations, and average standard error of the scores earlier in the Phase 3 output. The computation of these two quantities is carried out as follows. Theoretical reliability
The theoretical reliability value applies to IRT scores estimated by the maximum likelihood method (METHOD=1 of the SCORE command). It is based only on the item parameters passed from Phase 2 and does not depend in any way on the ability scores computed in Phase 3. Instead, it assumes that the true ability scores are distributed normally with mean zero and variance one in the population of examinees. The test information function is integrated numerically with respect to this assumed distribution to obtain the average information expected when the test is administered in the population. The formulas for evaluating test information for any given value of ability, assuming a one, two, or three parameter logistic item response model, are as follows: 1PL ∧
S .E.(1) (θ ) = {1/ D a 2
2
∑
n
∧
∧
1 2
∧
∧
1
P (θ )[1 − P(1) j (θ )]}
j =1 (1) j
2PL ∧
S .E.(2) (θ ) = {1/ D 2 ∑ j =1 a 2j P(2) j (θ )[1 − P(2) j (θ )]}2 n
3PL
1 − P(3) j (θ ) P(3) j (θ ) − g j n 2 2 S .E.(3) (θ ) = 1/ D ∑ j =1 a j ∧ P(3) j (θ ) 1 − g j ∧
∧
∧
2
1 2
Although the formulas are expressed in terms of standard errors, the information values can be obtained by taking the reciprocal of the squared standard error. Conversely, the reciprocal of the average information with respect to the ability distribution is the harmonic mean of the error variance. Since by assumption the variance of the true score (i.e., ability) distribution is equal to one when expressed in the scale of the Phase 2 item parameter calibration, the theoretical reliability is one divided by the quantity one plus the error variance. 33
2 BILOG-MG REFERENCE In the program the theoretical reliability is computed for each form of the test when there are multiple forms. Whether the analysis pertains to one group or multiple groups of examinees is not relevant; because the theoretical reliability is a function only of the item parameters, the presence of multiple groups has no effect on the results. This version of BILOG-MG has provisions for computing information curves and reliability for any set of item parameters supplied in Phase 1 as starting values for item parameter estimation. If alternative forms are to be constructed from the items set, the user can insert forms commands following the score command to indicate the item composition of the forms. See the documentation of these score-forms commands for instructions on how to set up these calculations (REFERENCE, READF and NFORMS on the SCORE command discussed in Section 2.6.16). Empirical reliability
The formulas for estimating the error and true score variances for calculating empirical reliability differ depending on how the ability scores of the examinees in the sample (or in the samples in the case of a multiple-group analysis) are estimated:
For maximum likelihood scores (method 1), the estimated error variance is the reciprocal of the mean of the best information evaluated at the ability estimates of all examinees in the sample or samples. The score variance is just the variance of the maximum likelihood scores in the sample or samples. The true score variance can therefore be estimated simply by subtracting the error variance from the score variance. The empirical reliability in each sample is then given by that value for the true score variance divided by the score variance. For Bayes EAP scores (METHOD=2 on the SCORE command), the estimate of the error variance is the mean of the variances of the posterior distribution’s ability for all examinees in the sample or samples. Because the ability scores are regressed estimates in Bayes estimation, the true score variance is estimated directly by the variance of the means of the posterior distribution (i.e., the EAP scores) in the sample or samples. An empirical reliability is therefore the true score variance divided by the sum of the true score variance and the error variance. The formulas for computing, by numerical integration, the means and variances of the examinee posterior distributions of ability are as follows.
The Bayes estimate is the mean of the posterior distribution of θ , given the observed response pattern x i (Bock & Mislevy, 1982). It can be approximated as accurately as required by the Gaussian quadrature, q
−
θi ≅
∑ X k P(x i | X k ) A( X k ) k =1 q
∑ P(x i | X k ) A( X k ) k =1
34
2 BILOG-MG REFERENCE This function of the response pattern x i has also been called the expected a posteriori (EAP) estimator. A measure of its precision is the posterior standard deviation (PSD), approximated by −
q
−
PSD(θ i ) ≅
∑ ( X k − θ i )2 P(xi | X k ) A( X k ) k =1
q
∑ P(x k =1
i
.
| X k ) A( X k )
The weights, A( X k ) , in these formulas depend on the assumed distribution of θ . Theoretical weights, empirical weights A* ( X k ) , or subjective weights are possibilities. The EAP estimator exists for any answer pattern and has a smaller average error in the population than any other estimator, including the ML estimator. It is in general biased toward the population mean, but the bias is small within ±3σ of the mean when the PSD is small (e.g., less than 0.2σ , see Bock & Mislevy, 1982). Although the sample mean of the EAP estimates is an unbiased estimator of the mean of the latent population, the sample standard deviation is in general smaller than that of the latent population. In most applications, this effect is not apparent because the sample standard deviation is adjusted arbitrarily when the scale scores are standardized. Thus, the bias is not a serious problem if all the respondents are compared using alternative test forms that have much different PSDs. The same problem occurs, of course, when number-right scores from alternative forms with differing reliabilities are used to compare respondents. Users of tests should avoid making comparisons between respondents who have taken alternative forms that differ appreciably in their reliability or precision. A further implication is that, if EAP estimates are used in computerized adaptive testing, the trials should not terminate after a fixed number of items, but should continue until a prespecified PSD is reached. For Bayes MAP scores, the estimated error variance is the mean of the reciprocal of the test information at the modes of the posterior distributions of all examinees in the sample or samples. Similarly the true score variance is estimated by the mean of the variances of the posterior distributions at the mode. As in the case of Bayes EAP scores, the empirical reliability for the MAP scores is equal to the true score variance divided by the sum of the true score variance and the error variance. The formulas for computing the posterior mode and test information at the mode are as follows. Similar to the Bayes estimator but with a somewhat larger average error is the Bayes modal, or so-called maximum a posteriori (MAP) estimator. It is the value of θ that maximizes n
P(θ | xi ) = ∑ {xij log e Pi (θ ) + (1 −xij ) log e [1 − Pi (θ )]} + log e g (θ ), j =1
where g (θ ) is the density function of a continuous population distribution of θ .
35
2 BILOG-MG REFERENCE The stationary equation is xij − Pj (θ )
n
∑ P (θ )[1 − P (θ )] ⋅ j =1
j
j
∂Pj (θ ) ∂θ
+
∂ log e g (θ ) =0 ∂θ
Analogous to the maximum likelihood estimate, the MAP estimate is calculated by Fisher scoring, employing the posterior information, J (θ ) = I (θ ) − ∂ 2 log e g (θ ) / ∂θ 2 ,
where the right-most term is the second derivative of the population log density of θ . In the case of the 2PL model and a normal distribution of θ with variance σ 2 , the posterior information is n
1
j =1
σ2
J (θ ) = ∑ a 2j Pj (θ )[1 − Pj (θ )] +
.
The PSD of the MAP estimate, θ , is approximated by ∧
PSD(θ ) = 1/ J (θ ).
Like the EAP estimator, the MAP estimator exists for all response patterns but is generally biased toward the population mean. Because empirical reliabilities are estimated from the results of test score estimation, they are reported separately for each group of examinees in a multiple-group analysis. Note, however, that the test forms are not distinguished in these computations. If there are multiple forms of the test, the empirical reliabilities are aggregations over the test forms. Information curves and reliabilities for putative test forms
It may be useful in test development to preview the information and theoretical reliability of test forms that might be constructed from items drawn from a calibrated item bank. This can now be done using the FIX keyword on the TEST commands. Starting values for the item parameters are supplied to the program (see definition of the FIX keyword in Section 2.6.3 for details) or the parameters may be read from an IFNAME file. Then all of the items are designated as fixed using the FIX keyword. If the INFO keyword appears in the SCORE command, the required information and reliability analysis will be performed in Phase 3. In order for this procedure to work, however, the program must have data to process in Phases 1 and 2 for at least a few cases. Some artificial response data can be used for this purpose. The only calculations that will be performed in Phase 2 are preparations for the information analy36
2 BILOG-MG REFERENCE sis in Phase 3. The number of EM cycles in the CALIB command can therefore be set to 1 and the number of Newton cycles to 0. The NOADJUST option must also be invoked. Output files Phase 1 results appear in the *.ph1 file. They include test and item identification and classical item statistics. Phase 2 results appear in the *.ph2 file. They include assumed prior distributions, estimated item parameters, standard errors and goodness-of-fit statistics, DRIFT parameters, estimates of differential item functioning, posterior distributions for the groups, group means, and standard deviations, and estimates of their standard errors. Phase 3 results appear in the *.ph3 file. They include assumed prior distributions of the scale scores for MAP and EAP estimation, correlations among the subtest scores, rescaling constants, rescaled item parameters, scale scores for the subjects, test information plots, and parameters of the rescaled latent distribution.
2.3 The BILOG-MG interface When the BILOG-MG program is opened the first time, a blank window is displayed with only three active options: File, View and Help. By default, however, BILOG-MG will open with the last active file syntax displayed. In this case, or when a command file is opened, the main menu bar shown below is displayed.
There are 12 menu titles available on the main menu bar. The main purpose of each is summarized in Table 2.1.
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2 BILOG-MG REFERENCE Table 2.1: Menu titles on the main menu bar Menu title
Purpose
File
Creating or opening files, printing files and exiting the program
Edit
Standard Windows editing functions
Setup
Model specification
Data
Description of the data, with option to enter new data
Technical
Specifying starting values and priors for calibration and/or scoring
Save
Saving output to external files
Run
Generating syntax and running one or all phases of the program; accessing the graphics procedure.
Output
Viewing output files for the current analysis.
View
Show or hide the tool bar and status bar
Options
Changing program settings and user preferences
Window
Switching between open files
Help
Access to the online help, build number and contact information for SSI
2.3.1 File menu
The File menu provides the user with options to open an existing syntax or text file, to create a new file, to save or to print files.
38
BILOG-MG INTERFACE
When the New or Open options are selected from the File menu, the user is prompted for the name of a command file. This can be either a new file, in which case a new name is entered in the File name field, or an existing file, in which case one can browse and select the previously created command file to be used as the basis for the current analysis.
The Close option is used to close any file currently open in the main BILOG-MG window, while the Save option is used to save any changes made to the file since it was opened. With the Save As option a file may be saved under the same or a different name in a folder of the user’s choosing. The Print and Print Setup options represent the usual Windows printing options, while selection of the Print Preview option opens a new window, in which the current file is displayed in print preview mode. Options to move between pages and to zoom in and out are provided. The printing options are followed by the names of the last files opened, providing easy access to recently used files. The Exit option is used to exit the program and return to Windows.
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2 BILOG-MG REFERENCE
2.3.2 Edit menu
The Edit menu has the standard Windows options to select, cut, copy and paste contents of files. In addition, the user can search for text strings and/or replace them with new text using the Find and Replace options.
2.3.3 Setup menu
The Setup menu is used to provide general information to be used in the analysis. The three options on the Setup menu are:
General: used for entering general information on the type of analysis required. Item Analysis: used to specify the allocation of items to forms, subtests, and/or groups and to control the item parameter estimation procedure. Test Scoring: used to request the scoring of individual examinees or of response patterns, item and test information and rescaling of scores.
40
BILOG-MG INTERFACE
The menu options are used to activate dialog boxes. The function of each dialog box is described below. Setup menu: General dialog box
The General dialog box has four tabs on which the job description, model, type of response and test, group and item labels may be specified. The Job Description tab is shown below.
41
2 BILOG-MG REFERENCE The top half of the Job Description tab on the General dialog box is used to provide a title and additional comments for the analysis. Below these fields, the number of items, subtests, groups and/or forms (if any), and the reference group in the case of a multiple-group analysis are entered. On the images shown here, links between the fields and the corresponding keywords are provided. Related topics
CALIB command: REFERENCE keyword (see Section 2.6.3) COMMENT command (see Section 2.6.4) GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NFORM, NGROUP, and NTOTAL keywords (see Section 2.6.9) TITLE command (see Section 2.6.18)
The second tab, Model, is used to select a 1-, 2-, or 3PL model and to specify the response function metric to be used. If variant items are to be included in the analysis, or a DIF or DRIFT multiple-group analysis is required, this is indicated in the Special Models group box.
42
BILOG-MG INTERFACE
Note that the selection of some models is dependent on the presence of other keywords in the syntax. For example, in order to request Variant Item Analysis the NVTEST keyword on the GLOBAL command should have a value larger than the default of 0, or the NVARIANT keyword on the LENGTH command should have a non-zero entry. Related topics
GLOBAL command: LOGISTIC option, NPARM and NVTEST keywords (see Section 2.6.7) INPUT command: DIF and DRIFT options (see Section 2.6.9) LENGTH command: NVARIANT keyword (see Section 2.6.11)
The Response tab allows specification of the number of response alternatives, and codes for the responses, not-presented and/or omitted items. In the case of a 3-PL model, the user may also request that omitted responses are scored fractionally correct. If the NPARM keyword on the
43
2 BILOG-MG REFERENCE GLOBAL command is not set to 3 to indicate a 3-PL model (see the previous tab) any instructions
in the Omits will be scored group box will not be used.
Related topics
GLOBAL command: OMITS option (see Section 2.6.7) INPUT command: NALT keyword (see Section 2.6.9)
Finally, the Labels tab provides the default item labels and group/test names. The user may enter names in the respective fields, or import item labels from an external file by using the Browse button next to the Item Label File Name field. After entering or selecting the file containing the 44
BILOG-MG INTERFACE item labels, click the Import button. Alternatively, after completion of the Item Labels and Test or Group fields, the user may save the labels to file using the Save button.
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2 BILOG-MG REFERENCE
Related topics
GROUP command: GNAME keyword (see Section 2.6.8) ITEMS command: INAMES keyword (see Section 2.6.10) TEST command: TNAME keyword (see Section 2.6.17)
Setup menu: Item Analysis dialog box
The Item Analysis dialog box has 5 tabs and is used to assign items to subtests, forms, and/or groups. In addition, subtests to be calibrated are selected here. Calibration specifications controlling the iterative procedure are also entered on this dialog box. On the Subtests tab shown below, labels for the subtests are entered in the first fields. The next two fields are used to indicate the number of items per test. Note that variant items should also 46
BILOG-MG INTERFACE be indicated here. The final column is used to select the subtests for which item parameter estimation is required. Related topics
CALIB command: SELECT keyword (see Section 2.6.3) LENGTH command: NITEMS and NVARIANT keywords (see Section 2.6.11)
On the images below, links between the fields and the corresponding keywords are provided.
: Number of subtest-only item = –
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2 BILOG-MG REFERENCE Table 2.2: Effect of test length
Add or remove entries in: >LENGTH NITEMS Add or remove entries in the >PRIORS command: TMU, TSIGMA
Add or remove entries in the variant’s >TEST command: INUMBER, INTERCPT, SLOPE, THRESHLD, GUESS, DISPERSN, FIX Add or remove entries in the subtest’s >TEST command: INUMBER, INTERCPT, SLOPE, THRESHLD, GUESS, DISPERSN, FIX Add or remove entries in the >PRIORS command: SMU, SSIGMA, ALPHA, BETA
The Subtest Items tab allows the user to assign specific items to the main and variant tests. Note that, if fewer items are selected here than were indicated on the Subtests tab, the information on the Subtest Items tab will be adjusted accordingly (see table above for specific information).
The Select and Unselect buttons may be used to include or exclude single items or sets of items (selected by holding down the mouse button and dragging over a selection of items). Double-clicking a single item also reverses the state of the item. To reverse the state of a block of items, highlight the items and click the Toggle button. A variant item can only be selected when its corresponding subtest item is selected. Note that the table only supports rectangular blocks of items. There are two ways to highlight a rectangular block of items:
Click and drag: Left-click on any one corner of the block you want to highlight, hold the mouse button down and drag the mouse to the opposite corner of the block before releasing the mouse button. All items bounded by the opposite corners used will be highlighted. Click-Shift-Click: Left-click on any corner of the block you want to highlight. Press and hold down the Shift key, move the mouse pointer to the opposite corner of the block and left-click. All items bounded by the opposite corners used will be highlighted. Related topics
LENGTH command: NITEMS keyword (see Section 2.6.11) TEST command: INUMBERS keyword (see Section 2.6.17)
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BILOG-MG INTERFACE
The next two tabs, Form Items and Group Items, are only available when a multiple-group or multiple-form analysis was indicated on the Job Description tab of the General dialog box. Both dialog boxes have the same form and mode of operation as the Subtest Items tab previously discussed, the only difference being that information entered here are recorded on the FORMS and GROUPS commands respectively. Both are used to indicate the length of and assignment of items to forms/groups.
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2 BILOG-MG REFERENCE Related topics
FORM command: INAMES, INUMBERS, and LENGTH keywords (see Section 2.6.6) GROUP command: INAMES, INUMBERS, and LENGTH keywords (see Section 2.6.8)
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BILOG-MG INTERFACE
The final tab of the Item Analysis dialog box is the Advanced tab that controls the estimation of item parameters. Most of the information pertains to the CALIB command. The number of iterations and convergence criterion are set at the top of the dialog box, while the number of items and ability intervals for calculation of χ 2 item fit statistics are specified in the Chi-square Item Fit Statistics group box. At the bottom of the dialog box, prior item constraints may be requested and the estimation of the means of the prior distributions on the item parameters specified to be kept at a fixed value or to be estimated along with the parameters. If a 3PL model is selected, all the prior check boxes in the Prior Item Constraints group box will be enabled. In the case of a 2PL model, the Prior on Guessing check box is disabled, while both the Prior on Guessing and Prior on Slope check boxes are disabled when a 1PL model is fitted to the data. Related topics
CALIB command: CHI, CRIT, CYCLES, NEWTON, NQPT, FLOAT, EMPIRICAL, GPRIOR, SPRIOR, and TPRIOR keywords (see Section 2.6.3) QUAD command: POINTS and WEIGHTS keywords (see Section 2.6.13)
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2 BILOG-MG REFERENCE
Setup menu: Test Scoring dialog box
Information entered on the Test Scoring dialog box controls the type of scoring performed in Phase 3 of the analysis. The General tab of this dialog box is used to select the method of scoring and to import item parameters for scoring from previously saved files. In the latter case, the Browse button at the bottom of the tab can be used to locate the file containing the item parameters to be used for scoring. Group-level fit statistics, the suppression of printing of scores to the output file when scores are saved to an external file using the SCORE keyword on the SAVE command, and biweighted estimates robust to isolated deviant responses are requested using the Group Level Fit Statistics, 52
BILOG-MG INTERFACE List Scores, and Biweight Items radio buttons. On the images below, links between the fields and the corresponding keywords are provided. Related topics
GLOBAL command: IFNAME and PRN keywords (see Section 2.6.7) SCORE command: BIWEIGHT, FIT, and NOPRINT options (see Section 2.6.16) SCORE command: IDIST and METHOD keywords
The Rescaling tab is associated with the RSCTYPE, LOCATION and SCALE keywords on the SCORE command and is used to request the scaling of the ability scores according to user-specified values. Provision is made for different scaling options for different subtests.
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2 BILOG-MG REFERENCE
Related topics
SCORE command: LOCATION, RSCTYPE, and SCALE keywords (see Section 2.6.16)
2.3.4 Data menu
The Data menu is used to enter data or to provide information on the data file; type and number of records in the data file; and answer, omit and not-presented keys if applicable (Item Keys option). A distinction is made between single-subject and group-level data (Examinee Data and Group-level Data tabs respectively).
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BILOG-MG INTERFACE
Data menu: Examinee Data dialog box
The Examinee Data dialog box deals with single-subject data. On the General tab of this dialog box, the type and number of data records to be used in the analysis are specified. All of the entries on this dialog box correspond to keyword values on the INPUT command, as indicated on the image below. Note that when the check box labeled External Ability Criterion and Standard Error is checked, the External Ability and Ability S.E. data fields on the Data File tab are enabled. Related topics
INPUT command: EXTERNAL, NIDCHAR, SAMPLE, TAKE, and TYPE keywords (see Section
2.6.9)
INPUT command: PERSONAL option
The name of the raw data file and the format of the data are specified on the Data File tab. An external data file may be selected using the Browse button at the top of the tab. Data may be displayed in the window below the Data File Name field by clicking the Show Data button.
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Data can be read in free- or fixed column format. For fixed-format data, a format string is required to tell the program where in the file each data element is located. To ensure the accuracy of the format information, the column locations of the various data elements can be determined directly using the spreadsheet in which the data are displayed: clicking directly in the display 56
BILOG-MG INTERFACE paces a cursor whose exact position is shown by the Line: and Col: indicators.
Related topics
GLOBAL command: DFNAME keyword (see Section 2.6.7)
Input files (see Section 2.6.20) INPUT command: NFMT keyword (see Section 2.6.9) Variable format statement (see Section 2.6.18)
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2 BILOG-MG REFERENCE Data may be entered interactively on the third tab of the Examinee Data dialog box. The Append button is used to add a new case to the end of the data file. The Insert button is used to insert a new case at the current cursor location, while the Delete button is used to delete lines of entered data. For example, if case 10 is highlighted in the table, pressing the Insert button will insert a new case at case 10, and all cases starting from 10 will move one row down in the table.
In the Read as Fixed-Column Records group box, the user can indicate the number of data records per case and then fill in the information on the positions of the case ID, the form and group numbers (if applicable), and the responses. The Set Format button is then clicked to create automatically a format statement in the Format String data field. Alternatively, the format statement may be entered directly in the Format String data filed. Clicking the Set Fields button will then automatically fill in the fields in the Read as Fixed-Column Records group box. Note that with either method the response string must be continuous, that is, there can be no spaces in the response string. Any attempt to specify non-continuous response data will result in incor58
BILOG-MG INTERFACE rect format and/or response information, and the data will not be read correctly. For example, if the response string is “10A,1X,10A,1X,15A” clicking the Set Fields button does not correctly set the Format String data field. Related topics
GLOBAL command: DFNAME keyword (see Section 2.6.7) INPUT command: NFMT keyword (see Section 2.6.9)
Variable format statement (see Section 2.6.18)
Data menu: Item Keys dialog box
The sole purpose of the Item Keys option on the Data menu is to provide the option to use answer, not-presented or omit keys. The three tabs on the Item Keys dialog box are similar. Possible key codes consist of the entire list of “possible keys”. The information is taken from the Response Codes edit box on the Response tab of the General dialog box on the Setup menu. On the first tab, Answer Key, an answer key may be read from an external file using the Open button and browsing for the file containing the answer key, or entered interactively in the window towards the top of the tab. In the case of multiple forms, a separate answer key for each form should be provided. The format of the keys should be the same as that used for the raw response data. If a key is entered interactively, the Save button may be used to save the entered information to an external file. The file used as answer key is referenced by the KFNAME keyword on the INPUT command. Related topics
INPUT command: KFNAME keyword (see Section 2.6.9)
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The second tab is used for the not-presented key (if any) and information entered here is echoed to the NFNAME keyword on the INPUT command.
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Related topics
INPUT command: NFNAME keyword (see Section 2.6.9)
The Omit Key tab is used for the omit key, if any. This tab corresponds to the OFNAME entry on the INPUT command in the completed command file.
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Related topics
INPUT command: OFNAME keyword (see Section 2.6.9)
Data menu: Group-Level Data dialog box
The Group-Level Data dialog box is similar in purpose to the Examinee Data dialog box where single-subject data may be entered. On this dialog box, however, information on the structure of group-level data to be used in analysis is provided. The General tab is used to provide information on the number of groups, group ID, and number of data records and weights, if any, to use in analysis. All entries correspond to keywords on the INPUT command.
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Related topics
INPUT command: EXTERNAL, NIDCHAR, SAMPLE, TAKE, and TYPE keywords (see Section
2.6.9) The Browse button on the Data File tab allows the user to browse for the file containing the group-level data. After clicking the Show Data button the contents of the selected file are dis-
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2 BILOG-MG REFERENCE played in the window below these buttons. The Format String field should be completed according to the contents of the file. In contrast to item responses in the case of single-subject data, which are read in “A” format, the frequencies in group-level data files are read in “F” format as shown below.
Related topics
GLOBAL command: DFNAME keyword (see Section 2.6.7) INPUT command: NFMT keyword (see Section 2.6.9)
Variable format statement (see Section 2.6.18)
2.3.5 Technical menu
The first set of options on the Technical menu is used to assign starting values, prior constraints, and information on prior latent distributions for both calibration and scoring during the analysis. The last three options on this menu provide the user with the option to exercise even more con64
BILOG-MG INTERFACE trol over the sampling and EM and Newton cycles (Data Options); request a Rasch model, plots per group or to prevent the adjustment of the latent distribution to a mean of 0 and S.D. of 1 (Calibration Options), and finally to calculate domain scores based on a user-supplied file containing information on previously calibrated items (Score Options).
Technical menu: Item Parameter Starting Values dialog box
The Assign Item Parameter Starting Values option on the Technical menu may be used to import starting values for item parameters from a saved item parameter or user-supplied file or, alternatively, to enter starting values interactively. The first tab on the Item Parameter Starting Values dialog box is used to select a previously created file. To use an item parameter file created during a previous BILOG-MG analysis, check the radio button next to the Import Saved Item Parameter File option. If starting values are provided through a user-supplied file, check the radio button next to the Import User Supplied File … option. The Browse button is used to locate the file.
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Enter starting values for the item parameters on the Enter Values tab to set values for the corresponding keywords on the TEST command. A subset of slope, threshold or asymptote parameters may be selected by holding the mouse button down and dragging until the selection is complete. Clicking the right mouse button will display a pop-up menu that can be used to assign values to the parameters. All selected parameters may be set to a specific value or to the default value. In addition, the user may select one parameter and assign a value to this and all other parameters below it by selecting the appropriate option from the pop-up menu. Alternatively, the Default Value or Set Value buttons may be used to assign values to the selected parameters. There are two ways to select the cell for parameter values:
Select a rectangular block of cells. Use either the click-and-drag or click-shift-click method described in the discussion of the Item Analysis dialog box of the Setup menu. Note that cells selected must be one continuous rectangular block. Select one or more columns by clicking on the column header. The click-and-drag 66
BILOG-MG INTERFACE method works when selecting a continuous block of columns. To select a disjoint block of columns, press and hold the Ctrl key down when clicking the header.
Note that when selecting a block of cells, the Shift key is used. When selecting a block of columns through clicking column headers, the Ctrl key is used. Clicking the column header changes the selection state of the entire column. It toggles the items in the column from the “selected” state to the “unselected” state and vice versa. The Save as 67
2 BILOG-MG REFERENCE User Data option may be used to provide a name for the external file to which input is saved with the file extension *.prm. Related topics
TEST command:
DISPERSN, GUESS, INTERCPT, SLOPE, and THRESHLD keywords (see
Section 2.6.17) Technical menu: Assign Fixed Items dialog box
This dialog box is associated with the FIX keyword on the TEST command, which is used to indicate which items of a subtest are free to be estimated, and which are to be held fixed at their starting values. As with the Enter Values tab on the Item Parameter Starting Values dialog box discussed above, cells may be selected in rectangular blocks or by columns. The same conventions for the use of the Shift and Control keys apply. Additionally, double-clicking on any one cell under the Fixed column also toggles the cell state: fixed to free or free to fixed.
Related topics
TEST command: FIX keyword (see Section 2.6.17)
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BILOG-MG INTERFACE Technical menu: Item Parameter Prior Constraints dialog box
The Item Parameter Prior Constraints dialog box is associated with the PRIORS command. The number of tabs on the dialog box depends on the number of subtests – priors may be entered for each subtest separately.
The user can set values by selecting an item or group of items and clicking the Set Value button. A subset of cells in the displayed table may be selected by holding the mouse button down and dragging until the selection is complete. Clicking the right mouse button will display a pop-up menu, which can be used to assign values to the cells. All selected cells may be set to a specific value or to the default value.
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2 BILOG-MG REFERENCE In addition, the user may select one parameter and assign a value to this and all other parameters below it by selection the appropriate option from the pop-up menu. A dialog box appears, prompting the user to enter the value to be assigned. Alternatively, the Default Value or Set Value buttons may be used to assign values to the selected parameters. To set the priors of a selection of items to their default value, the Default Value button may be used. Links between the fields on this dialog box and the corresponding keywords on the PRIOR command are shown on the image below. Related topics
CALIB command: READPRIOR option (see Section 2.6.3) PRIORS command: ALPHA, BETA, SMU, SSIGMA, TMU, and TSIGMA keywords (see Section
2.6.12) Technical menu: Calibration Prior Latent Distribution dialog box
The Assign Calibration Prior Latent Distribution option provides the opportunity to assign prior latent distributions, by subtest, to be used during item parameter estimation. This dialog box is associated with the QUAD command(s). This option is only enabled when the IDIST keyword is set to 1 or 2 on the CALIB command. There is no user’s interface to select this option. It must be set manually in the command file. For assigning prior latent distributions to be used during scoring, see the Assign Scoring Prior Latent Distribution dialog box. The first image below shows the dialog box for a single group analysis. Quadrature points and weights may be provided separately for each subtest. On the second image, the Calibration Prior Latent Distribution dialog box for a multiple-group analysis is shown. Note that quadrature points and weights may be entered per group and subtest, as a tab for each subtest is provided in this case, and that the set of positive fractions entered as Weights should sum to 1.0.
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The format of the table on the Calibration Prior Latent Distribution dialog box depends on the values of the NTEST, NGROUP and IDIST keywords. Examples are shown below.
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2 BILOG-MG REFERENCE >GLOBAL NTEST=1, … >INPUT NGROUP=1, … >CALIB IDIST=1 or 2, …
>GLOBAL NTEST>1, … >INPUT NGROUP=1, … >CALIB IDIST=1, …
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BILOG-MG INTERFACE >GLOBAL NTEST>1, … >INPUT NGROUP=1, … >CALIB IDIST=2, …
>GLOBAL NTEST=1, … >INPUT NGROUP>1, … >CALIB IDIST=1 or 2, …
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2 BILOG-MG REFERENCE >GLOBAL NTEST>1, … >INPUT NGROUP>1, … >CALIB IDIST=1, …
>GLOBAL NTEST>1, … >INPUT NGROUP>1, … >CALIB IDIST=2, …
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BILOG-MG INTERFACE Related topics
QUAD command: POINTS and WEIGHTS keywords (see Section 2.6.13)
Technical menu: Scoring Prior Latent Distribution dialog box
The Assign Scoring Prior Latent Distribution dialog box provides the opportunity to assign prior latent distributions, by subtest, to be used during scoring. This dialog box is associated with keywords on the SCORE and QUAD commands. For assigning prior latent distributions to be used during the item parameter estimation phase, see the Assign Calibration Prior Latent Distribution dialog box. On the Normal tab of this dialog box, the type of prior distribution to be used for the scale scores is the first information required. This tab is used when separate arbitrary discrete prior for each group or for each group for each subtest are to be read from a QUAD command. These options are only available when the Expected A Posteriori (EAP) method of scale score estimation is used. When maximum likelihood (ML) or Maximum A Posteriori (MAP) estimation is selected, these options are disabled and the PMN and PSD keywords may be used to specify real-numbered values for the means and standard deviation of the normal prior distributions. The default values of these keywords for each group for each subtest, 0 and 1 respectively, are displayed. To provide alternative values for the PMN and PSD keywords, click in the fields and enter the new values. Information in the table below corresponds to numbers on the image shown overleaf. Set the number of entries in the >QUAD commands: POINTS, WEIGHTS to NGROUP * NTEST.
Set the number of entries in the >QUAD commands: POINTS, WEIGHTS to NGROUP.
Related topics
SCORE command: IDIST, PMN and PSD keywords (see Section 2.6.16)
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The User Supplied tab allows the user to change the number of quadrature points to be used by subtest. Different quadrature points and weights may be supplied for each group per subtest, as shown in the image below where two subtests were administered to two groups of examinees.
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Related topics
QUAD command: POINTS and WEIGHTS keywords (see Section 2.6.13) SCORE command: NQPT keyword (see Section 2.6.16)
Technical menu: Data Options dialog box
To set the values for the random number generator seed used with the SAMPLE keyword on the INPUT command, or to change the value of the acceleration constant used during the E-steps in item calibration, the Data Options dialog box may be used. To use default values, the Set to Default Value buttons may be clicked after which the program defaults will be displayed in the corresponding fields. 77
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Note that:
The Item Analysis and/or Scoring from Saved Master File Name section is the same as the Master Data edit box in the Save Output to File dialog box . The dialog box does not read any data from the specified master file. The filename is simply copied to the MASTER keyword on the SAVE command.
Related topics
CALIB command: ACCEL and NFULL keywords (see Section 2.6.3) INPUT command: ISEED and SAMPLE keywords (see Section 2.6.9)
Technical menu: Calibration Options dialog box
The Calibration Options dialog box is associated with keywords on the CALIB command. To request a Rasch model, the One Parameter Logistic Model option should be checked. Separate item plots for each group may be requested using the Separate Plot for Each Group check box while adjustment of the latent distribution to a mean of 0 and S.D. of 1 may be suppressed using the first check box. To keep the prior distributions of ability in the population of respondents fixed at the value specified in the IDIST keyword and/or the QUAD commands, the Fixed Prior Distribution of Ability check box should be checked. This corresponds to the FIXED option on the CALIB command.
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Related topics
CALIB command: FIXED, GROUP-PLOTS, NOADJUST and RASCH options (see Section 2.6.3)
Technical menu: Score Options dialog box
This dialog box allows the user to request the calculation of domain scores based on a usersupplied file containing the item parameters for a sample of previously calibrated items for a domain and to request the computation and listing of the coefficients of skewness and kurtosis of the ability estimates and of the latent distribution. Related topics
SCORE command: DOMAIN and FILE keywords (see Section 2.6.16) SCORE command: MOMENTS option
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2.3.6 Save menu
The Save Output to File dialog box is accessed through the Save menu. Various types of data may be saved to external files using the SAVE command. On the image below, links are provided between the fields of this dialog box and the corresponding keywords on the SAVE command. Related topics
GLOBAL command: SAVE option (see Section 2.6.7) SAVE command (see Section 3.2.13)
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2.3.7 Run menu
The Run menu provides the necessary options to generate syntax from the input provided on the dialog boxes accessed through the Setup, Data, and Technical menus (Build Syntax option) or to run separate or all phases of the analysis. This menu is also used to access the graphics procedure described in Chapter 6 via the Plot option. Note that this option is only enabled after completion of the three phases of analysis.
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2 BILOG-MG REFERENCE Select the Build Syntax option to generate a syntax or command file based on the contents of the previous dialog boxes and menus. When the Initialize option is selected, changes made to an existing command file in the syntax window are transferred to the dialog boxes and menus. Run only the first phase of the analysis to obtain the classical statistics by selecting the Classical Statistics Only option. The item parameter estimation may be performed next by selecting the Calibration Only option, and scoring after that using the Scoring Only option. These options are provided to allow the user to run and verify information in the output for each phase of the analysis before continuing to the next step. When running the analysis phase by phase, the option to run the next phase will only be enabled after successful completion of the previous phase. Alternatively, the user can request to run all three phases in succession by selecting the Stats, Calibration and Scoring option. A message indicating the normal or abnormal termination of each phase will appear in the main window between phases to alert the user to possible problems in a particular phase of the analysis. This message may be suppressed using the Options menu.
2.3.8 Output menu
To view the output obtained during any of the three phases of analysis, the options on the Output menu may be used. Options will be enabled or disabled depending on the number of completed phases of the analysis. When any of these options is selected, the relevant output file will be displayed. After inspection, the user may close this file to return to the main BILOG-MG window, where the command file on which the analysis was based will be displayed.
2.3.9 View menu
The View menu allows the user to add or remove the status bar displayed at the bottom of the main BILOG-MG window. The toolbar, allowing the standard Windows editing functions, is displayed by default.
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2.3.10 Options menu
The Options menu provides access to the Settings dialog box. This dialog box has three tabs: General, Editor, and Server. On the General tab as shown below, the size of the application window and document window can be set. The user may opt to always open the last active document when opening BILOG-MG (default) or to start with a blank screen instead by unchecking the Open last active document on start check box.
To change the font in which the contents of the editor window are displayed, or to use tabs, the Editor tab of the Settings dialog box may be used. Reminders of file changes and automatic reloading of externally modified documents may also be requested.
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The Server tab of the Settings dialog box may be used to show or hide the windows in which details of the analysis are displayed during the run. To open multiple command files, which can then be accessed using the Windows menu, check the box next to the Allow multiple command file… option on this tab.
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BILOG-MG INTERFACE 2.3.11 Window menu
The Window menu allows the user to arrange multiple windows or to switch between open files. To open multiple command files simultaneously that may be accessed through this menu, use the Server tab on the Settings dialog box accessed through the Options menu.
2.3.12 Help menu
The Help menu provides access to the BILOG-MG help file (Help Topics option) and to the About BILOG-MG for Windows dialog box in which the version and build number of the application are displayed. This box may also be used to directly e-mail SSI for technical support or product information or to link to the SSI website. 2.3.13 Location of keywords in interface Command and keyword
Menu and option
Tab on dialog box
TITLE command
Setup, General
Job Description
COMMENT command
Setup, General
Job Description
DFNAME
Data, Examinee Data/Data, Group-Level Data
Data File/Enter Data
MFNAME
-
-
CFNAME
-
-
IFNAME
Setup, Test Scoring
General
NPARM
Setup, General
Model
NWGHT
-
-
NTEST
Setup, General
Job Description
Setup, General
Model
GLOBAL command:
NVTEST LOGISTIC
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Menu and option
Tab on dialog box
OMITS
Setup, General
Response
SAVE
Save
-
PRNAME
Setup, Test Scoring
General
MASTER
Save
-
CALIB
Save
-
PARM
Save
-
SCORE
Save
-
COVARIANCE
Save
-
TSTAT
Save
-
POST
Save
-
EXPECTED
Save
-
ISTAT
Save
-
DIF
Save
-
DRIFT
Save
-
PDISTRIB
Save
-
NITEMS
Setup, Item Analysis
Subtests
NVARIANT
Setup, Item Analysis
Subtests
Setup, General
Job Description
SAVE command:
LENGTH command:
INPUT command: NTOTAL NFMT TYPE SAMPLE NALT
Data, Examinee Data/Data, Group-Level Data Data, Examinee Data/Data, Group-Level Data Data, Examinee Data/Data, Group-Level Data Setup, General
Data File General General Response
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BILOG-MG INTERFACE Command and keyword NIDCHAR TAKE
Menu and option
Data, Examinee Data/Data, Group-Level Data Data, Examinee Data/Data, Group-Level Data
Tab on dialog box
General General
NGROUP
Setup, General
Job Description
NFORM
Setup, General
Job Description
KFNAME
Data, Item Keys
Answer Key
NFNAME
Data, Item Keys
Not Presented Key
OFNAME
Data, Item Keys
Omit Key
DRIFT
Setup, General
Model
DIF
Setup, General
Model
PERSONAL
Data, Examinee Data
General
EXTERNAL
Data, Examinee Data/Data, Group-Level Data
General
ISEED
Technical, Data Options
-
INUMBERS
-
-
INAMES
Setup, General
Labels
TNAME
Setup, General
Labels
INUMBERS
Setup, Item Analysis
Subtest Items
INAMES
Setup, General
Labels
DIAGNOSE
ITEMS command:
TEST command:
INTERCPT SLOPE THRESHLD GUESS
Technical, Assign Item Parameter Starting Values Technical, Assign Item Parameter Starting Values Technical, Assign Item Parameter Starting Values Technical, Assign Item Parameter Starting Values
Import/Enter Values Import/Enter Values Import/Enter Values Import/Enter Values
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Command and keyword
Menu and option
Tab on dialog box
DISPERSN
Technical, Assign Item Parameter Starting Values
Import/Enter Values
FIX
Technical, Assign Fixed Items
-
LENGTH
Setup, Item Analysis
Form Items
INUMBERS
Setup, Item Analysis
Form Items
INAMES
Setup, General
Labels
GNAME
Setup, General
Labels
LENGTH
Setup, Item Analysis
Group Items
INUMBERS
Setup, Item Analysis
Group Items
INAMES
Setup, General
Labels
MAXPOWER
-
-
MIDPOINT
-
-
Data, Examinee Data / Data, Group-Level Data
Data File
CHI
Setup, Item Analysis
Advanced
NQPT
Setup, Item Analysis
Advanced
CYCLES
Setup, Item Analysis
Advanced
NEWTON
Setup, Item Analysis
Advanced
PRINT
-
-
CRIT
Setup, Item Analysis
Advanced
IDIST
-
-
PLOT
-
-
FORM command:
GROUP command:
DRIFT command:
Variable format statement CALIB command:
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BILOG-MG INTERFACE Command and keyword
Menu and option
Tab on dialog box
DIAGNOSIS
-
-
REFERENCE
Setup, General
Job Description
SELECT
Setup, Item Analysis
Subtests
RIDGE
-
-
ACCEL
Technical, Data Options
-
NSD
-
-
COMMON
-
-
EMPIRICAL
Setup, Item Analysis
Advanced
NORMAL
-
-
FIXED
Technical, Calibration Options
-
TPRIOR
Setup, Item Analysis
Advanced
SPRIOR
Setup, Item Analysis
Advanced
GPRIOR
Setup, Item Analysis
Advanced
NOTPRIOR
Setup, Item Analysis
Advanced
NOSPRIOR
Setup, Item Analysis
Advanced
NOGPRIOR
Setup, Item Analysis
Advanced
READPRIOR
Technical menu: Item Parameter Prior Constraints dialog box
-
FLOAT
Setup, Item Analysis
Advanced
NOFLOAT
Setup, Item Analysis
Advanced
GROUP-PLOTS
Technical, Calibration Options
-
NOADJUST
Technical, Calibration Options
-
RASCH
Technical, Calibration Options
-
NFULL
Technical, Data Options
-
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Command and keyword
Menu and option
Tab on dialog box
QUAD command: POINTS WEIGHTS
Technical, Assign Calibration Prior Latent Distributions Technical, Assign Calibration Prior Latent Distributions
-
PRIORS command: TMU TSIGMA SMU SSIGMA ALPHA BETA
Technical, Assign Item Parameter Prior Constraints Technical, Assign Item Parameter Prior Constraints Technical, Assign Item Parameter Prior Constraints Technical, Assign Item Parameter Prior Constraints Technical, Assign Item Parameter Prior Constraints Technical, Assign Item Parameter Prior Constraints
-
SCORE command: METHOD
Setup, Test Scoring
General
NQPT
Technical, Assign Scoring Prior Latent Distribution
User-supplied
IDIST (values=0,3)
Setup, Test Scoring
General
IDIST (values=1,2) PMN PSD
Technical, Assign Scoring Prior Latent Distribution Technical, Assign Scoring Prior Latent Distribution Technical, Assign Scoring Prior Latent Distribution
Normal Normal Normal
RSCTYPE
Setup, Test Scoring
Rescaling
LOCATION
Setup, Test Scoring
Rescaling
SCALE
Setup, Test Scoring
Rescaling
INFO
-
-
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BILOG-MG INTERFACE Command and keyword
Menu and option
Tab on dialog box
BIWEIGHT
Setup, Test Scoring
General
FIT
Setup, Test Scoring
General
NOPRINT
Setup, Test Scoring
General
YCOMMON
-
-
POP
-
-
REFERENCE
-
-
READF
-
-
NFORMS
-
-
MOMENTS
Technical, Score Options
-
DOMAIN
Technical, Score Options
-
FILE
Technical, Score Options
-
QUADS command: POINTS WEIGHTS
Technical, Assign Scoring Prior Latent Distribution Technical, Assign Scoring Prior Latent Distribution
User-Supplied User-Supplied
Related topics
Overview of required and optional commands.
2.4 Getting started with BILOG-MG To illustrate the use of the interface in creating syntax files, the data file exampl01.dat in the examples subfolder of the BILOG-MG installation folder is used. This problem is based on an example in Thissen, Steinberg & Wainer (1993). Other examples based on the same data (see complete description below) can be found in Chapter 10. In the late 1980s, R. Darrell Bock created a “College Level Spelling Test” comprising a sample of 100 words drawn from a large source list by simple random sampling. Data collected using that test are the basis for the empirical example in the paper “IRT Estimation of Domain Scores” (R.D. Bock, M.F. Zimowski, & D. Thissen, Journal of Educational Measurement, 1997, 34, 197211). Parameter estimates for the 2PL IRT model for the 100-item test are tabulated in that paper. Bock created the script for conventional oral presentation of the test, and recorded the original reading of the script (by Monica Marie Bock) on reel-to-reel magnetic tape. Subsequent copies onto cassette tape were used by Jo Ann Mooney in the collection of data from around 1000 91
2 BILOG-MG REFERENCE University of Kansas Undergraduates. We are using the file with 100 words (items) and 1000 records (examinees). The words for the test were randomly selected from a popular wordbook for secretaries. Students were asked to write the words as used in a sentence on the tape recording. Responses were scored 1 if spelled correctly and 0 if spelled incorrectly. Because the items are scored 1,0, according to the defaults assumed by the program, an answer key is not required. The purpose of this section is to give the new user a quick overview of the interface and the absolute minimum input needed to run the program. In Chapter 11, the syntax and keywords of each example are discussed in detail. A few lines of the data in exampl01.dat are shown below: 11 21 31 41
0000 0001 1000 1001
… 162 1111
The first three characters in each line represent the examinee identification field. This is followed by the responses to the four items in the test. 2.4.1 A first model: 2PL model for spelling data
As a first example, we wish to set up a simple 2-PL model for this data. To construct a command file, begin by selecting the New option from the File menu. The Open dialog box is now activated.
Assign a name, with the *.blm file extension, to the command file. In this case, the command file first.blm is created in the examples folder as shown below. Click Open when done to return to the main BILOG-MG window.
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Note that a number of options have been added to the main menu bar of the BILOG-MG window. Of interest for this example are the Setup, Data, Run and Output options. The Setup menu is used to describe the model to be fitted to the data. As a first step, select the General option from this menu to access the General dialog box.
The General dialog box has four tabs, on which both required and optional keywords may be set. On the Job Description tab below, the number of items in the test is indicated as 4. The type of model is selected on the Model tab. As the default model fitted by BILOG-MG is a 2PL model, this tab is not used now. Click OK to return to the main window.
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The next step in specifying the analysis is to assign the items to be calibrated to the test. To do this, select the Item Analysis option on the Setup menu to access the Item Analysis dialog box. Change the default value of 1 under Subtest Length to 4 by clicking in this field and typing in “4”. By default, all items will be analyzed, as indicated under the Analyze this run header. Click OK when done. This completes the model specification. All that remains to be done is to provide information on the data. To do so, the Data menu is used. In this case, we have examinee data and thus the Examinee Data option is selected from the Data menu.
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GETTING STARTED WITH BILOG-MG
On the Examinee Data dialog box, enter the number of characters representing the examinee identification (in this case 3) in the Number of Case ID Characters field. By default, all data are used as shown below. To provide information on the name and format of the data file, click the Data File tab.
Use the Browse button to browse for the data file. Next, indicate that the data are in fixed format by clicking the Read as Fixed-Column Records radio button. Complete the table in the Read as Fixed-Column Records group box by clicking in the cell next to Case ID under the First header. Enter a “1” here to indicate that the examinee identification starts in column 1 of the data. Next, enter a “3” under the Last header to indicate the end of the examinee identification. Note that Form Number, Group Number, etc is disabled due to the information we entered from the Setup menu. The only other fields to complete are the Response String fields. The response to the first item is in column 5 of the data, so a “5” is entered in the cell next to Response String under the First header. The response to the last item is in column 8, and an “8” is thus entered in the Last column. By default, BILOG-MG assumes that there is one line of data for each examinee, as indicated by the Number of Data Record per Case field. As this is the case for data in exampl01.dat, no further information is required. Click the Set Format button to write the 95
2 BILOG-MG REFERENCE format statement (3A1,1X,4A1) to the Format String field. Click OK to return to the main BILOG-MG window.
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GETTING STARTED WITH BILOG-MG
Having completed the specification in terms of model and data, the command file is created by selecting the Build Syntax option from the Run menu. The syntax created by the program is now displayed in the main window, as shown below. Note that no options are given on the ITEMS and SCORE commands in this file, indicating that all program defaults will be used. Save the completed syntax to file by selecting the Save option on the File menu.
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The analysis is now performed by using some of the other options on the Run menu. Although the analysis can be done phase by phase (using the Classical Statistics Only, Calibration Only, and Scoring Only options) all three phases can be run in sequence by selecting the Stats, Calibration and Scoring option from this menu.
After successful completion of all three phases of the analysis, a message to this effect is displayed on the screen. If a problem was encountered during analysis, this message box will indicate that all phases were not completed successfully. Access the output from the analysis through the Output menu. Classical statistics are given in the *.ph1 file.
98
GETTING STARTED WITH BILOG-MG A section of this output file is shown below.
In the first.ph2 file, the results of the item calibrations are given. The item parameter estimates for the four items in the test are shown below.
Scoring results are given in the first.ph3 file. The complete list of scores is printed to this file by default. A section of this output, showing summary statistics for the score estimates, is shown below.
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2.4.2 A second model: DIF model for spelling data
The data analyzed in the previous example actually came from two groups of respondents. The groups in this example are the two sexes. The same four items are presented to both groups on a single test form. The group indicator is found in column 3 of the data records. 11 21 31 41
0000 0001 1000 1001
… 162 1111
The third column of the data contains either a 1 or a 2, indicating whether an examinee belonged to group 1 (male) or group 2 (female). The previous single-group analysis for this group, contained in the command file first.blm, is modified to perform a DIF analysis for the two groups. As a first step, the General option on the Setup menu is used to indicate the presence of multiple groups. On the Job Description tab of the General dialog box, change the Number of Examinee Groups from the default value of 1 to 2, as shown below.
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GETTING STARTED WITH BILOG-MG
In the case of a DIF model, a 1PL model is required. To change the model from the default 2PL model previously used, click the Model tab and check the 1-Parameter Logistic (1PL) radio button in the Response Model group box. To request a DIF model, click the Differential Item Functioning (DIF) radio button in the Special Models group box. By default, the first group will be used as reference group as indicated in the Reference Group field. Once this is done, all necessary changes to the General dialog box have been made. Click OK to return to the main BILOG-MG window.
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The allocation of items to be calibrated for each of the two groups is specified using the Item Analysis option of the Setup menu. Once this option is selected, the Item Analysis dialog box is displayed. Leaving the Subtests tab as previously completed, click the Group Items tab. By default, all items will be selected for the first group. This is indicated by the display of the item names in a bold font in the first column of the table. To select all four items for the second group, click on ITEM0001 in the second column. While holding the Shift button on the keyboard down, click on ITEM0004. All four items are now highlighted. Click the Select button at the bottom left of the dialog box to select all items. This completes the model specification. Click OK to return to the main window.
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GETTING STARTED WITH BILOG-MG
The only remaining task is to revise the reading of the data file so that the group identification field can be recognized and processed by the program. To do so, select the Examinee Data option from the Data menu. On the General tab of the Examinee Data dialog box, the number of case identification characters is now decreased to 2, as shown below. (Recall that previously this field was set to 3: in effect, a combination of actual examinee ID and group ID was used to identify the cases in the previous example.)
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The format statement is now adjusted accordingly by changing the entries in the Read as FixedColumn Records group box:
The Last value for the Case ID is set to 2. The Group Number field, now enabled due to our selection of a multiple-group analysis from the Setup menu, is set to 3 under both the First and Last headers, as the group identification, given in column 3 of the data, is one character in length. Finally, these changes are made to the format statement by clicking the Set Format button. Note that the format statement now contains an additional “I1” indicating the group number in integer format.
This completes the syntax specification. Return to the main window by clicking the OK button.
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The revised command file is generated by selecting the Build Syntax option from the Run menu.
After generating the syntax, it is saved to file using the Save As option on the File menu. The revised syntax is saved in the file second.blm in the examples folder. Click the Save button after specifying a name for and path to the new command file.
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When the syntax displayed in the main BILOG-MG window is compared to the first example, we note the addition of two GROUP commands and the NGROUP and DIF keywords on the INPUT command. The revised format statement is also included. The NPARM keyword on the GLOBAL command (not shown here) indicates that a 1-PL model is requested. The three phases of the analysis can be run separately using the Classical Statistics Only, Calibration Only, and Scoring Only options on the Run menu. To run the phases sequentially, select the Stats, Calibration, and Scoring option from this menu. Output for the analysis is accessed as before using the Output menu from the main menu bar.
In the partial output from the second.ph1 file for this DIF analysis classical item statistics are provided by group. Similar statistics are also given for the combined group (not shown below).
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The Phase 2 output in the second.ph2 file provides item parameter estimates, and DIF specific output as shown below.
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2 BILOG-MG REFERENCE Although the second.ph3 file is created as shown below, no scoring is performed in the case of a DIF analysis.
2.5 Syntax 2.5.1 Data structures: ITEMS, TEST, GROUP and FORM commands
In addition to conventional IRT analysis of one test administered to one group of examinees, BILOG-MG is capable of analyzing data from test development and scoring applications in which multiple alternative test forms, each consisting of multiple subtests or scales, are administered to persons in one or more groups. BILOG-MG relies on a system of four commands, ITEMS, TEST, FORM, and GROUP, describing the assignment of items to subtests, forms, and groups. The syntax of these commands is discussed in detail in the syntax section. Here a description is given of how the commands work together to accommodate a wide range of applications. Related topics
The FORM command The GROUP command The ITEMS command The TEST commands How the FORM and GROUP commands work Setup menu: General dialog box Setup menu: Item Analysis dialog box
The ITEMS command
The ITEMS command attaches names and numbers to items of the test instrument. In the TEST, FORM, and GROUP commands, the user can select items either by name or number. As a convenience to the user, the program can automatically create sequences of eight-character item 108
OVERVIEW OF SYNTAX names consisting of a user-supplied alphabetical section and a sequential numerical section. An ITEMS command is required in all applications of the program. It lists the entire set of items appearing in the test instrument. In the TEST, FORM, and GROUP commands, the full set, or subsets of it, may appear. The examples illustrate the use of the ITEMS command in a variety of applications
of the program. Related topics
The FORM command The GROUP command The TEST commands How the FORM and GROUP commands work Setup menu: General dialog box
The TEST command
The TEST commands describe the subsets (or scales) that will be scored in the test. There is a separate TEST command for each subtest. A subtest may consist of a combination of items in the instrument including items that appear on different test forms making up the instrument. The TEST commands identify the items belonging to each subtest. In addition, when the LENGTH command indicates variant items are present in a particular subtest (items that are included in the test to obtain item statistics for a subsequent form of the test but are not used in computing test scores), the user identifies these items with the corresponding subtest by means of an additional TEST command that immediately follows the TEST command of the subtest. If the entire instrument is analyzed in a single subtest without variant items, the problem setup requires a single TEST command that lists all the items in the test instrument. Chapter 10 illustrate this type of application. If multiple subtests of items are selected for analysis, a separate TEST command is required for each subscale. The example in Section 10.6 illustrates the problem setup for an analysis with multiple subtests within a single test form. The example discussed in Section 10.8 shows the setup for analysis with multiple subtests for an instrument and with multiple test forms. Section 10.7 illustrates the special TEST command setup for an instrument with variant items. Related topics
LENGTH command
Setup menu: General dialog box Setup menu: Item Analysis dialog box Technical menu: Assign Fixed Items dialog box Technical menu: Item Parameter Starting Values dialog box
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2 BILOG-MG REFERENCE The FORM command
The FORM command controls the input of the response record. It lists the items in the order in which they appear in the data records. Most applications of BILOG-MG require at least one FORM command. There are two arrangements in which multiple forms data can be supplied to the program. We refer to them as the expanded format and the compressed format (see also the file structure specifications): Expanded format
The response record of each examinee spans the entire set of items appearing in the test instrument. Each item of the test instrument has a unique location (column) in the input records. A notpresented code appears in the locations of the items belonging to forms not presented to a given examinee. Expanded format is convenient for users who store data in two-dimensional (row by column) arrays typical of many database systems. This format requires only a single FORM command, even though the data arise from multiple forms. Note that the order of the items in the input records, and thus the order of their listing on the FORM command, does not have to be the same as that in the list of names and numbers in the ITEMS command (although ordinarily it would be). Note also that a code to identify the form administered to a particular examinee is not read by the program from an expanded format record. Compressed format
The data record for each examinee contains responses only to the items presented to that person, and the responses appear in the same column field of each record (the number of columns is equal to the number of items in the longest test form). Data entry in the compressed format is easier than in the expanded format and results in smaller data files. With compressed-format data, the locations of the items in the input records are not unique. An item in one record may occupy the same column as a different item in another record. A separate FORM command is therefore required for each test form in the instrument. In addition, each response record must contain a number identifying the FORM command that applies to that record. The number (1, 2, 3, etc.) refers to the order of the FORM command in the command file. The item list of the corresponding FORM command gives the names or numbers of the items in the order that they appear in the response field of the data records (see Section 2.6.18 for details). Internally, the program works by expanding the compressed records and inserting not-presented codes in locations corresponding to the forms not administered to the examinee. Related topics
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box
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OVERVIEW OF SYNTAX The GROUP command GROUP commands are required whenever a multiple-group analysis is specified. The number of commands is equal to the number of groups in the analysis. GROUP commands serve two purposes. First, they identify groups of respondents for multiple-group analysis. Second, they identify the set of items administered to each group. Note that whenever a multiple-group analysis is requested, each response record must contain a number identifying the GROUP command that applies to that record.
Related topics
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box
How the FORM and GROUP commands work
The FORM and GROUP commands control the input of the individual response records. How they work together depends on the following:
the format of the response records (expanded or compressed) the number of forms in the test instrument the assignment of test forms to the respondents the number of groups in the IRT analysis
The sections below describe how these factors determine the structure of the FORM and GROUP commands. Instruments with a single test form
When an instrument consists of a single test form, a single FORM command is assumed, and it applies to all records. The program reads the entire response records according to the specifications on the command. If a FORM command is not included in the problem setup, the program reads the response records according to the order of items in the ITEMS command list. As in all applications with a single FORM command, the response records do not contain a form indicator. Single-group analysis
The examples in Sections 10.1 and 10.3 illustrate the simple case of a single-group analysis of a single test form. The program reads all response records according to the specifications on the FORM or ITEMS commands. GROUP commands are not required for the analysis. Multiple-group analysis
In multiple-group analysis of a single test form, the groups may represent naturally occurring subgroups within a population of respondents, or groups of respondents drawn from different populations. In either case, the structure of the FORM and GROUP commands is the same. A single FORM command applies to all response records and a separate GROUP command is required for 111
2 BILOG-MG REFERENCE each group of respondents in the analysis. Because all respondents receive the same test form, and thus respond to the same set of items, the lists of items in the GROUP commands are the same for all groups in the analysis. The lists include all of the items specified in the FORM or ITEMS commands. The primary function of GROUP commands in applications of this type is to identify the groups of respondents for multiple-group analysis. The example in Section 10.2 shows how this command structure applies to examinations of differential item functioning in subgroups of a population. Group differences in the latent distributions of ability may also be examined in this way. Instruments with multiple test forms
When an instrument consists of multiple test forms, the structure of the FORM and GROUP commands depends in part on whether the forms are administered to equivalent or nonequivalent groups of respondents. If the forms of the instrument are randomly assigned to respondents drawn from a single population, the groups are equivalent, and the data may be analyzed with a single-group IRT model. GROUP commands are not required in this case, but may be added to examine subgroup differences in item functioning. When test forms are administered to nonequivalent groups of respondents, the forms must contain common “linking” items, and a multiplegroup analysis is necessary to place the items from the forms on the same scale. GROUP commands are required in this case. The number of GROUP commands corresponds to the number of groups in the analysis. In multiple-form applications the response records may follow either of the two formats. The sections below show how the structure of the FORM and GROUP commands depends on these formats. Single-group analysis
When there are multiple forms in the test instrument but only one group of examinees, multiple FORM commands are required if the compressed data format is used, but a GROUP command or a group indicator on the response records is not required. Section 10.4 illustrates an application of this type. Multiple-group analysis
In the case of multiple forms and multiple groups, all such applications can be handled by expanded format. Only one FORM command is then required and the data records will not contain a forms indicator. Similarly, if the assignment of items to groups is performed in expanded format, including the codes for items presented to a given examinee in a given group, the GROUP commands require only the group names, not the item identifications. Specification of the items assigned to each group will, however, shorten the run time. The example in Section 10.5 illustrates this type of data structure. The expanded style of data entry is mandatory in applications where the test forms contain more than one subtest and the examinee is assigned to different groups for different subtests. This can occur in complex two-stage testing designs. In more typical applications, however, whole forms rather than subtests are assigned to groups. In this case, the compressed style of data entry is suitable and may be more convenient. The 112
OVERVIEW OF SYNTAX GROUP commands must then contain, in addition to the group name, a list of all items on all forms assigned to the corresponding groups. The data records must include both a forms identifier and a group identifier. The advantage of this method is that response records need not contain codes for not-presented items. Examples illustrating this type of data input are discussed in Sections 10.4 and 10.8 respectively.
Related topics
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box
2.6 Using the command language 2.6.1 Overview of syntax
BILOG-MG uses command lines employing the general syntax: >NAME
KEYWORD1=N, KEYWORD2=(list), …, OPTIONn;
The following rules apply:
A greater-than sign (>) must be entered in column 1 of the first line of a command and followed without a space by the command name. All command names, keywords, options, and keyword values may be entered in upper and/or lower case. Command names, keywords, options, and keyword values may be entered in full or abbreviated to the first three characters. At least one space must separate the command name from any keywords or options. Commas must separate all keywords and options. The equals sign is used to set a keyword equal to a value, which may be integer, real or character. A real value must contain a decimal point. A character value must be enclosed in single quotes if it: o Contains more than eight characters o Begins with a numeral o Contains embedded blanks, commas, slashes, or semi-colons
For example: DFNAME=‘EXAMPL01.DAT’, TNAME=’20-ITEMS’;
A keyword may be vector valued, i.e., set equal to a list of integer, real or character constants, separated with commas or spaces, and enclosed in left and right parentheses (as KEYWORD2 above). If the list is an arithmetic progression of integer or decimal numbers, the short form, first (increment) last, may be used. Thus, a selection of items 1, 3, 7, 8, 9, 10, 15 may be entered as 1,3,7(1)10,15. Real values may be entered in a similar way. If the values in the list are equal, the form, value (0) number of values, may be used. Thus 1.0, 1.0, 1.0, 1.0, 1.0 may be entered as 1.0(0)5. The italic elements in the format description are variables that the user needs to replace. 113
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Command lines may not exceed 80 columns. Continuation on one or more lines is permitted. Each command terminates with a semi-colon (;). The semi-colon signals the end of the command and the beginning of a new command.
2.6.2 Order of commands
The table below lists all available BILOG-MG commands in their necessary order. Commands marked below as “Required” must appear in each problem in the order shown. All other commands are optional. Note that, in the rest of this chapter, description for the commands follow an alphabetical order. The data layout must be described in a variable format statement. This statement is entered within parentheses. Table 7.3: keywords and options in BILOG-MG Command
Keywords and options
Required
TITLE1
*
TITLE2
*
COMMENT GLOBAL
CFNAME, DFNAME, IFNAME, LOGISTIC, MFNAME, NTEST, NVTEST, NWGHT, OMITS, SAVE, PRN;
SAVE
CALIB, COVARIANCE, DIF, DRIFT, EXPECTED, ISTAT, MASTER, PARM, POST, SCORE, TSTAT, PDISTRIB;
LENGTH
NITEMS, NVARIANT;
*
INPUT
DIAGNOSE, DIF, DRIFT, KFNAME, NALT, NFMT, NFNAME, NFORM, NGROUP, NIDCHAR, NTOTAL, OFNAME, PERSONAL, SAMPLE, TAKE, TYPE, EXTERNAL;
*
ITEMS
INAMES, INUMBERS;
*
TESTi
DISPERSN, GUESS, INAME, THRESHLD, TNAME, FIX;
FORMj
INAMES, INUMBERS, LENGTH;
GROUPk
GNAME, INAMES, INUMBERS, LENGTH;
DRIFT
MAXPOWER, MIDPOINT;
(variable format statement)
INTERCPT,
INUMBER,
NPARM,
SLOPE,
*
*
*
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OVERVIEW OF SYNTAX Table 7.3 (continued) Command
Keywords and options
CALIB
ACCEL, COMMON, CRIT, CYCLES, DIAGNOSIS, EMPIRICAL, FIXED, FLOAT, GPRIOR, IDIST, NEWTON, NOFLOAT, NOGPRIOR, NORMAL, NOSPRIOR, NOTPRIOR, NQPT, NSD, PLOT, PRINT, READPRIOR, REFERENCE, RIDGE, SELECT, SPRIOR, TPRIOR, CHI, GROUP-PLOTS, NOADJUST, RASCH, NFULL;
QUADk (for group k)
POINTS, WEIGHTS;
PRIORSi (for subtest I)
Required
*
ALPHA, BETA, SMU, SSIGMA, TMU, TSIGMA;
SCORE
BIWEIGHT, FIT, IDIST, INFO, LOCATION, METHOD, NOPRINT, NQPT, PMN, POP, PSD, RSCTYPE, SCALE, YCOMMON, MOMENTS, DOMAIN, FILE, REFERENCE, READF, NFORMS;
QUADSk (for group k)
POINTS, WEIGHTS;
Note that if there are not variant items in the subtest, there is one TEST command for each subtest. If a subtest contains variant test items, there must be exactly two TEST commands for that subtest. The first identifies the main test items while the second identifies the variant test items. Related topics
Location of keywords in interface (see Section 2.3.13)
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2 BILOG-MG REFERENCE 2.6.3 CALIB command Purpose
To control the item parameter estimation procedure and the specification of prior distributions on the item parameters. Format >CALIB
NQPT=n, CYCLES=n, NEWTON=n, PRINT=n, CRIT=n, IDIST=n, PLOT=n, DIAGNOSIS=n, REFERENCE=n, SELECT=(list), RIDGE=(a,b,c), ACCEL=n, NSD=n, COMMON, EMPIRICAL, NORMAL, FIXED, TPRIOR, SPRIOR, GPRIOR, NOTPRIOR, NOSPRIOR, NOGPRIOR, READPRI, NOFLOAT, FLOAT, NOADJUST, GROUP-PLOT, RASCH, NFULL, CHI=(a,b);
Examples
This example uses simulated responses to illustrate nonequivalent groups equating of two forms of a 25-item multiple-choice examination administered to different populations. Separate latent distributions are estimated for each population (EMPIRICAL option). The indeterminacy in location and scale of the distributions is resolved by setting the mean and standard deviation of Group 1 to 0 and 1, respectively, with REF=1 on the CALIB command. >CALIB NQPT=10, EMPIRICAL, CYCLES=25, NEWTON=5, CRIT=0.01, PLOT=0.05, REFERENCE=1, TPRIOR;
In the following example of vertical equating of test forms over three grade levels, students at each of three grade levels were given grade-appropriate versions of a arithmetic examination. The distributions of ability are assumed to be normal at each grade level (NORMAL option). The second group serves as the reference group in the calibration of the items. A prior is placed on the item thresholds by the addition of the TPRIOR option. >CALIB
NQPT=20, NORMAL, CYCLE=30, TPRIOR, NEWTON=2, CRIT=0.01, REFERENCE=2;
In the following example of a 3-PL model, the PLOT keyword has been set to 0.99 so that all item response functions will be plotted. The FLOAT option is added to request the MML (under normal distribution assumptions) of the means of the prior distributions on the item parameters along with the parameters. This option should not be invoked when the data set is small and the items few. The acceleration constant (ACCEL keyword) is set to 0.5 instead of the default value of 1.0 for a single group analysis. >CALIB NQPT=6, FLOAT, PLOT=0.99, CYCLES=15, NEWTON=3, ACCEL=0.5;
The next example, again of a 3-PL model, illustrates the command’s usage in the presence of aggregate-level, multiple-matrix sampling data. In this case, the data come from eight forms of a rather difficult, multiple-choice instrument. Since aggregate-level data are always 116
CALIB COMMAND more informative than individual-level item responses, it is worthwhile to increase the number of quadrature points (NQPT), to set a stricter convergence criterion (CRIT), and to increase the CYCLES limit. A prior on the thresholds (TPRIOR) and a ridge constant of 0.8 (RIDGE) are required for convergence with the exceptionally difficult second subtest. Aggregate-level data typically have smaller slopes in the 0,1 metric than do person-level data. Thus, the mean of the prior for log slopes is set to 0.5 with the READPRIOR option and the succeeding PRIOR commands as shown. >CALIB
NQPT=3, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
Related topics
PRIORS command (see Section 3.2.10) QUAD command (see Section 2.6.13)
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box Technical menu: Calibration Options dialog box (see Section 2.3.5) Technical menu: Data Options dialog box Technical menu: Item Parameter Prior Constraints dialog box
ACCEL keyword (optional) Purpose
To set the acceleration constant for the E-steps. Format ACCEL=n
Default
for NGROUP=1 and 0.5 for NGROUP>1. Related topics
CALIB command: CYCLES keyword (see Section 2.6.3) INPUT command: NGROUP keyword (see Section 2.6.9)
Technical menu: Data Options dialog box (see Section 2.3.5)
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2 BILOG-MG REFERENCE CHI keyword (optional) Purpose
To specify the number of items required and the number of intervals used for χ 2 computations. Format CHI=(a,b)
where a is the number of items for computation of the χ 2 fit statistics, and b is the number of intervals into which the score continuum will be divided for purposes of computation of χ 2 item fit statistics. Default CHI=(20,9).
Example
In the CALIB command shown below, the CHI keyword is used to request the calculation of the χ 2 item fit statistics on 18 items and 7 intervals. >CALIB
CYCLE=30, TPRIOR, NEWTON=2, CRIT=0.01, CHI=(18,7);
Related topics
Setup menu: Item Analysis dialog box (see Section 2.3.3)
COMMON option (optional) Purpose
To estimate a common value for the lower asymptote of all items in the 3PL model. Format COMMON
Default
Separate values for the lower asymptotes.
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CALIB COMMAND Example
If the CALIB command >CALIB NQPT=10, CYCLES=30, NEWTON=5, COMMON;
is used for a 3PL model, output as shown below is obtained. Note that the asymptote parameter is estimated at a common value of 0.031 for all items. SUBTEST SIM
;
ITEM PARAMETERS AFTER CYCLE
15
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------T01| 1.320 | 0.968 | -1.363 | 0.695 | 0.031 | 3.5 6.0 | 0.185* | 0.192* | 0.198* | 0.138* | 0.006* | (0.7490) | | | | | | T02| 1.516 | 0.984 | -1.541 | 0.701 | 0.031 | 3.2 6.0 | 0.212* | 0.197* | 0.212* | 0.140* | 0.006* | (0.7783) | | | | | | T03| 1.020 | 1.131 | -0.902 | 0.749 | 0.031 | 2.8 6.0 | 0.170* | 0.227* | 0.142* | 0.150* | 0.006* | (0.8294) | | | | | | T04| 0.603 | 0.787 | -0.766 | 0.619 | 0.031 | 8.2 8.0 | 0.118* | 0.134* | 0.155* | 0.105* | 0.006* | (0.4113) | | | | | | T05| 0.780 | 0.695 | -1.123 | 0.571 | 0.031 | 6.3 7.0 | 0.119* | 0.124* | 0.208* | 0.102* | 0.006* | (0.5066)
Related topics
GLOBAL command: NPARM keyword (see Section 2.6.7)
CRIT keyword (optional) Purpose
To set the convergence criterion for EM and Newton iterations. Format CRIT=n
Default
0.01.
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Here, the convergence criterion has been set to the more restrictive value of 0.0050 in order to deal with a more informative aggregate-level data set. >CALIB NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT;
Related topics
CALIB command: CYCLES and NEWTON keywords (see Section 2.6.3)
Setup menu: Item Analysis dialog box
CYCLES keyword (optional) Purpose
To set the maximum number of EM cycles. If CYCLES=0 and NEWTON=0, item parameter estimates will be calculated from the classical item statistics from Phase 1 or from the starting values of the TEST command. The former will be corrected for guessing if the 3-parameter model is selected. Format CYCLES=n
Default
10 (for all subtests). Examples
In this example of vertical equating of test forms over three grade levels, a maximum of 30 EM cycles and 2 Newton-Gauss iterations are requested. >CALIB
NQPT=20,NORMAL,CYCLE=30,TPRIOR,NEWTON=2,CRIT=0.01,REFERENCE=2;
Here, the CYCLES limit is increased in order to deal with a more informative aggregate-level data set. >CALIB NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, NOFLOAT, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI;
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CALIB COMMAND Related topics
CALIB command: NEWTON keyword (see Section 2.6.3) TEST command (see Section 2.6.17)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
DIAGNOSIS keyword (optional) Purpose
To set the level of diagnostic printout. Format DIAGNOSIS=n
Default
0. Example
When DIAGNOSIS is set to 1, for example, item parameter estimates are printed to the Phase 2 output phase at each iteration. EMPIRICAL option (optional) Purpose
To estimate the score distribution in the respondent population in the form of a discrete distribution on NQPT points. This empirical distribution is used in place of the prior in the MML estimation of the item parameters. If NGROUP >1, separate score distributions are estimated for each group. Format EMPIRICAL
Default
Not-empirical (FIXED) if NGROUP=1; empirical if NGROUP>1.
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2 BILOG-MG REFERENCE Example
For this example, which comes from a simulation of non-equivalent groups equating, the EMPIRICAL option is used to estimate separate latent distributions for each population. >CALIB NQPT=10, EMPIRICAL, CYCLES=25, NEWTON=5, CRIT=0.01, PLOT=0.05, REFERENCE=1, TPRIOR;
Related topics
CALIB command: FIXED option (see Section 2.6.3) CALIB command: NQPT keyword INPUT command: NGROUP keyword (see Section 2.6.9)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
FIXED option (optional) Purpose
To keep the prior distributions of ability in the population of respondents fixed at the values specified in the IDIST keyword and/or the QUAD commands. Format FIXED
Default
Same as for EMPIRICAL. Related topics
CALIB command: EMPIRICAL option (see Section 2.6.3) CALIB command: IDIST keyword QUAD command (see Section 2.6.13)
Technical menu: Calibration Options dialog box (see Section 2.3.5)
FLOAT option (optional) Purpose
To estimate the means of the prior distributions on the item parameters by marginal maximum likelihood (under normal distribution assumptions), along with the parameters. To keep the means of the prior distributions on the item parameters fixed at their specified values during estimation, the NOFLOAT option should be used.
122
CALIB COMMAND Standard deviations of the priors are fixed in either case. The FLOAT option should not be invoked when the data set is small and the items few. The means of the item parameters may drift indefinitely during the estimation cycles under these conditions. Format FLOAT
Default NOFLOAT if NGROUP=1; FLOAT if NGROUP>1
Example
In this example of a 3-PL model, the FLOAT option is added to request the MML (under normal distribution assumptions) of the means of the prior distributions on the item parameters along with the parameters. This option should not be invoked when the data set is small and the items few. The acceleration constant (ACCEL keyword) is set to 0.5 instead of the default value of 1.0 for a single group analysis. >CALIB NQPT=6, FLOAT, PLOT=0.99, CYCLES=15, NEWTON=3, ACCEL=0.5;
Related topics
INPUT command: NGROUP keyword (see Section 2.6.9)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
GPRIOR/NOGPRIOR option (optional) Purpose
To select or suppress prior distributions on the lower asymptote (guessing) parameter. This may be needed in order to give plausible values for easy items (which carry little or no information about guessing). Priors on the slope parameters are sometimes required to prevent Heywood cases. Priors on the lower asymptote parameters may be needed to give plausible values for easy items. Format GPRIOR/NOGPRIOR
Default
1PL model, NOGPRIOR 2PL model, NOGPRIOR
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2 BILOG-MG REFERENCE
3PL model, GPRIOR
Examples
For a 3PL model, priors on slopes and asymptote parameters are assumed. To remove these priors, the CALIB command >CALIB NQPT=10, CYCLES=15, NOSPRIOR, NOGPRIOR;
may be used. To remove the default prior distribution on the asymptote parameters and use a prior distribution on the thresholds instead, use >CALIB NQPT=10, CYCLES=15, SPRIOR, NOGPRIOR, TPRIOR;
Related topics
CALIB command: SPRIOR/NOSPRIOR and TPRIOR/NOTPRIOR options
Setup menu: Item Analysis dialog box (see Section 2.3.3)
GROUP-PLOTS option (optional) Purpose
To provide plots showing the proportions of correct responses for each separate group in a multiple-group analysis. These plots may provide more information than the combined plot provided by the PLOT keyword. Format GROUP-PLOTS
Default
Combined plots, if PLOT keyword is used. Example
In the CALIB command from a two-group analysis below, the PLOT keyword has been set to 0.99 so that all item response functions will be plotted. In order to obtain plots by group, the GROUP-PLOT keyword has been added. >CALIB NQPT=6, REFERENCE=2, PLOT=0.99, CYCLES=15, NEWTON=3, GROUP-PLOT;
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CALIB COMMAND Related topics
CALIB command: PLOT keyword
Technical menu: Calibration Options dialog box (see Section 2.3.5)
IDIST keyword (optional) Purpose
To designate the type of prior distribution in the population of respondents. Format IDIST=n
n=0
standard normal approximation
n=1
separate, arbitrary discrete priors for each group for each subtest read from QUAD command
n=2
separate, arbitrary discrete priors for each group read from QUAD command
Default
0. Example
This example illustrates how user-supplied priors for the latent distributions are specified with IDIST=1 on the CALIB command. The points and weights for these distributions are supplied in the QUAD commands. Note that with IDIST=1, there are separate QUAD commands for each group for each subtest. Within each subtest, the points are the same for each group. This is a requirement of the program. But as the example shows, the points for the groups may differ by subtest. >CALIB >QUAD1
>QUAD2
IDIST=1,READPR,EMPIRICAL,NQPT=16,CYCLE=25,TPRIOR,NEWTON=5, CRIT=0.01,REFERENCE=1,NOFLOAT; POINTS=(-0.4598E+01 -0.3560E+01 -0.2522E+01 -0.1484E+01 -0.4453E+00 0.5930E+00 0.1631E+01 0.2670E+01 0.3708E+01 0.4746E+01), WEIGHTS=(0.2464E-05 0.4435E-03 0.1724E-01 0.1682E+00 0.3229E+00 0.3679E+00 0.1059E+00 0.1685E-01 0.6475E-03 0.8673E-05); POINTS=(-0.4598E+01 -0.3560E+01 -0.2522E+01 -0.1484E+01 -0.4453E+00 0.5930E+00 0.1631E+01 0.2670E+01 0.3708E+01 0.4746E+01), WEIGHTS=(0.2996E-04 0.1300E-02 0.1474E-01 0.1127E+00
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2 BILOG-MG REFERENCE
>PRIOR >QUAD1
>QUAD2
>PRIOR
0.3251E+00 0.3417E+00 0.1816E+00 0.2149E-01 0.1307E-02 0.3154E-04); TSIGMA=(1.5(0)35); POINTS=(-0.4000E+01 -0.3111E+01 -0.2222E+01 -0.1333E+01 -0.4444E+00 0.4444E+00 0.1333E+01 0.2222E+01 0.3111E+01 0.4000E+01), WEIGHTS=(0.1190E-03 0.2805E-02 0.3002E-01 0.1458E+00 0.3213E+00 0.3213E+00 0.1458E+00 0.3002E-01 0.2805E-02 0.1190E-03); POINTS=(-0.4000E+01 -0.3111E+01 -0.2222E+01 -0.1333E+01 -0.4444E+00 0.4444E+00 0.1333E+01 0.2222E+01 0.3111E+01 0.4000E+01), WEIGHTS=(0.1190E-03 0.2805E-02 0.3002E-01 0.1458E+00 0.3213E+00 0.3213E+00 0.1458E+00 0.3002E-01 0.2805E-02 0.1190E-03); TSIGMA=(1.5(0)35);
Related topics QUAD command (see Section 2.6.13)
NEWTON keyword (optional) Purpose
To specify the number of Gauss-Newton (Fisher-scoring) iterations following EM cycles. If CYCLES=0 and NEWTON=0, item parameter estimates will be calculated from the classical item statistics from Phase 1 or from the starting values of the TEST command. The former will be corrected for guessing if the 3-parameter model is selected. Format NEWTON=n
Default
2. Example
In this example, the value of NEWTON is increased to 4 in order to deal with a more informative aggregate-level data set. >CALIB NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT;
126
CALIB COMMAND
Related topics
CALIB command: CYCLES keyword TEST command (see Section 2.6.17)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
NFULL keyword (optional) Purpose
To specify that the Fisher-scoring steps for estimating item parameters use the full information matrix (if the number of items n is less than p) or the block-diagonal approximation to the information matrix (if n is greater than or equal to p). Format NFULL=p
Default p=20.
Example
The NFULL keyword is used on the CALIB command to request the use of the full information matrix in the Newton steps for this data set where only 4 items were presented to subjects. In the absence of the NFULL keyword, the block diagonal approximation to the information matrix would have been used in this case, as NITEMS=4 is less than the default threshold of 20 items. >CALIB
TPRIOR,SPRIOR,NFULL=4;
Related topics
LENGTH command: NITEMS keyword (see Section 2.6.11)
Technical menu: Data Options dialog box (see Section 2.3.5)
NOADJUST option (optional) Purpose
In multiple-group applications, each group has its own latent distribution. To resolve the indeterminacy of origin and scale of measurement in the IRT analysis, the user can choose to set the mean and standard deviation to 0.0 and 1.0 in a reference group specified by the REF keyword of the CALIB command; alternatively, the user can choose to assign these 127
2 BILOG-MG REFERENCE keyword of the CALIB command; alternatively, the user can choose to assign these values to the combined distributions weighted by their sample sizes. BILOG-MG routinely rescales the origin and scale of the latent distribution (i.e., linearly transforms the quadrature points) exactly to these values even in the case of one group. The item slopes and thresholds are then linearly transformed to match the adjusted scale. This results in small differences between the values estimated in BILOG and BILOG-MG because the posterior latent distribution has mean and standard deviation equal to only approximately zero and one. To obtain the BILOG values (when all other conditions of estimation are identical), the user may include the option NOADJUST in the CALIB command, as in the example below. Format NOADJUST
Default
Rescaling the origin and scale of the latent distribution. Example
In the syntax below, a single subtest is analyzed in a single group analysis. The NOADJUST option is used on the CALIB command to suppress the adjustment of the rescaling of the latent distribution. EXAMPLE 16: TRADITIONAL IRT ANALYSIS OF A FIFTEEN-ITEM TEST PARAMETERS OF ITEMS 6 THROUGH 10 ARE FIXED … >CALIB CYCLES=15,NEWTON=3,NQPT=11,DIAGNOS=1,NOADJUST;
Related topics
CALIB command: REFERENCE keyword
Technical menu: Calibration Options dialog box (see Section 2.3.5)
NORMAL option (optional) Purpose
To specify the estimation of the means and standard deviations of the prior distributions of ability in the population of respondents by marginal maximum likelihood (under normal distribution assumptions) along with the item parameters. If NGROUP>1, separate means and standard deviations are estimated for each group.
128
CALIB COMMAND Format NORMAL
Default
Same as for EMPIRICAL. Example
In this example of vertical equating of test forms over three grade levels, the distributions of ability are assumed to be normal at each grade level (NORMAL on the CALIB command). >CALIB
NQPT=20,NORMAL,CYCLE=30,TPRIOR,NEWTON=2,CRIT=0.01,REFERENCE=2;
Related topics
CALIB command: EMPIRICAL option INPUT command: NGROUP keyword (see Section 2.6.9)
NQPT keyword (optional) Purpose
To specify the number of quadrature points in MML estimation for each group. Format NQPT=n
Default
20 for each group when NGROUP>1; 10 (otherwise). Examples
In this example of a nonequivalent groups equating analysis, the number of quadrature points is set to 10 instead of the default of 20 for multiple-group analyses. >CALIB NQPT=10, EMPIRICAL, CYCLES=25, NEWTON=5, CRIT=0.01, PLOT=0.05, REF=1, TPRIOR;
Here, the value of NQPT is increased to 30 in order to deal with a more informative aggregate-level data set.
129
2 BILOG-MG REFERENCE >CALIB NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT;
Related topics
INPUT command: NGROUP keyword (see Section 2.6.9)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
NSD keyword (optional) Purpose
To specify the range of the prior distribution(s) for the population(s) in standard deviation units. Format NSD=n
Default
8 standard deviation units (from –4.0 to 4.0). PLOT keyword (optional) Purpose
To specify the significance level for the goodness-of-fit of the item-response functions to be plotted. All items for which the significance level is below the real-number value (decimal fraction) provided will be plotted. Format PLOT=n
n = 0.0
no plots produced
n = 1.0
plots all items
n = 0.01
(for example) plots only those of poor-fitting items for which the significance level is less than 0.01
Default 0.0.
130
CALIB COMMAND Examples
Plots of the item-response functions of all items for which the goodness-of-fit statistic is less than 0.05 are requested. >CALIB NQPT=10, EMPIRICAL, CYCLES=25, NEWTON=5, CRIT=0.01, PLOT=0.05, REFERENCE=1, TPRIOR;
In this example of a 3-PL model, the PLOT keyword has been set to 1.0 so that all item response functions will be plotted >CALIB NQPT=6, FLOAT, PLOT=1.0, CYCLES=15, NEWTON=3, ACCEL=0.5;
PRINT keyword (optional) Purpose
To print provisional item parameter estimates at each iteration during the calibration phase. If PRINT=1, provisional item parameter estimates are printed; if PRINT=0 printing is suppressed. Format PRINT=n
Default
0. Example
If the following CALIB command is used for a 2-group DIF analysis, only the information shown below is printed concerning the iterative process: >CALIB
NQPT=10,CYCLES=15,CRIT=0.005,NEWTON=2,REFERENCE=1, PRINT=0;
[E-M CYCLES] -2 LOG LIKELIHOOD = 3152.375 CYCLE 1; LARGEST CHANGE= 0.17572 -2 LOG LIKELIHOOD = 3128.806 CYCLE 2; LARGEST CHANGE= 0.15440 -2 LOG LIKELIHOOD = 3117.237 …
When the PRINT keyword is set to 1 >CALIB
NQPT=10,CYCLES=15,CRIT=0.005,NEWTON=2,REFERENCE=1, PRINT=1;
131
2 BILOG-MG REFERENCE the output provided in the Phase 2 output file is expanded and parameter estimates are given for each group after each cycle. The output obtained for both groups after the third EM cycle is given below as an example. QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.: EM CYCLE: 3 GROUP 1 MALES ; ITEM PARAMETERS AFTER CYCLE 3 ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE ---------------------------------------------------------------SP1 | 1.378 | 1.128 | -1.222 | 0.748 | 0.000 | 0.151* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP2 | 0.686 | 1.128 | -0.608 | 0.748 | 0.000 | 0.137* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP3 | -0.938 | 1.128 | 0.831 | 0.748 | 0.000 | 0.140* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP4 | 0.649 | 1.128 | -0.575 | 0.748 | 0.000 | 0.136* | 0.053* | 0.000* | 0.000* | 0.000* ---------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE = 0.101366 GROUP 2 FEMALES ; ITEM PARAMETERS AFTER CYCLE 3 ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE ---------------------------------------------------------------SP1 | 1.795 | 1.128 | -1.591 | 0.748 | 0.000 | 0.144* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP2 | 0.582 | 1.128 | -0.516 | 0.748 | 0.000 | 0.117* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP3 | -1.069 | 1.128 | 0.947 | 0.748 | 0.000 | 0.124* | 0.053* | 0.000* | 0.000* | 0.000* | | | | | SP4 | -0.200 | 1.128 | 0.177 | 0.748 | 0.000 | 0.115* | 0.053* | 0.000* | 0.000* | 0.000* ---------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE = 0.101366 PARAMETER MEAN STN DEV ----------------------------------GROUP: 1 NUMBER OF ITEMS: 4 THRESHOLD -0.393 0.869 GROUP: 2 NUMBER OF ITEMS: 4 THRESHOLD -0.246 1.078 -----------------------------------2 LOG LIKELIHOOD =
3112.870
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CALIB COMMAND
RASCH option (optional) Purpose
To rescale the parameter estimates according to Rasch-model conventions. That is, all the slopes will be rescaled so that their geometric mean equals 1.0, and the thresholds will be rescaled so that their arithmetic mean equals 0.0. If the 1-parameter model has been specified, all slope parameters will therefore equal 1.0. Because the threshold parameters are constrained in other ways in DIF and DRIFT analysis, the RASCH option cannot be used with these models. The posterior latent distribution displayed in Phase 2 is not rescaled in the Rasch convention. Format RASCH
Default
No Rasch rescaling. Example
In the syntax for a single-group analysis shown below, a 1-parameter model is fitted to the data (NPARM=1 on GLOBAL command). Rasch rescaling is requested on the CALIB command through inclusion of the RASCH keyword, and all slope parameters will therefore equal 1.0. >GLOBAL … >CALIB
DFNAME='EXAMPL04.DAT',NIDCH=5,NPARM=1; CYCLE=10,TPRIOR,NEWTON=2,CRIT=0.01,RASCH;
Related topics
GLOBAL command: NPARM keyword (see Section 2.6.7)
Technical menu: Calibration Options dialog box (see Section 2.3.5)
READPRIOR option (optional) Purpose
To specify that the prior distributions for selected parameters will be read from the ensuing PRIORS command(s). Otherwise, default priors will be used for these parameters.
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2 BILOG-MG REFERENCE Format READPRI
Default
thresholds: normal, mean = 0, SD = 2.0 log slope: normal, mean = 0, SD = 0.5 asymptote: beta, with parameters set so that the mean is 1/NALT with a weight of 20 observations of respondents who are marking randomly.
Example
In this example, the mean of the prior for the log slopes has been set to 0.5 by use of the READPRI option of the CALIB command and the following PRIORS commands. >CALIB
NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
Related topics
INPUT command: NALT keyword (see Section 2.6.9) PRIORS command (see Section 2.6.12)
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
REFERENCE keyword (optional) Purpose
To resolve the indeterminacy of the location and scale of the latent variable when NGROUP>1. When the groups originally came from one population as, for example, in two-stage testing, REFERENCE should be set to 0. When the groups represent separate populations, REFERENCE should be set to the value of one of the group indicators. It specifies the reference group for the DIF model and the reference cohort for the DRIFT model. Format REFERENCE=n
n=0
The mean and standard deviation of the combined estimated distributions of the groups weighted by their sample sizes are set to 0 and 1, respectively. 134
CALIB COMMAND n>0
The mean and standard deviation of group n are set to 0 and 1, respectively.
Default
1. Examples
In this example of a nonequivalent groups equating analysis, the indeterminacy in location and scale of the distributions is resolved by using REF=1 to specify Group 1 as the reference group. This sets the mean and standard deviation of Group 1 to 0 and 1, respectively. >CALIB NQPT=10, EMPIRICAL, CYCLES=25, NEWTON=5, CRIT=0.01, PLOT=0.05, REFERENCE=1, TPRIOR;
Here, the second group serves as the reference group in the calibration of the items. >CALIB NQPT=20,NORMAL,CYCLE=30,TPRIOR,NEWTON=2,CRIT=0.01,REFERENCE=2;
Related topics
INPUT command: DIF option (see Section 2.6.9) INPUT command: DRIFT option INPUT command: NGROUP keyword
Setup menu: General dialog box (see Section 2.3.3)
RIDGE keyword (optional) Purpose
To add a ridge constant (if a = 2) to the diagonal elements of the information matrix to be inverted during the EM cycles and Newton iterations. The ridge constant starts at the value 0 and is increased by b if the ratio of a pivot and the corresponding diagonal elements of the matrix is less than c. The old ridge option can be invoked with the RIDGE=1 specification. It is provided so users may duplicate old results from BILOG. The present default is an improvement of the old method. Format RIDGE=(a, b, c)
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2 BILOG-MG REFERENCE Default
(2, 0.1, 0.01). Example
This example emanates from an analysis of aggregate-level data that includes some fairly difficult items. A ridge constant of 0.8 is required for convergence as one of the subtests is exceptionally difficult. Aggregate-level data typically have smaller slopes in the 0,1 metric than do person-level data. For this reason, the mean of the prior for the log slopes has been set to -0.5 by use of the READPRI option of the CALIB command and the following PRIOR commands. >CALIB
NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
Related topics CALIB command: CYCLES keyword CALIB command: NEWTON keyword
SELECT keyword (optional) Purpose
To select, with a vector of ones and zeros, subtests for which item-parameter calibration is desired. Format SELECT=( n1 , n2 , ..., nNTEST )
where
n=0 n=1
Do not calibrate subtest i Calibrate subtest i
Default
Calibrate all subtests.
136
CALIB COMMAND Example
In this example with three subtests, only the second subtest is to be calibrated. >TEST1 INUMBERS=(1(1)10); >TEST2 INUMBERS=(11(1)30); >TEST3 INUMBERS=(31(1)45); (5A1,45A1) >CALIB NQPT=10, CYCLES=25, NEWTON=5, SELECT=(0,1,0);
Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
SPRIOR/NOSPRIOR option (optional) Purpose
The presence of these options selects or suppresses, respectively, prior distributions on the threshold, slope, and lower asymptote (guessing) parameter. Priors on the slope parameters are sometimes required to prevent Heywood cases. Format SPRIOR/NOSPRIOR
Default
1PL model, NOSPRIOR 2PL model, SPRIOR 3PL model, SPRIOR.
Examples
In the case of a 1PL model, no priors are used by default and thus the two CALIB commands >CALIB NQPT=10, CYCLES=15, NOSPRIOR;
and >CALIB NQPT=10, CYCLES=15;
are equivalent.
137
2 BILOG-MG REFERENCE In order to assume a prior distribution on the slopes in the 1PL case, the CALIB command >CALIB NQPT=10, CYCLES=15, SPRIOR;
may be used. In a 2PL model, a prior is placed on the slopes by default and thus the commands >CALIB NQPT=10, CYCLES=15, SPRIOR;
and >CALIB NQPT=10, CYCLES=15;
are equivalent. Related topics
CALIB command: GPRIOR/NOGRPRIOR and TPRIOR/NOTPRIOR options
Setup menu: Item Analysis dialog box (see Section 2.3.3)
TPRIOR/NOTPRIOR option (optional) Purpose
To select prior distributions on the threshold parameters. Although extreme threshold values do not affect the estimation of ability adversely, a diffuse prior distribution on the thresholds will keep their estimates within a reasonable range during the estimation cycle. Format TPRIOR/NOTPRIOR
Default
1PL model, NOTPRIOR 2PL model, NOTPRIOR 3PL model, NOTPRIOR.
Examples
In this example of vertical equating of test forms, a prior is placed on the item thresholds by the addition of the TPRIOR option to the CALIB command. >CALIB
NQPT=20,NORMAL,CYCLE=30,TPRIOR,NEWTON=2,CRIT=0.01,REFERENCE=2;
138
CALIB COMMAND This example emanates from an analysis of aggregate-level data that includes some fairly difficult items. A prior on the thresholds is required for convergence as one of the subtests is exceptionally difficult. >CALIB
NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
In the case of a 1PL model, no priors are used by default and thus the two CALIB commands >CALIB NQPT=10, CYCLES=15, NOSPRIOR;
and >CALIB NQPT=10, CYCLES=15;
are equivalent. In order to assume a prior distribution on the slopes in the 1PL case, the CALIB command >CALIB NQPT=10, CYCLES=15, SPRIOR;
may be used. In a 2PL model, a prior is placed on the slopes by default and thus the commands >CALIB NQPT=10, CYCLES=15, SPRIOR;
and >CALIB NQPT=10, CYCLES=15;
are equivalent. In a 2PL model, the command >CALIB NQPT=10, CYCLES=15, SPRIOR, TPRIOR;
indicates that an additional prior distribution should be assumed for the threshold parameters. For a 3PL model, priors on slopes and asymptote parameters are assumed. To remove these priors, the CALIB command >CALIB NQPT=10, CYCLES=15, NOSPRIOR, NOGPRIOR;
may be used.
139
2 BILOG-MG REFERENCE In a 3PL model, to remove the default prior distribution on the asymptote parameters and use a prior distribution on the thresholds instead, use >CALIB NQPT=10, CYCLES=15, SPRIOR, NOGPRIOR, TPRIOR;
Related topics
CALIB command: SPRIOR/NOSPRIOR and GPRIOR/NOGPRIOR options
Setup menu: Item Analysis dialog box (see Section 2.3.3)
140
COMMENT COMMAND
2.6.4 COMMENT command (Optional) Purpose
To enter one or more lines of explanatory remarks into the program output stream. This line and all subsequent lines preceding the GLOBAL command will be printed in the initial output stream. The maximum length of each line is 80 characters. A semicolon to signal the end of the command is not needed. Format >COMMENT …text… …text…
Example EXAMPLE 4 SIMULATED RESPONSES TO TWO 20-ITEM PARALLEL TEST FORMS >COMMENT This example illustrates the equating of equivalent groups with the BILOGMG program. Two parallel test forms of 20 multiple-choice items were administered to two equivalent samples of 200 examinees drawn from the same population. There are not common items between the forms. >GLOBAL DFNAME=’EXAMPL04.DAT’,NIDCH=5,NPARM=2;
Default
No comments. Related topics
GLOBAL command (see Section 2.6.7)
Setup menu: General dialog box (see Section 2.3.3)
141
2 BILOG-MG REFERENCE 2.6.5 DRIFT command (Required only if DRIFT is specified in the INPUT command) Purpose
To provide the maximum degree of the polynomial item parameter drift model and a vector of time points, n1 , n2 ,..., nn . Format >DRIFT MAXPOWER=a, MIDPOINT=( n1 , n2 ,..., nn );
Default
No DRIFT command. Example >DRIFT MAXPOWER=2, MIDPOINT=(-5,-2,0,2,4);
Related topics
INPUT command: DRIFT option (see Section 2.6.9)
MAXPOWER keyword (optional) Purpose
To specify the maximum degree of the drift polynomial included in the model. The maximum degree must be less than the number of groups. Format MAXPOWER=n
Default NGROUP-1.
Related topics
INPUT command: NGROUP keyword (see Section 2.6.9)
142
DRIFT COMMAND MIDPOINT keyword (optional) Purpose
To specify a vector of time points (or midpoints of time intervals). Format MIDPOINT=( n1 , n2 ,..., nn )
Default (1, 2, …, NGROUP-1)
Related topics
INPUT command: NGROUP keyword
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2 BILOG-MG REFERENCE 2.6.6 FORM command (Required only if the NFORM keyword appears in the INPUT command) Purpose
To supply the order of the item responses in the data records. Each FORM command gives the number of items in the form and lists the items in the order in which the item responses appear on the data records for that form. The items may be listed by name or number, but not by both. When NFORMS > 1, the FORM command requires a form number in the data record. The form numbers must range in value from 1 to the number of forms. The form indicator field follows the case ID field and is INTEGER in the variable format statement. Because the same format statement is used to read the data records for all forms, the item responses, the case ID and weight, and the form and group indicators must occupy the same columns on all records. If the forms are of unequal length, the size of the item-response field on the format statement should equal the number of items in the longest form. The order of the several FORM commands corresponds to the number of the respective form. Format >FORM LENGTH=n, INUMBERS=(list), INAMES=(list);
Default
None. Example
Form 1 consists of items 1, 2, 3, 4, and 6, and form 2 consists of items 1, 6, 7, 8, 9, and 10. The data records are as follows: SUBJECT001 1 21321 SUBJECT002 2 513122 … SUBJECT999 1 21422
Responses to item 1 appear in column 14 of the data records for form 1 and at the end of the data records for form 2. The FORM commands and format statement are as follows: >FORM1 LENGTH=5, INUMBERS=(1(1)5); >FORM2 LENGTH=6, INUMBERS=(6(1)10,1); (10A1, 1X, I1, 1X, 6A1)
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FORM COMMAND Related topics
INPUT command: NFORM keyword (see Section 2.6.9)
Variable format statement (see Section 2.6.18) Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box
INAMES keyword (optional) Purpose
To specify the list of item names, as specified in the ITEMS command, in the order in which the item response appear on the data records for FORMj. Format INAMES=( n1 , n2 ,..., nLENGTH )
Default
When NFORM = 1, the sequence of items specified on the ITEMS command. When NFORM > 1, no sequence is specified. Example
Assume, in the previous example, that the command >ITEMS INAMES=(I1(1)I10);
appears earlier in the command file to give the name Ix to item x. Then the FORM1 statement could be replaced with >FORM1 LENGTH=5, INAMES=(I1(1)I4,I6);
Note that if the item names are in a sequence, they can be specified using the variable list format “first (increment) last”, as “I1(1)I4” is used here to specify items 1 through 4. Related topics
ITEMS command INPUT command: NFORM keyword
Setup menu: General dialog box
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2 BILOG-MG REFERENCE INUMBERS keyword (optional) Purpose
To provide the list of item numbers, as specified in the ITEMS command, in the order in which the item response appear on the data records for FORMj. Format INUMBERS=( n1 , n2 ,..., nLENGTH )
Default
When NFORM = 1, the sequence of items specified on the ITEMS command. When NFORM > 1, none. Related topics
ITEMS command (see Section 2.6.10) INPUT command: NFORM keyword (see Section 2.6.9)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
LENGTH keyword (required) Purpose
The number of items in FORM. Format LENGTH=n
Default NTOTAL when NFORM = 1, none when NFORM > 1.
Related topics
INPUT command: NFORM and NTOTAL keywords (see Section 2.6.9) ITEMS command (see Section 2.6.10)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
146
GLOBAL COMMAND 2.6.7 GLOBAL command (Required) Purpose
To supply input filenames and other information used in the three phases of the program. The GLOBAL keywords DFNAME, MFNAME, CFNAME, and IFNAME enable the user to assign specific names to the program’s input files. A filename must be not more that 128 characters long and may include a drive prefix, a path name, and an extension. The filename must be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. Format >GLOBAL DFNAME=n, MFNAME=n, CFNAME=n, IFNAME=n, NPARM=n, NWGHT=n, NTEST=n, NVTEST=n, PRNAME=n, LOGISTIC, OMITS, SAVE;
Example >GLOBAL DFNAME=’EXAMPL04.DAT’, NPARM=2;
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Save menu (see Section 2.3.6) Setup menu: General dialog box (see Section 2.3.3) Setup menu: Test Scoring dialog box
CFNAME keyword (optional) Purpose
To supply the name of the previously created calibration file (if any) to be read in. If data are read from a previously generated calibration file, DFNAME must not appear, and TYPE=0 must appear in the INPUT command. The PARM keyword of the SAVE command must be specified to save updated parameter estimates to an external file. Format CFNAME=
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2 BILOG-MG REFERENCE
Example
In a previous run, a calibration file was created as shown below. The calibration file was saved to exampl03.cal using the CALIB keyword on the SAVE command. Note that a calibration file will be created only if the SAMPLE keyword is also specified on the INPUT command, with a number less that the total number of examinees. EXAMPLE: CREATING >COMMENT >GLOBAL >SAVE >LENGTH >INPUT
A CALIBRATION FILE DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; CALIB='EXAMPL03.CAL'; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NIDCHAR=5, NALT=5,NFORM=2,TYPE=1;
The previously created calibration file is now used as data source through the use of the CFNAME keyword on the GLOBAL command. Note that the TYPE keyword on the INPUT command is now set to 0, compared to 1 previously. The updated item parameter estimates are saved to the file latest.prm using the PARM keyword on the SAVE command. EXAMPLE: USING A MASTER FILE AS INPUT >COMMENT >GLOBAL CFNAME='EXAMPL03.CAL',NPARM=2, SAVE; >SAVE PARM='LATEST.PRM'; >LENGTH NITEMS=(45); >INPUT NTOTAL=45,SAMPLE=2000,NGROUP=2,NIDCHAR=5, NALT=5,NFORM=2,TYPE=0;
Related topics
GLOBAL command: DFNAME keyword (see Section 2.6.7) INPUT command: TYPE keyword (see Section 2.6.9) SAVE command: CALIB keyword (see Section 2.6.15)
DFNAME keyword (optional) Purpose
To supply the name of the raw data file that contains the original data. The format for this file is described in the section on input and output files. Format DFNAME=
148
GLOBAL COMMAND Notes
The path to and filename of this file may be longer than 80 characters. However, as the maximum length of any line in the command file is 80 characters, multiple lines may be used. It is important to continue up to and including the 80th column when specifying a long path and filename. For example, suppose the data file exampl06.dat is in a folder named: C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MGVERSION1.2\EXAMPLES
The correct way to enter this information in the command file is to enclose the name and path in single quotes, and continue until column 80 is reached. Then proceed in column 1 of the next line as shown below: >GLOBAL DFNAME=’C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MG -VERSION1.2\EXAMPLES\EXAMPL06.DAT’, NTEST=1, NVTEST=1, NPARM=2, SAVE;
If the data are stored in the same folder as the command file, it is sufficient to type DFNAME='EXAMPL06.DAT'
Examples
This example shows the use of the external data file exampl03.dat. >GLOBAL DFNAME='EXAMPL03.DAT'; >LENGTH NITEMS=(45); >INPUT NTOTAL=45,KFNAME='EXAMPL03.DAT',NIDCHAR=5,
Note that this file is referenced on both the GLOBAL command (DFNAME keyword) and on the INPUT command (KFNAME keyword). This indicates that the answer key for correct responses is given at the top of the data file, as shown below: ANSWER KEY person1 person2
1111111111111111111111111 1111111112221212211111121 2211111212222222222255222
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Input files (see Section 2.6.20) Output files Variable format statement (see Section 2.6.18)
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2 BILOG-MG REFERENCE IFNAME keyword (optional) Purpose
To supply the name of the previously created item parameter file (if any) to be used as input. The PARM keyword of the SAVE command must be specified to save updated parameter estimates to an external file. Format IFNAME=
Example
The previously created parameter file exampl03.par is used as data source through the use of the IFNAME keyword on the GLOBAL command. The updated item parameter estimates are saved to the file latest.par using the PARM keyword on the SAVE command. EXAMPLE: USING A ITEM PARAMETER FILE AS INPUT >COMMENT >GLOBAL IFNAME='EXAMPL03.PAR',NPARM=2, SAVE; >SAVE CALIB='LATEST.PAR'; >LENGTH NITEMS=(45); >INPUT NTOTAL=45,SAMPLE=2000,NGROUP=2,NIDCHAR=5, NALT=5,NFORM=2;
Related topics
GLOBAL command: IFNAME keyword INPUT command: TYPE keyword (see Section 2.6.9) SAVE command: PARM keyword (see Section 2.6.15)
Setup menu: Test Scoring dialog box (see Section 2.3.3)
LOGISTIC option (optional) Purpose
To assume the natural metric of the logistic response function in all calculations. Otherwise, the logit is multiplied by D = 1.7 to obtain the metric of the normal ogive model. Format LOGISTIC
150
GLOBAL COMMAND Default
Normal ogive model. Examples
For the 2-parameter model requested in this first GLOBAL command, the natural metric of the logistic response function is assumed: >GLOBAL NPARM=2, LOGISTIC, DFNAME=’EXAMPLE.DAT’;
while a similar normal ogive model can be obtained by using the command: >GLOBAL NPARM=2, DFNAME=’EXAMPLE.DAT’;
Related topics
Setup menu: General dialog box (see Section 2.3.3)
MFNAME keyword (optional) Purpose
To supply the name of a previously created master file to be read in. If data are read from a previously prepared master file, DFNAME must not appear, and TYPE=0 must appear in the INPUT command. The PARM keyword of the SAVE command may be specified to save updated parameter estimates to an external file. Format MFNAME='filename'
Example
The previously created master file exampl03.mas is used as data source through the use of the MFNAME keyword on the GLOBAL command. Note that the TYPE keyword on the INPUT command is now set to 0. EXAMPLE: USING A MASTER FILE AS INPUT >GLOBAL MFNAME='EXAMPL03.MAS',NPARM=2; >LENGTH NITEMS=(45); >INPUT NTOTAL=45,SAMPLE=2000,NGROUP=2,NIDCHAR=5, NALT=5,NFORM=2,TYPE=0;
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2 BILOG-MG REFERENCE Related topics
INPUT command: TYPE keyword (see Section 2.6.9) SAVE command: MASTER keyword (see Section 2.6.15)
NPARM keyword (optional) Purpose
To indicate the number of item parameters in the model:
1: 2: 3:
1-parameter logistic model 2-parameter logistic model 3-parameter logistic model
Format NPARM=n
Default NPARM=2.
Examples
The following GLOBAL commands are used to request a 1PL, 2PL and 3PL model respectively. >GLOBAL NPARM=1, NWGHT=3, LOGISTIC; >GLOBAL DFNAME=’EXAMPL03.DAT’, NPARM=2; >GLOBAL NPARM=3, DFNAME=EXAMPL07.DAT;
Related topics
Setup menu: General dialog box (see Section 2.3.3)
NTEST keyword (optional) Purpose
To indicate the number of subtests.
152
GLOBAL COMMAND Format NTEST=n
Default NTEST=1.
Examples
In the GLOBAL command below, the NTEST keyword is used to indicate that two subtests are used. Note the two TEST commands in the syntax. The LENGTH command is used to indicate the length of the subtests. >GLOBAL NPARM=3, NTEST=2, DFNAME=’EXAMPL08.DAT’; >LENGTH NITEM=(8,8); >INPUT NTOTAL=16; >ITEMS INUMBER=(1(1)16), INAMES=(N1(1)N8,A1(1)A8); >TEST1 TNAME=NUMCON, INUMBER=(1(1)8); >TEST2 TNAME=ALGCON, INUMBER=(9(1)16);
Related topics
GLOBAL command: NVTEST keyword LENGTH command (see Section 2.6.11)
Setup menu: General dialog box (see Section 2.3.3) TEST command
NVTEST keyword (optional) Purpose
To indicate the number of subtests with variant items. Format NVTEST=n
Default NVTEST=0.
Example
In the example below, both a main and variant test are used. In this case, NTEST is set to 1 to indicate the main test, and the NVTEST keyword is used to indicate the presence of a variant test. The first test command is that for the main test, while items for the variant test are selected by name in the next TEST command (here named TESTV purely for convenience). 153
2 BILOG-MG REFERENCE There are 20 main test items and 4 variant test items, selected from a total of 50 items in the data file. The LENGTH command is used to indicate the length of the subtests. >GLOBAL >LENGTH >INPUT >ITEMS >TESTM
NPARM=3, NTEST=1, NVTEST=1, DFNAME=’EXAMPL06.DAT’; NITEM=24, NVARIANT=4; NTOTAL=50, NIDCHAR=11; INUMBERS=(1(1)50), INAMES=(I26(1)I75); TNAME=MAIN, INAMES=(I26, I27, I28, I29, I31, I33, I34, I35, I36, I38, I39, I47, I48, I49, I50, I54, I60, I64, I68, I72); >TESTV TNAME=VARIANT, INAMES=(I53, I59, I69,I73); (11A1,T39,25A1/T13,25A1)
Related topics
GLOBAL command: NTEST keyword LENGTH command (see Section 2.6.11) TEST command (see Section 2.6.17)
NWGHT keyword (optional) Purpose
To specify the weighting of response records. A value larger than 0 is required when the data are input in the form of response patterns and frequencies, or when the sampling procedure requires the use of case weights. The data file (TYPE) in the INPUT command must also be set appropriately. See the information on format statements for the data format with weights in Section 2.6.18. Format NWGHT=n
The type of weighting associated with valid values of n is:
0: 1: 2: 3:
none for classical item statistics only for IRT item calibration only for both statistics and calibrations.
Default NWGHT=0.
Example
In this example, the data are accumulated into answer patterns. TYPE=2 and NWGHT=3 are in-
154
GLOBAL COMMAND cluded to indicate this form of data. >GLOBAL NPARM=1, NWGHT=3, LOGISTIC; >LENGTH NITEMS=4; >INPUT NTOTAL=4, NGROUPS=2, DIF, NIDCHAR=2, TYPE=2;
Related topics
INPUT command: TYPE keyword (see Section 2.6.9)
Variable format statement (see Section 2.6.18)
OMITS option (optional) Purpose
To specify that omits are treated as fractionally correct when the 3-parameter model is employed. The fraction is the reciprocal of the number of the alternatives in the multiple-choice items (see the NALT keyword on the INPUT command, Section 2.6.9). Also see Section 2.6.20 for more information on the specification of an omit key using the OFNAME keyword on the INPUT command. Format OMITS
Default
Omitted responses are treated as incorrect. Examples
For the following 3-parameter model, an omitted response will be scored fractionally correct with the fraction equal to 1/5 (NALT=5). The omit response key can be found in the data file. >GLOBAL >LENGTH >INPUT
NPARM=3, LOGISTIC, DFNAME=’EXAMPLE.DAT’, OMITS; NITEMS=40; NTOTAL=40, OFNAME=’EXAMPLE.DAT’, NALT=5;
In this example the omitted response will be scored fractionally correct with fraction 1/4. The key for omitted responses can be found in a separate, external file. >GLOBAL >LENGTH >INPUT
NPARM=3, LOGISTIC, DFNAME=’EXAMPLE.DAT’, OMITS; NITEMS=40; NTOTAL=40, OFNAME=’OMITKEY.DAT’, NALT=4;
Related topics
INPUT command: NALT and OFNAME keywords(see Section 2.6.9)
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2 BILOG-MG REFERENCE
Input files (see Section 2.6.20) Setup menu: General dialog box (see Section 2.3.3)
PRNAME keyword (optional) Purpose
To specify the name of the file from which the provisional (i.e. starting) values of parameters of selected items will be obtained. The values are read in space-delimited, free-format form. Format PRNAME=
Contents:
The contents of the file are as follows: Line 1:
The number of selected items in each subtest. Remaining lines:
The serial position of each item selected from the corresponding subtest, followed by the slope, threshold, and chance success (guessing) probability of the item. If a two-parameter model is assumed, the latter should be entered as 0. Default
None. Example 5
5 5 10 15 25 30 5 10 15 25 30
1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1
0.0 0.0 0.0 0.0 0.0 0.5 0.5 0.5 0.5 0.5
0.333 0.333 0.333 0.333 0.333 0.233 0.233 0.233 0.233 0.233
156
GLOBAL COMMAND Provisional values will be assigned to five items in each of two subtests. In each subtest, the 5-th, 10-th, 15-th, 25-th, and 30-th item will be assigned the values in the corresponding line. The following is an example of a command file that will input these values. Note that PRINT has been set to 1 on the CALIB command to print the item parameters at cycle zero and show the assigned values. EXAMPLE 15: ASSIGNED STARTING VALUES FOR TWO SUBTESTS >GLOBAL DFNAME='EXAMPL03.DAT',PRNAME='EXAMPL15.PRM',NPARM=2, NTEST=2,SAVE; >SAVE PDISTRIB='EXAMPL15.PST',SCORE='EXAMPL15.SCO'; >LENGTH NITEMS=(35,35); >INPUT NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT', NALT=5,NFORMS=2,NIDCHAR=5; >ITEMS INUMBERS=(1(1)45), INAME=(C01(1)C45); >TEST1 TNAME=SUBTEST1, INAME=(C01(1)C15,C21(1)C40); >TEST2 TNAME=SUBTEST2, INAME=(C06(1)C25,C31(1)C45); >FORM1 LENGTH=25,INUMBERS=(1(1)25); >FORM2 LENGTH=25,INUMBERS=(21(1)45); >GROUP1 GNAME=POP1,LENGTH=25,INUMBERS=(1(1)25); >GROUP2 GNAME=POP2,LENGTH=25,INUMBERS=(21(1)45); (T28,5A1,T25,I1,T25,I1/45A1) >CALIB IDIST=1,EMPIRICAL,NQPT=11,CYCLE=10,TPRIOR,NEWTON=1, CRIT=0.01,REF=1,NOFLOAT,PRINT=1; >SCORE IDIST=3,RSCTYPE=3,INFO=1,YCOMMON,POP,NOPRINT,MOMENTS;
Related topics
CALIB command: PRINT keyword (see Section 2.6.3)
Setup menu: Test Scoring dialog box (see Section 2.3.3)
SAVE option (optional) Purpose
To indicate that a SAVE command will follow the GLOBAL command. Format SAVE
Default
No SAVE command to follow.
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2 BILOG-MG REFERENCE Example
In the syntax below, the item parameters and scale scores are saved to file through the use of the SCORE and PARM keywords on the SAVE command. Note that, in order to use the SAVE command, the SAVE option is added to the GLOBAL command. >GLOBAL >SAVE >LENGTH
DFNAME=’EXAMPLE.DAT’, NPARM=2, SAVE; SCORE=’EXAMPLE.SCO’, PARM=’EXAMPLE.PAR’; NITEMS=(40);
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.6)
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GROUP COMMAND 2.6.8 GROUP command (Required when NGROUP > 1 on the INPUT command) Purpose
To specify information about the items in each particular group. When the NGROUP keyword on the INPUT command is greater than one, that same number of GROUP commands must follow the FORM commands. Each GROUP command specifies the group’s name, the length of the group’s form, and the items included in that form. Items may be identified by name or number, but not by both. The GROUP command requires a group number in the data record. The group numbers must range in value from 1 to the number of groups. If NFORM > 1, the group indicator field follows the form indicator field. If NFORM = 1, the group indicator field follows the case ID field. The group indicator field is INTEGER in the variable format statement. If the subtest is personalized (the option PERSONAL is present in the INPUT command) there are NTEST group indicators for each subject. The order of the several GROUP commands corresponds to the number of the respective group. If the same items are administered to all groups, the INUMBERS and INAMES lists are the same as those in the ITEMS command. Format >GROUP GNAME=n, LENGTH=n, INUMBERS=(list), INAMES=(list);
Default
No groups assumed. Example
If the form(s) for group 1 consists of items 1, 2, 4, and 5, and the form(s) for group 2 consists of items 3 through 8, then the corresponding group commands are as follows: >GROUP1 GNAME=GROUP1, LENGTH=4, INUMBERS=(1,2,4,5); >GROUP2 GNAME=GROUP2, LENGTH=6, INUMBERS=(3(1)8);
Related topics
GLOBAL command: NTEST and NVTEST keywords (see Section 2.6.7) INPUT command: NFORM keyword (see Section 2.6.9) INPUT command: PERSONAL option ITEMS command (see Section 2.6.10) LENGTH command (see Section 2.6.11)
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2 BILOG-MG REFERENCE
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box Variable format statement (see Section 2.6.18)
GNAME keyword (optional) Purpose
To specify the name of GROUPk (up to eight characters). Format GNAME=character string
Default
Blanks. Related topics
Setup menu: General dialog box (see Section 2.3.3)
INAMES keyword (optional) Purpose
To specify the list of item names, as specified in the ITEMS command, for all items in all forms administered to GROUPk. Format INAMES=( n1 , n2 ,..., nLENGTH )
Default
All names specified in the ITEMS command. Example
Assume, in the previous example, that the command >ITEMS INAMES=(I1(1)I8)
appears earlier in the command file to give the name Ix to item x. Then the two GROUP statestatements could be replaced with 160
GROUP COMMAND >GROUP1 GNAME=GROUP1, LENGTH=4, INAMES=(I1,I2,I4,I5) >GROUP2 GNAME=GROUP2, LENGTH=6, INAMES=(I3(1)I8)
Note the use of the list notation in the GROUP2 statement to specify items I3 through I8. Related topics
GROUP command: LENGTH keyword INPUT command: NTOTAL keyword (see Section 2.6.9) ITEMS command (see Section 2.6.10)
Setup menu: General dialog box (see Section 2.3.3)
INUMBERS keyword (optional) Purpose
To provide a list of item numbers, as specified in the ITEMS command, for all items in all forms administered to GROUPk. Format INUMBERS=( n1 , n2 ,..., nLENGTH )
Default
All items specified in the ITEMS command. Example
In the following example, the INUMBERS keywords specify the item list for each group. Note, again, the use of the “sequence” notation in the second statement to specify items 3 through 8. >GROUP1 GNAME=GROUP1, LENGTH=4, INUMBERS=(1,2,4,5); >GROUP2 GNAME=GROUP2, LENGTH=6, INUMBERS=(3(1)8);
Related topics
GROUP command: LENGTH keyword INPUT command: NTOTAL keyword (see Section 2.6.9) ITEMS command (see Section 2.6.10)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
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2 BILOG-MG REFERENCE LENGTH keyword (optional) Purpose
To specify the number of items in the test form(s) for GROUPk . Format LENGTH=n
Default NTOTAL.
Example
In the following example, the LENGTH keyword in each GROUP statement specifies the number of items for each group. >GROUP1 GNAME=GROUP1, LENGTH=4, INUMBERS=(1,2,4,5); >GROUP2 GNAME=GROUP2, LENGTH=6, INUMBERS=(3(1)8);
Related topics
INPUT command: NTOTAL keyword (see Section 2.6.9)
Setup menu: General dialog box (see Section 2.3.3)
162
INPUT COMMAND 2.6.9 INPUT command (Required) Purpose
To provide the information which describes the raw data file. One or more variable format statements describing the layout of the data must follow the FORM, GROUP, or DRIFT command, if present. The keywords KFNAME, NFNAME, and OFNAME enable the user to assign specific names to the program’s input files. A filename must be no more than 128 characters long and may include a drive prefix, a path name, and an extension. The filename must be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. Format >INPUT NTOTAL=n, NFMT=n, TYPE=n, SAMPLE=n, NALT=n, NIDCHAR=n, TAKE=n, NGROUP=n, NFORM=n, ISEED=n, DIAGNOSE=n, KFNAME=n, NFNAME=n, OFNAME=n, DRIFT, DIF, PERSONAL, EXTERNAL;
Examples
In the following example, responses from two groups are analyzed. There are two forms of a 25-item multiple-choice examination, with 5 items in common. In total, the responses of a sample of 2000 respondents to the 45 items are considered. >INPUT NTOTAL=45, SAMPLE=2000, NGROUP=2, NFORM=2;
The INPUT command below is used to request a DIF analysis on 4 items administered to two groups. >INPUT NTOTAL=4, DIF, NGROUP=2;
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Data menu: Item Keys dialog box DRIFT command (see Section 2.6.5) FORM command (see Section 2.6.6) GROUP command (see Section 2.6.8) Setup menu: General dialog box (see Section 2.3.3) Technical menu: Data Options dialog box (see Section 2.3.5) Variable format statement (see Section 2.6.18)
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2 BILOG-MG REFERENCE DIAGNOSE keyword (optional) Purpose
To specify a level of diagnostic printout for Phase 1. Larger values of n give increasing diagnostic output. Format DIAGNOSE=n
Default
No diagnostic printout. Related topics
Phase 1: INPUT (see Section 2.2)
DIF option (optional) Purpose
To specify a differential equation modeling (DIF) analysis for multiple groups, which assumes common slopes and guessing parameter for all groups. Format DIF
Default
No DIF analysis. Example
In the syntax below, a 1-parameter DIF model is fitted to data from two groups of examinees. DIF parameters are saved to the file exampl01.dif through use of the SAVE option on the GLOBAL command and the DIF option on the SAVE command. >GLOBAL >SAVE >LENGTH >INPUT
NPARM=1,LOGISTIC,SAVE; PARM='EXAMPL01.PAR',DIF='EXAMPL01.DIF'; NITEMS=4; NTOTAL=4,NGROUPS=2,DIF,NIDC=2;
164
INPUT COMMAND Related topics
DRIFT option GROUP command (see Section 2.6.8) INPUT command: NGROUP keyword SAVE command: DIF keyword (see Section 2.6.15)
Setup menu: General dialog box (see Section 2.3.3)
DRIFT option (optional) Purpose
To specify an item parameter drift model for multiple groups. A DRIFT command must also appear after the GROUP commands. Format DRIFT
Default
No DRIFT model. Example
In the syntax below, a 2-parameter DRIFT model is fitted to data from two groups of examinees. DRIFT parameters are saved to the file exampl01.drf by using the SAVE option on the GLOBAL command and the DRIFT option on the SAVE command. >GLOBAL >SAVE >LENGTH >INPUT
NPARM=1,LOGISTIC,SAVE; PARM='EXAMPL01.PAR',DRIFT='EXAMPL01.DRF'; NITEMS=4; NTOTAL=4,NGROUPS=2,DRIFT,NIDC=2;
Related topics
DRIFT command (see Section 2.6.5) GROUP command (see Section 2.6.8) INPUT command: NGROUP keyword SAVE command: DRIFT keyword (see Section 2.6.15)
Setup menu: General dialog box (see Section 2.3.3)
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2 BILOG-MG REFERENCE EXTERNAL keyword (optional) Purpose
To specify the computation of the item parameters with respect to an external variable, the values of which are supplied in the data records, rather than to a latent variable inferred from the item responses. When item parameters are estimated in this way and used to score test data of any other groups of examinees, the resulting scores are the best predictors of the ability measured by the external variable. In each record of the calibration data, each test in the analysis must be represented by a value of the external variable and its corresponding standard error. These two quantities for each test in the data record must precede the item responses in the same order as the tests appear in their successive command lines. The columns of the data records devoted to these pairs of scores and standard errors must be identified in the input variable format statement. Format EXTERNAL
Default
Calibration with respect to a latent variable inferred from the item responses. Example
Suppose a group of students took an end-of-term reading test and math test routinely administered to all students in a metropolitan school district. Suppose these students were also part of the sample for a state assessment of reading and math achievement. If scores and standard errors on the assessment tests for these students were available to the district, the district test could be calibrated to best predict the state reading and math scores of students of all students in the district. For this purpose, the state test results would serve as the external variables for calibrating items of the local tests to predict the state assessment’s scores. For the sake of generality, suppose also that there are three random parallel forms of the district tests and that these forms are assigned at random to students in two successive school grades. Then there will be two groups of students in the analysis and the record layout of the data might be the following:
Columns 1-4: Student ID Column 6: test form number Column 8: grade group number Columns 10-13: state reading test score Columns 15-18 state reading test standard error Columns 20-23 state math test score Columns 25-28 state math test standard error 166
INPUT COMMAND
Columns of 30-59: local reading test item responses Columns 60-89: local math test item responses The format statement for reading the data records would be (4A1,1X,I1,1X,I1,2(1X,F4.1,1X,F4.1),1X,60A1)
and the item parameter file from the calibration could be saved for use in scoring other students. Related topics
Data, Examinee Data /Data, Group-Level Data dialog boxes (see Section 2.3.4) Variable format statement (see Section 2.6.18)
ISEED keyword (optional) Purpose
To specify the seed for the random number generator used for sampling subjects. By default, the same seed will always be used for sampling subjects when the SAMPLE keyword on the INPUT command is used. ISEED may be used to change the seed, thus producing a different random sample of subjects. Format ISEED=n
Default ISEED=1.
Related topics
INPUT command: SAMPLE and TAKE keywords
Technical menu: Data Options dialog box (see Section 2.3.5)
KFNAME keyword (optional) Purpose
To specify the name of the file which contains the answer key. This key consists of the correct response alternative for each item, in the same format as the corresponding response records. Any single ASCII character can be used as a response alternative. If the answer key is in the same file as the item response data, the key must precede the first response record. If
167
2 BILOG-MG REFERENCE KFNAME does not appear on the INPUT command, then the data are assumed to be scored 1
for correct and 0 for incorrect. When NFORM > 1, separate answer, not-presented, and omit keys must be specified for each form in the order of the forms to which they apply. Again, if they are in the same file as the response data, all keys must precede the first response record. Format KFNAME=
Default
No answer key. Notes
The path to and filename of this file may be longer than 80 characters. As the maximum length of any line in the command file is 80 characters, multiple lines should be used. It is important to continue up to and including the 80th column when specifying a long path and filename. For example, suppose the data file exampl06.dat is in a folder named: C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MGVERSION1.2\EXAMPLES
The correct way to enter this information in the command file is to enclose the name and path in apostrophes, and continue until column 80 is reached. Then proceed in column 1 of the next line as shown below: >GLOBAL DFNAME=’C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MG -VERSION1.2\EXAMPLES\EXAMPL06.DAT’, NTEST=1, NVTEST=1, NPARM=2, SAVE;
If the data are stored in the same folder as the command file, it is sufficient to type DFNAME='EXAMPL06.DAT'
Example
In the analysis of single subject data from the file exampl04.dat, the answer key appears at the top of the file as indicated by the use of the KFNAME keyword. >INPUT
NTOTAL=40,NFORM=2,KFNAME='EXAMPL04.DAT',NALT=5;
168
INPUT COMMAND As two forms are used, answer keys are given by form before the actual data, and in the same format as the data records. The first few lines of exampl04.dat are as follows: ANSWER KEY FORM 1 11111111111111111111 ANSWER KEY FORM 2 11111111111111111111 Samp1 11111111122212122111 Samp1 11222212221222222112
1 2 12
1
12
1
Related topics
INPUT command: NFORM, NFNAME, and OFNAME keywords
Data menu: Item Keys dialog box (see Section 2.3.4)
NALT keyword (optional) Purpose
To specify the maximum number of response alternatives in the raw data. 1/NALT is used as the automatic starting value for estimating lower asymptotes (guessing parameters) of the 3parameter model. Format NALT=n
Default
5 for the 3PL model; 1000 for the 1PL and 2PL models. Examples
In the case of the following 2-parameter model, 5 responses to each item are given in the data file. The correct response to each item is noted in the answer key, which appears at the top of the data file (indicated by the KFNAME keyword on the INPUT command). >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NIDCHAR=5, NALT=5,NFORM=2,TYPE=1;
When a 3-parameter model is fitted to the same data, 1/5 will be used as starting value for the lower asymptote (guessing parameter) of each item. 169
2 BILOG-MG REFERENCE >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=3; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NIDCHAR=5, NALT=5,NFORM=2,TYPE=1;
In the following example, a 2-parameter model is fitted to the data. No answer key is given, and it is assumed that the 2 response alternatives (NALT=2) are coded 1 for correct responses and 0 for incorrect responses. If more than 2 response alternatives are present and no code is given, all responses other than 1 will be assumed incorrect. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL04.DAT',NPARM=2; NITEMS=(40); NTOTAL=40,NALT=2;
Related topics
GLOBAL command: NPARM keyword (see Section 2.6.7)
Setup menu: General dialog box (see Section 2.3.3)
NFMT keyword (optional) Purpose
To specify the number of format records for reading the respondent data records. Format NFMT=n
Default
1. Examples
In the format statement below, item responses are read from two lines: the first 25 responses are read on the first line of data for each examinee and the second 25 on the second line of data. Although responses are read over two lines, the format statement fits comfortably on one line in the command file, and thus NFMT=1. (11A1,T39,25A1/T13,25A1)
If, however, a large data file is used as input, and it becomes necessary to write the format statement over multiple lines in the command file, the value assigned to NFMT should be adjusted to reflect this. For example, NFMT=2 for the following format statement in which 15 items are selected and columns between items are passed over using the “X” operator:
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INPUT COMMAND (11A1,1X,A1,2X,A1,1X,A1,3X,A1,1X,A1,2X,A1,1X,A1,3X,A1,1X,A1,2X,A1,1X,A1, 3X,A1,1X,A1,2X,A1,1X,A1)
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Variable format statement (see Section 2.6.18)
NFNAME keyword (optional) Purpose
To specify the name of the file which contains the not-presented key. This key must be given in the same format as the corresponding response records. Any single ASCII character can be used to represent a not-presented item. If the not-presented key is in the same file as the item response data, the key must precede the first response record. If this key appears in the same file as the answer key, it must appear in the file after the answer key. If NFNAME does not appear on the INPUT command, then all items are assumed presented. When NFORM > 1, separate answer, not-presented, and omit keys must be provided for each form in the order of the forms to which they apply. Again, if they are in the same file as the response data, all keys must precede the first response record. Format NFNAME=
Default
No not-presented key. Examples
In the analysis of single subject data from the file exampl04.dat, the not-presented key appears at the top of the file as indicated below, using the NFNAME keyword. >INPUT NTOTAL=40,NFORM=2,NFNAME='EXAMPL04.DAT',NALT=5; >ITEMS INUMBERS=(1(1)40),INAME=(T01(1)T40); >TEST TNAME=SIM; >FORM1 LENGTH=20,INUMBERS=(1(1)20); >FORM2 LENGTH=20,INUMBERS=(21(1)40); (T28,5A1,T25,I1/40A1)
As two forms are used, the not-presented keys are given by form before the actual data, and in the same format as the data records. The first few lines of exampl04.dat are as follows:
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2 BILOG-MG REFERENCE Not-P KEY FORM 1 aaaaaaaaaaaaaaaaaaaa Not-P KEY FORM 2 aaaaaaaaaaaaaaaaaaaa Samp1 11a11111122212122111 Samp1 112222122a1222222112
1 2 12
1
12
1
Alternatively, the lines Not-P KEY FORM 1 aaaaaaaaaaaaaaaaaaaa Not-P KEY FORM 2 aaaaaaaaaaaaaaaaaaaa
1 2
can be saved to a not-presented key file exampl04.nfn, and referenced as such in a revised INPUT command: >INPUT
NTOTAL=40,NFORM=2,NFNAME='EXAMPL04.NFN',NALT=5;
If both a not-presented key and an omit key are used for the two forms, the following lines should appear at the top of the data file when the data file is referenced by the NFNAME and OFNAME keywords in the INPUT command: >INPUT
NTOTAL=40,NFORM=2,NFNAME='EXAMPL04.DAT', OFNAME='EXAMPL04.DAT',NALT=5;
Not-P KEY FORM 1 aaaaaaaaaaaaaaaaaaaa Omit KEY FORM 1 bbbbbbbbbbbbbbbbbbbb Not-P KEY FORM 2 aaaaaaaaaaaaaaaaaaaa Omit KEY FORM 2 bbbbbbbbbbbbbbbbbbbb
1 1 2 2
Related topics
Data menu: Item Keys dialog box (see Section 2.3.4) GLOBAL command: DFNAME keyword (see Section 2.6.7) INPUT command: NFORM keyword INPUT command: KFNAME keyword INPUT command: OFNAME keyword
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INPUT COMMAND NFORM keyword (optional) Purpose
To specify the number of test forms. If NFORM > 1, the response records must contain an indicator specifying the form to which the examinee responded. This keyword is used in combination with the FORM command and the variable format statement. The NFORM keyword is required when multiple-form data is supplied to the program in compressed form (see input file format discussed in Section 2.6.20 for more details). If the instrument consists of a single test form, or multiple-form data is supplied to the program in expanded format, the NFORM keyword, with NFORM=1, is required by the program if the order of items on the response records does not correspond to the order of items in the ITEMS command list. Format NFORM=n
Default
No FORM commands will be read and the order of items in the response records is assumed to be the same as that in the ITEMS command. Example
In the following example, two forms were administered to two groups of examinees. As both the NFORM and NGROUP keywords are used on the INPUT command, both FORM and GROUP commands are given. >INPUT >ITEMS >TEST >FORM1 >FORM2 >GROUP1 >GROUP2
NTOTAL=45,NGROUP=2,NIDCHAR=5,NALT=5,NFORM=2; INUMBERS=(1(1)45), INAME=(C01(1)C45); TNAME=CHEMISTRY; LENGTH=25,INUMBERS=(1(1)25); LENGTH=25,INUMBERS=(21(1)45); GNAME=POP1,LENGTH=25,INUMBERS=(1(1)25); GNAME=POP2,LENGTH=25,INUMBERS=(21(1)45);
Note that the format statement contains both a form and a group indicator. (5A1,T25,I1,T25,I1,25A1)
Related topics
FORM command (see Section 2.6.6)
Input files (see Section 2.6.20) ITEMS command (see Section 2.6.10)
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2 BILOG-MG REFERENCE Setup menu: General dialog box (see Section 2.3.3) Variable format statement (see Section 2.6.18)
NGROUP keyword (optional) Purpose
To specify the number of groups or cohorts of respondents. If NGROUP > 1, the response records must contain an indicator specifying the group or cohort to which the respondent belongs. This keyword is used in combination with the GROUP command and the variable format statement, where a group indicator is added. Format NGROUP=n
Default
1. Related topics
FORM command (see Section 2.6.6) GROUP command (see Section 2.6.8)
Setup menu: General dialog box (see Section 2.3.3) Variable format statement (see Section 2.6.18)
NIDCHAR keyword (required) Purpose
To specify the number of characters in the respondent’s identification field. Valid values are 1 to 30. Format NIDCHAR=n
Default
30. Example
Data from two groups, found on two forms are analyzed in this example. The NIDCHAR keyword is set to 5, indicating that the subject ID field is 5 columns in length. This corre174
INPUT COMMAND sponds with the format statement, where the first entry, for the subject ID, is 5A1. >INPUT NTOTAL=45,NGROUP=2,NIDCHAR=5,NALT=5,NFORM=2,TYPE=1; (5A1,T25,I1,T25,I1/25A1)
Related topics Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Variable format statement (see Section 2.6.18)
NTOTAL keyword (optional) Purpose
To specify the total number of unique items in the respondent data records. The number includes all main and variant test items on all forms. Format NTOTAL=n
Default
0. Examples
In this example, responses from two groups are analyzed. There are two forms of a 25-item multiple-choice examination, with 5 items in common. In total, the responses of a sample of 2000 respondents to the 45 items are considered. >INPUT NTOTAL=45, SAMPLE=2000, NGROUP=2, NFORM=2;
The INPUT command below is used to request a DIF analysis on 4 items administered to two groups. >INPUT NTOTAL=4, DIF, NGROUP=2;
In the following example, responses to 50 items are read from the data file. From the 50, 20 are selected as Main Test items and 4 as Variant Test items. Items for the main test are selected by name in the TESTM command; items for the variant test are selected by name in the TESTV command. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL06.DAT', NTEST=1,NVTEST=1,NPARM=2; NITEM=24,NVARIANT=4; NTOTAL=50,KFNAME='EXAMPL06.DAT',SAMPLE=200,NIDCH=11;
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2 BILOG-MG REFERENCE >ITEMS >TESTM
>TESTV
INUMBERS=(1(1)50),INAME=(I26(1)I75); TNAME=MAINTEST, INAMES=(I26,I27,I28,I29,I31,I33,I34, I35,I36,I38,I39,I47,I48,I49,I50,I54,I60,I64,I68,I72); TNAME=VARIANT,INAMES=(I53,I59,I69,I73);
Related topics
Setup menu: General dialog box (see Section 2.3.3)
OFNAME keyword (optional) Purpose
To specify the name of the file which contains the omit key. This key must be specified in the same format as the response records. Any single ASCII character can be used to represent a not-presented item. If the not-presented key is in the same file as the item response data, the key must precede the first response record. If this key appears in the same file as the answer and/or not-presented keys, it must appear in the file after the both keys. If OFNAME does not appear on the INPUT command, omits will not be distinguished from incorrect responses. When NFORM > 1, separate answer, not-presented, and omit keys must be provided for each form in the order of the forms to which they apply. Again, if they are in the same file as the response data, all keys must precede the first response record. Format OFNAME=character string
Default
No omit key. Examples
In the analysis of single subject data from the file exampl04.dat, the omit key appears at the top of the file as indicated by the use of the OFNAME keyword. >INPUT NTOTAL=40,NFORM=2,OFNAME='EXAMPL04.DAT',NALT=5; >ITEMS INUMBERS=(1(1)40),INAME=(T01(1)T40); >TEST TNAME=SIM; >FORM1 LENGTH=20,INUMBERS=(1(1)20); >FORM2 LENGTH=20,INUMBERS=(21(1)40); (T28,5A1,T25,I1/40A1)
As two forms are used, omit keys are given by form before the actual data, and in the same format as the data records. The first few lines of exampl04.dat are as follows: 176
INPUT COMMAND Omit KEY FORM 1 bbbbbbbbbbbbbbbbbbbb Omit KEY FORM 2 bbbbbbbbbbbbbbbbbbbb Samp1 11a11111122212122111 Samp1 112222122a1222222112
1 2 12
1
12
1
Alternatively, the lines Omit KEY FORM 1 bbbbbbbbbbbbbbbbbbbb Omit KEY FORM 2 bbbbbbbbbbbbbbbbbbbb
1 2
can be saved to a omit key file exampl04.ofn, and referenced as such in a revised INPUT command: >INPUT
NTOTAL=40,NFORM=2,NFNAME='EXAMPL04.OFN',NALT=5;
If both a not-presented key and an omit key are used for the two forms, the following lines should appear at the top of the data file when the data file is referenced by the NFNAME and OFNAME keywords in the INPUT command: >INPUT
NTOTAL=40,NFORM=2,NFNAME='EXAMPL04.DAT', OFNAME='EXAMPL04.DAT',NALT=5;
Not-P KEY FORM 1 aaaaaaaaaaaaaaaaaaaa Omit KEY FORM 1 bbbbbbbbbbbbbbbbbbbb Not-P KEY FORM 2 aaaaaaaaaaaaaaaaaaaa Omit KEY FORM 2 bbbbbbbbbbbbbbbbbbbb
1 1 2 2
Related topics
Data menu: Item Keys dialog box (see Section 2.3.4) GLOBAL command: DFNAME and NPARM keywords (see Section 2.6.7) INPUT command: KFNAME, NFNAME, and NFORM keywords
PERSONAL option (optional) Purpose
To specify the assumption that the group or cohort assignment of an examinee is personalized by subtest. The response records must contain NTEST indicators, one for each subtest, specifying the groups or group cohorts to which the respondent belongs. The NTEST group 177
2 BILOG-MG REFERENCE indicators must be specified in the variable format statement in the same order as the subtests. The PERSONAL option is especially useful for two-stage tests that measure ability in more than one area. Assignment to the second-stage booklets may differ among areas. Format PERSONAL
Default
None. Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box GLOBAL command: NTEST keyword (see Section 2.6.7) Variable format statement (see Section 2.6.18)
SAMPLE keyword (optional) Purpose
To specify the number of respondents to be randomly sampled from the raw data file. Format SAMPLE=n
Default
1000. Example
Here data are read from the file exampl03.dat, which also contains the answer key (DFNAME and KFNAME keywords). Although the data file contains only 400 records, a sample of 2000 is requested. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NIDCHAR=5, NALT=5,NFORM=2,TYPE=1;
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INPUT COMMAND If the first few records of the data file are to be used, the TAKE keyword should be used instead. Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box INPUT command: TAKE keyword
TAKE keyword (optional) Purpose
To specify an analysis using only the first n respondents in the data file. This option is useful for testing the problem setup on a smaller number of respondents when the sample size is large. Note that the maximum value for this keyword is the actual number of respondents in the data file. To obtain a random sample of the respondents, the SAMPLE keyword should be used. TAKE and SAMPLE are mutually exclusive keywords. Format TAKE=n
Default
Take all data specified by SAMPLE. Examples
In the following example, data are read from the file exampl03.dat, which also contains the answer key (DFNAME and KFNAME keywords). Although the data file contains only 400 records, a sample of 2000 is requested. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NIDCHAR=5,NALT=5,TYPE=1;
If, however, only the first 100 records are to be used in the analysis, the modified INPUT command >INPUT
NTOTAL=45,TAKE=100,NIDCH=5,NALT=5,TYPE=1;
should be used.
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2 BILOG-MG REFERENCE Related topics Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box INPUT command: SAMPLE keyword
TYPE keyword (optional) Purpose
To specify the type of data file to be used in the analysis: 0: 1: 2: 3: 4:
no raw data to read in single-subject data to read in single-subject data with case weights number tried, number right data, no case weights number tried, number right data, case weights
Format TYPE=n
Default
1. Examples
In a preliminary run, an item parameter file was created as shown below. The item parameter file was saved to exampl03.par using the PARM keyword on the SAVE command. As single-subject data were used in this run TYPE was set to 1 in the INPUT command. EXAMPLE: CREATING >COMMENT >GLOBAL >SAVE >LENGTH >INPUT
A ITEM PARAMETER FILE DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; PARM='EXAMPL03.PAR'; NITEMS=(45); NTOTAL=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NIDCH=5, NALT=5,NFORM=2,TYPE=1;
The previously created calibration file is now used as input through the use of the IFNAME keyword on the GLOBAL command. Note that the TYPE keyword on the INPUT command is now set to 0, compared to 1 previously. The updated item parameter estimates are saved to the file latest.par using the PARM keyword on the SAVE command.
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INPUT COMMAND
EXAMPLE: USING A ITEM PARAMETER FILE AS INPUT >COMMENT >GLOBAL CFNAME='EXAMPL03.PAR',NPARM=2, SAVE; >SAVE CALIB='LATEST.PAR'; >LENGTH NITEMS=(45); >INPUT NTOTAL=45,SAMPLE=2000,NGROUP=2,NIDCHAR=5, NALT=5,NFORM=2,TYPE=0;
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box GLOBAL command: IFNAME keyword (see Section 2.6.7) SAVE command: PARM keyword (see Section 2.6.15)
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2 BILOG-MG REFERENCE 2.6.10 ITEMS command (Required) Purpose
To specify the names and corresponding numbers for all items in the data records. The items may be listed in any order, but the order in which the names appear must correspond with the order of the numbers. The names and numbers specified in the ITEMS command are used to refer to the items in the TEST, FORM, and GROUP commands. Strings of consecutive numbers may be abbreviated as m(1)n, where m is the number of the first item and n is the number of the last item. Strings of up to 8 character names including consecutive numbers may be abbreviated as Xm(1)Xn, where X is a string of up to 4 letters of the alphabet, m is the up-to-4 character integer number of the first item and n is the up-to-4 character integer number of the last item. Format >ITEMS INUMBERS=(list), INAMES=(list);
Default
None. Examples
In the first example, 15 items are assigned the names MATH01 through MATH15. >ITEMS
INAME=(MATH01(1)MATH15);
In the syntax that follows, 16 items belonging to 2 subtests are identified. From the LENGTH command, we see that each subtest has 8 items. The ITEMS command is used to first number these items, and then to assign the names N1 through N8 to items belonging to the first subtest. Items belonging to the second subtest are named A1 through A8. On the TEST commands, items are referenced by number. Referencing by the names assigned in the ITEMS command is another option. >LENGTH >INPUT >ITEMS >TEST1 >TEST2
NITEMS=(8,8); NTOTAL=16,NALT=5,NIDCHAR=9,TYPE=3; INUMBERS=(1(1)16),INAMES=(N1(1)N8,A1(1)A8); TNAME=NUMCON,INUMBERS=(1(1)8); TNAME=ALGCON,INUMBERS=(9(1)16);
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ITEMS COMMAND Related topics
FORM command (see Section 2.6.6) GROUP command (see Section 2.6.8) TEST command (see Section 2.6.17)
Setup menu: General dialog box (see Section 2.3.3)
INAMES keyword (optional) Purpose
To specify a list of NTOTAL unique names (up to eight characters each). Item names that do not begin with letters must be enclosed in single quotes. Strings of up to 8 character names including consecutive numbers may be abbreviated as Xm(1)Xn, where X is a string of up to 4 letters of the alphabet, m is the up-to-4 character integer number of the first item and n is the up-to-4 character integer number of the last item. Format INAMES=( n1 , n2 ,..., nNTOTAL )
Default 1, 2, …, NTOTAL.
Related topics
INPUT command: NTOTAL keyword (see Section 2.6.9)
Setup menu: General dialog box (see Section 2.3.3)
INUMBERS keyword (optional) Purpose
To specify the list of NTOTAL unique numbers. Strings of consecutive numbers may be abbreviated as m(1)n, where m is the number of the first item and n is the number of the last item. Format INUMBERS=( n1 , n2 ,..., nNTOTAL )
Default 1, 2, …, NTOTAL.
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2 BILOG-MG REFERENCE Example
In the syntax that follows, 16 items belonging to 2 subtests are identified. From the LENGTH command we see that each subtest has 8 items. The ITEMS command is used to first number these items, and then to assign the names N1 through N8 to items belonging to the first subtest. Items belonging to the second subtest are named A1 through A8. On the TEST commands, items are referenced by number. Referencing by the names assigned in the ITEMS command is another option. >LENGTH >INPUT >ITEMS >TEST1 >TEST2
NITEMS=(8,8); NTOTAL=16,NALT=5,NIDCHAR=9,TYPE=3; INUMBERS=(1(1)16),INAMES=(N1(1)N8,A1(1)A8); TNAME=NUMCON,INUMBERS=(1(1)8); TNAME=ALGCON,INUMBERS=(9(1)16);
Related topics
INPUT command: NTOTAL keyword (see Section 2.6.9)
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LENGTH COMMAND 2.6.11 LENGTH command (Required) Purpose
To supply the number of items in subtests and the number of variant items in the subtests. Format >LENGTH NITEMS=(list), NVARIANT=(list);
Example
Consider two subtests. Subtest 1 has a total of 20 items; subtest 2 has a total of 15 items. Five of the items in subtest 1 are variant items. None of the items in subtest 2 are variant items. Note that the number of variant tests has to be specified using the NVTEST keyword on the GLOBAL command. The corresponding number of TEST commands must also be included in the syntax. >GLOBAL DFNAME=’EXAMPL04.DAT’,NTEST=2,NVTEST=1; … >LENGTH NITEMS=(20,15), NVARIANT=(5,0);
Related topics
GLOBAL command: NVTEST and NVTEST keywords (see Section 2.6.7)
Setup menu: Item Analysis dialog box (see Section 2.3.3) TEST command (see Section 2.6.17)
NITEMS keyword (required) Purpose
To provide a list of the number of items in the successive subtests to be analyzed. If a subtest contains variant items, they are included in this count of items. Format NITEMS=( n1 , n2 ,..., nNTESTS )
Default
None.
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2 BILOG-MG REFERENCE Example
In the example below, 20 of the 24 items are selected as main test items and 4 as variant test items. The number of variant tests is specified using the NVTEST keyword on the GLOBAL command. The TEST command for the main test is followed by a TEST command in which the variant items are specified by item number. >GLOBAL >LENGTH >INPUT >ITEMS >TESTM >TESTV
DFNAME='example.dat', NTEST=1, NVTEST=1; NITEM=24, NVARIANT=4; NTOTAL=24; INUMBER=(1(1)24); TNAME=MAINTEST, INUMBER=(1(1)20); TNAME=VARIANT, INUMBER=(21(1)24);
Related topics
GLOBAL command: NTEST and NVTEST keywords (see Section 2.6.7)
Setup menu: Item Analysis dialog box (see Section 2.3.3) TEST command (see Section 2.6.17)
NVARIANT keyword (optional) Purpose
To specify the number of variant items, if any, in the successive subtests to be analyzed. Although parameter estimates for these items will be obtained, these items are not used in scoring of tests/forms. Format NVARIANT=( nv1 , nv2 ,..., nvNVTESTS )
Default
0. Related topics
GLOBAL command: NTEST and NVTEST keywords (see Section 2.6.7)
Setup menu: Item Analysis dialog box (see Section 2.3.3) TEST command (see Section 2.6.17)
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PRIORS COMMAND 2.6.12 PRIORS command (Optional) Purpose
To specify prior distributions for constrained estimation of the item parameters of the main test and for the variant items, if any. This command is required when the READPR keyword appears in the CALIB command. There is one prior command for each subtest. Values are read in order of the items in the subtest beginning with the main test items and ending with the variant test items. If NGROUP>1, more than one set of prior means and standard deviations for the item thresholds may be required when the DIF or DRIFT models are specified. See the TMU and TSIGMA keywords below. Format >PRIORS TMU=(list),TSIGMA=(list),SMU=(list) SSIGMA=(list), ALPHA=(list), BETA=(list);
Notes
If the same value applies to all items of the subtest, you may use the “repeat” form: “value (0) number of values” (see Section 2.6.2). For a mean of p with a weight of n observations for the beta prior distribution, set ALPHA=np+1 BETA=n(1–p)+1
To set an item parameter to a fixed value, set the mean of the prior to the parameter value and set the corresponding standard deviation to a very small value. Suitable values for TSIGMA are 0.005, for SSIGMA, 0.001 and for ALPHA and BETA, n = 1000. The priors for free parameters should be set to the default values above. The PRIORS command for each test should appear immediately after the QUAD commands for that test. Examples
The following example emanates from an analysis of aggregate-level, multiple-matrix sampling data. Aggregate-level data typically have smaller slopes in the 0,1 metric than do person-level data. For this reason, the mean of the prior for the log slopes has been set to 0.5 by the use of the READPRI option on the CALIB command and the successive PRIOR commands. The NOFLOAT option is used to keep the means of the prior distributions on the item parameters fixed at their specified values during estimation.
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2 BILOG-MG REFERENCE >CALIB
NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, TPRIOR, READPRI, NOFLOAT; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
The next example illustrates how user-supplied priors for the latent distributions are specified with IDIST=1 on the CALIB command. The points and weights for these distributions are supplied in the corresponding QUAD commands. Note that with IDIST=1, there are separate QUAD commands for each group for each subtest. Within each subtest the points are the same for each group. This is a requirement of the program. But as the example shows, the points for the groups may differ by subtest. The PRIOR command for each subtest is placed after the QUAD commands for that subtest. In this example, only the prior for the standard deviations of the thresholds is supplied on the PRIOR command. Default values are used for the other prior distributions. The means of the distributions are kept fixed at their specified values by using the NOFLOAT option on the CALIB command. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2,NTEST=2; NITEMS=(35,35); NTOT=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NALT=5, NFORMS=2,NIDCHAR=5; >ITEMS INUMBERS=(1(1)45), INAME=(C01(1)C45); >TEST1 TNAME=SUBTEST1,INAME=(C01(1)C15,C21(1)C40); >TEST2 TNAME=SUBTEST2,INAME=(C06(1)C25,C31(1)C45); >FORM1 LENGTH=25,INUMBERS=(1(1)25); >FORM2 LENGTH=25,INUMBERS=(21(1)45); >GROUP1 GNAME=POP1,LENGTH=25,INUMBERS=(1(1)25); >GROUP2 GNAME=POP2,LENGTH=25,INUMBERS=(21(1)45); (T28,5A1,T25,I1,T25,I1/45A1) >CALIB IDIST=1,READPR,EMPIRICAL,NQPT=16,CYCLE=25,TPRIOR,NEWTON=5, CRIT=0.01,REFERENCE=1,NOFLOAT; >QUAD1 POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01, 0.4746E+01), WEIGHTS=(0.2464E-05,0.4435E-03,0.1724E-01,0.1682E+00, 0.3229E+00,0.3679E+00,0.1059E+00,0.1685E-01,0.6475E-03, 0.8673E-05); >QUAD2 POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01, 0.4746E+01), WEIGHTS=(0.2996E-04,0.1300E-02,0.1474E-01,0.1127E+00, 0.3251E+00,0.3417E+00,0.1816E+00,0.2149E-01,0.1307E-02, 0.3154E-04); >PRIOR TSIGMA=(1.5(0)35); >QUAD1 POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01, -0.4444E+00,0.4444E+00,0.1333E+01,0.2222E+01,0.3111E+01, 0.4000E+01), WEIGHTS=(0.1190E-03,0.2805E-02,0.3002E-01,0.1458E+00, 0.3213E+00,0.3213E+00,0.1458E+00,0.3002E-01,0.2805E-02, 0.1190E-03); >QUAD2 POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01, -0.4444E+00,0.4444E+00,0.1333E+01,0.2222E+01,0.3111E+01, 0.4000E+01),
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PRIORS COMMAND WEIGHTS=(0.1190E-03,0.2805E-02,0.3002E-01,0.1458E+00, 0.3213E+00,0.3213E+00,0.1458E+00,0.3002E-01,0.2805E-02, 0.1190E-03); TSIGMA=(1.5(0)35);
>PRIOR
Suppose IDIST=1, NGROUP=2, and NTEST=2. The setup for the QUAD and PRIOR commands is as follows: >QUAD1 >QUAD2 >PRIOR1 >QUAD1 >QUAD2 >PRIOR2
(specifications (specifications (specifications (specifications (specifications (specifications
for for for for for for
Group 1, Group 2, Groups 1 Group 1, Group 2, Groups 1
subtest 1) subtest 1) and 2, subtest1) subtest2) subtest 2) and 2, subtest 2)
Related topics
CALIB command: READPRIOR option (see Section 2.6.3) GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) QUAD command (see Section 2.6.13)
Technical menu: Item Parameter Prior Constraints dialog box
ALPHA keyword (optional) Purpose
To specify the real-valued “alpha” parameters for the beta prior distribution of lower asymptote (guessing) parameters. Format ALPHA=( n1 , n2 ,..., nN )
Default 20p+1.
Related topics
CALIB command: READPRIOR option (see Section 2.6.3) GLOBAL command: NPARM keyword (see Section 2.6.7) PRIORS command: BETA keyword
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
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2 BILOG-MG REFERENCE BETA keyword (optional) Purpose
To specify the real-valued “beta” parameters for the beta prior distribution of lower asymptote (guessing) parameters. Format BETA=( n1 , n2 ,..., nN )
Default 20(1–p)+1.
Related topics
CALIB command: READPRIOR option (see Section 2.6.3) GLOBAL command: NPARM keyword (see Section 2.6.7) PRIORS command: ALPHA keyword
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
SMU keyword (optional) Purpose
To provide real-valued prior means for the item slopes. Format SMU=( n1 , n2 ,..., nN )
Default
1.0. Example
In the following example, SMU is used to specify prior means for the item slopes. >CALIB
NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, NOFLOAT, RIDGE=(2, 0.8000, 2.0000), ACCEL=1.0000, SPRIOR, READPRI; >PRIORS1 SMU=(0.5000(0)8); >PRIORS2 SMU=(0.5000(0)8);
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PRIORS COMMAND
Related topics
CALIB command: READPRIOR option (see Section 2.6.3)
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
SSIGMA keyword (optional) Purpose
To specify real-valued prior standard deviations of the item slopes. Format SSIGMA=( n1 , n2 ,..., nN )
Default
1.64872127. Example
In the calibration of a single subtest with 35 items, the following PRIOR command is used to provide a real-valued prior standard deviation of 1.75 for the item slopes. >CALIB >PRIOR
READPRI, NQPT=16,CYCLE=25, NEWTON=5; SSIGMA=(1.75(0)35);
Related topics
CALIB command: READPRIOR option (see Section 2.6.3)
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
TMU keyword (optional) Purpose
To specify real-valued prior means for the item thresholds (DIF) or polynomial coefficients (DRIFT) including intercept. Format TMU=( n1 , n2 ,..., nMITM1 , n1 , n2 ,..., nMITM 2 ,..., n1 , n2 ,..., nMITM L )
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2 BILOG-MG REFERENCE Default
0.0. Example
In the example, below, PRIOR commands are used to specify prior distributions for the constrained estimation of the thresholds in the calibration of two subtests with 8 items each. >CALIB NQPT=30, CYCLES=24, NEWTON=4, CRIT=0.0050, READPRI; >PRIORS1 TMU=(2.0500(0)8); >PRIORS2 TMU=(2.0500(0)8);
Related topics
CALIB command: READPRIOR option (see Section 2.6.3) INPUT command: DIF option (see Section 2.6.9) INPUT command: DRIFT option
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
TSIGMA keyword (optional) Purpose
To specify real-valued prior standard deviations of the threshold parameters. If neither the DIF nor the DRIFT model is selected, L = 1. If the DIF model is selected, L = NGROUP. If the DRIFT model is selected, L = MAXPOWER. Format TSIGMA=( n1 , n2 ,..., nMITM1 , n1 , n2 ,..., nMITM 2 ,..., n1 , n2 ,..., nMITM L )
Default
2.0. Related topics
CALIB command: DRIFT command: INPUT command: INPUT command: INPUT command:
Technical menu: Item Parameter Prior Constraints dialog box (see Section 2.3.5)
READPRIOR option (see Section 2.6.3) MAXPOWER keyword (see Section 2.6.5) DIF option (see Section 2.6.9) DRIFT option NGROUP keyword
192
QUAD COMMAND 2.6.13 QUAD command (Required if IDIST = 1 or 2 on CALIB command) Purpose
To read in user-supplied quadrature points and weights, or points and ordinates of the discrete finite representations of the prior distribution for the groups. This command follows directly after the CALIB command. If:
IDIST = 0: This command is not used. IDIST = 1: There must be a separate QUAD command for each group for each subtest. For
IDIST = 2: There must be a separate QUAD command for each group. The same set of QUAD
any subtest, the points for each group must have the same values commands applies to all subtests. The points for each group must have the same values. Format >QUAD POINTS=(list), WEIGHTS=(list);
Example
This example illustrates user-supplied priors for the latent distributions are specified with IDIST=1 on the CALIB command. The points and weights for these distributions are supplied in the QUAD commands. Note that with IDIST=1, there are separate QUAD commands for each group for each subtest. Within each subtest the points are the same for each group. This is a requirement of the program. But as the example shows, the points for the groups may differ by subtest. The PRIOR command for each subtest is placed after the QUAD commands for that subtest. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2,NTEST=2; NITEMS=(35,35); NTOT=45,SAMPLE=2000,NGROUP=2,KFNAME='EXAMPL03.DAT',NALT=5, NFORMS=2,NIDCHAR=5; >ITEMS INUMBERS=(1(1)45), INAME=(C01(1)C45); >TEST1 TNAME=SUBTEST1,INAME=(C01(1)C15,C21(1)C40); >TEST2 TNAME=SUBTEST2,INAME=(C06(1)C25,C31(1)C45); >FORM1 LENGTH=25,INUMBERS=(1(1)25); >FORM2 LENGTH=25,INUMBERS=(21(1)45); >GROUP1 GNAME=POP1,LENGTH=25,INUMBERS=(1(1)25); >GROUP2 GNAME=POP2,LENGTH=25,INUMBERS=(21(1)45); (T28,5A1,T25,I1,T25,I1/45A1) >CALIB IDIST=1,READPR,EMPIRICAL,NQPT=16,CYCLE=25,TPRIOR,NEWTON=5, CRIT=0.01,REFERENCE=1,NOFLOAT; >QUAD1 POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01,
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2 BILOG-MG REFERENCE
>QUAD2
>PRIOR >QUAD1
>QUAD2
>PRIOR
0.4746E+01), WEIGHTS=(0.2464E-05,0.4435E-03,0.1724E-01,0.1682E+00, 0.3229E+00,0.3679E+00,0.1059E+00,0.1685E-01,0.6475E-03, 0.8673E-05); POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01, 0.4746E+01), WEIGHTS=(0.2996E-04,0.1300E-02,0.1474E-01,0.1127E+00, 0.3251E+00,0.3417E+00,0.1816E+00,0.2149E-01,0.1307E-02, 0.3154E-04); TSIGMA=(1.5(0)35); POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01, -0.4444E+00,0.4444E+00,0.1333E+01,0.2222E+01,0.3111E+01, 0.4000E+01), WEIGHTS=(0.1190E-03,0.2805E-02,0.3002E-01,0.1458E+00, 0.3213E+00,0.3213E+00,0.1458E+00,0.3002E-01,0.2805E-02, 0.1190E-03); POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01, -0.4444E+00,0.4444E+00,0.1333E+01,0.2222E+01,0.3111E+01, 0.4000E+01), WEIGHTS=(0.1190E-03,0.2805E-02,0.3002E-01,0.1458E+00, 0.3213E+00,0.3213E+00,0.1458E+00,0.3002E-01,0.2805E-02, 0.1190E-03); TSIGMA=(1.5(0)35);
Related topics
CALIB command: IDIST keyword (see Section 2.6.3) GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9)
Technical menu: Calibration Prior Latent Distribution dialog box (see Section 2.3.5)
POINTS keyword (optional) Purpose
To specify the location of quadrature points. If:
IDIST = 1: a set of NQPT real-numbered values (with decimal points) of the quadrature points must be supplied for each group for each subtest. IDIST = 2: one set of points is required for each group.
Format POINTS=( n1 , n2 ,..., nNQPT )
194
QUAD COMMAND Default
Supplied by program. Example
See the example given above. Related topics
CALIB command: IDIST and NQPT keywords (see Section 2.6.3) GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9)
Technical menu: Calibration Prior Latent Distribution dialog box (see Section 2.3.5)
WEIGHTS keyword (optional) Purpose
To supply the weights for the quadrature points. If:
IDIST = 1 on the CALIB command: A set of NQPT positive fractions (with decimal points
and summing to 1.0) for weights for quadrature points must be supplied for each group for each subtest. This set of points applies to all subtests. IDIST = 2: One set of weights is required for each group. This set of weights applies to all subtests.
Format WEIGHTS=( n1 , n2 ,..., nNQPT )
Default
Supplied by program. Related topics
CALIB command: IDIST and NQPT keywords (see Section 2.6.3) GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9)
Technical menu: Calibration Prior Latent Distribution dialog box (see Section 2.3.5)
195
2 BILOG-MG REFERENCE 2.6.14 QUADS command (Required command if IDIST =1 or IDIST =2 on SCORE command) Purpose
To supply arbitrary prior distributions of scale scores for the respondents when EAP estimation is selected. This command follows directly after the SCORE command. If:
IDIST = 0: This command is not required IDIST = 1: There must be as separate QUADSj command for each group for each subtest. IDIST = 2: There must be a separate QUADSj command for each group. The same set of QUADS commands applies to all subtests. IDIST = 3: This command is not required. IDIST = 4: This command is not required.
If there are multiple groups (NGROUPS > 1) and IDIST = 1 or 2, the POINTS must have the same values for all groups. The WEIGHTS may differ by group, and the POINTS may differ by subtest. Format >QUADS POINTS=(list), WEIGHTS=(list);
Example
In the 2-group example below, an illustration is given of the use of user-supplied priors for the scale scores (IDIST=2) for the respondents when EAP estimation is selected (METHOD=2). The points and weights for these distributions are supplied in the QUADS commands. Note that with IDIST=2, there are separate QUADS commands for each group. >SCORE >QUADS1
>QUADS2
NQPT = 10, METHOD = 2, IDIST=2, INFO=1, YCOMMON, POP; POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01, 0.4746E+01), WEIGHTS=(0.2464E-05,0.4435E-03,0.1724E-01,0.1682E+00, 0.3229E+00,0.3679E+00,0.1059E+00,0.1685E-01,0.6475E-03, 0.8673E-05); POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01, -0.4453E+00,0.5930E+00,0.1631E+01,0.2670E+01,0.3708E+01, 0.4746E+01), WEIGHTS=(0.2996E-04,0.1300E-02,0.1474E-01,0.1127E+00, 0.3251E+00,0.3417E+00,0.1816E+00,0.2149E-01,0.1307E-02, 0.3154E-04);
196
QUADS COMMAND
Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) SCORE command: IDIST keyword (see Section 2.6.16)
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
POINTS keyword (optional) Purpose
To specify real-numbered, non-negative values (with decimal points) for the NQPT points of the arbitrary discrete prior distribution. Format POINTS=( n1 , n2 ,..., nNQPT )
Default
Supplied by program. Example
See example above. Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) SCORE command: IDIST or NQPT keywords (see Section 2.6.16)
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
WEIGHTS keyword (optional) Purpose
To specify real-numbered, non-negative values (with decimal points) for the NQPT weights of the arbitrary discrete prior distribution. The sum of the weights must equal unity. Format WEIGHTS=( n1 , n2 ,..., nNQPT )
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2 BILOG-MG REFERENCE
Default
Supplied by program. Example
See the example above. Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) SCORE command: IDIST or NQPT keywords (see Section 2.6.16)
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
198
SAVE COMMAND 2.6.15 SAVE command (Required when SAVE is specified on the GLOBAL command) Purpose
This command is used to supply output filenames. The filenames must be less than 128 characters long and may contain a drive prefix, a path name, and an extension. The filename must be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. All output files other than the MASTER and CALIB files are saved in a formatted form. See Section 2.6.20 on output files for more information. Note that, in order to use the SAVE command, the SAVE option must be included in the GLOBAL command. Format >SAVE MASTER=n, CALIB=n, PARM=n, SCORE=n, COVARIANCE=n, TSTAT=n, POST=n, EXPECTED=n, ISTAT=n, DIF=n, DRIFT=n, PDISTRIB=n;
Example
In the syntax below, the item parameters and scale scores are saved to file through use of the SCORE and PARM keywords on the SAVE command. Note that, in order to use the SAVE command, the SAVE keyword is added to the GLOBAL command. >GLOBAL DFNAME=’EXAMPLE.DAT’, NPARM=2, SAVE; >SAVE SCORE=’EXAMPLE.SCO’, PARM=’EXAMPLE.PAR’; >LENGTH NITEMS=(40);
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
CALIB keyword (optional) Purpose
To specify a filename for the calibration data file that is to be saved. The original response data are sampled and calibrated, then saved as a temporary binary file. If no sampling occurs, this temporary file cannot be created. Upon normal termination of the program this temporary file is deleted automatically. By assigning a specific name to the calibration data file, the user can save and reuse it as a master data file in subsequent analyses.
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2 BILOG-MG REFERENCE Format CALIB=
Default
Do not save. Example
The calibration file is saved to exampl03.cal using the CALIB keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; CALIB='EXAMPL03.CAL';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
COVARIANCE keyword (optional) Purpose
To specify a filename for the external file to which the covariances of item parameter estimates for each item are written. This file is written automatically in the calibration phase (Phase 2) as a temporary file, which passes necessary information to the scoring phase (Phase 3). Normally, it is deleted at the termination of the program, but by assigning a specific name to this file the user can save it as a permanent file. Format COVARIANCE=
Default
Do not save. Example
A covariance file from a previous calibration can be used to compute test information by specifying the name of the file with the COVARIANCE keyword on the SAVE command. During the scoring phase, the item information indices will be added to this file if requested. This feature is intended for use when scoring is based on a previously created item parameter file. 200
SAVE COMMAND It must be used in conjunction with an IFNAME specification on the GLOBAL command, as shown below: >GLOBAL IFNAME=’EXAMPLE.PAR’, SAVE; >SAVE COV=’EXAMPLE.COV’;
Related topics
GLOBAL command: SAVE option (see Section 2.6.7) GLOBAL command: IFNAME keyword
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
DIF keyword (optional) Purpose
To specify a filename for saving the DIF parameters if requested and computed during the calibration phase (Phase 2) to an external file. Format DIF=
Default
Do not save. Example
The DIF parameters are saved to the file exampl03.dif using the DIF keyword on the SAVE command. >GLOBAL >SAVE >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; DIF='EXAMPL03.DIF'; NGROUPS=2, DIF, …;
Related topics
GLOBAL command: SAVE option (see Section 2.6.7) INPUT command: DIF option (see Section 2.6.9)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
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2 BILOG-MG REFERENCE DRIFT keyword (optional) Purpose
To specify a filename for saving the DRIFT parameters computed during the calibration phase (Phase 2) to an external file. Format DRIFT=
Default
Do not save. Example
In the following example, the DRIFT parameters are saved to the file exampl03.dri using the DRIFT keyword on the SAVE command. >GLOBAL >SAVE >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; DRIFT='EXAMPL03.DRI'; NGROUPS=2, DRIFT, …;
Related topics
GLOBAL command: SAVE option (see Section 2.6.7) INPUT command: DRIFT option (see Section 2.6.9)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
EXPECTED keyword (optional) Purpose
To specify the filename to which the expected frequencies of correct responses, attempts, and proportions of correct responses, attempts, and proportions of correct responses for each item at each quadrature point by group will be saved. This file will also contain standardized posterior residuals and model proportions of correct responses. Format EXPECTED=
202
SAVE COMMAND Default
Do not save. Example
In the following example, the expected frequencies are saved to exampl03.frq using the EXPECTED keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; EXPECTED='EXAMPL03.FRQ';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
ISTAT keyword (optional) Purpose
To specify a filename for saving the classical item statistics computed in Phase 1 of the program to an external file. Format ISTAT=
Default
Do not save. Example
The classical item statistics are saved to the file exampl03.sta using the ISTAT keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; ISTAT='EXAMPL03.STA';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
203
2 BILOG-MG REFERENCE MASTER keyword (optional) Purpose
To specify a filename for the master data file. The original response data are scored and stored as a temporary binary file. Upon normal termination of the program this temporary file is deleted automatically. By assigning a specific name to this master data file, the user can save and reuse it as an input file in subsequent analyses. Format MASTER=
Default
Do not save. Example
The master file is saved to exampl03.mas using the MAS keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; MAS='EXAMPL03.MAS';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) SAVE command: CALIB keyword Save menu (see Section 2.3.6)
PARM keyword (optional) Purpose
To specify a filename for the item parameter file. Item parameter estimates are saved in a formatted form as an external output file. This file can be used as initial estimates of item parameters for further iterations or as final estimates of the item parameters for scoring new data. In either case, the user must specify the name of the previously created item parameter file using the IFNAME keyword of the GLOBAL command.
204
SAVE COMMAND Format PARM=
Default
Do not save. Example
In the syntax below, the item parameters are saved to file through use of the SCORE and PARM keywords on the SAVE command. Note that, in order to use the SAVE command, the SAVE option is added to the GLOBAL command. >GLOBAL DFNAME=’EXAMPLE.DAT’, NPARM=2, SAVE; >SAVE PARM=’EXAMPLE.PAR’; >LENGTH NITEMS=(40);
The use of this file as initial estimates for further iterations is illustrated in the syntax below: >GLOBAL DFNAME=’EXAMPLE.DAT’, IFNAME=’EXAMPLE.PAR’, NPARM=2; >LENGTH NITEMS=(40);
Related topics
GLOBAL command: IFNAME keyword (see Section 2.6.7) GLOBAL command: SAVE option
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
PDISTRIB keyword (optional) Purpose
To save the points and weights of the posterior latent distribution at the end of Phase 2 to an external file. These quantities can be included as prior values following the SCORE command for later EAP estimation of ability from previously estimated item parameters. Format PDISTRIB=
Default
Do not save.
205
2 BILOG-MG REFERENCE Related topics
SCORE command (see Section 2.6.16)
Save menu (see Section 2.3.6)
POST keyword (optional) Purpose
To save the case weight and marginal probability for each observation to an external output file. Format POST=
Default
Do not save. Example
The case weights and marginal probabilities are saved to the file exampl03.pos using the POST keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; POST='EXAMPL03.POS';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
SCORE keyword (optional) Purpose
To specify a filename when the score file is to be saved. Format SCORE=
206
SAVE COMMAND Default
Do not save. Example
In the following example, the score file is saved to exampl03.sco using the SCORE keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; SCORE='EXAMPL03.SCO';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
TSTAT keyword (optional) Purpose
To specify a filename when the tables of test information statistics are to be saved. Format TSTAT=
Default
Do not save. Example
The test information statistics file is saved to exampl03.tsa using the TSTAT keyword on the SAVE command. >GLOBAL >SAVE
DFNAME='EXAMPL03.DAT',NPARM=2, SAVE; TSTAT='EXAMPL03.TSA';
Related topics
GLOBAL command: SAVE option (see Section 2.6.7)
Output files (see Section 2.6.20) Save menu (see Section 2.3.6)
207
2 BILOG-MG REFERENCE 2.6.16 SCORE command (Optional) Purpose
To initiate the scoring of individual examinees or of response patterns; to compute item and test information and plot information curves; to rescale scores to a specified mean and standard deviation in either the sample or the latent distribution. Format >SCORE METHOD=n, NQPT=(list), IDIST=n, PMN=(list), PSD=(list), RSCTYPE=n, LOCATION=(list), SCALE=(list), INFO=n, BIWEIGHT, FIT, NOPRINT, YCOMMON, POP, MOMENTS, FILE, READF, REFERENCE=n, NFORMS=n;
Examples
The aggregate scores for the following analysis of school-level data are estimated by the EAP method using the empirical distributions from Phase 2. The number of quadrature points is set to 12 per subtest. The scores are rescaled to a mean of 250 and a standard deviation of 50 in the latent distribution of schools (IDIST=3, LOCATION=250, SCALE=50). The fit of the data to the grouplevel model is tested for each school (FIT). >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
The next SCORE command gives the specifications for a scoring phase that includes an information analysis (INFO=2) with expected information indices for a normal population (POP). Rescaling of the scores and item parameters to mean 0 and standard deviation 1 in the estimated latent distribution has been requested (RSC=3). Printing of the students' scores on the screen is suppressed (NOPRINT). >SCORE NQPT=6, NOPRINT, RSCTYPE=3, INFO=2, POP;
In the following SCORE command, the EAP scale scores of Phase 3 are computed from the responses to items in the main test as specified by setting METHOD to 2. Printing of scores is suppressed (NOPRINT). >SCORE
METHOD=2,NOPRINT;
In this score command, Maximum Likelihood estimates of ability (METHOD=1) are rescaled to a mean of 250 and standard deviation of 50 in Phase 3 (RSCTYPE=3, LOCATION=250, SCALE=50).
208
SCORE COMMAND >SCORE METHOD=1, RSCTYPE=3, LOCATION=(250.0000), SCALE=(50.0000), INFO=1, NOPRINT;
Related topics
Phase 2: CALIBRATE (see Section 2.2) Setup menu: Test Scoring dialog box (see Section 2.3.3) Technical menu: Score Options dialog box (see Section 2.3.5) Technical menu: Scoring Prior Latent Distribution dialog box
BIWEIGHT option (optional) Purpose
To request the calculation of biweighted estimates robust to isolated deviant responses. (See also Mislevy & Bock, 1982.) Format BIWEIGHT
Related topics
Setup menu: Test Scoring dialog box (see Section 2.3.3)
DOMAIN keyword (optional) Purpose
To convert the Phase 3 estimates into domain scores if the user supplies a file containing the item parameters for a sample of previously calibrated items. The FILE keyword on the SCORE command is used to specify this parameter file. Weights can be applied to the items to improve the representation of the domain specifications. This conversion may be useful as an aid to the interpretation of test results (see Bock, Thissen, & Zimowski (1997).) Note that the formula for the domain scores that appears in the paper cited here contains typographical errors. The computation of the domain scores in the program uses the corrected formula. The domain scores will appear in the score listing following the test percent correct score for each case in the Phase 3 output file. Format DOMAIN=n
where n represents the number of items in the domain.
209
2 BILOG-MG REFERENCE Default
No domain scores. Related topics
SCORE command: FILE keyword
Technical menu: Score Options dialog box (see Section 2.3.5)
FILE keyword (required if DOMAIN keyword is used) Purpose
To specify the name of the file containing the item parameters to be used for the domain score conversions. The first line of the file referenced by the FILE keyword must contain a variable format statement (in parentheses) describing the column layout of the weights and parameter in the following lines of the file. The values must be read in order—item weight, slope, threshold, and guessing parameter. The weights will be automatically scaled to sum to 1.0 by the program. The domain score will appear in the score listing following the test percent correct score for each case. Note that the parameter file produced by the SAVE command does not have the layout described above. Format FILE=
Default
No domain scores or supplied file. Related topics
SCORE command: DOMAIN keyword
Technical menu: Score Options dialog box (see Section 2.3.5)
FIT option (optional) Purpose
To request the computation of a likelihood ratio χ 2 goodness-of-fit statistic for each response pattern. This statistic is intended only for use with aggregate-level data.
210
SCORE COMMAND Format FIT
Default
No fit statistic. Example
The aggregate scores for this analysis of school-level data are estimated by the EAP method using the empirical distributions from Phase 2. The fit of the data to the group-level model is tested for each school (FIT). >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
Related topics Setup menu: Test Scoring dialog box (see Section 2.3.3)
IDIST keyword (optional) Purpose
To designate the type of prior distribution of scale scores. IDIST = 0 applies to both MAP and EAP estimation. IDIST = 1, 2, 3, or 4 applies only to EAP estimation. Format IDIST=n n=0 n=1
Standard normal approximation. Separate, arbitrary discrete priors for each group for each subtest read from QUADSj command. n = 2 Separate arbitrary discrete priors for each group read from QUADSj command. n = 3 Empirical prior estimated during Phase 2. n = 4 35-point rectangular prior on the interval ± 3.5. These scores may be transformed to the 150 – 850 range by setting LOCATION = 500.0 and SCALE = 100.0.
Default
0.
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2 BILOG-MG REFERENCE Examples
In the following aggregate-level example, IDIST=3 is used to estimate scores by the EAP method by using the empirical distributions from Phase 2. >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
In the next example, EAP estimates of ability are calculated (METHOD=2) using the information in the posterior distributions from Phase 2 (IDIST=3). The ability estimates are rescaled to a mean of 0 and standard deviation of 1 by specifying RSCTYPE=3 on the SCORE command. >SCORE
METHOD=2, IDIST=3, NOPRINT, RSCTYPE=3;
Related topics
QUADS command (see Section 2.6.14)
Setup menu: Test Scoring dialog box (see Section 2.3.3) Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
INFO keyword (optional) Purpose
To select information output. Format INFO=n n=0 n=1 n=2
none test information curves test information curves and table of information statistics
Default
0. Examples
The following SCORE command gives the specifications for a scoring phase that includes an information analysis (INFO=2) with expected information indices for a normal population (POP). >SCORE NQPT=6, NOPRINT, RSCTYPE=4, INFO=2, POP;
212
SCORE COMMAND In the following SCORE command, Maximum Likelihood estimates of ability (METHOD=1) are rescaled to a mean of 250 and standard deviation of 50 in Phase 3. >SCORE METHOD=1, RSCTYPE=3, LOCATION=(250.0000), SCALE=(50.0000), INFO=1, NOPRINT;
Related topics
SCORE command: POP and YCOMMON options
LOCATION keyword (optional) Purpose
To specify real-valued location constants (with decimal points) for rescaling. Format LOCATION=( n1 , n2 ,..., nNTEST )
Default
0.0. Examples
The scores are rescaled to a mean of 250 and a standard deviation of 50 in the latent distribution of schools (IDIST=3, LOCATION=250, SCALE=50). The fit of the data to the grouplevel model is tested for each school (FIT). >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
In the next SCORE command, Maximum Likelihood estimates of ability (METHOD=1) are rescaled to a mean of 250 and standard deviation of 50 in Phase. >SCORE METHOD=1, RSCTYPE=3, LOCATION=(250.0000), SCALE=(50.0000), INFO=1, NOPRINT;
Related topics
SCORE command: RSCTYPE keyword SCORE command: SCALE keyword
Setup menu: Test Scoring dialog box (see Section 2.3.3)
213
2 BILOG-MG REFERENCE METHOD keyword (optional) Purpose
To specify the method of estimating scale scores. If ML is selected, it is advisable to use the PMN keyword to set bounds on the estimated scores. If EAP or MAP is selected, the PMN and PSD keywords may be used to specify the means and standard deviations of the prior distributions. Format METHOD=n n=1 n=2 n=3
Maximum likelihood (ML) Expected a posteriori (EAP) (Bayes) Maximum a posteriori (MAP) (Bayes modal)
Default
2. Examples
In this score command, Maximum Likelihood estimates of ability (METHOD=1) are rescaled to a mean of 250 and standard deviation of 50 in Phase 3 (RSCTYPE=3, LOCATION=250, SCALE=50). >SCORE METHOD=1, RSCTYPE=3, LOCATION=(250.0000), SCALE=(50.0000), INFO=1, NOPRINT;
Related topics
SCORE command: PMN keyword SCORE command: PSD keyword
Setup menu: Test Scoring dialog box (see Section 2.3.3)
MOMENTS option (optional) Purpose
To request the computation and listing of the coefficients of skewness and kurtosis of the ability estimates and of the latent distribution.
214
SCORE COMMAND Format MOMENTS
Default
No computation or listing. Examples
The MOMENTS keyword on the SCORE commands below is used to obtain the coefficients of skewness and kurtosis for the rescaled ability. >SCORE
NQPT=11,RSCTYPE=3,LOCATION=250,SCALE=50,NOPRINT,INFO=1, POP,MOMENT;
>SCORE
IDIST=3,RSCTYPE=3,INFO=1,YCOMMON,POP,NOPRINT,MOMENTS;
Related topics
Technical menu: Score Options dialog box (see Section 2.3.5)
NFORM keyword (optional) Purpose
To indicate the number of additional FORM commands after the SCORE command. It is used when scoring is to be performed using these additional form specifications. The reference form for scoring is set using the REFERENCE keyword on the SCORE command. Format NFORMS=n
Default
No additional FORM commands are expected. Example
In the example below, two additional form commands follow the SCORE command. The first is the references group (as set by the REFERENCE keyword) while the READF keyword instructs the program to read and process the additional FORM commands. >SCORE IDIST=3,RSCTYPE=3,INFO=1,YCOMMON,POP,NOPRINT,REF=1,NFORMS=2,READF; >FORM1 LENGTH=25,INUM=(1(1)25); >FORM2 LENGTH=25,INUM=(21(1)45);
215
2 BILOG-MG REFERENCE Related topics
FORM command (see Section 2.6.6) SCORE command: READF option SCORE command: REFERENCE keyword
NOPRINT option (optional) Purpose
To suppress the display of the scores on screen and in the printed output of Phase 3. To shorten the run time for scoring a large subject response file, it is advisable to specify an external file using the SCORE keyword in the SAVE command, and the NOPRINT option. In this way, scores for all subjects are computed but are stored only in the external file. Format NOPRINT
Default
Scores will appear both on screen and in the Phase 3 output file. Examples
The EAP scale scores of Phase 3 are computed from the responses to items in the main test as specified by setting METHOD to 2. Printing of scores is suppressed (NOPRINT). >SCORE
METHOD=2, NOPRINT;
Related topics
Setup menu: Test Scoring dialog box (see Section 2.3.3)
NQPT keyword (optional) Purpose
To set the number of quadrature points for each subtest with the NQPT keyword when EAP estimation is selected by the METHOD keyword. To reduce computing time when there are items not-presented, use 2 x square root of the maximum number of items per respondent as the number of quadrature points.
216
SCORE COMMAND Format NQPT=( n1 , n2 ,..., nNTEST )
Default
Computed by program as a function of number of items in complete data. Example
The aggregate scores for this analysis of school-level data are estimated by the EAP method using the empirical distributions from Phase 2. The number of quadrature points is set to 12 per subtest. >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) SCORE command: METHOD keyword
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
PMN keyword (optional) Purpose
To specify real-numbered means (with decimal points) of the normal prior distributions for each group for each subtest. Format PMN=( n1,1 , n1,2 ,..., n1, NGROUP , n2,1 , n2,2 ,..., n2, NGROUP ,..., nNTEST ,1 , nNTEST ,2 ,..., nNTEST , NGROUP )
Default
0.0. Example
In the following two-group analysis for one subtest, the PMN and PSD keywords are used on the SCORE command to provide the means and standard deviations of the normal prior distributions for each subtest. >SCORE PMN=(0.00051,-0.16191), PSD=(0.00001,0.89707);
217
2 BILOG-MG REFERENCE Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) SCORE command: PSD keyword
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
POP option (optional) Purpose
To request the calculation of the expected information for the population when INFO > 0. This includes an estimate of the classical reliability coefficient for each subtest. The score metric after rescaling is used in these calculations. Format POP
Default
No expected information calculated for population. Example
This SCORE command gives the specifications for a scoring phase that includes an information analysis (INFO=2) with expected information indices for a normal population (POP). Rescaling of the scores and item parameters to mean 0 and standard deviation 1 in the estimated latent distribution has been requested (RSC=3). Printing of the students' scores on the screen is suppressed (NOPRINT). >SCORE NQPT=6, NOPRINT, RSCTYPE=4, INFO=2, POP;
Related topics
SCORE command: INFO keyword
Phase 3: SCORING (see Section 2.2)
PSD keyword (optional) Purpose
To specify real-numbered standard deviations (with decimal points) of the normal prior distributions for each group for each subtest.
218
SCORE COMMAND Format PSD=( n1,1 , n1,2 ,..., n1, NGROUP , n2,1 , n2,2 ,..., n2, NGROUP ,..., nNTEST ,1 , nNTEST ,2 ,..., nNTEST , NGROUP )
Default
1.0. Example
In the following two-group analysis for one subtest, the PMN and PSD keywords are used on the SCORE command to provide the means and standard deviations of the normal prior distributions for each subtest. >SCORE PMN=(0.00051, -0.16191), PSD=(0.00001, 0.89707);
Related topics
GLOBAL command: NTEST keyword (see Section 2.6.7) INPUT command: NGROUP keyword (see Section 2.6.9) SCORE command: PMN keyword
Technical menu: Scoring Prior Latent Distribution dialog box (see Section 2.3.5)
READF option (optional) Purpose
To indicate the presence of multiple FORM commands after the SCORE command. It is used to indicate that scoring is to be performed using this form specification. The reference form for scoring is set using the REFERENCE keyword on the SCORE command. Format READF
Default
No additional FORM commands are expected. Example
In the example below, two additional form commands follow the SCORE command. The first is the references group (as set by the REFERENCE keyword) while the READF keyword instructs the program to read and process the additional FORM commands.
219
2 BILOG-MG REFERENCE >SCORE IDIST=3,RSCTYPE=3,INFO=1,YCOMMON,POP,NOPRINT,REF=1,NFORMS=2,READF; >FORM1 LENGTH=25,INUM=(1(1)25); >FORM2 LENGTH=25,INUM=(21(1)45);
Related topics
FORM command (see Section 2.6.6) SCORE command: REFERENCE and NFORM keywords
REFERENCE keyword (optional) Purpose
To set the reference group for scoring when scoring is performed by forms, as specified with the READF and NFORM keywords on the same command. Note that, if this keyword is omitted while the READF and NFORM keywords are present, the reference form specified in the CALIB command will be used. Format REFERENCE=n
Default
Set by REFERENCE keyword on CALIB command. Example
In the example below, two additional form commands follow the SCORE command. The first is the references group (as set by the REFERENCE keyword) while the READF keyword instructs the program to read and process the additional FORM commands. >SCORE IDIST=3,RSCTYPE=3,INFO=1,YCOMMON,POP,NOPRINT,REF=1,NFORMS=2,READF; >FORM1 LENGTH=25,INUM=(1(1)25); >FORM2 LENGTH=25,INUM=(21(1)45);
Related topics
CALIB command: REFERENCE keyword (see Section 2.6.3) SCORE command: READF option SCORE command: NFORM keyword
220
SCORE COMMAND RSCTYPE keyword (optional) Purpose
To specify the type of rescaling required. Format RSCTYPE=n
Uses the LOCATION and SCALE constants specified by the options below. Note that there is no option 2. 0: no rescaling 1: linear transformation of scores: new score = SCALE x old score + LOCATION 3: rescale to SCALE and LOCATION in the sample of scale score estimates 4: only if EAP estimation has been selected: Set the mean of the latent population distribution equal to LOCATION and set the standard deviation equal to SCALE.
Default
0. Examples
The aggregate scores for this analysis of school-level data are estimated by the EAP method using the empirical distributions from Phase 2. The number of quadrature points is set to 12 per subtest. The scores are rescaled to a mean of 250 and a standard deviation of 50 in the latent distribution of schools (IDIST=3, LOCATION=250, SCALE=50). The fit of the data to the group-level model is tested for each school (FIT). >SCORE NQPT=(12, 12), IDIST=3, RSCTYPE=4, LOCATION=(250.0000, 250.0000), SCALE=(50.0000, 50.0000), FIT;
Related topics
SCORE command: LOCATION and SCALE keywords
Setup menu: Test Scoring dialog box (see Section 2.3.3)
SCALE keyword (optional) Purpose
To specify real-valued scale constants (with decimal points) for rescaling.
221
2 BILOG-MG REFERENCE Format SCALE=( n1 , n2 ,..., nNTEST )
Default
1.0. Examples
In the following example, Maximum Likelihood estimates of ability (METHOD=1) are rescaled to a mean of 250 and standard deviation of 50 in Phase 3 (RSCTYPE=3, LOCATION=250, SCALE=50). >SCORE METHOD=1, RSCTYPE=3, LOCATION=(250.0000), SCALE=(50.0000), INFO=1, NOPRINT;
Related topics
SCORE command: LOCATION and RSCTYPE keywords
Setup menu: Test Scoring dialog box (see Section 2.3.3)
YCOMMON option (optional) Purpose
To specify that the test information curves for subtests should be expressed in comparable units when INFO > 0. If INFO = 0, the curves for subsets will be adjusted separately to make each plot fill the available space. Format YCOMMON
Default
Plots adjusted separately. Example
The following SCORE command specifies a scoring phase that includes an information analysis (INFO=2) with expected information indices for a normal population (POP).
222
SCORE COMMAND
Test information curves for subtests will be expressed in comparable units and printed to the Phase 3 output file. >SCORE INFO=2, POP, YCOMMON;
Related topics
SCORE command: INFO keyword SCORE command: POP option
223
2 BILOG-MG REFERENCE 2.6.17 TEST command (Required) Purpose
To identify the main test items and the variant test items (if any) in each of the NTEST subtests. If the subtest contains only main test items, there is only one TEST command for that subtest. If there are variant items in the subtest, two TEST commands are required for that subtest. The first describes the main test items, while the second describes the variant test items. There are as many TEST commands as there are main and variant subtests specified in the NTEST and NVTEST keywords of the GLOBAL command. Items may be identified by name or number, but not by both. The names or numbers must correspond to those listed in the ITEMS command. If numbers are supplied, the program will refer to the names supplied in the ITEMS command only for printing of item information. Starting values for estimating the item parameters may also be supplied in the TEST command. Note that parameter estimation for variant items is non-iterative and does not require starting values. Format >TEST TNAME=n, INUMBER=(list), INAME=(list), INTERCPT=(list), SLOPE=(list), THRESHLD=(list), GUESS=(list), DISPERSN=(list), FIX=(list);
Default
All items are used. Examples
In the example below, two subtests are used, each with 8 items. The NTEST keyword on the GLOBAL command indicates that two subtests are to be used, and two TEST commands follow the ITEMS command. The TEST commands are assigned names through the TNAME keyword and items are referenced by number. >GLOBAL >LENGTH >INPUT >ITEMS >TEST1 >TEST2
NPARM=3,NTEST=2,DFNAME='EXAMPL08.DAT'; NITEMS=(8,8); NTOTAL=16,NALT=5,NIDCHAR=9,TYPE=3; INUMBERS=(1(1)16),INAMES=(N1(1)N8,A1(1)A8); TNAME=NUMCON,INUMBERS=(1(1)8); TNAME=ALGCON,INUMBERS=(9(1)16);
In the next example, the ITEMS command lists the four items in the order that they will be read from the data records. The INAMES and INUMBERS keywords assign each item a name and a corresponding number. Because there is only one form, the NFORM keyword is not re224
TEST COMMAND quired in the INPUT command and a FORM command is not required. Because examinees in both groups are presented all the items listed in the ITEMS command, the TEST command needs contain only the test name. >GLOBAL >LENGTH >INPUT >ITEMS >TEST >GROUP1 >GROUP2
NPARM=1,NWGHT=3,LOGISTIC; NITEMS=4; NTOTAL=4,NGROUPS=2,DIF,NIDCHAR=2,TYPE=2; INAMES=(SP1(1)SP4),INUMBERS=(1(1)4); TNAME=SPELL; GNAME=MALES; GNAME=FEMALES;
Related topics
GLOBAL command NTEST and NVTEST keywords (see Section 2.6.7) ITEMS command (see Section 2.6.10)
Setup menu: General dialog box (see Section 2.3.3) Setup menu: Item Analysis dialog box Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5) Technical menu: Assign Fixed Items dialog box
DISPERSN keyword (optional) Purpose
To specify positive real-numbered starting values for dispersion (2- and 3-parameter models only). Starting values may be specified for slopes or for dispersions, but not for both. Format DISPERSN=( n1 , n2 ,..., nn ( i ) )
Default
1/slope. Example
In the syntax below, starting values for the dispersion and intercepts of the four items considered in this 3-parameter model are provided on the TEST command. EXAMPLE: USING STARTING VALUES >GLOBAL NPARM=3,LOGISTIC'; >LENGTH NITEMS=4; >INPUT NTOTAL=4,NIDCHAR=2;
225
2 BILOG-MG REFERENCE >ITEMS >TEST
INAME=(SP01,SP02,SP03,SP04),INUMBERS=(1(1)4); TNAME=SPELL, INTERCPT = (1.284,0.287,-1.912,-0.309), DISPERSN=(0.957,0.623,0.545,0.620);
Related topics
Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5) TEST command: SLOPE keyword
FIX keyword (optional) Purpose
To specify whether the parameters of specific items are free to be estimated or are to be held fixed at their starting values. This keyword appears in the j-th TEST command as FIX=( n1 , n2 ,..., nLENGTH ( j ) ) where
ni = 0 if parameters of item i of test j are free to be estimated, or ni =1 if these item parameters are to be held fixed at their starting values. The starting values may be entered by the SLOPE, THRESHLD, and GUESSING keywords of the j-th TEST command, or read from an existing item parameter file (IFNAME) designated by IFNAME= on the GLOBAL command and saved in a previous job by the PARM= keyword on the SAVE command; or, alternatively, read from a file of provisional item parameters, designated by the PRNAME= keyword on the GLOBAL command. When only a few items are to be fixed, this method is the most convenient. If all items are designated as fixed, and the INFO keyword appears on the SCORE command, the required information and reliability analysis will be performed in Phase 3. In order for this procedure to work, however, the program must have data to process in Phases 1 and 2 for at least a few cases. Some artificial response data can be used for this purpose. The only calculations that will be performed in Phase 2 are preparations for the information analysis in Phase 3. The number of EM cycles in the CALIB command can therefore be set to 2 and the number of NEWTON cycles to 1. The NOADJUST option must also be invoked.
Format FIX=( u1 , u2 ,..., u LENGTH ( j ) )
Default
Do not fix.
226
TEST COMMAND Example
The following command file shows the fixing of five items by specifying values in a PRNAME file. EXAMPLE 16: TRADITIONAL IRT ANALYSIS OF A FIFTEEN-ITEM TEST PARAMETERS OF ITEMS 6 THROUGH 10 ARE FIXED >GLOBAL NPARM=3, DFNAME='EXAMPL07.DAT',PRNAME='EXAMPL7f.PRM',SAVE; >SAVE SCORE='EXAMPL7.SCO',PARM='EXAMPL7.PAR'; >LENGTH NITEMS=15; >INPUT NTOTAL=15,NALT=5,KFNAME='EXAMPL07.KEY',SAMPLE=600,NIDCHAR=4; >ITEMS INAME=(MATH01(1)MATH15); >TEST TNAME=PRETEST,FIX=(0(0)5,1(0)5,0(0)5); (2X,4A1,T25,15A1) >CALIB CYCLES=15,NEWTON=3,NQPT=11,NOADJUST; >SCORE NQPT=11,RSCTYPE=3,LOCATION=250,SCALE=50,NOPRINT,INFO=1,POP;
The exampl7f.prm file contains the following 6 lines: 5 6 7 8 9 10
1.27168 1.79009 0.81238 1.33017 1.06557
0.10504 0.10221 0.24523 -0.22387 0.58430
0.14011 0.07543 0.22179 0.15453 0.08921
DIAGNOS has been set equal to 1 to produce more detailed output which show that these val-
ues do not change during the Phase 2 estimation cycles. They will, of course, be rescaled along with those of the estimated items in Phase 3. Related topics
CALIB command: NOADJUST option (see Section 2.6.30) GLOBAL command: IFNAME and PRNAME keywords (see Section 2.6.7) SAVE command: PARM keyword (see Section 2.6.15) SCORE command: INFO keyword (see Section 2.6.16) TEST command: GUESS, SLOPE, and THRESHLD keywords
Technical menu: Assign Fixed Items dialog box (see Section 2.3.5)
GUESS keyword (optional) Purpose
To specify starting values for the lower asymptote (guessing) parameters (3-parameter model only). These values should be positive fractional numbers with decimal points.
227
2 BILOG-MG REFERENCE Format GUESS=( n1 , n2 ,..., nn ( i ) )
Default
0.0. Example
In the syntax below, starting values for the slopes and guessing parameters of the four items considered in this 3-parameter model are provided on the TEST command. EXAMPLE: USING STARTING VALUES >GLOBAL NPARM=3,LOGISTIC'; >LENGTH NITEMS=4; >INPUT NTOTAL=4,NIDCHAR=2; >ITEMS INAME=(SP01,SP02,SP03,SP04),INUMBERS=(1(1)4); >TEST TNAME=SPELL, SLOPE=(1.045,1.604,1.836,1.613), GUESS=(0.189,0.168,0.101,0.152);
Related topics
GLOBAL command: NPARM keyword (see Section 2.6.7)
Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5)
INAMES keyword (optional) Purpose
To provide a list of names, as specified in the ITEMS command for items in TEST. Item names that do not begin with letters must be enclosed in single quotes. Format INAME=( n1 , n2 ,..., nn ( i ) )
Default
If NTEST =1, and NVTEST = 0, all NTOTAL items are as specified in the INPUT command. There is no default if NTEST > 1 or NVTEST ≠ 0.
228
TEST COMMAND Example
In the following example, responses to 50 items are read from those of 100 items in the data file. From the 50, 20 are selected as Main Test items and 4 as Variant Test items. Items for the main test are selected by name in the TESTM command; items for the variant test are selected by name in the TESTV command. The item names correspond to the sequence numbers in the original set of 100 items. >GLOBAL >LENGTH >INPUT >ITEMS >TESTM
>TESTV
DFNAME='EXAMPL06.DAT', NTEST=1,NVTEST=1,NPARM=2; NITEM=24,NVARIANT=4; NTOTAL=50,KFNAME='EXAMPL06.DAT',SAMPLE=200,NIDCH=11; INUMBERS=(1(1)50),INAME=(I26(1)I75); TNAME=MAINTEST, INAMES=(I26,I27,I28,I29,I31,I33,I34, I35,I36,I38,I39,I47,I48,I49,I50,I54,I60,I64,I68,I72); TNAME=VARIANT,INAMES=(I53,I59,I69,I73);
Related topics
GLOBAL command: NTEST and NVTEST keywords (see Section 2.6.7) INPUT command: NTOTAL keyword (see Section 2.6.9) ITEMS command (see Section 2.6.10) LENGTH command (see Section 2.6.11)
Setup menu: General dialog box (see Section 2.3.3)
INTERCPT keyword (optional) Purpose
To specify real-numbered starting values (with decimal points) for estimating the item intercept. Starting values may be specified for intercepts or for thresholds, but not for both. Format INTERCPT=( n1 , n2 ,..., nn ( i ) )
Default
Supplied by the program. Example
In the syntax below, starting values for the intercepts of the four items considered in this 3parameter model are provided on the TEST command.
229
2 BILOG-MG REFERENCE EXAMPLE: USING STARTING VALUES >GLOBAL NPARM=3,LOGISTIC'; >LENGTH NITEMS=4; >INPUT NTOTAL=4,NIDCHAR=2; >ITEMS INAME=(SP01,SP02,SP03,SP04),INUMBERS=(1(1)4); >TEST TNAME=SPELL,INTERCPT = (1.284,0.287,-1.912,-0.309);
Related topics
TEST command: THRESHLD keyword
Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5)
INUMBERS keyword (optional) Purpose
To provide a list of numbers, as specified in the ITEMS command for items in TEST. If TEST refers to main test items, n(i) is the number of main test items. If TEST refers to variant test items, n(i) is the number of variant test items. The notation “first (increment) last” in these lists may be used when the item numbers form an arithmetic progression. Format INUMBER=( n1 , n2 ,..., nn ( i ) )
Default
If NTEST=1, and NVTEST=0, all NTOTAL items as specified in the INPUT command. There is no default if NTEST>1 of NVTEST ≠ 0. Examples
For the case where NTEST=1 and NVTEST=1 in the GLOBAL command, NITEMS=10 and NVARIANT=4 in the LENGTH command, and NTOT=10 in the INPUT command, the main test items of subtest i might be specified in the first TEST command with ITEMS=(1,2,3,6,8,10). The variant test items of subtest i might be specified in the second TEST command with ITEMS=(4,5,7,9). In the example below, two subtests are used, each with 8 items. The NTEST keyword on the GLOBAL command indicates that two subtests are to be used, and two TEST commands follow the ITEMS command. The subtests are assigned names through the TNAME keyword and items are referenced by number.
230
TEST COMMAND >GLOBAL >LENGTH >INPUT >ITEMS >TEST1 >TEST2
NPARM=3,NTEST=2,DFNAME='EXAMPL08.DAT'; NITEMS=(8,8); NTOTAL=16,NALT=5,NIDCHAR=9,TYPE=3; INUMBERS=(1(1)16),INAMES=(N1(1)N8,A1(1)A8); TNAME=NUMCON,INUMBERS=(1(1)8); TNAME=ALGCON,INUMBERS=(9(1)16);
Related topics
GLOBAL command: NTEST or NVTEST keywords (see Section 2.6.7) ITEMS command (see Section 2.6.10) LENGTH command (see Section 2.6.11)
Setup menu: Item Analysis dialog box (see Section 2.3.3)
SLOPE keyword (optional) Purpose
To provide starting values for slopes (2- and 3-parameter models only). These starting values should be positive, real numbers with decimal points. Starting values may be specified for slopes or for dispersions, but not for both. Format SLOPE=( n1 , n2 ,..., nn ( i ) )
Default
1.0. Example
In the syntax below, starting values for the intercepts and slopes of the four items considered in this 3-parameter model are provided on the TEST command. EXAMPLE: USING STARTING VALUES >GLOBAL NPARM=3,LOGISTIC'; >LENGTH NITEMS=4; >INPUT NTOTAL=4,NIDCHAR=2; >ITEMS INAME=(SP01,SP02,SP03,SP04),INUMBERS=(1(1)4); >TEST TNAME=SPELL, INTERCPT = (1.284,0.287,-1.912,-0.309), SLOPE=(1.045,1.604,1.836,1.613);
231
2 BILOG-MG REFERENCE Related topics
Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5) TEST command: DISPERSN keyword
THRESHLD keyword (optional) Purpose
To specify real-numbered starting values (with decimal points) for estimating the item thresholds. Starting values may be specified for intercepts or for thresholds, but not for both. Format THRESHLD=( n1 , n2 ,..., nn ( i ) )
Default
0.0. Example
In the syntax below, starting values for the slopes and thresholds of the four items considered in this 3-parameter model are provided on the TEST command. EXAMPLE: USING STARTING VALUES >GLOBAL NPARM=3,LOGISTIC'; >LENGTH NITEMS=4; >INPUT NTOTAL=4,NIDCHAR=2; >ITEMS INAME=(SP01,SP02,SP03,SP04),INUMBERS=(1(1)4); >TEST TNAME=SPELL, SLOPE=(1.045,1.604,1.836,1.613), THRESHLD=(-1.229,-0.179,1.041,0.192);
Related topics
Technical menu: Item Parameter Starting Values dialog box (see Section 2.3.5) TEST command: INTERCPT keyword
TNAME keyword (optional) Purpose
To supply a name for subtest i (up to eight characters), if there are not variant test items in subtest i; or name of the main test items in subtest i, if there are variant test items in subtest i. 232
TEST COMMAND Format TNAME=character string
Default
None. Examples
In the example below, two subtests are used, each with 8 items. The NTEST keyword on the GLOBAL command indicates that two subtests are to be used, and two TEST commands follow the ITEMS command. The TEST commands are assigned names through the TNAME keyword and items are referenced by number. >GLOBAL >LENGTH >INPUT >ITEMS >TEST1 >TEST2
NPARM=3,NTEST=2,DFNAME='EXAMPL08.DAT'; NITEMS=(8,8); NTOTAL=16,NALT=5,NIDCHAR=9,TYPE=3; INUMBERS=(1(1)16),INAMES=(N1(1)N8,A1(1)A8); TNAME=NUMCON,INUMBERS=(1(1)8); TNAME=ALGCON,INUMBERS=(9(1)16);
In the next example, the ITEMS command lists the four items in the order that they will be read from the data records. Because examinees in both groups are presented all the items listed in the ITEMS command, the TEST command needs contain only the test name. >GLOBAL >LENGTH >INPUT >ITEMS >TEST >GROUP1 >GROUP2
NPARM=1,NWGHT=3,LOGISTIC; NITEMS=4; NTOTAL=4,NGROUPS=2,DIF,NIDCHAR=2,TYPE=2; INAMES=(SP1(1)SP4),INUMBERS=(1(1)4); TNAME=SPELL; GNAME=MALES; GNAME=FEMALES;
Related topics
GLOBAL command: NTEST and NVTEST keywords (see Section 2.6.7) ITEMS command (see Section 2.6.10)
Setup menu: General dialog box (see Section 2.3.3) TEST command: INUMBERS keyword
233
2 BILOG-MG REFERENCE 2.6.18 TITLE command (Required) Purpose
To provide a label that will be used throughout the output to identify the problem run. The first two lines of the command file are always the title lines. If the title fits on one line, a second, blank line must be entered before the next command starts. The maximum length of each line is 80 characters. The text will be printed verbatim at the top of each output section, as well as at the start of some output files. The two title lines are required at the start of the command file. No special delimiters (> or ;) are required. Format …text… …text…
Example EXAMPLE 4 SIMULATED RESPONSES TO TWO 20-ITEM PARALLEL TEST FORMS
Related topics
Setup menu: General dialog box (see Section 2.3.3)
234
VARIABLE FORMAT STATEMENT 2.6.19 Variable format statement (Required) Purpose
To supply variable format statements describing the column assignments of fields in the data records. Format (aA1,nX,Ib,Ic,Fd.e,Tw,fA1)
where: a
is the number of columns in the ID field
b
is the number of columns in the form indicator field, if any.
c
is the number of columns in the group indicator field, if any. If PERSONAL is present on the INPUT command there will be multiple-group indicator fields.
d
is the number of columns in the case-weight or pattern-frequency field, if any.
e
is the number of columns to the right of the decimal place in the case-weight or pattern frequency field, if any.
f
is the total number of items in the form, when NFORM=1 and the total number of items in the longest form when NFORM>1.
Notes
Columns skipped between fields are indicated by nX, where n is the number of columns to be passed over. If the fields in the data records are not in the above order, the format tab designator (Tw) may be inserted in front of any of the fields (w is the position of the first column of the field, counting from column one). Check the input data carefully when left tabs are used. A forward slash (/) means “skip to the next line”. For example, (5A1,5X,15A1/10X,15A1)
would read the case ID and 15 item responses from line 1; then, skip ten columns and read 15 item responses from line 2.
235
2 BILOG-MG REFERENCE The variable format statement for aggregate-level data has the general form: (aA1,Ib,Ic,Fd.e,f(Fg.h,Fi.j))
where:
g h i j
is the number of columns in the “number tried” field is the number of columns to the right of the decimal point in the “number tried” field is the number of columns in the “number right” field. is the number of columns to the right of the decimal point in the “number right” field.
Examples
The following example uses simulated responses to illustrate nonequivalent groups equating of two forms of a 25-item multiple-choice examination administered to different populations. The two forms have five items in common: C21, C22, C23, C24, and C25. The items for each group are specified in the GROUP1 and GROUP2 commands. Note that the item lists on the GROUP commands are the same as those on the FORMS command. This is because Group 1 took Form 1 of the examination and Group 2 took Form 2 of the examination. As an answer key is provided in the raw data file (KFNAME=EXAMPL03.DAT on the INPUT command), the answer key appears first. Note that, when multiple forms are used, an answer key for each form should be provided. The answer key is in the same format as the data. For each examinee, two lines of data are provided. The first line contains identifying information and the second the item responses. The first information read from the data file is the examinee’s ID, which is in column 35 (5A1). For the first examinee the ID is 0001, and for the last 0200. Using the T operator to move to column 25, the form indicator is read next (I1). Because the values for form and group are the same for any given subject, a single form/group indicator appears on each data record. The indicator is read twice, first for forms and then for groups. The “/” operator is used to move to the first column of the second line. The 25 item responses are then read as (25A1). >GLOBAL DFNAME='EXAMPL03.DAT', NPARM=2; >LENGTH NITEMS=(45); >INPUT NTOTAL=45, SAMPLE=2000, NIDCHAR=5, NALT=5, NGROUP=2, NFORM=2, TYPE=1, KFNAME='EXAMPL03.DAT'; >ITEMS INAMES=(C01(1)C45), INUMBERS=(1(1)45); >TEST1 TNAME='CHEMISTR', INUMBERS=(1(1)45); >FORM1 LENGTH=25, INUMBERS=(1(1)25); >FORM2 LENGTH=25, INUMBERS=(21(1)45); >GROUP1 GNAME='POP1', LENGTH=25, INUMBERS=(1(1)25); >GROUP2 GNAME='POP2', LENGTH=25, INUMBERS=(21(1)45); (T35,5A1,T25,I1,T25,I1/25A1)
236
VARIABLE FORMAT STATEMENT ANSWER KEY FORM 1 1111111111111111111111111 ANSWER KEY FORM 2 1111111111111111111111111 Samp1 GROUP1 11 1111111112221212211111121 Samp1 GROUP1 11 2211111212222222222255222 Samp2 GROUP2 22 2422221211222211222221121 … Samp2 GROUP2 22 1111111111111111212111111
1 2 1
00001
2
00002
2
00199
100
00200
The following example illustrates the equating of equivalent groups with the BILOG-MG program. Two parallel test forms of 20 multiple-choice items were administered to two equivalent samples of 200 examinees drawn from the same population. There are no common items between the forms. Because the samples were drawn from the same population, GROUP commands are not required. The FORM1 command lists the order of the items in Form 1 and the FORM2 command lists the order of the items in Form 2. As in the previous example, two lines of data are provided for each examinee. The first line contains identifying information and the second the item responses. The first information read from the data file is the examinee’s ID, which is in column 35 (5A1). For the first examinee the ID is 0001, and for the last 0200. Using the “T” operator to move to column 25, the form indicator is read next (I1). The “/” operator is used to move to the first column of the second line. The 20 item responses per form are then read in. >GLOBAL DFNAME='EXAMPL04.DAT', NIDCH=5, NPARM=2; >LENGTH NITEMS=(40); >INPUT NTOTAL=40, SAMPLE=2000, NALT=5, NIDCHAR=5, NFORM=2, KFNAME='EXAMPL04.DAT'; >ITEMS INAMES=(T01(1)T40), INUMBERS=(1(1)40); >TEST1 TNAME='SIM', INMBERS=(1(1)40); >FORM1 LENGTH=20, INUMBERS=(1(1)20); >FORM2 LENGTH=20, INUMBERS=(21(1)40); (T35,5A1,T25,I1/40A1) ANSWER KEY FORM 1 11111111111111111111 ANSWER KEY FORM 2 11111111111111111111 Samp1 GROUP1 11111111122212122111 Samp1 GROUP1 11222212221222222112 …
1 2 12
1
00001
12
1
00002
Two hundred students at each of three grade levels, grades four, six, and eight, were given grade-appropriate versions of a 20-item arithmetic examination. Items 19 and 20 appear in the grade 4 and 6 forms; items 37 and 38 appear in the grade 6 and 8 forms. Because each 237
2 BILOG-MG REFERENCE item is assigned a unique column in the data records, a FORM command is not required. Both an answer and not-presented key are given at the top of the raw data file (KFNAME=EXAMPL05.DAT, NFNAME=EXAMPL05.DAT on the INPUT command). In the case of the answer key, a “1” represents a correct response. A not-presented item is indicated by a blank, ” “. As no FORM command is required, only a group indicator has to be read in. The case ID, given in column 35, is read first (5A1), followed by the group indicator in column 25 (I1). The 56 item responses are read from the second line of data (56A1) after using the “/”operator to move to the start of this line. >GLOBAL >LENGTH >INPUT
DFNAME='EXAMPL05.DAT',NPARM=2; NITEMS=(56); NTOTAL=56,SAMPLE=2000,NGROUPS=3,KFNAME='EXAMPL05.DAT', NFNAME='EXAMPL05.DAT',NIDCHAR=5; >ITEMS INUMBERS=(1(1)56),INAME=(M01(1)M56); >TEST TNAME=MATH; >GROUP1 GNAME='GRADE 4',LENGTH=20,INUMBERS=(1(1)20); >GROUP2 GNAME='GRADE 6',LENGTH=20,INUMBERS=(19(1)38); >GROUP3 GNAME='GRADE 8',LENGTH=20,INUMBERS=(37(1)56); (T35,5A1,T25,I5/56A1) ANSWER KEY 11111111111111111111111111111111111111111111111111111111 NOT-PRESENTED KEY Samp1 GROUP1 11111112221211222212 Samp1 GROUP1 21121211121111121212 Samp3 GROUP3
1
1
00001
1
1
00002
3
3
Samp3
3
3
00199 12212212212211112121 00200 11111111121111111111
GROUP3
The following example illustrates the use of the TYPE=3 specification on the INPUT command to analyze aggregate-level, multiple-matrix sampling data. The data in exampl08.dat are numbers tried and numbers correct for items from eight forms of a matrix sampled assessment instrument. The groups are selected 8-th grade students from 32 public schools. The first record for each school contains the data for the items of a Number Concepts scale, NUMCON, and the second record contains the data for items of an Algebra Concepts scale, ALGCON. An answer key is not relevant for aggregate-level data in number-tried, numberright summary form. Note the format statement for reading the two sets of eight numbertried, number-right observations from the two data lines. Again, the “/” operator is used to move to the start of the second line of data for each school. >GLOBAL DFNAME='EXAMPL08.DAT', NPARM=3, NTEST=2; >LENGTH NITEMS=(8, 8); >INPUT NTOTAL=16, NALT=5, TYPE=3, NIDCHAR=9; >ITEMS INAMES=(N1(1)N8, A1(1)A8), INUMBER=(1(1)16);
238
VARIABLE FORMAT STATEMENT >TEST1 TNAME='NUMCON', INUMBERS=(1(1)8); >TEST2 TNAME='ALGCON', INUMBERS=(9(1)16); (1X,9A1,5X,8(2F3.0)/15X,8(2F3.0)) SCHOOL SCHOOL SCHOOL SCHOOL
1 1 2 2
NUM ALG NUM ALG
1 1 5 5
0 0 3 2
3 3 4 4
2 1 4 2
2 2 3 3
1 0 2 2
4 3 3 3
4 2 3 2
3 3 2 2
2 2 2 2
2 2 4 4
1 1 3 2
4 4 4 4
3 1 3 2
4 4 5 5
1 0 3 3
The next example illustrates the use of BILOG-MG with multiple groups and multiple subtests. Based on previous test performance, examinees are assigned to two groups for adaptive testing. Out of a set of 45 items, group 1 is assigned items 1 through 25, and group 2 is assigned items 21 through 45; thus, there are 5 items linking the test forms administered to the groups. Twenty of the 25 items presented to group 1 belong to subtest 1 (items 1-15 and 21-25). Twenty items also belong to subtest 2 (items 6-25). Of the 25 items presented to group 2, 20 belong to subtest 1 (items 21-40) and 20 to subtest 2 (items 21-25 and 31-45). In all, there are 35 items from the set of 45 assigned to each subtest. (This extent of item overlap between subtests is not realistic, but it illustrates that more than one subtest can be scored adaptively provided they each contain link items between the test forms.) Note that, in this case, the item responses on the second line of data for each examinee represent responses to different items. When we previously considered these data, the response in the first column of the second line represented the response to item 1, regardless of group membership. Here, that response would be the response to item 1 for a member of group 1, but the response to item 21 for an examinee from group 2. >GLOBAL >SAVE >LENGTH >INPUT
DFNAME='EXAMPL03.DAT',NPARM=2,NTEST=2,SAVE; SCORE='EXAMPL09.SCO'; NITEMS=(35,35); NTOTAL=45, SAMPLE=2000, NGROUP=2, KFNAME='EXAMPL03.DAT', NALT=5, NFORMS=2,NIDCHAR=5; >ITEMS INUMBERS=(1(1)45), INAME=(C01(1)C45); >TEST1 TNAME=SUBTEST1, INAME=(C01(1)C15,C21(1)C40); >TEST2 TNAME=SUBTEST2, INAME=(C06(1)C25,C31(1)C45); >FORM1 LENGTH=25,INUMBERS=(1(1)25); >FORM2 LENGTH=25,INUMBERS=(21(1)45); >GROUP1 GNAME=POP1,LENGTH=25,INUMBERS=(1(1)25); >GROUP2 GNAME=POP2,LENGTH=25,INUMBERS=(21(1)45); (T35,5A1,T25,I1,T25,I1/45A1) ANSWER KEY FORM 1 1111111111111111111111111 ANSWER KEY FORM 2 1111111111111111111111111 Samp1 GROUP1 11 1111111112221212211111121 Samp1 GROUP2 22 2211111212222222222255222
1 2 1
00001
1.0
.48900
2
00002
1.0
-.92734
239
2 BILOG-MG REFERENCE Default
None. Related topics
Data Structures (see Chapter 1) Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box FORM command (see Section 2.6.6) GLOBAL command: DFNAME and NWGHT keywords (see Section 2.6.7) INPUT command: NFORM, TYPE and PERSONAL keywords (see Section 2.6.9)
240
INPUT AND OUTPUT FILES 2.6.20 Input and output files Input files
The following data files contain problem information that must be supplied by the user as needed. Any text editor that writes an ASCII file may be used to prepare these files. File
Keyword
Answer key
KFNAME on INPUT command
Not-presented key
NFNAME on INPUT command
Omit key
OFNAME on INPUT command
Original data file
DFNAME on GLOBAL command
Provisional starting values PRNAME on GLOBAL command Note:
The assignment of specific names to these files in the INPUT command causes the program to read external files. These files may be combined into one file, using the order above. The arrangement is to construct an arbitrarily named file consisting of the answer key, if any, the not-presented key, if any, the omit key, if any and the item-response data. Any of the above files would then have the name of that file. Section 10.5 illustrates the combination of an answer key and not-presented key within the data file. Format of the input records
The keys and the data records must have the same fixed-column formats. The fields of the data records are read in the following order: The respondent identification field (up to 30 columns of characters as specified by the NIDCHAR keyword on the INPUT command). The form number (only if NFORMS>1). The group number or numbers (integer) (only if specified by a value larger than 1 for the NGROUP keyword of the INPUT command). A real-valued (with decimal point) case weight for the respondent or frequency for a response pattern (only if specified by the NWGHT keyword of the GLOBAL command). The individual item-response records or patterns. The type of entries in the item-response field is determined by the TYPE keyword of the INPUT command and by the presence or absence of the KFNAME keyword of the INPUT command: if KFNAME is not present, the item responses are scored 1 = correct and 0 = not correct. 241
2 BILOG-MG REFERENCE
if KFNAME is present, the item responses are arbitrary single ASCII characters, the correct alternatives of which appear in the same columns of the answer key. In either of the above types of data, not-presented items may be coded by an arbitrary character defined in the corresponding column of the not-presented key. (See the NFNAME keyword of the INPUT command in Section 2.6.9.) Similarly, omitted items may be coded by another character defined in the corresponding column of the omit key. (See the OFNAME keyword of the INPUT command.) The path to and filename of any of these files may be longer than 80 characters. As the maximum length of any line in the command file is 80 characters, multiple lines should be used. It is important to continue up to and including the 80th column when specifying a long path and filename. For example, suppose the data file exampl06.dat is in a folder named: C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MGVERSION1.2\EXAMPLES
The correct way to enter this information in the command file is to enclose the name and path in apostrophes, and continue until column 80 is reached. Then proceed in column 1 of the next line as shown below: >GLOBAL DFNAME=’C:\PROGRAM FILES\ITEM RESPONSE THEORY\IRT_2002\MARCH20\BILOG-MG -VERSION1.2\EXAMPLES\EXAMPL06.DAT’, NTEST=1, NVTEST=1, NPARM=2, SAVE;
If the data are stored in the same folder as the command file, it is sufficient to type DFNAME='EXAMPL06.DAT'
Related topics
Data menu: Examinee Data dialog box (see Section 2.3.4) Data menu: Group-Level Data dialog box Data menu: Item Keys dialog box GLOBAL command (see Section 2.6.7) INPUT command (see Section 2.6.9)
Output files
Through use of the keywords on the SAVE command, the following output files may be created.
Ability score file Classical item statistics file DIF parameter file DRIFT parameter file Estimated covariance file Expected frequency file Item parameter file 242
INPUT AND OUTPUT FILES
Marginal posterior probability file Test information file
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.6)
Ability score file Keyword: SCORE
This file is created during Phase 3 of the program if SCORE is specified in the SAVE command. It consists of the title records and two records per subtest for each respondent. The format is as follows: Records Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the ability score file.
3+
Two records per subtest for each respondent, containing the following information
Columns First cord
Format
Description
re-
1–3
I3
group indicator
4–5
2X
blank filler
6 – 35
30A1
respondent identification
1–6
F6.2
respondent case weight
7–7
A1
* if the subject is not calibrated; a blank otherwise
Second record
243
2 BILOG-MG REFERENCE 8 – 15
A8
subtest name
16 – 20
I5
number of attempts to items in the subtest
21 – 25
I5
number of correct responses to the subtest
26 – 35
F10.6
percent-correct score
36 – 47
F12.6
scale score estimate
48 – 59
F12.6
estimated standard error of scale score
60 – 60
A1
* if standard error of scale score is inestimable; a blank otherwise
61 – 70
F10.6
group fit probability, if requested
71 – 80
F10.6
marginal probability of response pattern if EAP scoring is chosen
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Classical item statistics file Keyword: ISTAT
This file contains all classical item statistics computed and printed by Phase 1 of the program. The following items are written to this external file in the same format as used in the result output from Phase 1, *.ph1:
the title records in format (20A4/20A4) item facilities (percent correct) number of attempts and correct responses to each item item-subscore correlations
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
244
INPUT AND OUTPUT FILES DIF parameter file Keyword: DIF Records Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the DIF parameter file
3+
Three sets of item records for each subtest. The first set contains the unadjusted item threshold parameters and s.e.s for each group. The second set contains adjusted threshold parameters and s.e.s for each group. The last set contains estimates of group differences in adjusted threshold parameters.
First set of item records: Columns
Format
Description
First record
1–8
A8
test name
8 – 10
2X
blank filler
11 - 18
A8
item name
19 – 20
2X
blank filler
21 - 220
20(F10.5)
unadjusted threshold parameters for groups
1 – 20
20X
Blank filler
21 – 220
20(F10.5)
Estimated s.e. of unadjusted threshold parameters for groups
Second record
245
2 BILOG-MG REFERENCE Second set of item records: First record
1–8
A8
test name
8 – 10
2X
blank filler
11 – 18
A8
item name
19 – 20
2X
blank filler
21 – 220
20(F10.5)
adjusted threshold parameters for groups
1 – 20
20X
blank filler
21 – 220
20(F10.5)
s.e. of adjusted threshold parameter for group contrasts
Second record
Third set of item records: First record
1–8
A8
test name
8 – 10
2X
blank filler
11 – 18
A8
item name
19 – 20
2X
blank filler
21 – 210
20(F10.5)
group differences in threshold parameters for group contrasts
211 – 220
10X
blank filler
1 – 20
20X
blank filler
21 – 210
20(F10.5)
s.e. of group differences for group contrasts
211 – 220
10X
blank filler
Second record
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INPUT AND OUTPUT FILES Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
DRIFT parameter file Keyword: DRIFT
This file is saved during Phase 2 if DRIFT is specified on the SAVE command. It consists of title records and two records for each item. The format is as follows: Records Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the DRIFT parameter file
3+
Two records for each item, containing the following information
Columns
Format
Description
First record
1–8
A8
item name
9 – 10
2X
blank filler
11 – 21
F11.5
Intercept
22 – 32
F11.5
linear coefficient
33 – 43
F11.5
quadratic coefficient
44 – 54
F11.5
cubic coefficient
55 – 65
F11.5
quartic coefficient
66 – 76
F11.5
quintic coefficient
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Second record
1 – 10
10X
blank filler
11 – 21
F11.5
estimated s.e. of intercept
22 – 32
F11.5
estimated s.e. of linear coefficient
33 – 43
F11.5
estimated s.e. of quadratic coefficient
44 – 54
F11.5
estimated s.e. of cubic coefficient
55 – 65
F11.5
estimated s.e. of quartic coefficient
66 – 76
F11.5
estimated s.e. of quintic coefficient
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Estimated covariance file Keyword: COVARIANCE
This file is created by Phase 2 of the program and passed to Phase 3, where item information indices are added if requested. It contains title records and the item parameter estimates at the conclusion of Phase 2 and the added item information indices at the conclusion of Phase 3. The format is as follows: Records Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the covariance file.
3+
Four records for each item, containing the following information:
248
INPUT AND OUTPUT FILES Columns
Format
Description
First record
1–8
A8
item name
9 – 16
A8
subtest name
17 – 21
I5
group indicator
22 – 33
F12.6
slope estimate
34 – 45
F12.6
threshold estimate
46 – 57
F12.6
lower asymptote estimate
58 – 69
F12.6
estimation error variance for slope
70 – 81
F12.6
estimation error covariance for slope and threshold
1 – 17
17X
blank filler
18 – 29
F12.6
estimation error variance for threshold
30 – 41
F12.6
estimation error covariance for slope and asymptote
F12.6
estimation error covariance for threshold and asymptote
54 – 65
F12.6
estimation error variance for lower asymptote
66 – 81
16X
blank filler
Second record
42 – 53
population-independent indices
Third record
1 – 17
17X
blank filler
18 – 29
F12.5
value of maximum information
30 – 41
F12.5
estimated s.e. of value of maximum information
42 – 53
F12.5
point of maximum information
54 – 65
F12.5
estimated s.e. of point of maximum information
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2 BILOG-MG REFERENCE 66 – 81
16X
blank filler population-dependent indices
Fourth record
1 – 17
17X
blank filler
18 – 29
F12.5
value of maximum effectiveness (info*density)
30 – 41
F12.5
point of maximum effectiveness
42 – 53
F12.5
average information
54 – 65
F12.5
reliability index (s.d./(s.d. + 1/(ave.info)2 ))
66 – 81
16X
blank filler
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Expected frequency file Keyword: EXPECTED
This file is created by Phase 2 of the program. It contains expected sample sizes, expected number of correct responses, expected proportions of correct responses, standardized posterior residuals and model proportions of correct responses. These values are evaluated at each quadrature point and item. The format of each item and each of the quadrature points is as follows: Records Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the expected file
3+
Seven records for each item, containing the following information
250
INPUT AND OUTPUT FILES Column
Format
Description
First record
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “POINT”
28 – 82
5(F10.5,1X)
five values of quadrature points
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “WEIGHT”
28 – 82
5(F10.5,1X)
five values of quadrature weights
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “TRIED”
28 – 82
5(F10.5,1X)
five values of expected sample sizes
Second record
Third record
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Fourth record
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “RIGHT”
5(F10.5,1X)
five values of expected number of correct responses
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “PROPORTION”
5(F10.5,1X)
five values of expected proportions of correct responses
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “s.e.”
28 – 82
5(F10.5,1X)
five values of standardized posterior residuals
28 – 82 Fifth record
28 – 82 Sixth record
252
INPUT AND OUTPUT FILES Seventh record
1–8
A8
item name
9 – 10
2X
blank filler
11 – 15
I5
group indicator
16 – 17
2X
blank filler
18 – 27
A10
label “MODEL PROP”
5(F10.5,1X)
five values of model proportions of correct responses
28 – 82 Remark:
If more than five quadrature points are used, each record is duplicated with the same format. If there is more than one group, the item information is presented for each group. Sets of records within an item are separated by single-dashed lines. Sets of records between items are separated by double-dashed lines. Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Item parameter file Keyword: PARM
This file is saved during Phase 2 of the program if PARM is specified in the SAVE command. The file contains the item parameter estimates and other information. The format is as follows: Records
Description
1&2
In 20A4/20A4 format, the title records of the BILOG-MG run that created the item parameter file
3
In 2I4 format, the number of subtests and the total number of items appearing in this file
4
In 20I4 format, the numbers of items in the main and variant subtest on as many records as necessary. 253
2 BILOG-MG REFERENCE 5+
One record for each item in the main and variant subtests (if any), containing the following information
Columns
Format
Description
1–8
A8
item name
9 – 16
A8
subtest name
17–26
F10.5
intercept parameter
27 – 36
F10.5
intercept s. e.
37 – 46
F10.5
slope parameter
47 – 56
F10.5
slope s. e.
57 – 66
F10.5
threshold parameter
67 – 76
F10.5
threshold s. e.
F10.5
dispersion parameter (reciprocal of slope)
87 – 96
F10.5
dispersion s. e.
97 – 106
F10.5
lower asymptote parameter
107 – 116
F10.5
lower asymptote s.e.
117 – 126
F10.5
DRIFT parameter
127 – 136
F10.5
DRIFT s. e.
137 – 146
F10.5
unused columns
147 – 150
I4
location of item in input stream
151
A1
answer key
152
I1
dummy values
77 – 86
254
INPUT AND OUTPUT FILES Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Marginal posterior probability file Keyword: POST
This file is created by Phase 2 of the program. It contains title records, the respondent’s identification and group numbers, the case weight, and the marginal posterior probability of its response pattern. The format of each respondent’s record is as follows: Records Description
1&2
In 20A4/20A4 format, the records of the BILOG-MG run that created the posterior file
3+
Two records for each response pattern, containing the following information
Columns
Format
Description
First record
1–5
I5
group indicator
6 – 10
5X
blank filler
11 – 40
30A1
respondent’s identification number
41 – 80
40X
blank filler
1–8
A8
subtest name
9 – 10
2X
blank filler
11 – 20
F10.3
case weight
21 – 25
5X
blank filler
Second record
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26 – 40
marginal posterior probability of the F15.10 response pattern
41 – 80
40X
blank filler
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7)
Test information file Keyword: TSTAT
This file contains all summary item and test information computed and printed by Phase 3 of the program. The following items are written to this external file in the same format as used in the result output from Phase 3, *.ph3:
the title records in format (20A4/20A4) correlations among subtest scale scores means and estimates of scale scores
The following items are written only if the appropriate INFO keyword on the SCORE command has been specified:
test information and standard error curves table of item information indices, including the point and value of maximum information and the corresponding estimated standard errors for those indices.
Related topics
SAVE command (see Section 2.6.15)
Save menu (see Section 2.3.7) SCORE command (see Section 2.6.16)
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3 PARSCALE The PARSCALE program developed in the early 1990s by Eiji Muraki (then from Educational Testing Service) and R. Darrell Bock (University of Chicago), implements a powerful extension of Item Response Theory (IRT) measurement methods ranging from binary-item analysis to multiple-category and rating-scale items. PARSCALE was originally developed with large-scale social surveys and educational assessments in mind. More recently, however, the program has become a popular tool for a wider variety of applications, seeing use by governmental statistical agencies, marketing researchers, policy and management consultants, and investigators of the many different “classical” (psychological, sociological, educational, medical) assessment studies. Its flexibility and the wealth of information it can provide have kept it in regular use by researchers around the world. The program can handle a great diversity of data types. The simple survey is probably the most common of these. In such a case, items are rated in a common set of categories (known to behavioral scientists as a “Likert”-type scale). Whereas the original Likert approach assigned arbitrary, successive integer values to the categories, the IRT procedures implemented in PARSCALE estimate optimal, empirical values for the boundaries between categories. These boundaries, as well as item locations and respondent scores, can all be represented as points along the latent dimension of measurement. Tests that utilize this type of data might be behavioral surveys in which the answers are “always,” “sometimes,” “often,” or “never”; expressions of opinion such as “agree,” “undecided,” or “disagree”; or ratings of status, as perhaps a physician using “critical", “stable,” “improved,” or “symptom-free” as levels of evaluation. For instruments of assessment, PARSCALE can also be used to analyze rating-scale items (such as open-ended essay questions) and multiple-choice items. With multiple-choice, simple “rightwrong” scoring and analysis is achieved by treating items as if only two categories are available (collapsing all wrong choices into a single category). However, if more information is desired, the choices can remain separated within each item so that the identity of the chosen alternative is retained during the analysis. In this way, information on wrong responses can be recovered for detailed analysis. The effects of guessing can also be included in the analysis. Often an instrument will consist of a mixture of item types, some having common categories and some with unique categories. PARSCALE handles this kind of diversity by allowing items to be assigned to “blocks” within which the item categories are common. Any item that has unique category definitions will be assigned to its own block. An educational test, for example, may contain open-ended exercises rated in five categories in one block and multiple-choice items in another block. PARSCALE’s multiple-group capability adds the options of Differential Item Functioning (DIF) analysis for trends between groups or over time, and Rater’s-Effect analysis in order to allow for rater bias or differences in rater severity. PARSCALE for Windows allows for both easier manipulation of the command (syntax) file and more efficient review of the output files.
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3.1 The PARSCALE interface This section describes those elements in the user’s interface that may not be immediately clear to the user or that behave in a somewhat nonstandard way.
Main menu bar Workspace Run menu Output menu Font option Window menu
3.1.1 Main menu At the center of the interface is the main menu bar, which adapts to the currently active function. For example, when you start the program, the menu bar shows only the menu choices File, View, and Help.
However, as soon as you open a PARSCALE output file or any other text file (by using the File menu), the Windows and Edit options show up on the menu bar. At the same time, the File menu choices are expanded with selections like Save and Save As. In addition, the View menu now includes a Font option following the Status Bar and Toolbar options.
The opening of an existing PARSCALE command (*.psl) file, or starting a new one, adds additional choices to the main menu bar: the Output, Run, and Workspace menus. Note that you can open only one command file at a time. If you want to paste some part from an existing command file in your current one, opening the old file will automatically close the current one. After you copy the selection you want to the clipboard, you have to reopen the *.psl file for pasting.
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3 PARSCALE REFERENCE Note also that, by choosing “All Files (*.*)” in the Open File dialog box, score files, parameter files, or other files created during the run can be reviewed. 3.1.2 Workspace The Workspace option on the main menu bar provides access to a dialog box that shows the current values that are reserved for the numeric and the character workspace.
The defaults are 50 Kbytes for character workspace and 200KBytes for the numeric workspace. Most problems will run with these settings. If there is insufficient workspace for an analysis to finish, the program will alert you with a message box and you will find a message at the end of the output file. For example: ***** NOT ENOUGH SPACE--
1024 4 BYTES LOCATIONS EXHAUSTED *****
When you encounter such a message, increase the workspace and run the problem again. Remember that the changes remain in effect until you change the settings again. Allocating too much workspace may slow down your analysis, or other programs that are running simultaneously, so increase the workspace in reasonable steps. If a run is successful, the program reports at the end of the output file how much memory it actually used. The values are reported in bytes and you should divide them by 1024 to arrive at the values for the numbers used in the Workspace dialog box. 3.1.3 Run menu The Run menu includes you the option to run All Phases of the program or to run them one at the time. If you opt for the latter, remember that the different program phases build on each other. In other words, you need calibration (Phase 2) before you can do the scoring (Phase 3). That is why the program interface disallows the possibility of running the phases out of order. 259
3 PARSCALE REFERENCE If you have a new (or changed) command file, initially only Phase 0, Phase 1 and All Phases are enabled on the Run menu.
When you run an analysis by clicking on one of the options under the Run menu, the current command file will first be saved, if you made any changes. You can easily tell if a command file has changed by looking at the filename above the menu bar. An asterisk after the filename shows that the current file has changed but has not been saved yet. Once all phases have been completed, the Plot option, providing access to the graphics procedure described in Chapter 6, is enabled. 3.1.4 Output menu By using the Output menu, you can open the output files for the four different program phases, named with the file extensions ph0, ph1, ph2, and ph3, respectively. Always check the end of each output file to see if it reports: NORMAL END. If it does not, something went wrong and the output file should include some information on that.
3.1.5 Font option The Font option on the View menu displays the Font dialog box with the fonts that are available on your system. You may use different fonts for command and output files. At installation, they are both set to a special Arial Monospace font that ships with the program. To keep the tables in the output aligned, you should always select a monospace or fixed pitch font where all the characters in the font have the same width. Once you select a new font, that font becomes the default font. This gives you the option to select a font (as well as font size and font style) for your command (*.psl) files that is different from the one for your output (*.ph*) files as a quick visual reminder of the type of file. 260
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3.1.6 Window menu The Window menu is only available when you have at least one file open. You can use the CtrlTab key combination to switch between open files, or use the Window menu to arrange the open files (cascade, tile). If you have several or all output (*.ph*) files open for a particular analysis, you could use the Window menu to arrange them for convenient switching.
3.2 Command syntax PARSCALE uses the command conventions of other IRT programs published by SSI. Commands employ the general syntax: >NAME KEYWORD1=n, KEYWORD2=(list), …, OPTION1….
The following general rules apply.
A greater-than sign (>) must be entered in column 1 of the first line of a command and followed without a space by the command name. All command names, keywords, options, and keyword values must be entered in UPPER CASE. Command names, keywords, and options may be entered in full or abbreviated to the first three characters. At least one space must separate the command name from any keywords or options. All keywords and options must be separated by commas. The equals sign is used to set a keyword equal to a value, which may be integer, real, or character. A real value must contain a decimal point. A character string must be enclosed in single quotes if: 261
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it contains more than eight characters it begins with a numeral it contains blanks, commas, semicolons, or slashes
Example: DFNAME=’EXAMPL0l.DAT’, TNAME=’20-ITEMS’.
A keyword may be vector valued; i.e., set equal to a list of integer, real, or character constants, separated by commas or spaces, and enclosed in parentheses. If the list is an arithmetic progression of integer or decimal numbers, the short form, first(increment)last, may be used. Thus, a selection of items 1,3,7,8,9,10,15 may be entered as 1,3,7(1)10,15. Real values may be used in a similar way. If the values in the list are equal, the form, value(0)number of values, may be used. Thus, 1.0,1.0,1.0,1.0,1.0 maybe entered as 1.0(0)5.
The italic elements in the command format description are variables that the user needs to replace. Command lines may not exceed 128 columns. Continuation on one or more lines is permitted. See Section 3.2.6 for more information. Filenames, including the path, may not exceed 128 characters, including path to file. Each command terminates with a semicolon (;). The semi colon functions as the command delimiter; it signals the end of the command and the beginning of a new command.
Related topics For information on the order of commands and keywords associated with each command, please see Section 3.2.1. 3.2.1 Order of commands The table below lists all available PARSCALE commands in their necessary order. This order is also used in the remainder of this section of the user’s guide. Commands marked as “required” must appear in the command file for each problem setup. All other commands are optional. In other words, at a minimum the command file should start with two TITLE lines, followed by the FILES, INPUT, TEST (or SCALE), BLOCK, CALIB, and SCORE command lines. Note that INPUT and the variable format statement may be followed by data. The variable format statement is also required in the command file when raw data are read in from an external file. Note that, in the remainder of this chapter, the commands are discussed in alphabetical order, and not in the required order as shown below.
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OVERVIEW OF SYNTAX Table 8.1: Order of PARSCALE commands Required *
Command Keywords
Options
TITLE COMMENT
*
*
*
*
*
FILES
DFNAME=, FNAME=, CFNAME=, FNAME=, OFNAME=, NFNAME=
SAVE
MASTER=, CALIB=, PARM=, SCORE=, FIT=, COMBINE=, INFORMATION=
INPUT
NTEST=n, LENGTH=(list), NFMT=n, SAMPLE=n, TAKE=n, NIDCH=n, NTOTAL=n, INOPT=n, COMBINE=n, MGROUP/MRATER=n, NRATER=(list), R-INOPT=n
SAVE
WEIGHT, GROUPLEVEL
(variable format statement) TEST
TNAME=n, NBLOCK=n, ITEMS=(list),INAME=(list), INTERCEPT=(list), THRESHOLD=(list), SLOPE=(list)
BLOCK
BNAME=(list), NITEMS=n, NCAT=n, ORIGINAL=(list), MODIFIED=(list), CNAME=(list), CADJUST=n, CATEGORY=(list), GPARM=(list), GUESSING=(list), SCORING=(list), REPEAT=n, SKIP=(list), RATER=(list),
MGROUP
GNAME=(list), GCODE=(list), DIF=(list), REFERENCE=n, COMMON=(list)
MRATER
RNAME=(list), RCODE=(list), RATER=(list)
CSLOPE, NOCADJUST
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*
*
GRADED/PARTIAL, LOGISTIC/NORMAL, POSTERIOR, FLOAT, QPREAD, ESTORDER, SPRIOR,TPRIOR, GPRIOR, PRIORREAD, NOCALIB, SKIPC, ACCEL/NOACCEL, CSLOPE, THRESHOLD, NRATER
CALIB
SCALE=n, NQPT=n, DIST=n, CYCLES=(list), CRIT=(list), DIAGNOSIS=n, QRANGE=(list), ITEMFIT=n, RIDGE=(list), NEWTON=n, FREE=(list),
QUADP
POINTS=(list),WEIGHTS=(list)
PRIORS
TMU=(list), TSIGMA=(list), SMU=(list), SSIGMA=(list), GMU=(list), GSIGMA=(list),
SOPTION
SCORE
NQPT=n, DIST=n, QRANGE=(list), SMEAN=n, SSD=n, NAME=n, PFQ=n, ITERATION=(list), SCORING=(list)
PRINT, QPREAD, NOSCORE, SAMPLE, EAP/WML/MLE, RESCALE, NOADJUST, FlT, NRATER
QUADS
POINTS=(list),WEIGHTS=(list)
COMBINE
NAME=n, WEIGHTS=(list)
Notes
A series of commands from TEST to QUADS should be repeated for the number of subtests, specified by the NTEST keyword in the INPUT command. The BLOCK command should be repeated for the number of blocks, specified by the NBLOCK keyword in the TEST (or SCORE) command. Repetition of the BLOCK commands can be shortened by utilizing the REPEAT keyword in the TEST (or SCORE) command. The COMBINE command is optional and must be placed at the end of the PARSCALE command file.
Related topics
Command syntax (Section 3.2)
264
BLOCK COMMAND 3.2.2 BLOCK command (Required) Purpose To provide a block name, and to identify the items that belong to block j in subtest or subscale i. Format >BLOCK
BNAME=(list), NITEMS=n, NCAT=n, ORIGINAL=(list), MODIFIED=(list),CNAME=(list), CATEGORY=(list), CADJUST=n, GPARM=(list),GUESSING=(n, FIX/ESTIMATE),SCORING=(list), REPEAT=n, SKIP=(list), RATER=(list),CSLOPE, NOCADJUST;
Notes
There should be as many BLOCK commands as the total number of blocks specified with the NBLOCK keyword on each TEST (or SCALE) command. These BLOCK commands are required commands. Each of the BLOCK commands provides a block name (BNAME), the number of items in the block (NITEMS), the number of categorical responses that those items share (NCAT), and the identification of those items. Categorical responses of the raw data are assumed to be coded as consecutive integers, such as 1, 2, 3, and so forth. (Notice that the first categorical response is coded 1 instead of 0.) Use the ORIGINAL keyword to describe categorical responses that are coded differently in the input file. The ORIGINAL and MODIFIED keywords may be used to re-order or concatenate the original categorical responses in the block. See the examples in Chapter 11. The user may supply the initial values of the parameters for the estimation phase with the CATEGORY keyword. Block names or category names that do not begin with a letter, or contain blanks and/or special (non-alphanumeric) symbols, or consist of more than 8 characters, must be enclosed in single quotes.
Related topics
Examples of BLOCK commands TEST/SCALE command: NBLOCK keyword (Section 3.2.15)
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3 PARSCALE REFERENCE BNAME keyword Purpose To provide the block name, which may be up to eight characters in length. If the REPEAT keyword is used, all values of the keywords including the block name are replicated for subsequent blocks. A user can supply unique block names for those replicated blocks by using the BNAME keyword. Format BNAME= ( n1 , n2 ,..., nREPEAT )
Default
Supplied by program Related topics
BLOCK command: REPEAT keyword Examples of BLOCK commands
CADJUST keyword Purpose
To control the location adjustment: n sets the mean of the category parameters. Format CADJUST=n
Default
0.0. Related topics
Examples of BLOCK commands
CATEGORY keyword Purpose
To provide initial category parameter values for the estimation process. If the CATEGORY keyword is supplied, but no values are specified, then the constant values from “scores 266
BLOCK COMMAND for ordinal or ranked data” (Statistical Tables for Biological, Agricultural, and Medical Research, R. A. Fisher & F. Yates, p. 66) substitute the default initial values of the category parameters. Format CATEGORY= ( n1 , n2 ,..., nMODIFIED )
Default
Supplied by program. Related topics
BLOCK command: MODIFIED keyword Examples of BLOCK commands
CNAME keyword Purpose
To provide a list of names for categories. Format CNAME= ( n1 , n2 ,..., nMODIFIED )
Default
Blanks. Related topics
BLOCK command: MODIFIED keyword Examples of BLOCK commands
CSLOPE option Purpose
To request the estimation of a single common slope parameter for all items in the block. Format CSLOPE
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Examples of BLOCK commands
GPARM keyword Purpose
To provide guessing parameters that are used only for the correction of dichotomous item response probabilities if GUESSING is specified. If GUESSING is specified, these guessing parameters are used for the initial parameter values. Format GPARM= ( n1 , n2 ,..., nMODIFIED )
Default
0.0. Related topics
BLOCK command: GUESSING keyword Examples of BLOCK commands
GUESSING keyword Purpose
To request the use of the item-response model with a lower asymptote (guessing) parameter, g. P* = g +(1 - g)P for the k-th response category and P* = (1- g)P for others. The lower asymptote (guessing) parameters are estimated if ESTIMATE is specified; otherwise, the probabilities of categorical responses are only corrected by fixed parameter values, which are supplied by the item-parameter file or the keyword GPARM in the BLOCK command. Format GUESSING=(n,FIX/ESTIMATE)
Default (2,FIX) (lower asymptote (guessing) parameters are not estimated)
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BLOCK COMMAND Related topics BLOCK command: GPARM keyword Examples of BLOCK commands
MODIFIED keyword Purpose
To provide a list of integers corresponding to the original response codes. The first category should correspond to n = 1, not n = 0. The number of arguments should be equal to NCAT. The program computes automatically the number of response categories after the modification, as specified by the MODIFIED keyword. If some categories are collapsed, making the modified number less than NCAT, the modified number is used to read the keywords CNAME and CATEGORY. Format MODIFIED = ( n1 , n2 ,..., nNCAT )
Default
1 through NCAT Related topics BLOCK command: CNAME, CATEGORY, and NCAT keyword Examples of BLOCK commands
NCAT keyword Purpose
To provide the number of response categories in the block. Format NCAT=n
Default
2. Related topics
Examples of BLOCK commands 269
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NITEMS keyword Purpose
To indicate the number of items in the block. Format NITEMS=n
Default
The number of items in the subtest (LENGTH on INPUT command). Related topics
Examples of BLOCK commands INPUT command: LENGTH keyword
NOCADJUST option Purpose
To omit the adjustment provided by the CADJUST keyword during the calibration. Format NOCADJUST
Related topics
BLOCK command: CADJUST keyword Examples of BLOCK commands
ORIGINAL keyword Purpose
To provide a list of the original categorical response codes (up to four characters each). The number of arguments should be equal to NCAT.
270
BLOCK COMMAND Format ORIGINAL= ( n1 , n2 ,..., nNCAT )
Default
1 through NCAT Related topics
BLOCK command: NCAT keyword Examples of BLOCK commands
RATER keyword Purpose
To provide the ratio of a rater variance and an error variance per item. This ratio is used for the correction of the information function per item. If “ n1 ” is specified, but no other “ n ” is specified, then the default values of those unspecified “ n ” are “ n1 ” (the first “ n ” value). Format RATER= ( n1 , n2 ,..., nNITEMS )
Default
n1 = 0 Related topics
Examples of BLOCK commands
REPEAT keyword Purpose
To request the repetition of a BLOCK command. The ij-th BLOCK command will be automatically repeated n times. This option maybe used to estimate different category values for each item (Samejima’s model).
271
3 PARSCALE REFERENCE Format REPEAT=n
Default
0. Related topics
Examples of BLOCK commands
SCORING keyword Purpose
To specify the scoring function of the partial credit models using scoring function values. Values can be fractional. Format SCORING= ( n1 , n2 ,..., nMODIFIED )
Default
1.0, 2.0, 3.0, .... Related topics
BLOCK command: MODIFIED keyword Examples of BLOCK commands
SKIP keyword Purpose
To skip the parameter estimation for this particular block and use the parameter values supplied by a user or the program.
n1 : If the estimation of the slope parameters needs to be skipped, set this value to one, otherwise 0. n2 : If the estimation of the threshold parameters needs to be skipped, set this value to one, otherwise 0. n3 : If the estimation of the category parameters needs to be skipped, set this value to one, otherwise 0.
272
BLOCK COMMAND
n4 : If the estimation of the lower asymptote parameters needs to be skipped, set this value to one, otherwise 0.
If the keyword SKIP appears without arguments, all of the parameter estimations are skipped, that is, SKIP=(1,1,1,1). If no SKIP keyword appears, none of the parameter estimations is skipped, that is, SKIP=(0,0,0,0). Format SKIP= ( n1 , n2 , n3 , n4 )
Related topics
Examples of BLOCK commands
Examples of BLOCK commands
The four categorical responses are coded as A, B, C, and D and the user wants to concatenate the categories A and B as the first category. Note that NCAT specifies the number of categories before the modification. >BLOCK NCAT=4, ORIGINAL=(A,B,C,D), MODIFIED=(1,1,2,3)
The four categorical responses are coded as 1, 2, 3, and 4 and the user wants to reverse the order of the categories. The ORIGINAL keyword is not really needed in this case, because it specifies the default. Note the single quotes around the specified block name, due to the presence of the hyphen. >BLOCK
BNAME=’OBS—RHET’, NCAT=4, ORIGINAL=(1,2,3,4), MODIFIED=(4,3,2,1);
273
3 PARSCALE REFERENCE 3.2.3 CALIB command (Required) Purpose
To control the item and category parameter estimation and to specify prior distributions on the parameters for subtest or sub-scale i. Format >CALIB
GRADED/PARTIAL, LOGISTIC/NORMAL, SCALE=n, NQPT=n, DIST=n, CYCLES=(list), CRIT=(list), DIAGNOSIS=n, QRANGE=(list), ITEMFIT=n, RIDGE=(list), NEWTON=n, POSTERIOR, FLOAT, QPREAD, ESTORDER, SPRIOR,TPRIOR, GPRIOR, PRIORREAD, NOCALIB, SKIPC, FREE=(t/NOADJUST,u/NOADJUST,REFERENCE/COMBINED,POSTERIOR/MLE), ACCEL/NOACCEL, CSLOPE, THRESHOLD, NRATER;
Notes
This is a required command. There should be as many CALIB commands as there are subtests, in the same order as the TEST commands. The values for both the CYCLES and CRIT keywords are positional. To change a default value after the first position, blanks must be supplied for the earlier positions (delimited by commas). To use the same values for all the CRIT parameters, for instance 0.05, specify CRIT = 0.05, without parentheses. The CYCLES keyword may be used to limit the iterations. One reason would be to check the problem setup. Another reason to do so is to check the convergence of the estimates. Sometimes the priors specified for slopes and thresholds are not strict enough, preventing some estimates from converging. In that case, save the intermediate estimates in an external file by specifying the PARM keyword in the SAVE command, and use these estimates as starting values by specifying the IFNAME keyword in the FILES command in a following run. Repeat this process until convergence of the parameters is reached. For the DIF model, the optimal estimation process is obtained by specifying FREE=(0,1) and POSTERIOR.
Related topics
FILES command: IFNAME keyword (Section 3.2.6) SAVE command: PARM keyword (Section 3.2.13) TEST/SCALE command (Section 3.2.15)
274
CALIB COMMAND
ACCEL/NOACCEL option Purpose
To specify whether or not the acceleration routine should be used after each cycle of the EM iterations. ACCEL specifies that it will be used, while NOACCEL specifies that it will not be used. Format ACCEL/NOACCEL
Default NOACCEL
CRIT keyword Purpose
To control the parameters of the iterative procedure.
j
Convergence criterion for EM cycles (ACRIT). (Default 0.001)
k
l
m
n
o
Convergence criterion for inner EM cycles of category parameter estimation. (Default equal to the ACRIT value above) Convergence criterion for inner EM cycles of threshold parameter estimation. (Default equal to the ACRIT value above) Convergence criterion for inner EM cycles of slope parameter estimation. (Default equal to the ACRIT value above) Convergence criterion for inner EM cycles of guessing parameter estimation. (Default equal to the ACRIT value above) Convergence criterion for inner EM cycles of multiple-group parameter estimation. (Default equal to the ACRIT value above)
Format CRIT= (j,k,l,m,n,o)
CSLOPE option Purpose
To request the estimation of a single common slope parameter for all items in the subtest.
275
3 PARSCALE REFERENCE Format CSLOPE
CYCLES keyword Purpose
To specify parameters for the EM cycles.
d
e
f
g
h
i
The maximum number of EM cycles. (Default 10, if LENGTH < 50 (see INPUT command, Section 3.2.7); 5, otherwise) The maximum number of inner EM iterations of item and category parameter estimation. (Default 1) The maximum number of inner EM iterations of category parameter estimation. (Default 1) The maximum number of inner EM iterations of item parameter estimation. (Default 1) The maximum number of inner EM iterations of the multiple rater parameter estimation. (Default 1) The minimum number of the inner EM iterations of item and category parameter estimation. (Default 1)
Format CYCLES = (d,e,f,g,h,i)
Related topics
INPUT command: LENGTH keyword (Section 3.2.7)
DIAGNOSIS keyword Purpose
To request diagnostic output and to specify the level of diagnostic output, from zero (no diagnostic out put) through 6 (maximum diagnostic output).
n = 0: n = 1: n = 2: n = 3 to 6:
Minimum printout of parameter estimates after each calibration cycle. Summary statistics of parameter estimates after each calibration cycle. Intermediate parameter estimates after each calibration cycle. Detailed diagnostic printout for checking the program or computations.
Diagnostic output of higher numbers includes the printout of the lower ones. n = 3 or higher is not recommended for normal use.
276
CALIB COMMAND Format DIAGNOSIS=n
Default
0, no diagnostic output DIST keyword Purpose
To designate the type of prior distribution specified for the ability distribution in the population of respondents. n = 1: n = 2: n = 3: n = 4:
Uniform distribution Normal on equally spaced points Normal on Gauss-Hermite points User supplied
Format DIST=n
Default
2 ESTORDER option Purpose
To reverse the estimation order of the EM cycles. This implies that the item parameters will be estimated before the category parameters, rather than the other way around. Format ESTORDER
FLOAT option Purpose
To specify that the means of the prior distributions on the item parameters are estimated by marginal maximum likelihood, along with the parameters. If this option does not appear, the means are kept fixed at their specified values during estimation.
277
3 PARSCALE REFERENCE Format FLOAT
Remark:
Standard deviations of the priors are fixed in either case. This option should not be invoked when the data set is small and the items few. The means of the item parameters may drift indefinitely during the estimation cycles under these conditions. FREE keyword Purpose
To specify the posterior latent distributions to be used.
If the DIF model is chosen, a prior latent trait distribution is normally used for each subgroup. If the FREE keyword is specified, the posterior distribution is substituted for the prior distribution. If this keyword is specified with numerical values of t and u, the multiple posterior distributions are rescaled to mean t and standard deviation u. If NOADJUST is specified for either argument, no rescaling will be done with respect to mean or standard deviation or both. The defaults are rescaling with t = 0.0 and u = 1.0. If the third argument is COMBINED, the multiple posterior distributions are combined and a total distribution is rescaled. Otherwise, only the reference group is rescaled to mean t and standard deviation u and other groups are adjusted accordingly. If the fourth argument is specified, the MLE scores are computed and used for the posterior distributions. The MLE option is not generally recommended.
Format FREE=(t/NOADJUST,u/NOADJUST, REFERENCE/COMBINED, POSTERIOR/MLE)
Default t=0.0, u=1.0, REFERENCE, POSTERIOR
GPRIOR option Purpose
To request the use of a Beta prior distribution on the guessing parameter.
278
CALIB COMMAND Format GPRIOR
GRADED/PARTIAL option Purpose
To specify the response model to be used: GRADED specifies the graded response model, and PARTIAL specifies the partial credit model. Format GRADED/PARTIAL
ITEMFIT keyword Purpose
To specify the number of frequency score groups to be used for the computation of item-fit statistics. If the ITEMFIT value specified is greater than NQPT, the NQPT value specified will replace the ITEMFIT value. Format ITEMFIT=n
Default
None Related topics
CALIB command: NQPT keyword
LOGISTIC/NORMAL option Purpose
To specify the response function metric to be used: LOGISTIC specifies that the natural metric of the logistic response function is used in all calculations, while NORMAL specifies the use of the metric of the normal response function (normal ogive model). This choice is effective only if the graded response model is used. For the partial credit model, only the logistic response function is available.
279
3 PARSCALE REFERENCE Format LOGISTIC/NORMAL
NEWTON keyword Purpose
To specify the maximum number of Newton-Gauss (Fisher scoring) iterations following the EM cycles. Format NEWTON=n
Default
0. NOCALIB option Purpose
To request that the calibration of both item and category parameter estimation will be skipped. This option permits tests to be scored from previously estimated parameters (see FILES and INPUT commands in Sections 3.2.6 and 3.2.7). Format NOCALIB
Related topics
FILES command (Section 3.2.6) INPUT command (Section 3.2.7)
NQPT keyword Purpose
To specify the number of quadrature points to be used in the EM and Newton estimation. Format NQPT=n
280
CALIB COMMAND Default
30. NRATER option Purpose
To specify that the correction for the information function, specified with the RATER keyword on the BLOCK command, is not to be used for calibration. Format NRATER
Related topics
BLOCK command: RATER keyword (Section 3.2.2)
POSTERIOR option Purpose
To specify the computation of the posterior distribution after the M-step in the EM cycle, in addition to normally doing so after the E-step. This allows the expected proportions computed in each succeeding E-step to be based on an updated posterior distribution. Format POSTERIOR
PRIORREAD option Purpose
To specify the use of the slope, threshold, and category parameter priors specified by the user in the PRIORS command. Format PRIORREAD
Related topics
PRIORS command (Section 3.2.10)
281
3 PARSCALE REFERENCE QPREAD option Purpose
To specify that quadrature points and weights are to be read from the following QUADP command. Otherwise, the program supplies the quadrature points and weights (and no QUADP command follows). Format QPREAD
Related topics QUADP command (Section 3.2.11)
QRANGE keyword Purpose
To specify the upper (q) and lower (r) range of the quadrature points. Format QRANGE=(q,r)
Default
(-4.0, +4.0) Note:
This keyword is effective only if DIST = 1 or 2 (see SCORE command, Section 3.2.14). Related topics
SCORE command: DIST keyword (Section 3.2.14)
RIDGE keyword Purpose
To specify that a ridge constant is to be added to the diagonal elements of the information matrix to be inverted during the EM cycles and the Newton iterations.
282
CALIB COMMAND
The ridge constant starts at the value of 0.0 and is increased by v if the ratio of a pivot and the corresponding diagonal elements of the matrix is less than w. Format RIDGE=(v,w)
Default
No ridge. SCALE keyword Purpose
To provide a scale constant for the item response model. Format SCALE=n
Default
1.0 for the normal ogive item response model; 1.7 for the logistic item response model. SKIPC option Purpose
To request the skipping of the calibration of the category parameters. Format SKIPC
SPRIOR option Purpose
To request the use of a log-normal prior distribution on the slope parameter. Format SPRIOR
283
3 PARSCALE REFERENCE THRESHOLD option Purpose
To specify that the item location parameter for a dichotomous item is to be estimated directly as a threshold. Otherwise, an intercept parameter is estimated and converted to a threshold. It is only effective for dichotomously scored items. Format THRESHOLD
TPRIOR option Purpose
To request the use of a normal prior distribution on the threshold parameter. Format TPRIOR
284
COMBINE COMMAND 3.2.4 COMBINE command (Optional) Purpose
To provide the weighting coefficients of a combined subtest or subscale score. Format >COMBINE NAME=n, WEIGHTS=(list);
Notes
The keyword COMBINE on the INPUT command establishes the number of COMBINE commands that should be inserted here, if any. Each of these COMBINE commands gives the name for the combined score and the weights corresponding to the subscale scores. The number of weight constants is the same as the total number of subscales (the total number of SCORE commands). Specific subscores may be excluded from the combined score by entering a zero for that subscore. Combined score names that
do not begin with a letter, or contain embedded blanks and/or special (non-alphanumerical) symbols, or consist of more than 8 characters, must be enclosed in single quotes.
Related topics
INPUT command: COMBINE keyword (Section 3.2.7) SAVE command: COMBINE keyword (Section 3.2.13) SCORE command (Section 3.2.14)
NAME keyword Purpose
To specify the name of the combined score (up to eight characters). Format NAME=character string
285
3 PARSCALE REFERENCE Default
Blank. WEIGHTS keyword Purpose
To specify the weights for combining of the subscores. The subscores are combined linearly.
For sums and means: a set of positive fractions with decimal points, summing to 1.0, for weights of subscale scores. For DIF: a set of fractions with decimal points, summing to 0.0, for weights of subscale scores.
Format WEIGHTS= ( n1 , n2 ,..., nn ) ;
Default
None.
286
COMBINE COMMAND 3.2.5 COMMENT command (Optional) Purpose
To enter one or more lines of explanatory remarks into the program output stream. Format >COMMENT
...text... ...text...
Notes
This line and all subsequent lines preceding the FILES command will be printed verbatim in the initial output stream. The maximum length of each line is 80 characters. A semicolon to signal the end of the command is not needed. Comments are optional. Example: EXAMPLE 4. MODEL
BIOLOGY LABORATORY PERFORMANCE ASSESSMENT PARTIAL CREDIT
>COMMENT Data for this example are from the study described by Doran, et al., in the April, 1992, Science Teacher. The ratings of the student’s laboratory reports with different numbers of graded categories are assigned to different blocks. Categories 1 and 2 of the item in block 4, which had low frequency of use, were collapsed in the modified category assignments. Because of the limited … are estimated and saved. >FILES DFNAME=’EXAMPLO4.DAT’,SAVE;
Default
No comments. Related topics
FILES command (see below)
287
3 PARSCALE REFERENCE 3.2.6 FILES command (Required) Purpose
To assign names to the input files. Format >FILES DFNAME=, MFNAME=, CFNAME=, IFNAME=, OFNAME=, NFNAME=, SAVE;
Notes
The master and calibration files are binary files and created by the program. They can be saved for reuse by specifying the MASTER and CALIB keywords on the SAVE command, respectively. Otherwise, they are automatically deleted at the end of the analysis.
Other files are ASCII (plain text) files and their specifications are described in Section 3.3.1 of the manual. FILES is a required command. If filenames are supplied, the files must already exist. Names must be enclosed in single quotes. The maximum length of filenames is 128 characters, including the directory path, if needed. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. The original response data are recoded into a binary form and saved in the master file (MFNAME). If the SAMPLE keyword on the INPUT command is specified, the additional binary file, the calibration file (CFNAME), is created and the responses of the randomly sampled respondents are saved in this calibration file. The calibration file is used for the item parameter estimation. For the scoring of respondents, however, the master file is used and all respondents’ scores are computed. This option shortens the calibration stage, but still computes all respondents’ scores. If only the sampled respondents need to be scored, the user must specify the SAMPLE keyword on the SCORE command. If no SAMPLE keyword on the INPUT command is specified, only the master file is created, and it is used for both the calibration and scoring phases. To read data from a previously prepared master file, specify the MFNAME keyword instead of the DFNAME keyword. If an existing item-parameter file is specified by the IFNAME keyword, and the NOCALIB option is evoked in the CALIB command for the test, scores for the test will be computed from the previously estimated parameters in the IFNAME file.
288
FILES COMMAND Example >FILES DFNAME=’c89conv.dat’, IFNAME=’cap90ctl.if1’, SAVE;
Related topics
CALIB command (Section 3.2.3) INPUT command (Section 2.6.9) SAVE command (Section 3.2.13) SCORE command (Section 3.2.14)
Key files (Section 3.3.4)
CFNAME keyword Purpose
To provide the name of the calibration file. Format CFNAME=
Default
Supplied by program. DFNAME keyword Purpose
To specify the name of the raw data file. This file contains the original data. Format DFNAME=
Default
Command file contains the raw data after the format code(s). IFNAME keyword Purpose
To specify the name of the item-parameter file.
289
3 PARSCALE REFERENCE Format IFNAME=
Default
Supplied by program. MFNAME keyword Purpose
To provide the name of the master file. Format MFNAME=
Default
Supplied by program. NFNAME keyword Purpose
To specify the name of the not-presented-key file. Format NFNAME=
Default
Blank. OFNAME keyword Purpose
To specify the name of the omit-key file. Format OFNAME=
290
FILES COMMAND Default
Blank. SAVE option Purpose
To indicate that additional output files are requested. If this option is present, then the SAVE command must follow the FILES command. Otherwise, the next command is the INPUT command. In other words, this option has to be specified if you want to save any or all of the intermediate output files; the specific output files are selected with the following SAVE command. Format SAVE
Related topics
SAVE command (Section 3.2.13)
291
3 PARSCALE REFERENCE 3.2.7 INPUT command (Required) Purpose
To describe the original data file and to supply other information used in all three phases of the program. Format >INPUT NTEST=n, LENGTH=(list),NFMT=n, SAMPLE=n, TAKE=n, NIDCH=n, NTOTAL=n, INOPT=n, COMBINE=n, MGROUP/MRATER=n, WEIGHT, GROUPLEVEL, NRATER=(list), R-INOPT=n;
Notes
INPUT is a required command. The TAKE keyword is useful for testing the command file specifications on a small number
of respondents when the sample size is large. Related topics
Examples of INPUT commands
COMBINE keyword Purpose
To specify the number of COMBINE commands that will be used to compute weighted score combinations (see Section 3.2.4) in the case of multiple subtests or subscores. Format COMBINE=n
Default
No combined scores. Related topics
SCORE command (Section 3.2.14) COMBINE command (Section 3.2.4) Examples of INPUT commands
292
INPUT COMMAND GROUPLEVEL option Purpose
To indicate that group-level frequency data will be used as input instead of the default single respondent data (see Section 3.3.1). Note that this option is not available for the Raterseffect model. Format GROUPLEVEL
Related topics
Examples of INPUT commands
INOPT keyword Purpose
To specify the nature of group-level input records (note that this only applies if the GROUP option has been specified). The possible values for INOPT are:
1: 2: 3: 4: 5:
Categorical responses Not-presented categorical responses plus frequencies Omit categorical responses plus frequencies Not-presented plus Omit categorical responses plus frequencies A series of categorical response code plus its frequency
Format INOPT=n
Default
15. Related topics
Examples of INPUT commands INPUT command: GROUPLEVEL option
293
3 PARSCALE REFERENCE LENGTH keyword Purpose
To specify the number of items in each subtest or subscale. If there is only one subtest (the default), the format LENGTH=n may be used. Format LENGTH= ( n1 , n2 ,..., na )
Default NTOTAL.
Related topics
Examples of INPUT commands
MGROUP/MRATER keyword Purpose
To specify the number of subgroups. The keyword MGROUP should be specified if the DIF model is used. MGROUP is the number of subgroups. In this case, an MGROUP command should also be present in the command file, after the BLOCK command(s) and before the CALIB command. Format MGROUP/MRATER=n
Default MGROUP = 1 for the multiple-group models; MRATER = 0 or no Rater-effect model.
Notes
Note that either MGROUP or MRATER can be specified, but not both. The keyword MRATER should be used if the Raters-effect model is used, in which case MRATER specifies the number of raters. If MRATER is specified, an MRATER command must be present after the BLOCK command(s) and before the CALIB command.
294
INPUT COMMAND
Related topics MGROUP command (Section 3.2.8) BLOCK command (Section 3.2.2) CALIB command (Section 3.2.3) Examples of INPUT commands
NFMT keyword Purpose
To indicate the number of lines used for the format statement(s) that specify how to read the original data records. Format NFMT=n
Default
1. Related topics
Examples of INPUT commands
NIDCHAR keyword Purpose
To specify the number of characters in the respondent’s identification field, at least 1 and at most 30 characters long. Format NIDCH=n
Default
30. Related topics
Examples of INPUT commands 295
3 PARSCALE REFERENCE NRATER keyword Purpose
To specify the number of times each of k items is rated by each rater. Note that this keyword can only be used when multiple raters rate examinees. Note
When rater data are analyzed, data are read in a different format. See Section 3.2.17 for examples of variable format statements for such data. Format NRATER= ( n1 , n2 ,..., nk )
Default
1. Related topics INPUT command: MRATER keyword INPUT command: R-INOPT keyword
NTEST keyword Purpose
To indicate the number of subtests or subscales to be analyzed. Format NTEST=n
Default
1. Related topics
Examples of INPUT commands
296
INPUT COMMAND NTOTAL keyword Purpose
To specify the total number of items in the original data records. The items for particular subtests or subscales are selected from these items using the TESTi (or SCALEi) commands. Format NTOTAL=n
Default
0. Related topics
Examples of INPUT commands TEST/SCALE command (Section 3.2.15)
R-INOPT keyword Purpose
This keyword is exclusively used when examinees are rated by multiple raters. By default, it is assumed that all the data for an examinee are given on the same line. If multiple lines are used, n should be set to the number of lines containing information for an examinee. Note
When rater data are analyzed, data are read in a different format. See Section 3.2.17 below for examples of variable format statements for such data. Format R-INOPT=n
Default R-INOPT=1.
Related topics
INPUT command: MRATER keyword INPUT command: NRATER keyword
297
3 PARSCALE REFERENCE SAMPLE keyword Purpose
To request a percentage (0-100) of respondents to be randomly sampled from the raw data file. Format SAMPLE=n
Default SAMPLE=100.
Related topics
Examples of INPUT commands
TAKE keyword Purpose
To request the analysis of only the first n respondents in the raw data file. Format TAKE=n
Default
Use all data WEIGHT option Purpose
To indicate the presence of case weights. If this option is present, each input record has a case weight. In each data record, the weight follows the case ID and precedes the item responses. Format WEIGHT
298
INPUT COMMAND Related topics
Examples of INPUT commands
Examples of INPUT commands
The following INPUT command specifies a 160 item test divided in 16 subtests of 10 items each. The first fifteen characters of each record are for identification purposes, and one format statement will follow describing each record. >INPUT NIDCH=5, NTOTAL=160, NTEST=16,NFMT=1, LENGTH=(10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10); (15A1,5X,60A1,/,80A1,/,20A1)
The next example is a variation on the first in that the data are now weighted. The option WEIGHT specifies that each record will have a case weight. It follows immediately after the case ID, and—as the following format statement describes—has a field width of five columns. >INPUT WEIGHT, NIDCH=15, NTOTAL=160, NTEST=17, LENGTH= (6,6,6,6,6,6,6,6,6,6,6,6,5,5,6,6,66); 15A1,F5.0,12(2X,6A2),2(2X,5A1),2(2X,6A2),/,66A1)
299
3 PARSCALE REFERENCE 3.2.8 MGROUP command (Optional) Purpose
To provide necessary information about the DIF model. Format >MGROUP GNAME=(list), GCODE=(list), DIF=(list), REFERENCE=n, COMMON=(list);
Notes
This command is required if the MGROUP keyword is specified in the INPUT command. Group names and group codes must be enclosed in single quotes if they do not begin with a letter or if they contain blanks or special (non-alphanumeric) symbols. Note that group codes in the data records do not need quotes, regardless of what characters are used.
Related topics
INPUT command: MGROUP/MRATER keyword
COMMON keyword Purpose
To specify the positions of the common blocks for each subtest. Note that this can be used only with the DIF model. A common block contains items for which the model parameters are the same among the multiple groups in spite of the DIF model. Format COMMON= ( n1 , n2 ,...)
Default
None.
300
MGROUP COMMAND DIF keyword Purpose
To specify the DIF model. If the value of nn is 1, separate item parameters for multiple subgroups are estimated. If the value of nn is 0, a common item parameter for multiple subgroups is obtained. Each position of DIF arguments corresponds to a particular item parameter:
n1 : n2 : n3 :
If slope parameter differs among groups, set to 1, otherwise 0. If threshold parameters differ among groups, set to 1, otherwise 0. If category parameters differ among groups, set to 1, otherwise 0.
n4 :
If lower asymptote parameters differ among groups, set to 1, otherwise 0.
Format DIF= ( n1 , n2 , n3 , n4 )
Default DIF=(0,1,0,0).
GCODE keyword Purpose
To specify the subgroup identification code, which appears in the data field of the original response file (DFNAME) in the same order as the group names, up to four characters. Format GCODE= ( n1 , n2 ,..., nMGROUP )
Default GCODE=(‘nnnl’, ‘nnn2’, ...), where n is a blank character.
Related topics
FILES command: DFNAME keyword (Section 3.2.6) INPUT command: MGROUP/MRATER keyword (Section 3.2.7)
301
3 PARSCALE REFERENCE GNAME keyword Purpose
To supply a list of names of subgroups, up to eight characters each. Format GNAME= ( n1 , n2 ,..., nMGROUP )
Default GROUP 01, GROUP 02, …
Related topics
INPUT command: MGROUP/MRATER keyword (Section 3.2.7)
REFERENCE keyword Purpose
To specify the position of the reference subgroup in the GCODE list (i.e., the subscript of the reference group corresponding to symbols n11 , n21 ,..., nMGROUP ). The parameter values for other subgroups are adjusted to this reference subgroup. If REFERENCE=0, no reference subgroup is set and no adjustment is performed. This keyword is used only for the DIF model. Format REFERENCE=n
Default n=1.
302
MRATER COMMAND 3.2.9 MRATER command (Optional) Purpose
To provide necessary information about the Raters-effect model. Format >MRATER RNAME=(list), RCODE=(list), RATER=(list);
Notes
This command is required if the MRATER keyword is specified on the INPUT command. Rater names and rater codes must be enclosed in single quotes if they do not begin with a letter or if they contain blanks or special (non-alphanumeric) symbols. Note that group codes in the data records do not need quotes, regardless of what characters are used.
Related topics
INPUT command: MGROUP/MRATER keyword (Section 3.2.7)
RATER keyword Purpose
To specify the raters’ weights. For the Raters-effect model, the ability score for each respondent is computed for each subtest (or subscale) and each rater separately. A total score of each respondent for each subtest (or subscale) is computed by summing those scores over items within each subtest and all raters who have rated the respondent. The rater weights of this keyword are used to compute the weighted subtest or subscale score for each respondent. Since the number of raters who rated each respondent’s responses may vary, the weights are normalized (divided by their sum) for each respondent. Format RATER= ( n1 , n2 ,..., nMRATER ) .
Default n = 1.0.
303
3 PARSCALE REFERENCE Related topics
INPUT command: MGROUP/MRATER keyword (Section 3.2.7)
RCODE keyword Purpose
To specify the rater identification code, which appears in the data field of the original response file (DFNAME) in the same order as the rater names, up to four characters. Format RCODE= ( n1 , n2 ,..., nMRATER ) .
Default RCODE=(‘nnnl’,’nnn2’, ...),where n is a blank character.
Related topics
INPUT command: MGROUP/MRATER keyword (Section 3.2.7) FILES command: DFNAME keyword (Section 3.2.6)
RNAME keyword Purpose
To supply a list of names of raters, up to eight characters each. Format RNAME= ( n1 , n2 ,..., nMRATER )
Default RATER001, RATER002, ….
Related topics
INPUT command: MGROUP/MRATER keyword (Section 3.2.7)
304
PRIORS COMMAND 3.2.10 PRIORS command (Optional) Purpose
To specify prior distributions for constrained estimation of item parameters of subtest or subscale i. Format >PRIORS TMU=(list), TSIGMA=(list), SMU=(list), SSIGMA=(list), GMU=(list), GSIGMA=(list), SOPTION;
Notes
If the PRIORREAD option has been specified on the CALIB command, the PRIORS command is required. Of course, since there should be as many CALIB commands as there are subtests, the number and order of the PRIORS commands should mimic the CALIB commands. The program assumes a normal prior distribution for the thresholds and a lognormal prior distribution for the slopes.
Related topics
CALIB command: PRIORREAD option (Section 3.2.3)
GMU keyword Purpose
To specify the real-valued “alpha” parameters for the Beta prior distribution of the lower asymptote (guessing) parameter. Format GMU= ( n1 , n2 ,..., nn )
Default
Supplied by the program.
305
3 PARSCALE REFERENCE GSIGMA keyword Purpose
To specify the real-valued “beta” prior parameters for the Beta prior distribution of the lower asymptote (guessing) parameter. Format GSIGMA= ( n1 , n2 ,..., nn )
Default
Supplied by the program. SMU keyword Purpose
To supply real-valued prior means for the item slopes. Format SMU= ( n1 , n2 ,..., nn )
Default
Supplied by the program. SOPTION option Purpose
To indicate that the means and the standard deviations for prior slopes are already in the log(e) metric. Format SOPTION
Default
The regular arithmetic metric.
306
PRIORS COMMAND SSIGMA keyword Purpose
To specify real-valued prior standard deviations of the item slopes. Format SSIGMA= ( n1 , n2 ,..., nn )
Default
Supplied by the program. TMU keyword Purpose
To specify real-valued prior means for the item thresholds. Format TMU= ( n1 , n2 ,..., nn )
Default
Supplied by the program. TSIGMA keyword Purpose
To specify real-valued prior standard deviations of the item thresholds. Format TSIGMA= ( n1 , n2 ,..., nn )
Default
Supplied by the program.
307
3 PARSCALE REFERENCE 3.2.11 QUADP command (Optional) Purpose
To specify that user-supplied quadrature points and weights, or points and ordinates of the discrete finite representation of the prior ability for subtest or subscale i are provided. Format >QUADP
POINTS=(list), WEIGHTS=(list);
Notes
If the QPREAD option has been specified on the CALIB command, the QUADP command is required. Of course, since there should be as many CALIB commands as there are subtests, the number and order of the QUADP commands should mimic the CALIB commands. Related topics
CALIB command: NQPT keyword (Section 3.2.3) CALIB command: QPREAD option
POINTS keyword Purpose
To provide a set of NQPT (on CALIB command) real-numbered values (with decimal points) of the quadrature points of the discrete distribution. Format POINTS= ( n1 , n2 ,..., nNQPT )
Default
Supplied by the program. Related topics
CALIB command: NQPT keyword (Section 3.2.3)
308
QUADP COMMAND WEIGHTS keyword Purpose
To supply a set of NQPT (on CALIB command) positive fractions (with decimal points and summing to 1.0) for weights of probabilities of points in the discrete distribution. Format WEIGHTS= ( n1 , n2 ,..., nNQPT )
Default
Supplied by the program. Related topics
CALIB command: NQPT keyword (Section 3.2.3)
309
3 PARSCALE REFERENCE 3.2.12 QUADS command (Optional) Purpose
To specify that user-supplied quadrature points and weights, or points and ordinates of the discrete step-function representation of the scale scores for the respondents on subtest or subscale i are provided. Format >QUADS
POINTS=(list), WEIGHTS=(list);
Notes
If the QPREAD option has been specified on the SCORE command, the QUADS command is required. Of course, since there should be as many SCORE commands as there are subtests, the number and order of the QUADS commands should mimic the SCORE commands. Related topics
SCORE command: NQPT keyword (Section 3.2.14) SCORE command: QPREAD option
POINTS keyword Purpose
To specify a set of NQPT (on SCORE command) real-numbered values (with decimal points) of the quadrature points of the discrete distribution. Format POINTS= ( n1 , n2 ,..., nNQPT )
Default
Supplied by the program. Related topics
SCORE command: NQPT keyword (Section 3.2.14)
310
QUADS COMMAND WEIGHTS keyword Purpose
To specify a set of NQPT (on SCORE command) positive fractions (with decimal points and summing to 1.0) for weights of probabilities of points in the discrete distribution. Format WEIGHTS= (b1 , b2 ,..., bNQPT )
Default
Supplied by the program. Related topics
SCORE command: NQPT keyword (Section 3.2.14)
311
3 PARSCALE REFERENCE 3.2.13 SAVE command (Optional) Purpose
To specify the output files to be saved. Format >SAVE
MASTER=, CALIB=, PARM=, SCORE=, INFORMATION=, FIT=, COMBINE=;
Notes
The master and calibration data files are saved in a binary form. Other files are saved as ASCII (plain text) files; their formats are described in Section 3.4.1. The SAVE command is required if the SAVE option on the FILES command has been entered. There are no default filenames for this command. If a specific name is supplied with a keyword, then that particular output file will be saved after the analysis is completed. If the same filename is used in both the FILES and the SAVE command, then the existing file will be overwritten after it has been read. Thus, different filenames should be supplied for the IFNAME keyword on the FILES command and the PARM keyword on the SAVE command to avoid replacing old item-parameter values with new values. Names must be enclosed in single quotes. The maximum length of filenames is 128 characters, including the path, if needed. See Section 3.2.6 for more details.
Related topics
FILES command (Section 3.2.6)
Format of output files (Section 3.4.1)
CALIB keyword Purpose
To specify a calibration data filename. Format CALIB=
312
SAVE COMMAND Default
None. Related topics
Format of output files (Section 3.4.1)
COMBINE keyword Purpose
To specify a combined score filename. Format COMBINE=
Default
None. Related topics
Combined score file (COMBINE) (Section 3.4.2)
FIT keyword Purpose
To specify a fit-statistics filename. Format FIT=
Default
None. Related topics
Fit statistics file (Section 3.4.3)
313
3 PARSCALE REFERENCE INFORMATION keyword Purpose
To specify an item information filename. Format INFORMATION=
Default
None. Related topics
Item information file (Section 3.4.5)
MASTER keyword Purpose
To specify a master data filename. Format MASTER=
Default
None. Related topics
Format of output files (Section 3.4.1)
PARM keyword Purpose
To specify an item parameter filename. Format PARM=
314
SAVE COMMAND Default
None. Related topics
Item parameter file (Section 3.4.4)
SCORE keyword Purpose
To specify a subject scores filename. Format SCORE=
Default
None. Related topics
Subject scores file (Section 3.4.6)
315
3 PARSCALE REFERENCE 3.2.14 SCORE command (Required) Purpose
To request the scoring of individual respondents or of response frequencies in group-level data. There is a SCORE command for each subtest or subscale. Format >SCORE NQPT=n, DIST=n, QRANGE=(list), SMEAN=n, SSD=n, NAME=n, PFQ=n, ITERATION=(list), PRINT, QPREAD, NOSCORE, SAMPLE, RESCALE, SCORING=STANDARD/CALIBRATION, EAP/MLE/WML, NOADJUST, FlT, NRATER;
Notes
There should be as many SCORE commands as there are subtests, in the same order as the TEST commands. If a score file has been specified by the SCORE keyword on the SAVE command, all subject scores will be printed to the output file, whether the PRINT option on the SCORE command has been selected or not. If the option RESCALE is present, the keywords SMEAN and SSD are rescaling constants. Let the rescaled score be θ * and the original score θ . Then, θ * = sθ + t , where s is the scaling constant (SSD) and t is the location constant (SMEAN).
Related topics
Examples of SCORE commands SAVE command: SCORE keyword (Section 3.2.13) TEST command (Section 3.2.15)
DIST keyword Purpose
To specify the type of prior distribution. This keyword is to be used when EAP scoring is selected.
n = 1: n = 2: n = 3:
Uniform distribution Normal on equally spaced points Normal on Gauss-Hermite points
316
SCORE COMMAND Format DIST=n
Default
2. Related topics
Examples of SCORE commands SCORE command: EAP/MLE/WML option
EAP/MLE/WML option Purpose
To specify a method of estimating scale scores.
EAP: MLE: WML:
Expected a posteriori estimation (Bayes) Maximum likelihood estimation Warms’ weighted maximum likelihood estimation
Format EAP/MLE/WML
Default EAP
Related topics
Examples of SCORE commands
FIT option
To request the printing of fit statistics for score estimates for the group-level data. This keyword is not effective for individual response data. Format FIT
Related topics
Examples of SCORE commands 317
3 PARSCALE REFERENCE ITERATION keyword Purpose
In maximum likelihood scoring, stops the iterative solution when the changes are less than i, or the number of iterations is greater than j. Format ITERATION=(i,j)
Default
(0.01, 20). Related topics
Examples of SCORE commands
NAME keyword Purpose
To specify a score name different from the subtest or subscale name. Format NAME=character string
Default
Test name Related topics
Examples of SCORE commands
NOADJUST option Purpose
To suppress the calibration adjustment of the category parameter mean during scoring.
318
SCORE COMMAND Format NOADJUST
Related topics
Examples of SCORE commands
NOSCORE option Purpose
To suppress the scoring of responses. Format NOSCORE
Related topics
Examples of SCORE commands
NQPT keyword Purpose
To set the number of quadrature points if EAP scoring has been selected. Format NQPT=n
Default
30. Related topics
Examples of SCORE commands
NRATER option Purpose
To stop the correction for the information function (specified with the RATER keyword on the BLOCK command) from being used for scoring.
319
3 PARSCALE REFERENCE Format NRATER
Related topics
BLOCK command: RATER keyword (Section 3.2.2) Examples of SCORE commands
PFQ keyword Purpose
To specify the response percentage to be moved to the immediately adjacent category to enable the computation of ML scale scores if the input data are group-level frequency data (see the INPUT command, Section 3.2.7) and all item responses are in the lowest or highest categories. The edited response records are printed out if DIAG=2 or higher on the CALIBRATION command. Format PFQ=n
Default
(n = 1 to 99). Related topics
CALIB command: DIAGNOSIS keyword (Section 3.2.3) Examples of SCORE commands INPUT command (Section 3.2.7)
PRINT option Purpose
To request the printing of the subject scores to the output file. Format PRINT
320
SCORE COMMAND QPREAD option Purpose
To indicate that quadrature points and weights will be read from the QUADS command. Otherwise, the program supplies the quadrature points and weights (and no QUADS command follows). Format QPREAD
Related topics
Examples of SCORE commands QUADS command (Section 3.2.12)
QRANGE keyword Purpose
To specify the upper (c) and lower (d ) range of quadrature points. Format QRANGE=(c,d)
Default
(-4.0, +4.0). Related topics
Examples of SCORE commands
RESCALE option Purpose
To use the values specified for the keywords SMEAN and SSD as rescaling constants instead of a mean and a standard deviation, respectively, of the sample distribution. Format RESCALE
321
3 PARSCALE REFERENCE Related topics
Examples of SCORE commands SCORE command: SMEAN keyword SCORE command: SSD keyword
SAMPLE option Purpose
To request that only the sampled subjects are scored (see the SAMPLE keyword on the INPUT command, Section 3.2.7). Format SAMPLE
Related topics
Examples of SCORE commands INPUT command: SAMPLE keyword (Section 3.2.7)
SCORING keyword Purpose
To specify the scoring function to be used for scoring. STANDARD specifies that the standard scoring function (1.0, 2.0,…) is to be used, even if a different function is used for calibration. CALIBRATION specifies that the calibration function specified in the BLOCK commands is to be used for scoring. Format SCORING=STANDARD/CALIBRATION
Default STANDARD.
Related topics
BLOCK command (Section 3.2.2) Examples of SCORE commands
322
SCORE COMMAND SMEAN keyword Purpose
To request that the original scale scores be rescaled such that the mean equals n. Format SMEAN=n
Default
No rescale. Related topics
Examples of SCORE commands
SSD keyword Purpose
To request that the original scale scores are rescaled such that the standard deviation equals n. Format SSD=n
Default
No rescale. Related topics
Examples of SCORE commands
Examples of SCORE commands
This example shows how an existing item-parameter file is used for scoring observations. Calibration is not needed, therefore the NOCALIB option of CALIB has been invoked. Scoring will be done with maximum likelihood estimation, and the score distribution will be adjusted to the mean and the standard deviation specified with the SMEAN and SSD keywords, respectively. >FILES NAME=’c89conv.dat’, IFNAME=’cap90ctl.ifl’, SAVE; >SAVE SCORE = ’cap89.scr’ ;
323
3 PARSCALE REFERENCE >INPUT WEIGHT, NIDCH=15, NTOTAL=120, LENGTH=6, NTEST=1, NFMT=3; (15A1,F5.0,2(7X,6A3,1X,19X,6A3,1X) 3(/,20X,2(7X,6A3,1X,19X,6A3,1X)),/, 20x,2(7X,6A3,1X, 19X,6A3,1X)) >TEST TNAME=AUTORHET, NBLOCK=1, ITEM=(1,3,5,11,17,19), INAME=(A20R,A21R,A22R,A25R,A28R,A29R) ; >BLOCK BNAME=’AUT-RHET’, NITEMS=6, NCATEGORIES=6, MODIFIED=(6,5,4,3,2,1) ; >CAL NOCALIB ; >SCORE SMEAN=254.182, SSD=66.496, MLE ;
Related topics
CALIB command: NOCALIB option (Section 3.2.3) SCORE command: SSD keyword SCORE command: SMEAN keyword
324
TEST/SCALE COMMAND 3.2.15 TEST/SCALE command (Required) Purpose
To identify the test or scale, or subtest i or subscale i. The keyword NTEST on the INPUT command supplies the number of subtests or subscales. The same number of TEST (or SCALE) commands is expected. The order of these TEST (or SCALE) commands is the same as the order in which the subtest lengths are specified on the INPUT command. If there is only one test or scale, there is only one test command. Location of the items, names of the items, and starting values for estimating the item parameters can also be supplied with the TEST (or SCALE) command. Format >TEST/SCALE
TNAME=n, NBLOCK=n, ITEMS=(list), INAME=(list), INTERCEPT=(list), THRESHOLD=(list), SLOPE=(list);
Notes
One TEST command is required for each subtest as specified by the NTEST keyword on the INPUT command. If there are no subtests (NTESTS=1), only one TEST command is needed. The order of the TEST commands is the same as the order used in the specification of the length of each subtest on the INPUT command. If the keywords INTERCEPT, THRESHOLD, or SLOPE are given without any arguments, the values 0.0, 0.0, and 1.0 are used for the initial intercept, threshold, and slope parameters, respectively. In this case, no initial values are computed by the program. Test or item names that o do not begin with a letter, or o contain blanks and/or special (non-alphanumerical) symbols, or o consist of more than 8 characters, must be enclosed in single quotes. Also see the section of examples of TEST/SCALE commands.
Related topics
INPUT command: NTEST keyword (Section 3.2.7) Examples of TEST/SCALE commands
325
3 PARSCALE REFERENCE INAMES keyword Purpose
To specify a list of names (up to four characters each) for the items in this (sub)test or (sub)scale. Format INAME= ( n1 , n2 ,..., nn1 )
Default
Supplied by the program. Related topics
Examples of TEST/SCALE commands
INTERCEPT keyword Purpose
To provide real-numbered starting values (with decimal points) for estimating the item intercepts. Starting values may be specified by INTERCEPT or THRESHOLD, but not by both. Format INTERCEPT= ( n1 , n2 ,..., nn1 )
Default
Supplied by the program. Related topics
Examples of TEST/SCALE commands
ITEMS keyword Purpose
To supply a list of the serial position numbers of the items in the total response record.
326
TEST/SCALE COMMAND
Format ITEMS= ( n1 , n2 ,..., nn1 )
Default
through LENGTH. Related topics
Examples of TEST/SCALE commands INPUT command: LENGTH keyword (Section 3.2.7)
NBLOCK keyword Purpose
To indicate the number of blocks of items that share common categorical parameters. When items are rated by a single Likert scale, for example, the number and meaning of their categories is the same and all may be assigned to the same block. The items must be selected or rearranged so that all those TEST or SCALES in block 1 precede those in block 2, which precede those in block 3, etc. (see BLOCK command, discussed in Section 3.2.2). Format NBLOCK=n
Default
1. Related topics
BLOCK command (Section 3.2.2) Examples of TEST/SCALE commands TEST/SCALE command
SLOPE keyword Purpose
To specify real-numbered starting values (with decimal points) for estimating the item slopes.
327
3 PARSCALE REFERENCE Format SLOPE= ( n1 , n2 ,..., nn1 )
Default
Supplied by the program. Related topics
Examples of TEST/SCALE commands
THRESHOLD keyword Purpose
To specify real-numbered starting values (with decimal points) for estimating the item thresholds. Starting values may be specified by INTERCEPT or THRESHOLD, but not by both. Format THRESHOLD= ( n1 , n2 ,..., nn1 )
Default
Supplied by the program. Related topics
Examples of TEST/SCALE commands TEST/SCALE command: INTERCEPT keyword
TNAME keyword Purpose
To provide a name for the test or scale, subtest or subscale i, up to eight characters. Format TNAME=character string
328
TEST/SCALE COMMAND Default
Supplied by the program. Related topics
Examples of TEST/SCALE commands
Examples of TEST/SCALE commands
The first TEST command describes a subtest with the name “AUTORHET” consisting of one block of items with the serial positions 1, 3, 5, 7, 9, 11, 13, 15, 17, and 19, and with names like “A20R.” The command file will have sixteen TEST commands, as specified with NTEST on the INPUT command. Note the order of the commands. >INPUT
NIDCH=15,NTOTAL=160, NTEST=16, GROUP, NFMT=2, LENGTH=(10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10); (15A1,5X,3(5(1OA1,2X),3X),/,10A3) >TEST1 TNAME=AUTORHET,NBLOCK=1,ITEM=(1(2)19), INAME=(A20R,A21R,A22R,A23R,A24R, A25R,A26R,A27R,A28R,A29R); >BLOCK BNAME=’AUT-HET’,NITEM=10, NCAT=6, MODIFIED=(6,5,4,3,2,1); >CAL DIAGNOS=1, SCALE=1.7, LOGISTIC, CYCLES=50, CRITERION=0.0075, SPRIOR, TPRIOR; >SCORE NOSCORE,MLE; >TEST2 TNAME=AUTOCONV,NBL=1,ITEM=(2(2)20), .... ....
Related topics
INPUT command (Section 3.2.7) INPUT command: NTEST keyword
329
3 PARSCALE REFERENCE 3.2.16 TITLE command (Required) Purpose
To provide a label that will be used throughout the output to identify the problem run. Format ...text... ...text...
Notes
The first two lines of the command file are title lines. If the title fits on one line, a second, blank line should be entered before the next command starts. The text will be printed verbatim at the top of each output section, as well as at the start of some output files. The two title lines are required at the start of the command file. No special delimiters (> or ;) are required. Example: CALIFORNIA ASSESSMENT PROJECT ‘90 CALIBRATION OF ESSAY-TEST RATINGS >COMMENT GROUP-LEVEL ANALYSIS
330
VARIABLE FORMAT STATEMENT 3.2.17 Variable format statements
The data layout must be described in a variable format statement. This statement is entered within parentheses and immediately follows the INPUT command. When data (labels, raw data, summary statistics) are used in fixed format, a format statement is needed to instruct the program how to read the data. The general form of such a statement is (rCw) or (rCw.d), where: r Repeat count; if omitted, 1 is assumed. C Format code: A Code for character values I Code for integer values F Code for real numbers w Field width, or number of columns. d Number of decimal places (for F-format).
The format statement should be enclosed in parentheses. Blanks within the statement are ignored: (r C w. d) is acceptable. Anything after the right parenthesis and on the same line is also ignored by the program, thus comments may be placed after the format statement. The following example shows three ways to read five integers, with the same result: (5I1) 12345
(5I2) 1 2 3 4 5
(I1,I2,3I3) 1 2 3 4 5
The F-format requires the number of decimal places in the field description. If there are none (and eight columns) specify (F8.0); (F8) is not allowed. However, if a data value con-
331
3 PARSCALE REFERENCE tains a decimal point, then it overrides the location of the decimal point as specified by the general field description. If the general field description is given by (F8.5), then 12345678 would result in the real number +123.45678, but the decimal point in —1234.56 would not be changed. Only blanks will result in the value zero. The plus sign is optional. The “X” operator can be used to skip spaces or unused variables in the data file. For example, (F7.4,8X,2F3.2) informs the program that the data file has 21 columns per record. The first value can be found in the first seven columns (and there are four decimal places), then eight columns should be skipped, and a second and third value are in columns 16 - 21, both occupying three columns (with two decimal places). Note that the ITEMS keyword on the TEST (or SCALE) command also allows selection and reordering of variables. Another option is the use of the tabulator format descriptor T, followed by a column number n. For example, (F8.5, T61, 2F5.1) describes three data fields; in columns 1 - 8, with five decimal digits, next in columns 61 - 65 and 66 - 70, both with one decimal digit. If the number n is smaller than the current column position, left-tabbing results. Left tabs can be unreliable in PC systems and should be used cautiously. A forward slash (/) in an F-format means “skip the rest of this line and continue on the next line. Thus, (F10.3/5F10.3) or (F10.3,/,5F10.3) instructs the program to read the first variable on the first line, then to skip the remaining variables on that line and to read five variables on the next line. For other uses of a format statement, a FORTRAN textbook should be consulted. Related topics
ITEMS keyword on the TEST/SCALE command (Section 3.2.15)
332
INPUT AND OUTPUT FILES
3.3 Input files 3.3.1 Specification of input files
The following types of data can be used as input for a PARSCALE analysis:
Original response data for individual respondents Individual response data for a single-group model Individual response data for a DIF multiple-group model Individual response data for a rater-effect multiple-group model Original response data for group-level frequencies
In addition to these, item parameter files from previous analyses may be used as input. The use of an omitted key file and not-presented key file is also permitted. Each of these data types will now be discussed in turn. 3.3.2 Individual level data Original response data for individual respondents (DFNAME)
Each record is read by a variable format statement supplied by the user. The following fields are contained in each record. Field
Format
Description
Identification A1
Required. is specified by the NIDCHAR keyword in the INPUT command.
Subgroup A identification
For a single model, this field should be omitted. For DIF multiplegroup models, the subgroup code is read as characters. For the Rater-Effect Model, the rater’s code is read as characters. The length of the characters (b), must be less than eight, and they should be specified by the GCODE keyword on the MGROUP command. The maximum number of subgroups should be specified in the MGROUP keyword on the INPUT command.
Fw.d
If the WEIGHT option appears in the INPUT command, this WEIGHT field must be read, in floating point format.
Weight
Response vector
should correspond to the NTOTAL keyword on the INPUT command, can be specified by the user, but only the first four A
characters are significant. These character responses are converted into integers according to the list of response codes, specified by the ORIGINAL keyword in the BLOCK command.
333
3 PARSCALE REFERENCE Notes
For a single group model and a DIF multiple-group model, each respondent’s responses are represented by a single response vector. For the DIF model, response vectors are not necessarily to be sorted by subgroups. Response vectors of all subgroups can be mixed. If the identification field is a blank or the end of file is reached, the program terminates the input procedure. For a Rater-effect multiple-group model, the responses of a single respondent to constructed items may be rated by more than one rater. The program assumes that multiple rated response vectors of each respondent are consecutively read, and only the first record of each respondent has a non-blank identification field. Subsequent response vectors rated by different raters for the respondent have a blank field of identification and non-blank field of subgroup identification (Rater Identification). If both respondent and rater’s identification are blanks, or the end of file is reached, the program assumes that it is the end of the input file. Some of the items are not rated. These objective items must be duplicated for each rater response vector. If a constructed response item is not rated by a certain value, the response should be coded as not-presented. Related topics
BLOCK command: ORIGINAL keyword (Section 3.2.2) INPUT command: NIDCH, NTOTAL, and MGROUP/MRATER keywords (Section 3.2.7) INPUT command: WEIGHT option MGROUP command: GCODE keyword (Section 3.2.8)
Variable format statements (Section 3.2.17)
Individual response data for single-group model
In this case, the format of the data should be: Respondent 1 [ID] <WEIGHT> [ITEM RESPONSES] Respondent 2 [ID] <WEIGHT> [ITEM RESPONSES] ... ... [Blank Record or End-of-file]
Individual response data for DIF multiple-group model
In this case, the format of the data should be: Respondent 1 [ID.] [GROUP CODE] <WEIGHT> [ITEM RESPONSES] Respondent 2 [ID.] [GROUP CODE] <WEIGHT> [ITEM RESPONSES] ... ... [Blank Record or End-of-file]
334
INPUT AND OUTPUT FILES Individual response data for rater-effect multiple-group model
In this case, the format of the data should be: Respondent 1 [ID.] <WEIGHT> [RATER CODE,ITEM RESPONSE].... [RATER CODE,ITEM RESPONSE] Respondent 2 [ID.] <WEIGHT> [RATER CODE,ITEM RESPONSE].... [RATER CODE,ITEM RESPONSE] ... ... [Blank Record or End-of-file]
3.3.3 Group-level data Original response data for group-level Frequencies (DFNAME)
If the GROUP keyword appears in the INPUT command, the input data are assumed to be grouplevel frequencies of categorical responses. Each record is read by a format statement supplied by the user. The following fields are contained in each record. Field
Format
Description
Identification
A1
Required. is specified by the NIDCHAR keyword in the INPUT command.
Subgroup identification
A
For a single-group model, this field should be omitted. For DIF multiple-group models, the subgroup code is read as characters. For the Rater-effect Model, the rater’s code is read as characters. The length of characters, (b), must be less than eight, and they should be specified by the GCODE keyword on the MGROUP command. The maximum number of subgroups should be specified in the MGROUP keyword on the INPUT command.
Weight
Fw.d
If the WEIGHT option appears in the INPUT command, this WEIGHT field must be read, in floating point format. The INOPT keyword on the INPUT command allows the program to read the following input vector:
Response vector
Integer
INOPT=1:
Frequencies of Categorical Responses[1][2][3]...[ m j ]
335
3 PARSCALE REFERENCE INOPT=2:
Frequencies of Not-Presented Responses and Categorical Responses [N-P][1][2]...[ m j ] INOPT=3:
Frequencies of Omitted Responses and Categorical Responses [Omit][1][2]..[ m j ] INOPT=4:
Frequencies of Not-Presented, Omitted, and Categorical Responses [N-P][Omit][1][2]...[ m j ] INOPT=5: A series of Response Codes and Frequencies [Code][Fre][Code][Fre]
If INOPT=5 is used, the response code is read in as characters and specified by the ORIGINAL keyword on the BLOCK command. The distinctions between input data streams for the single-group and multiple-group (DIF and Rater-effect) models are the same as those in the individual response data discussed earlier. Related topics
BLOCK command: ORIGINAL keyword (Section 3.2.2) INPUT command: INOPT, MGROUP/MRATER, and NIDCHAR keywords (Section 3.2.7) INPUT command: GROUPLEVEL and WEIGHT options MGROUP command: GCODE keyword (Section 3.2.8)
Variable format statements (Section 3.2.17)
3.3.4 Key files Item parameter file
See the format specification for PARM file in the SAVE command. Omitted key file
This file should contain a single record in the same format as the individual response data. The fields of identification, subgroup identification, and weight are not processed and do not need to be occupied. The current version of PARSCALE treats omitted response as not-presented.
336
INPUT AND OUTPUT FILES Not-presented file
This file should contain a single record in the same format as the individual response data. The fields of identification, subgroup identification, and weight are not processed and do not need to be occupied. For multiple-group models (DIF and Raters’ Effect), this file is particularly important because for those situations, not all items are presented to all subgroups of respondents or not all items are rated by all raters. If a not-presented code is present in the original data file and this file is not specified, response records containing the code will be rejected. Related topics
FILES command: OFNAME and NFNAME keywords (Section 3.2.6) SAVE command: PARM keyword (Section 3.2.13)
3.4 Output files 3.4.1 Format of output files
Apart from the standard 4 list output files produced (*.ph0, *.ph1, *.ph2, and *.ph3), the user can instruct the program to create the following additional output files, using keywords on the SAVE command:
combined score file (COMBINE) fit statistics file (FIT) item information file (INFORMATION) item parameter file (PARM) subject scores file (SCORE)
3.4.2 Combined score file
In the combined score file, the first eight records form the file’s title lines. Format: ('1',//,T25,30'*',/,T32,'COMBINED SCORES',/,T25,30'*',///)
The following specifications are repeated for all respondents or sampled respondents. Format
Description
(A1,
Identification of respondent
T22,'|',2X,I7,2X
The respondent number
A8,2X,
Group name
F7.2)
Weight for respondent
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3 PARSCALE REFERENCE Finally, the next specifications are repeated for each subtest (from 1 through NTEST) within each respondent (NTEST is the number of subtests specified by the NTEST keyword in the INPUT command) Format
Description
(1X,I3,2X,
The subtest number
A8,2X,
The subtest name
T22,'|',2X,F7.3,2X,
Combined score
F7.3)
S.E. of combined scores
Related topics
INPUT command: NTEST keyword (Section 3.2.7) INPUT command: COMBINE keyword
3.4.3 Fit statistics file
The first four records of the fit statistics file describe the run as follows: Records Format
Description
1&2
(20A4,/,20A4)
The title records from the PARSCALE run
3
(I4,
The number of subtests
I4)
The number of subgroups
(I4,
The subtest number
A8,
The subtest name
I4)
The number of boundaries for the fit statistics computation (NBOUND)
4
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INPUT AND OUTPUT FILES The following information is repeated for each group (from 1 through MGROUP): Format
Description
(I4,
Subgroup number
A8)
Subgroup name
(8F10.5,/)
Mean ability for NBOUND boundaries
The information shown below, together with its format description, is written to the fit statistics file for each block (from 1 through NBLOCK) within each group, and for each item (from 1 through NITEMS) within each block. Format
Description
(I4,
The block number
A8,
The block name
I4,
The number of categories
I4,
The item number
A4)
The item name
(8F10.5,/)
Observed sample sizes for NBOUND boundaries
Finally, the following information is repeated for each response category (1 through NCAT) within each block (NCAT is the number of response categories of the current block): Format
Description
(8F10.5,/)
Observed frequencies for NBOUND boundaries
(8F10.5,/)
Model based frequencies for NBOUND boundaries
Related topics
TEST/SCALE command: NBLOCK keyword (Section 3.2.15) BLOCK command: NITEMS keyword (Section 3.2.2) BLOCK command: NCAT keyword SAVE command: FIT keyword (Section 3.2.13)
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3 PARSCALE REFERENCE 3.4.4 Item parameter file
Records 1 and 2 are the TITLE lines from the command file. Format: (20A4,/,20A4)
The codes in record 3 describe the model as follows: Format
Description
(A8,
The test name from the PARSCALE run
I5,
The number of blocks (NBLOCK)
I5,
The total number of items
I5,
The model code
I5,
The number of subgroups (MGROUP)
I5)
The model code for multiple-groups (see below)
Notes:
The model codes are as follows:
1: Normal ogive graded response model with item and category parameters separated 2: Normal ogive graded response model with item-category parameters 3: Logistic graded response model with item and category parameters separated 4: Logistic graded response model with item-category parameters 5: Normal ogive partial credit model with item and category parameters separated 6: Normal ogive partial credit model with item-category parameters (not implemented) 7: Logistic partial credit model with item and category parameters separated 8: Logistic partial credit model with item-category parameters
The model codes for multiple-groups are:
DIF model (the default for a single-group model) Raters’ effect model
Line 4 shows the number of items per block.
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INPUT AND OUTPUT FILES Format: (30I5)
The rest of the data show the parameters grouped by block within each group. For each group (from 1 through MGROUP), the subgroup name is listed first formatted as (A8). Note that for a single-group or Rater's-Effect model there will be only one group name. Within each group, the following block information appears: Format
Description
(A8,
Block name
I5,
The number of categories
A4,
Item name
F10.5,
Slope parameter
F10.5,
S.E. of slope parameter
F10.5,
Location parameter
F10.5,
S.E. of location parameter
F10.5,
Guessing parameter
F10.5)
S.E. of guessing parameter
(15F10.5,/)
Category parameters for this block
(15F10.5,/)
S.E. of category parameters for this block
Lastly, in the case of a Rater's-Effect model, the following rater information is provided for each rater: Format
Description
(A8,
Rater’s name
F10.5,
Rater's-Effect parameter
F10.5)
S.E. of Raters’ Effect parameter
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3 PARSCALE REFERENCE Related topics
BLOCK command: NITEMS keyword (Section 3.2.2) INPUT command: MGROUP/MRATER keyword (Section 3.2.7) TEST/SCALE command: NBLOCK keyword (Section 3.2.15) SAVE command: PARM keyword (Section 3.2.13)
3.4.5 Item information file
The information file begins with the TITLE lines from the command file in records 1 and 2. Format: (20A4,/,20A4)
In the remainder of the file, item information is listed as follows: the results are grouped by quadrature points (1 through NQPT), within items (1 through NITEMS), within blocks (1 through NBLOCK), within groups (1 through MGROUP, or just 1 for a single-group or Rater's-Effect model): Format
Description
(A8,2X,
The test name
I4,2X,
Group number
I4,2X,
Block number
A8,2X,
Block name
I4,2X,
Item number
A4,2X,
Item name
I2,2X,
Node number
F10.5,2X
Quadrature point value
F18.10)
Item information at each quadrature node
Related topics
TEST/SCALE command: NBLOCK keyword (Section 3.2.15) BLOCK command: NITEMS keyword (Section 3.2.2) CALIB command: NQPT keyword (Section 3.2.3) INPUT command: MGROUP/MRATER keyword (Section 3.2.7) SAVE command: INFORMATION keyword (Section 3.2.13)
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INPUT AND OUTPUT FILES 3.4.6 Subject scores file
Records: 1 Format
Description
(/,1X,I2,
Subtest number
‘SUBTEST:',A8)
Subtest name
Note that if the number of subtests is one, this record is skipped. Repeat for all respondents or sampled respondents: Format
Description
(A1,
Identification of respondents
T22,'|', I7,2X,
Respondent number
A8,2X,
Group name
F7.2)
Weight for respondent
Notes
The length of identification specified by the NIDCHAR keyword in the INPUT command is automatically supplied for A1. Repeat for each rater (from 1 through the number of response vectors, NVEC) within each respondent. (For a single-group model or DIF model, it repeats only once (NVEC=1), and for Raters’ Effects models, it repeats for the number of raters rated for this particular respondent. Therefore, for the Raters’ Effect model, NVEC varies depending on the number of raters who rated this respondent). Format
Description
(1X,I3,2X,
Score number
A8,2X,
Score name
I3,2X,
Rater identification of this response vector
'|',2X,
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F7.2,4X,
Weight for this response vector
F7.2,2X,
Mean category
F7.2,4X,
The number of items attempted
F10.4,2X,
Ability estimate
F10.4)
S.E. of ability estimate
Note that if the DIF model is used, the rater identification is the subgroup identification. Format
Description
(T22,'|',2X, F7.2,4X,
Fit statistics
F7.2,2X,
Probability of fit statistics
F7.2)
Degree of freedom
Note that this record is saved only if the original response data is frequency (group-level) data and the FIT option in the SCORE command has been given. Related topics
INPUT command: NIDCHAR keyword (Section 3.2.7) SCORE command: FIT option (Section 3.2.14) SAVE command: FIT option (Section 3.2.13)
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4 MULTILOG MULTILOG, written by David Thissen, is a computer program designed to facilitate item analysis and scoring of psychological tests within the framework of Item Response Theory (IRT). As the name implies, MULTILOG is for items with MULTIple alternatives and makes use of LOGistic response models, such as Samejima’s (1969) model for graded responses, Bock’s (1972) model for nominal (non-ordered) responses, and Thissen & Steinberg’s (1984) model for multiple-choice items. The commonly used logistic models for binary item response data are also included, because they are special cases of the multiple category models. MULTILOG provides Marginal Maximum Likelihood (MML) item parameter estimates for data in which the latent variable of IRT is random, as well as Maximum Likelihood (ML) estimates for the fixed-effects case. χ 2 indices of the goodness-of-fit of the model are provided. In IRT, the item parameter estimates are the focus of item analysis. MULTILOG also provides scaled scores estimates of the latent variable for each examinee or response pattern. MULTILOG is best suited to the analysis of multiple-alternative items, such as those on multiple-choice tests or Likert-type attitude questionnaires. It is the only widely available program capable of fitting a wide variety of models to these kinds of data using optimal (MML) methods. MULTILOG also facilitates refined model fitting and hypothesis testing through general provisions for imposing equality constraints among the item parameters and for fixing item parameters at a particular value. MULTILOG may also be used to test hypotheses about differential item functioning (DIF; sometimes called “item bias”) with either multiple response or binary data, through the use of its facilities to handle data from several populations simultaneously and test hypotheses about the equality of item parameters across groups.
4.1 The MULTILOG user’s interface Although MULTILOG syntax can still be created and submitted in batch mode as was done with previous versions, MULTILOG version 7.0 has new features designed to make the program more user-friendly.
The user no longer has to create syntax using INFORLOG. The functionality of INFORLOG and MULTILOG has been combined into a single executable file. In addition, the MULTILOG syntax wizard described in Section 4.2 can be used to create a skeleton command file that can then be edited according to the user’s needs.
This document describes those elements in the user’s interface that may not be immediately clear to the user or that behave in a somewhat nonstandard way. Each element will be discussed in turn in the following sections.
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4 MULTILOG REFERENCE 4.1.1 Main menu At the center of the interface is the menu bar, which adapts to the currently active function. For example, when you start the program, the menu bar shows only the menu choices File, View, and Help.
However, as soon as you open a MULTILOG output file (through the File menu), the Window and Edit menu choices show up on the menu bar. At the same time, the File menu choices expand with selections like Save and Save As And the View menu now has a Font option after the Status bar and Toolbar choices.
Opening an existing MULTILOG command (*.mlg) file, or starting a new one, adds further choices to the main menu bar: the Output and Run menus. Note that you can open only one command file at a time. If you want to paste some part from an existing command file in your current one, opening the old file will automatically close the current one. After you copy the part you want to the clipboard, you have to reopen the *.mlg file for pasting. 4.1.2 Run menu The Run menu gives you the option to run the command file displayed in the main window.
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OVERVIEW OF MULTILOG INTERFACE When you run an analysis by clicking Run, the current command file will first be saved, if you made any changes. You can easily tell if a command file has changed by looking at the filename above the menu bar. An asterisk after the filename shows that the current file has changed, but has not been saved yet. Once the analysis has been completed, the Plot option, providing access to the graphics procedure, is enabled. For a description of the plots that can be produced, see Chapter 6.
4.1.3 Output menu Through the Output menu you can open the list output, named with the file extension out. Always check the end of each output file to see if it reports: NORMAL END. If it does not, something went wrong and the output file should have some information on that.
4.1.4 Window menu The Window menu is only available when you have at least one file open. You can use the CtrlTab key combination to switch between open files, or use the Window menu to arrange the open files (Cascade, Tile). If you have the output (*.out) file open for a particular analysis, you could use the Window menu to arrange this file and the command file for convenient switching.
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4.1.5 Font option Clicking on the Font option on the View pull-down menu displays a dialog box with the fonts that are available on your system.
You may use different fonts for command and output files. At installation, they are both set to a special Arial Monospace font that ships with the program. To keep the tables in the output aligned, you should always select a monospace or fixed pitch font where all the characters in the font have the same width. Once you select a new font, that font becomes the default font. This gives you the option to select a font (as well as font size and font style) for your command (*.mlg) files that is different from the one for your list output (*.out) files as a quick visual reminder of the type of file.
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4.2 Creating syntax using the MULTILOG syntax wizard The MULTILOG syntax wizard, used to create new MULTILOG command files, uses successive dialog boxes to generate the syntax. The boxes displayed during the process depend on the user’s choices in previous boxes. The dialog boxes are described below, approximately in order of appearance. 4.2.1 New Analysis dialog box The New Analysis dialog box is used to select the type of problem and/or to create a new MULTILOG command file. This dialog box is activated when the File, New option is selected from the main menu bar.
The type of problem is specified by selecting one of the three mutually exclusive options in the Select type of problem group box:
MML item parameter estimation (RANDOM option on PROBLEM command) Fixed-theta item parameter estimation (FIXED option on PROBLEM command) MLE or MAP computation (SCORES option on PROBLEM command).
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4 MULTILOG REFERENCE Enter the folder location and the name for the MULTILOG command file in the Folder location and File name edit box respectively. If the Fixed-theta Item Parameter Estimation option or the MLE or MAP computation option is chosen, the Fixed Theta dialog box, in which you are asked about the reading of a fixed value of θ with the data, is activated once the OK button is clicked, followed by the Input Data dialog box. Selecting MML Item Parameter Estimation or MLE or MAP Computation will activate the Input Data dialog box when OK is clicked. If the Blank MULTILOG Command File option is selected, the Folder location and File name for the new file should be provided in the appropriate fields of this dialog box. Clicking OK in this case will open an editor window in which you can enter syntax manually. Related topics
RANDOM, FIXED and SCORES options on the PROBLEM command (Section 4.4.7)
Fixed Theta dialog box (Section 4.2.2) Input Data dialog box (Section 4.2.3)
4.2.2 Fixed Theta dialog box The Fixed Theta dialog box is activated when you select fixed- θ item parameter estimation or the MLE or MAP computation option in the New Analysis dialog box. It is used to indicate whether a fixed value of θ should be read with the data. If the Yes radio button is clicked, the position of the fixed value to be read must be indicated using the Data Format field in the Input Data dialog box. Clicking the Back button will return the user to the New Analysis dialog box while clicking the Next button will activate the Input Data dialog box. Related topics
FIXED option on the PROBLEM command (Section 4.4.7)
New Analysis dialog box (Section 4.2.1) Input Data dialog box (Section 4.2.3) Variable format statement (Section 4.4.14)
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OVERVIEW OF MULTILOG INTERFACE
4.2.3 Input Data dialog box The Input Data dialog box is used to specify the type and location of the data to be analyzed. You can enter the name of the data file in the Data file name field provided, or use the Browse button to browse for the file. The program automatically enters the name of the command file (specified in New Analysis dialog box) with file extension dat as the default name.
MULTILOG can handle three types of data, each associated with one of the mutually exclusive options in the Type of data group box:
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Counts of response patterns (PATTERN option on PROBLEM command) Individual item response vectors (INDIVIDUAL option on PROBLEM command) Fixed-effects table of counts (TABLE option on PROBLEM command) .
In all cases, the format statement describing the data must be entered in the Data Format field. Depending on the option selected, different versions of the Input Parameters dialog box, reflecting the selection made here, will be displayed when the Next button is clicked. Related topics
PATTERNS, INDIVIDUAL and TABLE options on the PROBLEM command (Section 4.4.7) DATA keyword on the PROBLEM command
Input Parameters dialog box (Section 4.2.3) Variable format statement (Section 4.4.14)
4.2.4 Input Parameters dialog box The Input Parameters dialog box is used to describe the contents of the data file to be analyzed. The version of this dialog box displayed depends on the type of data specified in the Input Data dialog box. In each case, the type of data previously selected is noted at the top of the Input Parameters dialog box. In general, this dialog box is used to indicate the number of items, groups, tests, patterns, examinees and the number of characters in the ID field. You can use the Back button to return to any of the previously completed dialog boxes. Clicking Next activates the Test Model dialog box. All fields in these dialog boxes are associated with keywords on the PROBLEM command, with the exception of the Number of tests field. This field is used by the program to determine the number of tabs in the Test Model dialog box, displayed later in the setup process. When counts of response patterns are analyzed, the Input Parameters dialog box shown below is used to provide the following information:
The number of items. Previous limits on the number of items that can be analyzed have been removed in the current version of MULTILOG (NITEMS keyword on PROBLEM command). The number of groups. Previously, a maximum of 10 groups could be used. This limit has also been removed (NGROUPS keyword on PROBLEM command) The number of patterns. This field is only displayed when response pattern data are analyzed (NPATTERNS keyword on PROBLEM command) The number of characters in the ID field. By default, it is assumed to be zero (NCHAR keyword on PROBLEM command)
In the case of analysis of individual item response vectors, the same options as described above are available, with one exception: the Number of patterns field is replaced by the Number of examinees field. The number of examinees for which response vectors are available should be entered in this field. 352
OVERVIEW OF MULTILOG INTERFACE
Related topics
NITEMS, NGROUPS, NEXAMINEES, NPATTERNS and NCHARS keywords on the PROBLEM com-
mand (Section 4.4.7) Input Data dialog box (Section 4.2.3) Test Model dialog box (Section 4.2.5)
For the analysis of a fixed-effects table of counts, only three fields need to be completed: the number of items, the number of groups and the number of tests. The Input Parameters dialog box for this type of analysis is shown below. 353
4 MULTILOG REFERENCE
4.2.5 Test Model dialog box The Test Model dialog box is used to specify details for a subtest. The number of tabs in the Test Model dialog box depends on the value entered in the Number of tests field in the Input Parameters dialog box.
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OVERVIEW OF MULTILOG INTERFACE The model to be fitted to the data is specified in the Test Model group box. One of six mutually exclusive options may be selected:
1-parameter logistic model, corresponding to the L1 option on the TEST command 2-parameter logistic model, corresponding to the L2 option on the TEST command 3-parameter logistic model, corresponding to the L3 option on the TEST command Graded model, corresponding to the GR option on the TEST command Nominal model, corresponding to the NO option on the TEST command Multiple-choice model, corresponding to the BS option on the TEST command.
In the Test Items group box, the items to be analyzed are described. By default, no items are selected. Clicking the All check box under the Use header will invoke the ALL option on the TEST command, and all items will be included in the analysis. Also, unchecking any of the items will uncheck the All check box. Clicking the check box next to an item will select or deselect an item. Such a selection corresponds to use of the ITEMS keyword on the TEST command. In the image above, all items have been included in the analysis. For each item, the number of response categories must be specified under the Category heading. By default, it is assumed that each item has two response categories. The admissible number of categories is between 2 and 10. This column is only available for the graded model, nominal model, and multiple-choice model and corresponds to the NC keyword on the TEST command. Finally, the number of the highest category has to be indicated in the case of a nominal model. Select either A for Ascending or D for Descending under the Order header to indicate the order of categories in each case. The Order column is only available for the nominal and multiplechoice models and corresponds to the HIGH keyword on the TEST command. Click the Back button to return to the Input Parameters dialog box and the Next button to proceed to the Response Codes dialog box. Related topics
L1, L2, L3, GR, NO and BS options on the TEST command (Section 4.4.11) NC, HIGH and ITEMS keywords and ALL option on the TEST command
Input Parameters dialog box (Section 4.2.4) Response Codes (Binary Data) dialog box (Section 4.2.6) Response Codes (Non-Binary Data) dialog box (Section 4.2.7)
4.2.6 Response Codes (Binary Data) dialog box The Response Codes dialog box for binary data is used to provide information on the response and missing codes and the answer key for the data to be used in the analysis. The Response Codes field is used to list all possible codes occurring in the data. The Correct response codes field is used to provide an answer key for the total number of items to be analyzed. The Missing Code check box is checked when a value indicating “missing” for popula355
4 MULTILOG REFERENCE tion membership other than the default 9.0 assumed by the program is used. Use the drop-down list box on the right to select the appropriate missing value code for the data.
After completing the dialog box, click Next to display a summary of the information entered. Click Finish in this dialog box to generate the command file. Related topics
VAIM keyword on the ESTIMATE command (Section 4.4.4)
Format of data (binary items) (Section 4.4.14)
4.2.7 Response Codes (Non-Binary Data) dialog box The Response Codes dialog box for non-binary data is used to provide information on the response codes and the answer key for the data to be used in analysis. The Response Codes field is used to list all possible codes occurring in the data. The Correct response code fields are used to provide an answer key for each of the items to be analyzed. After completing the dialog box, click Next to display a summary of the information entered. Click Finish in this dialog box to generate the command file. Related topics
Format of data (multiple response items) (Section 4.4.14)
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4.3 Getting started with MULTILOG 4.3.1 Two-parameter model for the skeletal maturity data In this example, the generation of syntax that includes the reading of an external criterion is illustrated. For a complete discussion of the problem, please see Section 12.19. The first step in creating a new command file using the syntax wizard is to select the New option from the File menu to activate the New Analysis dialog box. The type of problem and the name and location of the new command file are defined using the New Analysis dialog box. As we wish to score (estimate θ ) in this run, the MLE or MAP Computation option is selected in the Select type of problem group box. This selection corresponds to the SCORES option on the PROBLEM command. The location in which the new command file is to be stored is specified next. By default, the folder in which MULTILOG has been installed will be displayed. This can be changed by either typing an alternative path in the Folder Location field or by using the Browse button to the right of this field. Finally, the name of the command file is entered in the File name field. In this case, we want to create the command file knee.mlg in the (default) mlgwin folder. Click OK to continue with the syntax specification.
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The Fixed Theta dialog box is now displayed, allowing you to include the reading of a fixed value with the data. Click the radio button next to the Yes option to add the CRITERION option to the PROBLEM command. Then click Next to go to the Input Data dialog box.
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GETTING STARTED WITH MULTILOG The Input Data dialog box is used to provide information on the position and contents of the raw data file. By default, the Data file name will be assumed to be in the same folder and to have the same filename as the new command file. This may be changed by either correcting the entry in this field or by using the Browse button ( ) to the right of this field. The variable format statement describing the contents of this file must be entered in the Data Format field. Recall that the data are in the format: 40 33 33 29
1 1 1 1
0.5 1.0 2.0 3.0
2112111111112111111111111111111111 3113211111112122111111111111111111 4333211111113122111111111111111111 4543211111113122111111011111111111
As the data file contains individual data identification in the first 10 columns, an identification field is required as the first entry in the variable format statement. The format statement shown below reflects the position of the examinee identification field (10A1), the 34 item responses (34A1) and the criterion (F4.0). MULTILOG will read the chronological age of each individual and use that as a starting value for the iterative modal estimation procedure. The “T” format is used to tab to the correct positions of the respective fields. The first ten characters on each record, which are read as an identification field, are also used to assign a value to the NCHARS keyword on the PROBLEM command through the Input Parameters dialog box (see later in the example). Note that if the NCHARS keyword is set to 0, no ID field needs to be included in the format statement. Select the Individual item response vectors option from the Type of data group box. The selection made in this case will add the INDIVIDUAL option of the PROBLEM command, while the entry in the Data file name field will be used in conjunction with the DATA keyword on the same command. The variable format entered in the Data format field will be echoed to the generated command file. For more on format specification rules please see Section 4.4.14. The parameters for the 34 indicators are in a file called knee.par. This file was produced by MULTILOG in a (previous) calibration run. As the parameters for the SAVE and other optional commands cannot be set using the syntax wizard, these commands will be added after generating the command file. Instructions concerning this can be found at the end of this example. Having completed the Input Data dialog box, click Next to go to the Input Parameters dialog box.
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The problem uses 34 items, and this is indicated by setting the value in the Number of items field to 34. Data from 13 examinees are available, and this is specified using the Number of examinees field. Finally, the NCHAR keyword on the PROBLEM command is set to 10, as previously indicated in the variable format statement description, using the Number of characters in ID field. Entries in this dialog box correspond to the following MULTILOG keywords on the PROBLEM command: Dialog box field
Keyword in command file
Number of items
NITEMS
Number of groups
NGROUP
Number of tests
None; used to set number of tabs in Test Model dialog box.
Number of patterns
NPATTERNS
Number of characters in ID field
NCHARS
Click Next to go to the Test Model dialog box.
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A graded model is used here, and is specified by clicking the radio button next to the Graded model option in the Test model group box. This corresponds to the GR option on the TEST command. The “test” has varying numbers of response categories for the 34 indicators, which are entered in the NC list on the TEST command. As all items are used, the All check box in the Use column of the Test Items group box is clicked, and the number of categories is set by item in the Category column as shown below. Once the number of categories for each item has been indicated, click OK to go to the Response Code (Non-Binary data) dialog box.
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All possible responses in the data are entered in the Response Code field. The corresponding correct response codes are entered in the Correct response codes group box. On each line, the number of entries permitted corresponds to the number of items specified in the Input Parameters dialog box. Once the response codes (123450) are entered in the Response Code string field, these codes appear as the first column of the Correct response code group box. For each response code and each item, a category number is entered. Permissible values are 1, 2, …, NCAT, where NCAT denotes the total number of categories for a given item. In any row, a “0” indicates that the response code value is excluded from the analysis. A valid entry for item 1, for example, is Code 1 = 5, Code 2 = 2, Code 3 = 3, Code 4 = 4, and Code 5 = 1. The entry specifies that data values equal to 1 is assigned to the 5-th category of item 1, while data value 5 is assigned to the first category. Start by entering the correct responses for the first code, and press the Enter key on you keyboard when done to proceed to the next line of the window. Note that, if an attempt is made to specify response codes not in agreement with previous selections, no value will appear in this box. Only when valid codes are entered, will the results be displayed. Once all codes have been entered, click OK to go to the Project Settings dialog box. Entries in the Response Code dialog box will appear after the END command in the generated command file.
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The Project Settings dialog box displays a summary of all selections made up to this point. To go back to any of the previous dialog boxes, the Back button may be used. To generate the syntax, click Finish. Syntax generated using the wizard is now displayed in the main MULTILOG
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4 MULTILOG REFERENCE window. Before running this problem, the following (optional) commands are added to the syntax in this window by using standard Windows editing functions: >START ALL, FORMAT, PARAM=’KNEE.PAR’; >SAVE;
The START command is used to override the default starting values for all the item parameters and enter others, in this case from the file knee.par. Click the Run option on the main menu bar to start the analysis. Once the analysis has been completed, the output generated may be viewed using the Output option on the same menu bar. The output file will then be displayed in the main window, and the Window option may be used to switch between syntax and output files.
4.3.2 Three-parameter (and guessing) model for the LSAT6 data For a description of the problem for which syntax is generated here, please see Sections 12.1 to 12.3. Select the New option from the File menu to activate the New Analysis dialog box. In the New Analysis dialog box, the type of problem and the name and location of the new command file are defined. For the LSAT data, we wish to perform MML item parameter estimation. Note that this corresponds to the RANDOM option on the PROBLEM command. Click on the MML Item Parameter Estimation option.
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GETTING STARTED WITH MULTILOG
Next, the location in which the new command file is to be stored is specified. By default, the folder in which MULTILOG has been installed will be displayed. This can be changed by either typing an alternative path in the Folder Location field or by using the Browse button to the right of this field. Finally, the name of the command file is entered in the File name field. In this case, we want to create the command file lsat6_2.mlg in the (default) mlgwin folder. Click OK to go to the Input Data dialog box.
The Input Data dialog box is used to provide information on the position and contents of the raw data file. By default, the Data file name will be assumed to be in the same folder and to have the same filename as the new command file. This may be changed by either correcting the entry in this field or by using the Browse button ( ) to the right of this field.
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4 MULTILOG REFERENCE The variable format statement describing the contents of this file must be entered in the Data Format field. For this example, recall that the data are of the form 1 2 3 4
00000 00001 00010 00011
3 6 2 11
As the data file contains patterns and frequencies, no identification field is required. The format statement entered reflects the position of the pattern (5A1) and frequency (F4.0) only. In each row, the first 4 columns are skipped, and this is indicated by the value “4” in combination with the “X” operator. Select the Counts of response patterns option from the Type of data group box as shown below. The selection made in this case will add the PATTERN option of the PROBLEM command, while the entry in the Data file name field will be used with the DATA keyword on the same command. The variable format entered in the Data format field will be echoed to the generated command file. For more on format specification rules please see Section 4.4.14. After completing the Input Data dialog box, click Next to go to the Input Parameters dialog box.
The number of items, groups, tests, patterns/examinees, and characters in the identification field are specified using the Input Parameters dialog box. When the dialog box is first displayed, all entries are set to 1, assumed to be the default. For this problem, we need only indicate the number of items (5) and the number of patterns (32) in the Number of items and Number of patterns fields respectively. Note that the buttons to the right of each of these fields may be used to increase or decrease the value displayed in a particular field. Entries on this dialog box correspond to the following MULTILOG keywords on the PROBLEM command:
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GETTING STARTED WITH MULTILOG Dialog box field
Keyword in command file
Number of items
NITEMS
Number of groups
NGROUP
Number of tests
None; used to set number of tabs on Test Model dialog box.
Number of patterns
NPATTERNS
Number of characters in ID field NCHARS Click Next to go to the Test Model dialog box.
The Test Model dialog box is used to describe the items assigned to the test(s) and the model to be fitted. All entries on this dialog box correspond to keywords/options on the TEST command:
The Test Model group box corresponds to the choice of one of the following options: L1/L2 /L3/GR/NO/BS. Entries in the Use column of the Test items group box correspond to the ALL option and ITEMS keyword on the TEST command. The Categories and Order columns of the Test items group box (not used in this example) correspond to the NC and HIGH keywords respectively.
The number of tabs displayed at the top of this dialog box depends on the entry in the Number of tests field in the Input Parameters dialog box. This problem requires the use of all 5 items in 367
4 MULTILOG REFERENCE the data on a single test, so the All check box at the top of the Use column is checked to select all items simultaneously. To select single items, the check boxes next to the items selected for inclusion should be clicked individually. At this point, the data file and the model specification are complete. All that remains to be done is to indicate the response codes. To do this, click Next to go to the Response Codes (Binary Data) dialog box.
The response patterns in the data file consist of combinations of “0” and “1” values. These two values are entered in the Response Codes field, which should reflect all possible response codes present in the data. No missing code is used here, so the Missing code check box is left unchecked. The response to each of the items that indicates the correct response is entered in the Correct response codes field. Note that the number of entries allowed in this field is equal to the number of items specified in the Input Parameters dialog box. All entries in this dialog box are echoed to the command file and can be found directly after the END command that is automatically added to the command file, but before the variable format
statement that is also written to the command file.
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GETTING STARTED WITH MULTILOG
Problem specification is now complete. When the Next button is clicked on the Response Codes dialog box, a list of the options specified is displayed in the Project Settings dialog box. To go back to any of the previous dialog boxes, click the drop-down list button next to the Back button and select from the list that will be displayed. To generate the command file, click Finish.
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4 MULTILOG REFERENCE Once the Finish button has been clicked in the Project Settings dialog box, you are returned to the main MULTILOG window, where the generated syntax is displayed. In this example, no changes are needed but, if additional optional commands are to be used, you can insert such commands in this window using standard Windows editing functions. To run the generated command file, click the Run option on the main menu bar.
4.3.3 Generating syntax for a fixed- θ model This example illustrates user input for a fixed- θ analysis. For a discussion of these data, see the previous section. To start the process, select the New option from the File menu. The New Analysis dialog box shown below will be displayed. On the New Analysis dialog box, the type of problem and the name and location of the new command file is defined. As we wish to perform a fixed- θ analysis for the mouse data, the Fixed-theta Item Parameter Estimation option is selected by clicking on it. Note that this corresponds to the FIXED option on the PROBLEM command. Next, the location in which the new command file is to be stored is specified. By default, the folder in which MULTILOG has been installed will be displayed. This can be changed by either typing an alternative path in the Folder Location field or by using the Browse button to the right of this field. Finally, the name of the command file is entered in the File name field. In this case, we want to create the command file mouse.mlg in the (default) mlgwin folder. Click OK to proceed with the specification.
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GETTING STARTED WITH MULTILOG
As the Fixed-theta Item Parameter Estimation option was selected in the New Analysis dialog box, the Fixed Theta dialog box is displayed next. The Fixed Theta dialog box is only activated in the user-selected fixed- θ item parameter estimation in the New Analysis dialog box. It is used to indicate whether a fixed value of θ should be read with the data. Clicking the Back button will return you to the New Analysis dialog box while clicking the Next button will activate the Input Data dialog box. Leaving the default entry (No) as it is displayed on this dialog box, click Next.
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4 MULTILOG REFERENCE
Recall that the data are in a file called mouse.dat, which contains the following four lines: 1 7 0 6 0 2 3 10
0 0 0 2
2 11 6 10 5 11 0 2
Each of the four lines of data represents one of four groups of mice; each group of mice represents a cell of a 2 x 2 experimental design. The response variable (measured on an ordinal scale) is the severity of audiogenic seizures. The column categories are “crouching”, “wild running”, “clonic seizures”, “tonic seizures”, and “death”. The variable format statement describing the contents of this file must be entered in the Data Format field. As the data file contains frequencies for each cell of the table, no identification field is required and the format statement entered reflects the frequency in each cell (5F3.0) only. Select the Fixed-effect table of counts option from the Type of data group box as shown below to indicate that cell frequencies from a table are used as input. The selection made in this case will add the TABLE option of the PROBLEM command, while the entry in the Data file name field will be used in conjunction with the DATA keyword on the same command. The variable format entered in the Data format field will be echoed to the end of the generated command file. For more on format specification rules, please see Section 3.2.17. Having completed the Input Data dialog box, click Next to go to the Input Parameters dialog box.
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GETTING STARTED WITH MULTILOG
The Input Parameters dialog box reflects the selections made in previous dialog boxes. Only the numbers of items, groups, and tests need to be specified for this type of problem. When the dialog box is first displayed, all entries are set to 1, assumed to be the default. For this problem, you need only indicate the number of items (1) and the number of groups (4) in the Number of items and Number of groups fields respectively. Note that the buttons to the right of each of these fields may be used to increase or decrease the value displayed in a particular field. Entries in this dialog box correspond to the following MULTILOG keywords on the PROBLEM command: Dialog box field
Keyword in command file
Number of items
NITEMS
Number of groups
NGROUP
Number of tests
None; used to set number of tabs on Test Model dialog box.
Click Next to go to the Test Model dialog box.
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4 MULTILOG REFERENCE
In the Test Model dialog box, only one tab is displayed. In addition, only one item is available for inclusion on the test. This corresponds to the number of items and tests entered on the Input Parameters dialog box.
As a graded model (corresponding to the GR option on the TEST command) is required, click the Graded model radio button. 374
GETTING STARTED WITH MULTILOG The item can be selected by either clicking the check box next to All or the check box next to “1” in this case. The entries in the Use column correspond to the ALL option and ITEMS keyword respectively. In the case of a graded model, the number of categories must be specified. The presence of 5 categories is indicated using the buttons to the right of this field. This sets the value for the NC keyword on the TEST command. This completes the model specification, and clicking the Next button on the Test Model dialog box now generates the syntax. The generated syntax is displayed in the main MULTILOG window. To add the additional optional commands >TGROUPS NUMBER=4, MIDDLES=(1,1,1,-1); >FIX ITEMS=1, AJ, VALUE=1.0; >FIX ITEMS=1, BK=4, VALUE=0.4756;
to the syntax, use standard Windows editing functions. When done, click the Run option on the main menu bar to start the analysis. The output generated during the analysis may be accessed using the Output option after completion of the analysis.
4.4 Command syntax 4.4.1 Overview of syntax In the table below, the MULTILOG commands are listed in the order in which they should appear in the command file. MULTILOG command files should have a *.mlg suffix. In the rest of this section, these commands are listed and discussed in alphabetical order.
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4 MULTILOG REFERENCE Command
Required/ Keyword / Option Optional
TITLE TITLE
Required
PROBLEM
Required
RANDOM/FIXED/SCORE, NITEMS=n, NGROUP=n, PATTERNS/INDIVIDUAL/TABLE, NPATTERNS=n/NEXAMINEES=n, NCHARS=n, CRITERION, NOPOP, DATA=filename;
TEST
Required
ALL/ITEMS=(list), L1/L2/L3/GRADED/NOMINAL/BS, NC=(list), HIGH=(list);
EQUAL
Optional
ALL/ITEMS=(list)/GROUPS=(list), WITH=(list), AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list) /MU/SD;
ESTIMATE
Optional
NCYCLES=n, ITERATIONS=n, ICRIT=n, CCRIT=n, ACCMAX=n, VAIM=n;
END
Required
FIX
Optional
ALL/ITEMS=(list)/GROUPS=(list), VALUE=n, AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list) /MU/SD;
LABELS
Optional
ALL/ITEMS=(list), NAMES=(‘lab1’,’lab2’,...);
PRIORS
Optional
ALL/ITEMS=(list)/GROUPS=(list), PARAMS=(n,n), AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list) /MU/SD;
SAVE
Optional
START
Optional
ALL/ITEMS=(list), PARAM=‘filename’, FORMAT, PARAM=file;
TGROUPS
Optional
NUMBER=n, QP=(list), MIDDLE=(list);
TMATRIX
Optional
ALL/ITEMS=(list), AK/CK/DK, DEVIATION/POLYNOMIAL/TRIANGLE;
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OVERVIEW OF SYNTAX A basic command file may be created using the MULTILOG interface. Values can be assigned to the following keywords in this way: Command
Keyword / Option
Dialog box in which this is set
PROBLEM
RANDOM/FIXED/SCORE
New Analysis Fixed Theta
PROBLEM
PATTERNS/INDIVIDUAL/TABLE
Input Data
PROBLEM
NITEMS=n
Input Parameters
PROBLEM
NGROUP=n
Input Parameters
PROBLEM
NPATTERNS=n/NEXAMINEES=n
Input Parameters
PROBLEM
NCHARS=n
Input Parameters
PROBLEM
DATA=filename
Input Data
TEST
ALL/ITEMS=(list)
Test Model
TEST
L1/L2/L3/GRADED/NOMINAL/BS
Test Model
TEST
NC=(list)
Test Model
TEST
HIGH=(list)
Test Model
ESTIMATE
VAIM=n
Response Codes (Binary Data)
Variable format statement
Input Data
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4 MULTILOG REFERENCE 4.4.2 END command (Required) Purpose Terminates command line entry. Format >END;
378
EQUAL COMMAND 4.4.3 EQUAL command (Optional) Purpose To impose equality constraints among the item parameters. Format >EQUAL
ALL/ITEMS=(list)/GROUPS=(list), WITH=(list), AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(List)/MU/SD;
AJ/BJ/CJ/BK/AK/CK/DK/MU/SD keyword Purpose The set of parameters is specified by one of the following mutually exclusive keywords: AJ, BJ, CJ, BK=(list), AK=(list), CK=(list), DK=(List), MU, or SD.
AJ is the slope for the graded model and 3PL model BJ is the threshold for binary graded items and 3PL model CJ is the lower asymptote for the 3PL model BK=(list) specifies the listed threshold parameters for the graded model AK=(list) specifies the listed contrasts among the ak s for the nominal and multiple-
choice models CK=(list) specifies the listed contrasts among the ck s for the nominal and multiplechoice models DK=(list) specifies the listed contrasts among the d k s for the multiple-choice model MU is the mean of the population distribution for a group SD is the standard deviation of the population distribution for a group.
Format AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list)/MU/SD
Related topics
TEST command: L1/L2/L3/GRADED/NOMINAL/BS options) (Section 4.4.11)
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4 MULTILOG REFERENCE ALL/ITEMS/GROUPS keyword Purpose
The set of items is specified with one of the following: ALL, ITEMS=(list), or GROUPS=(list).
ALL refers to all of the items in the data ITEMS refers to a subset of the items in the data GROUPS specifies groups, for reference to MU and SD
Format ALL/ITEMS=(list)/GROUPS=(list)
WITH keyword Purpose
This keyword specifies pairwise constraints, as illustrated in examples 2 and 3 below: Format WITH=(list)
Example 1
If the item parameters on the EQUAL command are to be set equal for all items in a set, the set of items may be given as ALL if the equality constraint applies to all items on the test, or ITEMS=(list) if the equality constraint is to be imposed for a subset of the items given in the list. For example, the following sequence specifies the 1PL model: >TEST ALL, L1; >EQUAL AJ, ALL;
(It is easier to specify L1 on the TEST command). Example 2
There are cases in which it is desirable to impose equality constraints between (a number of) pairs of items; this is done by using WITH=(list) in conjunction with ITEMS=(list). A one-to-one relationship between the items in the ITEMS list and the WITH list is required; the parameters are made equal within the implied pairs. For example, >EQUAL AJ, ITEMS=(2,4), WITH(1,3);
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EQUAL COMMAND has the effect of setting aitem1 = aitem 2 and aitem 3 = aitem 4 . When WITH is used in this way, it must refer to the lower-numbered item of each pair; the form must be >EQUAL parameter ITEMS=(higher numbers) WITH=(lower numbers);
Example 3
For the parameter of BS items, if the WITH list is identical to the ITEMS list, equality constraints are imposed on the specified contrasts among the parameters within each item. For example, >EQUAL DK=(1,2,3), ITEMS=(1,2,3,4), WITH=(1,2,3,4);
sets the first three contrasts among the d k s equal within each item for items 1 – 4. For fouralternative multiple-choice items, such as those considered in Section 12.6, this would have the effect of setting d 2 = d3 = d 4 ; the identifiability constraint that the sum of the ds must be one would then give d1 = 1 − d 2 − d3 − d 4 . Similar forms may be used to impose constraints on the ak s and ck s . See Section 12.10 for further discussion of the use of the EQUAL command with the multiple-choice model. The parameters of Gaussian population distributions may also be constrained if there are several groups. The default arrangement fixes µ = 0 for the last group, as well as σ = 1 for all groups. If there are three groups, >EQUAL MU, GROUP=(1,2);
constrains the means of the first two groups to be equal. Related topics
EQUAL command: ALL/ITEMS/GROUPS keyword (Section 4.4.3)
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4 MULTILOG REFERENCE 4.4.4 ESTIMATE command (Optional) Purpose
To reset internal program parameters controlling the estimation. Format >ESTIMATE NCYCLES=n, ITERATIONS=n, ICRIT=n, CCRIT=n, ACCMAX=n, VAIM=n;
ACCMAX keyword Purpose
Specifies the maximum value for the acceleration parameter; more negative is more acceleration. Format ACCMAX=n
Default
0.0. CCRIT keyword Purpose
Specifies the convergence criterion for the EM-cycles. Format CCRIT=n
Default
0.001.
382
ESTIMATE COMMAND ICRIT keyword Purpose
Specifies the convergence criterion for the M-step. It should always be smaller than CCRIT. Format ICRIT=n
Default
0.0001. Related topics ESTIMATE command: CCRIT keyword
ITERATIONS keyword Purpose
A control parameter for the number of iterations in the M-step; the actual number of iterations is ITERATIONSxNP, where NP is the number of parameters being jointly estimated, which usually means the number of parameters for a particular item. For very large problems, it may be useful (faster) to set ITERATIONS at 2. Format ITERATIONS=n
Default
4. NCYCLES keyword Purpose
Specifies the number of cycles of MML estimation. Format NCYCLES=n
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4 MULTILOG REFERENCE Default
25. VAIM keyword Purpose
Defines the value indicating “missing” for population membership. Format VAIM=n
Default
9.0. Related topics
This keyword may be set through the Response Codes (Binary Data) dialog box (Section 4.2.6)
384
FIX COMMAND 4.4.5 FIX command (Optional) Purpose
To fix item parameters at their starting values. Format >FIX ALL/ITEMS=(list)/GROUPS=(list), VALUE=n, AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list)/MU/SD;
The set of items is specified with one of the following: ALL, ITEMS=(list), or GROUPS=(list). The set of parameters is specified by one of the following mutually exclusive keywords: AJ, BJ, CJ, BK=(list), AK=(list), CK=(list), DK=(List), MU, or SD.
AJ/BJ/CJ/BK/AK/CK/DK/MU/SD keyword Purpose
The set of parameters is specified by one of the following mutually exclusive keywords: AJ, BJ, CJ, BK=(list), AK=(list), CK=(list), DK=(List), MU, or SD.
AJ is the slope for the graded model and 3PL model BJ is the threshold for binary graded items and 3PL model CJ is the lower asymptote for the 3PL model BK=(list) specifies the listed threshold parameters for the graded model AK=(list) specifies the listed contrasts among the ak s for the nominal and multiple-
choice models CK=(list) specifies the listed contrasts among the ck s for the nominal and multiplechoice models DK=(list) specifies the listed contrasts among the d k s for the multiple-choice model MU is the mean of the population distribution for a group SD is the standard deviation of the population distribution for a group.
Format AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list)/MU/SD
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4 MULTILOG REFERENCE ALL/ITEMS/GROUPS keyword Purpose
The set of items is specified with one of the following: ALL, ITEMS=(list), or GROUPS=(list).
ALL refers to all of the items in the data ITEMS refers to a subset of the items in the data GROUPS specifies groups, for reference to MU and SD
Format ALL/ITEMS=(list)/GROUPS=(list)
Related topics
FIX command: MU/SD keywords
VALUE keyword Purpose
This real constant is used to specify the value at which the parameter is to be fixed. Format VALUE=n.
For the 3PL model, the values are specified in “traditional 3PL, normal metric” form; for the other models, the actual values of the parameters or contrasts must be used. The parameters of Gaussian population distributions may also be fixed if there are several groups. The default arrangement fixes µ = 0 for the last group, as well as σ = 1 for all groups. If there are three groups, FIX MU, GROUPS=(1,2), VALUE=0.0;
fixes the means of the first two groups at 0.0.
386
LABELS COMMAND 4.4.6 LABELS command (Optional) Purpose
To enter item-labels for the output. Format >LABELS ALL/ITEMS=(list), NAMES=(‘lab1’,’lab2’, ...);
The set of items is specified with either the keyword ALL or ITEMS=(list). ALL/ITEMS option Purpose
The set of items for which labels is provided is specified with either the keyword ALL or ITEMS=(list).
The ALL option refers to all of the items in the data. The ITEMS keyword specifies a subset of the items.
Format ALL/ITEMS=(list)
Related topics
LABELS command: NAMES keyword
NAMES keyword Purpose
These labels are entered as a list; each label must have 4 or fewer characters. Format NAMES=(‘lab1’,’lab2’,…).
Related topics
LABELS command: ALL/ITEMS keyword
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4 MULTILOG REFERENCE 4.4.7 PROBLEM command (Required) Purpose
To set up the problem and to specify the type of data MULTILOG is to expect. Format >PROBLEM RANDOM/FIXED/SCORE, PATTERNS/INDIVIDUAL/TABLE, NITEMS=n, NGROUP=n, NPATTERNS=n/NEXAMINEES=n, NCHARS=n, CRITERION, NOPOP, DATA=filename;
The class of the problem is specified by selecting one of the mutually exclusive options: RANDOM/FIXED/SCORE. The type of input data is specified by selecting one of the mutually exclusive options: PATTERNS/INDIVIDUAL/TABLE. Related topics
The RANDOM, FIXED and SCORES options in the New Analysis dialog box may be used to select the type of problem (Section 4.2.1) The PATTERNS, INDIVIDUAL and TABLE options may be set using the Input Data dialog box (Section 4.2.3) The NITEMS, NGROUP, NPATTERNS, NEXAMINEES, NCHARS and DATA keywords may be accessed via the Input Parameters dialog box (Section 4.2.4) The CRITERION option is added to the PROBLEM command by clicking “Yes” in the Fixed Theta dialog box (Section 4.2.2)
CRITERION option Purpose
If FIXED or SCORE is entered, you may specify, by including this keyword, that a fixed value of θ is to be read with the data as the criterion for fixed- θ item parameter calibration or as a starting value for computation of MLE[ θ ]. Format CRITERION
388
PROBLEM COMMAND Related topics
PROBLEM command: FIXED/SCORES option (Section 4.4.7) The CRITERION option is added to the PROBLEM command by clicking “Yes” in the Fixed
Theta dialog box (Section 4.2.2) Input Parameters dialog box (Section 4.2.4)
DATA keyword Purpose
This keyword is used to enter the name and location of the raw data file. The name may by up to 128 characters in length and must be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. Format DATA=
Related topics
The DATA keyword may be accessed via the Input Parameters dialog box (Section 4.2.4)
RANDOM/SCORE/FIXED option Purpose
The class of problem is specified by selecting one of three mutually exclusive options.
The FIXED option is used for fixed- θ item parameter estimation. The RANDOM option is used for MML item parameter estimation. The SCORES option is used for computation of MLE[ θ ] or MAP[ θ ].
Format FIXED/RANDOM/SCORE
Related topics
The RANDOM, FIXED and SCORES options in the New Analysis dialog box may be used to select the type of problem (Section 4.2.1)
389
4 MULTILOG REFERENCE PATTERNS/INDIVIDUAL/TABLE option Purpose
The type of input data is specified by selecting one of three mutually exclusive options.
The PATTERN option is used for pattern data (also see NPATTERN keyword on PROBLEM command). The number of examinees (NEXAMINEES) and the number of patterns (NPATTERNS) are mutually exclusive options. INDIVIDUAL is used for individual item response vectors (also see NEXAMINEES keyword on PROBLEM command). TABLE is used for a fixed-effect table of counts.
Format PATTERN/INDIVIDUAL/TABLE
Related topics
The RANDOM, FIXED and SCORES options in the New Analysis dialog box may be used to select the type of problem (Section 4.2.1) The type of data—pattern, individual or table—is specified on the Input Data dialog box (Section 4.2.3) PROBLEM command: NPATTERNS/NEXAMINEES keywords
NCHARS keyword Purpose
To specify the number of characters in the ID field for individual response or pattern count data (see INDIVIDUAL/PATTERN/TABLE option on PROBLEM command). Format NCHARS=n
Related topics
The NCHARS keyword may be accessed via the Input Parameters dialog box (Section 4.2.4). PROBLEM command: INDIVIDUAL/PATTERN/TABLE option
390
PROBLEM COMMAND NEXAMINEES/NPATTERNS keyword Purpose
Used to indicate the number of patterns or examinees for which responses are present in the data.
PATTERN represents the number of response patterns tabulated for pattern data (also see PATTERN option on PROBLEM command). The number of examinees for individual data. To be used with INDIVIDUAL option on the PROBLEM option. The number of examinees (NEXAMINEES) and the number of patterns (NPATTERNS) are
mutually exclusive options. Format NPATTERNS=n/NEXAMINEES=n
Related topics
The NITEMS, NGROUP, NPATTERNS, NEXAMINEES, NCHARS and DATA keywords may be accessed via the Input Parameters dialog box (Section 4.2.4) PROBLEM command: INDIVIDUAL/PATTERN/TABLE options PROBLEM command: NPATTERNS/NEXAMINEES keywords
NGROUP keyword Purpose
Specifies the number of groups to be used in the analysis. Format NGROUP=n
Related topics
The NGROUP keyword may be accessed via the Input Parameters dialog box (Section 4.2.4)
NITEMS keyword Purpose
Specifies the number of items to be used in the analysis.
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4 MULTILOG REFERENCE Format NITEMS=n
Related topics
The NITEMS keyword may be accessed via the Input Parameters dialog box (Section 4.2.4)
NOPOP option Purpose
If SCORE is specified, the default is MAP estimation including the population distribution. If no population distribution is desired, enter NOPOP. If NOPOP is entered, some MLE[ θ ]s may not be finite and the program may stop. Format NOPOP
Related topics
PROBLEM command: SCORE/FIXED/RANDOM option
392
PRIORS COMMAND 4.4.8 PRIORS command (Optional) Purpose
To impose Gaussian prior distributions for the item parameters. Format >PRIORS ALL/ITEMS=(list)/GROUPS=(list), PARAMS=(n,n), AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list)/MU/SD;
The set of items is specified with one of the following: ALL, ITEMS=(list), or GROUPS=(list). The set of parameters is specified by one of the following mutually exclusive keywords: AJ, BJ, CJ, BK=(list), AK=(list), CK=(list), DK=(List), MU, or SD. The parameters of the Gaussian prior distribution are entered using the PARAMS keyword. In the special case of CJ or DK=1, indicating the asymptote for the 3PL model, the prior must be specified for the logit of the asymptote, which is the parameter in MULTILOG. A standard deviation of 0.5 works well for the asymptote. AJ/BJ/CJ/BK/AK/CK/DK/MU/SD keyword Purpose
The set of parameters is specified by one of the following mutually exclusive keywords: AJ, BJ, CJ, BK=(list), AK=(list), CK=(list), DK=(List), MU, or SD.
AJ is the slope for the graded model and 3PL model BJ is the threshold for binary graded items and 3PL model CJ is the lower asymptote for the 3PL model BK=(list) specifies the listed threshold parameters for the graded model AK=(list) specifies the listed contrasts among the ak s for the nominal and multiple-
choice models CK=(list) specifies the listed contrasts among the ck s for the nominal and multiplechoice models DK=(list) specifies the listed contrasts among the d k s for the multiple-choice model MU is the mean of the population distribution for a group SD is the standard deviation of the population distribution for a group.
Format AJ/BJ/CJ/BK=(list)/AK=(list)/CK=(list)/DK=(list)/MU/SD
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4 MULTILOG REFERENCE ALL/ITEMS/GROUPS option Purpose
The set of items is specified with one of the mutually exclusive options ALL, ITEMS=(list) or GROUP=(list).
The ALL option refers to all of the items in the data. The ITEMS keyword specifies a subset of the items. The GROUPS keyword specifies groups, with reference to MU and SD.
Format ALL/ITEMS=(list)/GROUPS=(list)
PARAMS keyword Purpose
Specify the mean and standard deviation of the normal prior to be imposed on the item parameter(s) as (mean, standard deviation). Format PARAMS=(n,n)
394
SAVE COMMAND 4.4.9 SAVE command Purpose
To instruct MULTILOG to write the parameter estimates in item calibration problems, or the MLE[ θ ] or MAP[ θ ] in scoring problems, to external files. Format >SAVE FORMAT;
The saved parameters may be used to restart the program or score examinees, with the parameters read after a START command. When item calibration is performed, the parameters are saved to <jobname>.par. Scores obtained from scoring problems are saved to jobname.sco. FORMAT option Purpose
The present form of the parameter file differs from that of previous versions of the program. Users who wish to have MULTILOG save the estimated parameters in the previous style may insert the FORMAT option on the SAVE command. The program will then write a parameter file in the previous file, but the format of the parameter values will be 5F12.5 rather than 8F10.3; the new format must be used in formatted reading of the saved file. If this option is not present, the file will be saved in free format. Related topics
START command (see below)
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4 MULTILOG REFERENCE 4.4.10 START command Purpose
To override the default starting values for the item parameters and enter others. Format >START ALL/ITEMS=(list), PARAM=‘filename’, FORMAT;
The set of item related options is specified with either the ALL or ITEMS=(list) keyword. ALL/ITEMS option Purpose
The set of items is specified with one of the mutually exclusive options ALL or ITEMS=(list).
The ALL option refers to all of the items in the data. The ITEMS keyword specifies a subset of the items.
Format ALL/ITEMS=(list)
FORMAT option Purpose
The present form of the parameter files differs from that of previous versions of the program. The FORMAT option is used in the processing of parameter files created with previous versions. When this option is present on the START command, the next line of the command file must contain the format statement for reading the parameter file. The statement (8F10.3) is the required format for previous style parameter files. The filename is specified using the PARM keyword. Format FORMAT
Related topics
START command: PARAM keyword
396
START COMMAND PARAM keyword Purpose
This option is used to give the name and location of an external file containing parameter values that should be used as starting values in the current analysis or for comparing examinee or pattern scores. The filename can be up to 128 characters in length and should be enclosed in single quotes. Note that each line of the command file has a maximum length of 80 characters. If the filename does not fit on one line of 80 characters, the remaining characters should be placed on the next line, starting at column 1. This option in used in combination with FORMAT option. If this keyword does not appear, the parameters are assumed to be in the command file immediately following the START command. Format PARAM=
Related topics
START command: FORMAT option
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4 MULTILOG REFERENCE 4.4.11 TEST command (Required) Purpose
To define the IRT model for a set of items. Note that ALL and ITEMS=(list) and L1, L2, L3, GRADED, NOMINAL and BS are mutually exclusive options. Format >TEST
ALL/ITEMS=(list), L1/L2/L3/GRADED/NOMINAL/BS, NC=(list), HIGH=(list);
Related topics
The L1, L2, L3, GRADED, NOMINAL and BS options in the Input Parameters dialog box may be used to select the type of model to be fitted to the data (Section 4.2.4). The ALL option and ITEMS, NC and HIGH keywords may be set by using the Test Model dialog box (Section 4.2.5).
ALL option/ITEMS keyword Purpose
One of the following mutually exclusive options may be selected:
The ALL option will select all of the items in the data as indicated in the variable format statement. The ITEMS keyword is used to select a subset of the items for inclusion in the subtest(s).
Format ALL/ITEMS=(list)
Related topics
The ALL option or ITEMS keyword may be set using the Test Model dialog box.
L1/L2/L3/GRADED/NOMINAL/BS option Purpose
Used to select the type of model to be fitted to the data. Note that L1, L2, L3, GRADED, NOMINAL and BS are mutually exclusive options.
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TEST COMMAND
L1 represents the one-parameter logistic (1PL). L2 represents the two-parameter logistic (2PL). L3 represents the three-parameter logistic (3PL). GRADED represents Samejima’s (1969) graded model. NOMINAL represents Bock’s (1972) nominal model. BS represents the multiple-choice model of Thissen and Steinberg (1984), a version of
proposals by Bock (1972) and Samejima (1979). For the multiple-category models(GRADED,NOMINAL and BS) it is also necessary to indicate the number of categories of response for each item. This is done with the NC keyword. For the nominal model, it is also necessary to specify that one of the categories is “HIGH”; this is usually the correct response on an ability test. Format L1/L2/L3/GRADED/NOMINAL/BS
Related topics
The L1, L2, L3, GRADED, NOMINAL and BS options in the Input Parameters dialog box may be used to select the type of model to be fitted to the data (Section 4.2.4). TEST command: NC and HIGH keywords
HIGH keyword Purpose
This keyword is used to enter the number of the highest category for each item for the nominal models; this is usually the correct response on an ability test. Format HIGH=(list)
Related topics
The NC and HIGH keywords may be set by using the Test Model dialog box (Section 4.2.5) TEST command: L1/L2/L3/GRADED/NOMINAL/BS options TEST command: NC keyword
NC keyword Purpose
This keyword is used to enter the number of response categories for each item. Note that the nominal model cannot be used for binary (NC=2) data; use L2. 399
4 MULTILOG REFERENCE Format NC=(list)
Default (2(0)NITEMS).
Maximum
10 for any item. Related topics
The NC and HIGH keywords may be set using the Test Model dialog box (Section 4.2.5) TEST command: L1/L2/L3/GRADED/ NOMINAL/BS options PROBLEM command: NITEMS keyword (Section 4.4.7)
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TGROUPS COMMAND 4.4.12 TGROUPS command (Optional) Purpose
To specify grouping on the θ -dimension, for quadrature in MML or fixed groups for the fixed-effects model. Format >TGROUPS NUMBER=n, QP=(list), MIDDLE=(list);
MIDDLE keyword Purpose
Specifies the NUMBER of fixed groups at the values of θ as given in the list. It is used in the context of fixed-effects estimation. Format MIDDLE=(list)
Related topics
TGROUPS command: NUMBER keyword
NUMBER keyword Purpose
Specifies the number of quadrature points for MML estimation, or the number of θ -groups for fixed-effects estimation. Format NUMBER=n
Maximum
150.
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4 MULTILOG REFERENCE QP keyword Purpose
Specifies the NUMBER of quadrature points, placed at values of θ as given in the list. It is used in the context of MML random-effects estimation. Format QP=(list)
Related topics
TGROUPS command: NUMBER keyword
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TMATRIX COMMAND 4.4.13 TMATRIX command (Optional) Purpose
Specifies the form of the T -matrices for the NOMINAL/BS model.
Format >TMATRIX ALL/ITEMS=(list), AK/CK/DK DEVIATION/POLYNOMIAL/TRIANGLE;
The set of item related options is specified with either the ALL or ITEMS=(list) keyword. One of the three vectors of parameters of the nominal or multiple-choice model is specified with one of the following options: AK, CK or DK. One of the following three T -matrix options specifies the matrix: DE, PO or TR. The vector of parameters estimated by MULTILOG is multiplied by the T -matrices listed to give the ak s , ck s and d k s of the model. For example, the version of Masters’ (1982) model in which the slopes are not constrained to be equal for all of the items is given by the sequence >TEST ALL, NOMINAL, NC=(m, m, m,), HIGH=(m, m, m,..); >TMATRIX ALL, CK, TRIANGLE; >TMATRIX ALL, AK, POLYNOMIAL; >FIX ALL, AK=(2,3,…,m–1), VALUE=0.0;
which identifies the c-contrasts as the crossover points, the slope contrasts as polynomial and fixes the quadratic and higher terms to zero. If in addition you enter >EQUAL ALL AK=1;
the constraint is added that the slopes are equal across items and it becomes the MML version of Masters’ (1982) model.
Related topics
TEST command: NOMINAL/BS options (Section 4.4.11)
ALL/ITEMS option Purpose The set of items is specified with one of the mutually exclusive options ALL or ITEMS=(list).
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4 MULTILOG REFERENCE
The ALL option refers to all of the items in the data. The ITEMS keyword specifies a subset of the items.
Format ALL/ITEMS=(list)
AK/CK/DK option Purpose One of the three vectors of parameters of the nominal or multiple-choice model is specified with one of the following mutually exclusive keywords: AK, CK=(list) or DK=(list).
AK refers to ak
CK refers to ck
DK refers to d k .
Format AK/CK/DK
DEVIATION/POLYNOMIAL/TRIANGLE option Purpose One of the following three T-matrix options specifies the matrix: DEVIATION, POLYNOMIAL or TRIANGLE.
DEVIATION specifies deviation contrasts; those used by Bock (1972) POLYNOMIAL specifies polynomial contrasts TRIANGLE gives Masters’ (1982) δ s as parameters
Format DEVIATION/POLYNOMIAL/TRIANGLE
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VARIABLE FORMAT STATEMENT
4.4.14 Variable format statement There are two formats for the key information: one is used if the items are all binary, and the other is used if any items on the test have more than two response categories. The two types of key entry will now be discussed in turn.
Key entry: binary items
The first line after the END command must contain a single integer that is the number of response codes in the data file. In this context “code” means a single alphanumeric character that appears in the data file to indicate a response; common codes are 0 and 1, or T and F, or Y and N. The next line contains, beginning in column 1, in one-column fields, the codes themselves, for instance 01, or TF, or YN. The next line (or lines) contains the “key”: a list of the correct- or positive-responsecodes, beginning in column 1, in one-column fields. The key codes for up to 79 items go on a single line. If there are more than 79 items, the key codes for items 80 and higher go on the next line, up to the code for item 158. If there are more than 158 items, the key codes for items 159 and higher go on the following line, and so on (79 codes per line). The next line of the binary-item key block contains N in column 1 if there are no explicitly specified codes that indicate missing-at-random (or “not-reached”) or Y if such a code exists. Usually this line contains N, because any code in the data file that is not listed among the codes on the line described in (4), above, is also treated as missing-at-random, so it is generally easier to simply omit the missing data code(s) from the list of codes. However, if it is desirable to specify the missing data code among the codes, you can put Y on this line. If the entry on the missing-data code line (4, above) is Y, then the final line of the key sequence has the missing data code in column 1.
Related topics
END command
Response Codes (Binary Data) dialog box
Key entry: multiple response items For tests that include one or more items with more than two response categories:
The first line after the END command must contain a single integer that is the number of response codes in the data file. In this context “code” means a single alphanumeric character that appears in the data file to indicate a response; common codes are 0, 1, 2, 3, … or A, B, C, D, …. The next line contains, beginning in column 1, in one-column fields, the codes themselves, for instance 01234, or ABCDE. Note that the codes must be single characters, regardless of the number of item response categories. For example, after using the digits from 0-9 for ten response categories, the letters A, B, C, … are often used for the eleventh, 405
4 MULTILOG REFERENCE twelfth, thirteenth, and so on, categories. For multiple-category data, the code-line in (2) is followed by one line (or set of lines) for each code, in the order the codes are typed in (2), above. The category numbers are in 1-column fields if all of the items on the test have fewer than ten (10) response categories. Each line indicates, beginning in column 1 for item 1, the number of the response category into which that data code is to be placed. The lowest response category for MULTILOG models is numbered 1; the next lowest is 2, and so on. Response category number 0 is reserved for missing data in MULTILOG. For tests with no items with 10 or more categories, each item’s category-number for a given data code occupies a single column. 79 items' categories fit on a single line; the next 79 go on the next line, and so on. For example: In the mixture.mlg file (derived from example 15), there are five (5) response codes in the data: 0, 1, 2, 3, and 9. The first 26 items are binary; for those items, 0 is incorrect and 1 is correct. The 27th item has three categories, coded in the data file 1, 2, and 3. 9 indicates missing data. For this example, the key sequence is: 5 01239 111111111111111111111111110 222222222222222222222222221 000000000000000000000000002 000000000000000000000000003 000000000000000000000000000
For items 1-26, this key sequence maps the response code 0 into category 1, and the response code 1 into category 2. The response codes 1, 2, and 3 are placed in model categories 1, 2, and 3 for item 27. Unacceptable values, and 9s, are made missing by placement in category 0 (zero). If any item on the test has ten or more response categories, the category number lines in the key are all entered in two-column fields, right-justified. In this case, 40 category numbers are entered on each line; for more than 40 items, additional lines are used for each data code.
Related topics
END command (Section 4.4.2)
Response Codes (binary data) dialog box (Section 4.2.6)
Following the key sequence, a format command is entered describing the layout of the data file. This is described below: The format begins (in column 1) with “(” and ends with “)”.
406
VARIABLE FORMAT STATEMENT
For PATTERN data
NCHAR A1 for the ID field (optional; include only if NCHAR>0; NCHAR is the number of ID characters entered on the PROBLEM command). I1 (or I2, if there are 10 groups) to read the group number, from 1 to NGROUP (optional; include only if NGROUP>1 on the PROBLEM command). NITEMS A1 for the item responses. Fn.0 for the frequency corresponding to that response pattern, where n is the number of
columns devoted to the frequency in the data file.
For INDIVIDUAL data
NCHAR A1 for the ID field (optional; include only if NCHAR>0; NCHAR is the number of ID characters entered on the PROBLEM command). I1 (or I2, if there are 10 groups) to read the group number, from 1 to NGROUP (optional; include only if NGROUP>1 on the >PROBLEM command). NITEMS A1 for the item responses. Fn.d for a criterion if there is one (optional), where n is the number of columns in the data file devoted to the criterion, and d is the number of places after the decimal point.
For TABLE data The format is for one row of the θ -group × item-response table, giving the frequencies in each θ -group responding in each category, in F-format. FORTRAN “X” and “T” formats may be used as required.
Examples An example for binary data is example 1: MML PARAMETER ESTIMATION FOR THE 1PL MODEL, LSAT6 DATA BINARY DATA >PROBLEM RANDOM, PATTERN, NITEMS=5, NGROUP=1, NPATTERNS=32, DATA=‘LSAT6.DAT’; >TEST ALL, L1; >END; 2 01 11111 N (4X,5A1,F4.0)
The *.mlg file for the COURTAB2 example is shown below, illustrating keying for multiple categories. If the problem requires an item parameter file as starting values or for computing examinee or pattern scores, the file is named in a similar way by the keyword PARM=‘filename’ of the START 407
4 MULTILOG REFERENCE command. If this keyword does not appear, the parameters are assumed to be in the command file immediately following the START command. The command file courtab2.mlg for version 7.0 provides an example of this type of problem setup: SCORE LIBERAL CONSERVATIVE/COURTS HARSH PARAMETER VALUES IN COMMAND FILE >PROBLEM SCORE, INDIVIDUAL, NITEMS=2, NGROUP=2, NEXAMINEES=18, NCHAR=2, DATA=‘COURTAB.DAT’; >TEST ALL, GRADED, NCATS=(3,3); >START ALL; 1.08 -2.86 -1.78 1.12 -0.93 0.97 -1.00 0.16 1.00 -1.00 0.00 1.00 >END; 3 123 11 22 33 (2X,2A1,T1,I1,1X,2A1)
Note the addition of DATA=‘COURTAB.DAT’ on the PROBLEM command, and the FORMAT after >START ALL; To simplify parameter input, the program default has been changed to format-free read. The values of the parameters must be space- or comma-delimited; no format statement is required (or permitted). Free-format parameter values may also be read using the keyword PARM=‘filename’ to read the values from a file. By default, when the estimated parameters are saved in the jobname.par file, the parameter values are in this file and are suitable for format-free read. (See comments about the SAVE command below). To use parameter files in the format used by previous versions of MULTILOG, the keyword FORMAT has been added to the START command. This keyword is used only in conjunction with the keyword PARM=‘filename’, to read output files produced by MULTILOG with the keyword FORMAT on the SAVE command line. Partial syntax for the Knee problem using this new setup is
shown below: ESTIMATION OF SKELETAL MATURITY BY THE RWT (1975) METHOD PARTIAL SYNTAX >PRO SCORE, INDIVIDUAL, NOPOP, CRITERION, NEXAMINEES=13, NITEMS=34, NCHARS=10, DATA=‘KNEE.DAT’; >TEST ALL, GRADED, NC=(5,5,5,3,2,2,2,3,2,3,3,3,5,5,2(0)12,3,3,5,2,3,2,2,4); >START ALL, FORMAT, PARAM=‘KNEE.PAR’; >END; …
If the SAVE command appears in an item calibration problem, the parameters are saved in a file named jobname.par. 408
VARIABLE FORMAT STATEMENT
If you wish to have the program save the estimated parameters in the previous style, you may insert the keyword FORMAT in the SAVE command. This keyword causes an item header line to be written ahead of the parameter values as previously. This item header line contains essential information if the nominal or multiple-choice models are used; free-format output is not useful for most purposes with those models. If FORMAT does not appear in the SAVE command, the item header line is omitted and the parameters are written in format-free, space delimited form. If the SAVE command appears in an examinee or pattern scoring, the scores are saved in a file named jobname.sco. If the SAVE command does not appear, the scores are listed in the jobname.out file.
Related topics
END command (Section 4.4.2) PROBLEM command: NCHARS/ NGROUP/ NITEMS keywords (Section 4.4.7) START command: PARAM keyword (Section 4.4.10) SAVE command (Section 4.4.9)
Input Data dialog box (Section 4.2.3)
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5 TESTFACT REFERENCE
5 TESTFACT 5.1 Introduction The TESTFACT program implements all the main procedures of classical item analysis, test scoring, and factor analysis of inter-item tetrachoric correlations. In addition, it performs modern methods of factor analysis based on item response theory (IRT). The program also includes a facility for simulating responses to test items having difficulties and factor loadings specified by the user. New features in TESTFACT are all part of full information item factor analysis (FIFA). The commands and procedures of classical item statistics and classical factor analysis of tetrachoric correlation coefficients remain unchanged. The changes to full information item factor analysis consist of a new and improved algorithm for estimating the factor loadings and scores – specifically, new methods of numerical integration used in the EM solution of the marginal maximum likelihood equations. Three different methods of multidimensional numerical integration for the E-step of the EM algorithm are provided: adaptive quadrature, non-adaptive quadrature, and Monte Carlo integration. In exploratory item factor analysis, these methods make possible the analysis of up to fifteen factors and improve the accuracy of estimation when the number of items is large. The previous non-adaptive method has been retained in the program as a user-selected option (NOADAPT), but the adaptive method is the default. The maximum number of factors with adaptive quadrature is 10; with non-adaptive quadrature, 5; with Monte Carlo integration, 15. Bayes estimates of scores for all factors can be estimated either by the adaptive or non-adaptive method. Estimation of the classical reliability of the factor scores is also included. TESTFACT includes yet another full information method that provides an important form of confirmatory item factor analysis namely “bifactor” analysis. The factor pattern in bifactor analysis consists of a general factor on which all items have some loading, plus any number of so-called “group factors” to which non-overlapping subsets of items, assigned by the user, are assumed to belong. The subsets typically represent small numbers of items that pertain to a common stem such as a reading passage or problem-solving exercise. The bifactor solution provides Bayes estimation of scores for the general factor, accompanied by standard errors that properly account for association among responses attributable to the group factors. Three new commands have been added:
BIFACTOR invokes and controls the bifactor solution. The FACTOR and FULL commands may not be used with BIFACTOR. TECHNICAL combines keywords and options of item factor analysis that would otherwise have to be duplicated in the BIFACTOR, FACTOR, FULL, and SCORE commands. SIMULATE is now a separate command instead of a keyword of the SCORE command. It has
additional options for input of item parameters to specify the simulation. The parameters may be entered either as item intercepts and factor slopes, or standard difficulties (i.e., 410
5 TESTFACT REFERENCE normal deviates corresponding to items percent correct) and factor loadings. The command now also allows the user to specify mean values for the factor scores. The default values of the means are zero, as in the previous version of the program.
5.2 The TESTFACT interface This document describes those elements in the user’s interface that may not be immediately clear to the user or that behave in a somewhat nonstandard way.
Main menu Run menu Output menu Font option Window menu
5.2.1 Main menu At the center of the interface is the main menu bar, which adapts to the currently active function. For example, when you start the program, the menu bar shows only the menu choices File, View, and Help.
However, as soon as you open a TESTFACT output file (through the File menu), the Windows and Edit menu choices show up on the menu bar. At the same time, the File menu choices have been expanded with selections like Save and Save As. And the View menu now has a Font option next to the Status bar and Toolbar choices. Opening an existing TESTFACT command (*.tsf) file, or starting a new one, adds further choices to the main bar: the Output and Run menus.
Note that you can open only one command file at a time. If you want to paste some part from an existing command file in your current one, opening the old file will automatically close the current one. After you copy the part you want to the clipboard, you have to reopen the *.tsf file for pasting. 411
5 TESTFACT REFERENCE 5.2.2 Run menu The Run menu gives you the option to run the command file displayed in the main window.
If you made any changes, the current command file will first be saved when you run an analysis by clicking Run. You can easily tell if a command file has been changed by looking at the filename above the menu bar. An asterisk after the filename shows that the current file has changed but has not been saved yet. 5.2.3 Output menu Through the Output menu, you can open the list output, named with the file extension *.out. Always check the end of each output file to see if it reports: NORMAL END. If it does not, something went wrong and the output file should have some information on that.
5.2.4 Window menu The Window menu is only available when you have at least one file open. You can use the CtrlTab key combination to switch between open files, or use the Window menu to arrange the open files (cascade, tile). 5.2.5 Font option Clicking on the Font option on the View menu displays a dialog box with the fonts that are available on your system. You may use different fonts for command and output files. At installation, they are both set to a special Arial Monospace font that ships with the program. To keep the tables in the output aligned, you should always select a monospace or fixed pitch font where all the characters in the font have the same width. Once you select a new font, that font becomes the default font. This gives you the option to select a font (as well as font size and font style) for your command (*.tsf) files that is different from the one for your list output (*.out) file as a quick visual reminder of the type of file.
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OVERVIEW OF SYNTAX
5.3 Command syntax TESTFACT uses the command conventions of other IRT programs published by SSI. Command lines employ the general syntax: >NAME
KEYWORD1=n, KEYWORD2=(list), …, OPTIONn;
The following rules apply:
Command lines may not exceed 80 columns. Continuation on one or more lines is permitted. Each command must be terminated by a semicolon (;). The semicolon functions as the command delimiter: it signals the end of the command and the beginning of a new command. A greater-than sign (>) must be entered in column 1 of the first line of a command and followed without a space by the command name. Command names, keywords, and options may be entered in full or abbreviated to the first three characters. Exceptions are the following keyword values and options, which must be entered in full: VARIMAX in the FACTOR command (Section 5.3.9) PROMAX in the FACTOR command PATTERN in the INPUT command (Section 5.3.12) CASE in the INPUT command RECODE in the BIFACTOR and FULL commands (Sections 5.3.3 and 5.3.11) MISS in the BIFACTOR and FULL commands LORD in BIFACTOR and FULL commands
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LOADINGS and SLOPES in the SIMULATE command (Section 5.3.23)
At least one space must separate the command name from any keywords or options. Commas must separate all keywords and options. The equal sign is used to set a keyword equal to a value, which may be integer, real, or character. A real value must contain a decimal point. A character value must be enclosed in single quotes if: o it contains more than eight characters o it begins with a numeral o it contains embedded blanks, commas, semicolons, or slashes A keyword may be vector valued, i.e., set equal to a list of integer, real, or character constants, separated by commas or spaces, and enclosed in left and right parentheses. If the list contains an arithmetic progression, the abbreviated form first(increment)last, may be used. Thus, a selection of items 1,3,7,8,9,10,15 may be entered as 1,3,7(1)10,15. Real values may be used in a similar way. If the values in the list are equal, the form, value(0)number of values, may be used. Thus, 1.0,1.0,1.0,1.0,1.0 may be entered as 1.0(0)5. Any number of problems may be stacked. Output from the problems will be concatenated in files <problem>.* (the <problem> part of the filenames is equal to the name of the command file being used). The STOP command indicates the end of the stack of problems.
Related topics
FACTOR command: ROTATE keyword (Section 5.3.9) INPUT command: CASE and PATTERN keywords (Section 5.3.12) LOADINGS and SLOPES in the SIMULATE command (Section 5.3.23) LORD in BIFACTOR and FULL commands (Sections 5.3.3 and 5.3.11) MISS in the BIFACTOR and FULL commands RECODE in the BIFACTOR and FULL commands
5.3.1 Order of commands All available TESTFACT commands are given in their necessary order below. Required commands (indicated with a “*”) must appear in the command file for each problem setup. All other commands are optional. In the sections that follow, commands are arranged in alphabetical order. TITLE (*) PROBLEM (*) COMMENT NAMES RESPONSE (*) KEY (*) SELECT SUBTEST CLASS FRACTILE EXTERNAL CRITERION
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OVERVIEW OF SYNTAX RELIABILITY PLOT TETRACHORIC BIFACTOR FACTOR FULL PRIOR SCORE TECHNICAL SAVE SIMULATE INPUT (*) (Variable format statement) (*) CONTINUE (*) STOP (*)
Note: INPUT and CONTINUE (or INPUT and STOP) must be the last two commands. The variable format is required in the command file when raw data are read in from an external file.
5.3.2 Overview of syntax Command
Keywords
Options
NITEM=n, SELECT=n, RESPONSE=n, SUBTEST=n, CLASS=n, FRACTILES=n, EXTERNAL=n, SKIP=n
NOTPRES
TITLE PROBLEM
COMMENT NAMES RESPONSE KEY SELECT SUBTEST
BOUNDARY=(list), NAMES=(list)
CLASS
IDENTITY=(list), NAMES=(list)
FRACTILES
BOUNDARY=(list)
SCORE/PERCENTIL
NAME=n, WEIGHTS=(list)
EXTERNAL/SUBTESTS/CRITMARK
EXTERNAL CRITERION RELIABILITY
KR2/ALPHA
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Command
Keywords
PLOT
Options BISERIAL/PBISERIAL, NOCRITERION/CRITERION, FACILITY/DELTA
TETRACHORIC
NDEC=n
RECODE/PAIRWISE/ COMPLETE, TIME, LIST, CROSS
BIFACTOR
NIGROUPS=n, IGROUPS=(list), LIST=n, CPARMS=(list), NDEC=n, OMIT=n, CYCLES=n, QUAD=n
TIME, SMOOTH, RESIDUAL, NOLIST
FACTOR
NFAC=n, NROOT=n, NIT=n, ROTATE=(list), NDEC=n
RESIDUAL, SMOOTH
FULL
OMIT=n, FREQ=n, CYCLES=n, CPARMS=(list), QUAD=n
TIME
PRIOR
SLOPE=n, INTER=(list)
SCORE
NFAC=n, FILE=, LIST=n, METHOD=n, PARAM=n, SPRECISION=n
MISSING, TIME, CHANCE, LOADINGS
TECHNICAL
ITER=(list), QUAD=n, SQUAD=n, PRV=n, FREQ=n, NITER=n, QSCALE=n, QWEIGHT=n, IQUAD=n, ITLIMIT=n, PRECISION=n, NSAMPLE=n, ACCEL=n, MCEMSEED=n
NOADAPT, FRACTION, NOSORT
SAVE
SCORE, MAIN, SUBTESTS, CRITERION, CMAIN, CSUB, CCRIT, CORRELAT, SMOOTH, ROTATE, UNROTATE, FSCORES, TRIAL, SORTED, EXPECTED, PARM
SIMULATE
NFAC=n, NCASES=n, SCORESEED=n, ERRORSEED=n, GUESSSEED=n, FILE=, MEAN=(list), FORM=n, GROUP=n, PARM=n
LOADINGS/SLOPES, CHANCE
INPUT
NIDCHAR=n, NFMT=n, TRIAL=, WEIGHT=(list), FILE=
SCORES/CORRELAT/FACTOR, FORMAT/UNFORMAT, LIST, REWIND
CONTINUE STOP
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OVERVIEW OF SYNTAX Note:
Keywords require a value after the equal sign. Options operate if present; a forward slash (/) indicates a choice between two (or three) options, the first being the default.
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5 TESTFACT REFERENCE 5.3.3 BIFACTOR command (Optional) Purpose To request full information estimation of loadings on a general factor in the presence of item-group factors, and Bayes EAP estimation of scores on the general factor, including standard errors allowing for conditional dependence introduced by the item-group factors. Note FACTOR and BIFACTOR are mutually exclusive commands. If RESIDUAL is not selected, it is
not necessary to compute the tetrachoric matrix. Format >BIFACTOR NIGROUPS=n, IGROUPS=(list), LIST=n, CPARMS=(list), NDEC=n, OMIT=RECODE/MISS/LORD, CYCLES=n, QUAD=n, TIME, SMOOTH, RESIDUAL, NOLIST;
Example >BIFACTOR LIST=1, NDEC=2, OMIT=LORD;
Default No bifactor analysis. Related topics
FACTOR command (Section 5.3.9)
CPARMS keyword Purpose To specify the probability of chance success on each item. If items have been specified in the SELECT command, the corresponding probabilities will be selected from this list. Format CPARMS= (n1 , n2 ,..., nn )
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BIFACTOR COMMAND Default All probabilities are set to zero. Related topics
SELECT command (Section 5.3.22)
CYCLES keyword Purpose To specify the number of EM cycles in the bifactor solution. Format CYCLES=n
Default 20. IGROUPS keyword Purpose To assign the items to the item groups, numbered from 1 to n. Assign 0 to any item that loads only on the general factor. If items have been specified in the SELECT command, the corresponding IGROUPS numbers will be selected from this list. Format IGROUPS= ( n1 , n2 ,..., nn )
For purpose of comparing results of a bifactor analysis with a one-factor analysis of the same data, the user may assign all items to the general factor (i.e., all values of the IGROUPS keyword are zero. In that case, NIGROUPS must also be set to zero). Default
None. Related topics
BIFACTOR command: NIGROUPS keyword SELECT command (Section 5.3.22)
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5 TESTFACT REFERENCE LIST keyword Purpose
To control the printout of the bifactor loadings as follows: n= n= n= n=
0: no printout 1: loadings will be listed in item order 2: loadings will be listed in item group order 3: loadings will be listed in both orders
If unrotated factor loadings are selected in the SAVE command, the loadings will be saved in item order in the format of a conventional two-factor solution. The group assignments will be included. Format LIST=n
Default
0. Related topics SAVE command (Section 5.3.20)
NDEC keyword Purpose
To specify the number of decimal places in the listing of a selected smoothed or residual correlation computed from the bifactor solution. Format NDEC=n
Default
3.
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BIFACTOR COMMAND NIGROUPS keyword Purpose
To specify the number of item-group factors. Format NIGROUPS=n
Default
None. NOLIST option Purpose
To suppress the listing of the smoothed or residual matrix in the program output. These matrices may be saved in an external file in either case (see the SAVE command, discussed in Section 5.3.20). Format NOLIST
Related topics
SAVE command (Section 5.3.20)
OMIT keyword Purpose
To specify the treatment of omitted items. Note that the option selected should be given in full.
If OMIT is set to RECODE, omitted items will be scored incorrect. If OMIT is set to MISSING, omitted items will be treated as not-presented. If OMIT is set to LORD, F.M. Lord’s convention of scoring omits of multiple-choice items as fractionally correct will be observed. The fraction is the chance success parameter for the items. It is set to the reciprocal of the number of alternatives in the item. Only factor scores are affected.
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5 TESTFACT REFERENCE Format OMIT=RECODE/MISSING/LORD
Default RECODE.
QUAD keyword Purpose
To control the number of quadrature points for the EM estimation of the parameters. Format QUAD=n
Default
9. RESIDUAL option Purpose
To compute the difference between the tetrachoric correlation matrix and the smoothed expected matrix. If RESIDUAL is not selected, it is not necessary to compute the tetrachoric matrix. Format RESIDUAL
SMOOTH option Purpose
To reproduce the expected correlation matrix from the bifactor solution; otherwise, the matrix will not be computed. Format SMOOTH
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BIFACTOR COMMAND TIME option Purpose
To specify that omitted items following the last non-omitted item should be treated as notpresented. Otherwise, they will be scored incorrect if the OMIT keyword is set to RECODE. Format TIME
Related topics
BIFACTOR command: OMIT keyword
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5 TESTFACT REFERENCE 5.3.4 CLASS command (Optional) Purpose
To assign class codes if item statistics are to be estimated separately for each class (group) of respondents in the sample. Format >CLASS IDENTITY=(list), NAMES=(list);
Examples >CLASS IDENTITY=(‘1000’,‘2000’,‘3000’);
Classes are identified by numbers. >CLASS IDENTITY=(N,S,E,W,C);
Classes are identified by letters. >CLASS IDENTITY=(N,S,E,W,C), NAMES=(NORTH, SOUTH, EAST, WEST, CENTRAL);
Classes are identified and named. Default
No classes. IDENTITY keyword Purpose
To specify a string of codes to identify classes of respondents.
Each code may be 1 to 4 characters. Codes other than letters must be enclosed in single quotes. Codes are separated by commas. The string of codes for all the classes is enclosed in parentheses.
Format IDENTITY= ( n1 , n2 ,..., nq )
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CLASS COMMAND Default
None. NAMES keyword Purpose
To specify a string of names to label classes of respondents.
Names are limited to 8 characters for each class name. Names are listed in the same order as the codes. The string of names for all the classes is enclosed in parentheses.
Format NAMES= ( n1 , n2 ,..., nq )
Default
Blank names.
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5 TESTFACT REFERENCE 5.3.5 COMMENT command (Optional) Purpose
To enter one or more lines of comment to appear in the output. Format >COMMENT …text… … … …text…
Note
The COMMENT command is given on a line by itself and followed by as many lines as desired, of 80 characters maximum, containing comments. A semicolon to end this command is not needed. Example >COMMENT 20 ITEM TEST, THE TOTAL SCORE AS THE CRITERION SCORE. THE ITEMS ARE TESTING THE FOLLOWING TOPICS. STRUCTURE AND LANDFORMS. EROSION, TRANSPORT AND DEPOSITION. CLIMATE AND VEGETATION. MINERAL RESOURCES. AGRICULTURE AND INDUSTRY. POPULATION AND TRANSPORT. MISCELLANEOUS. PERSONS SITTING TEST CLASSIFIED BY SEX, G=GIRL, B=BOY; THE DATA CARDS ARE LAYED OUT AS BELOW. COLUMN 1 TO 12 INCLUSIVE – IDENTITY COLUMN 13 – SEX COLUMNS 14 TO 33 – ITEM RESPONSES COLUMNS 36 TO 37 INCLUSIVE – CRITERION SCORE >NAMES…
Default
No comments.
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CONTINUE COMMAND
5.3.6 CONTINUE command (Optional) Purpose
To terminate each problem in a set of stacked problems. Format >CONTINUE
Note
A semicolon to signal the end of this command is not needed.
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5 TESTFACT REFERENCE 5.3.7 CRITERION command (Optional) Purpose
To define a criterion score to supplement the main test score of each respondent. Format >CRITERION
EXTERNAL/SUBTESTS/CRITMARK, NAME=n, WEIGHTS=(list);
Examples >CRITERION
CRITMARK, NAME=TESTMARK;
>CRITERION
SUBTESTS, NAME=TOTAL, WEIGHTS=(0.3,0.2,0.5);
Default
No criterion score. EXTERNAL/SUBTEST/CRITMARK option Format EXTERNAL/SUBTESTS/CRITMARK
EXTERNAL: A linear combination of external variables (see the EXTERNAL command, discussed in Section 5.3.8). In this case, weights are supplied by the user (see WEIGHTS keyword, with w = t). SUBTESTS: A linear combination of subtest scores (see SUBTEST command, Section 5.3.25). In this case, calculation is based weights supplied by the user (see WEIGHTS keyword, with w = p). CRITMARK: A score input with item responses. No weights required.
Default EXTERNAL.
Related topics
EXTERNAL command (Section 5.3.8) CRITERION command: WEIGHTS keyword SUBTEST command (Section 5.3.25)
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CRITERION COMMAND NAME keyword Purpose
To provide a name of 1 to 8 characters for the resulting criterion. The rules for naming items (see NAMES command, Section 5.3.14) apply to the criterion name. Format NAME=character string
Default
Blank name. Related topics
NAMES command (5.3.14)
WEIGHTS keyword Purpose
To enter weights when the criterion score must be calculated as a linear combination of other variables (see EXTERNAL option) .
Weights are separated by commas, and the list is enclosed in parentheses. Weights must be real-valued (that is, they must have decimal points).
Format WEIGHTS= ( n1 , n2 ,..., nw )
Default
1.0. Related topics
CRITERION command: EXTERNAL option
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5 TESTFACT REFERENCE 5.3.8 EXTERNAL command (Optional) Purpose
To provide names for the external variables.
Names may be 1 to 8 characters long. Values of the external variables are entered from each data record. See the SCORES option on the INPUT command, discussed in Section 5.3.12. The rules for naming items (see NAMES command, Section 5.3.14) apply to the external variables. This keyword is related to the use of the EXTERNAL keyword on the PROBLEM command (see Section 5.3.17) .
Format >EXTERNAL= ( n1 , n2 ,..., nt )
Example >EXTERNAL
ARITH,ALGEBRA,TRIG,GEOMETRY;
Default
External variables are unnamed. Related topics
INPUT command: SCORES option (Section 5.3.12) NAMES command (Section 5.3.14) PROBLEM command: EXTERNAL keyword (Section 5.3.17)
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FACTOR COMMAND 5.3.9 FACTOR command (Optional) Purpose
To control the item factor analysis. To perform MINRES factor analysis of the conditioned tetrachoric correlation matrix. To rotate the solution. To obtain residual and smoothed correlations.
Format >FACTOR
NFAC=n, NROOT=n, NDEC=n, ROTATE=(VARIMAX/PROMAX,d,e), RESIDUAL, SMOOTH, NIT=n;
Note VARIMAX and PROMAX may not be abbreviated in the FACTOR command.
Example >FACTOR NFAC=5, ROTATE=(PROMAX,4,2), RESIDUAL;
Default
No factor analysis. FACTOR command: NDEC keyword Purpose
To specify the number of decimal places printed for the residuals. Format NDEC=n
Default
3. NFAC keyword Purpose
To specify the number of factors to be extracted. 431
5 TESTFACT REFERENCE
Must be less than NITEMS in the PROBLEM command. Must not exceed 15 in MINRES factor analysis or Monte Carlo EM analysis. Must not exceed 10 in adaptive full information factor analysis. Must not exceed 5 in non-adaptive full information item factor analysis (see the TECHNICAL command for the NOADAPT option, discussed in Section 5.3.26).
Format NFAC=n
Default NITEM/2.
Related topics
PROBLEM command: NITEMS keyword (Section 5.3.17) TECHNICAL command: NOADAPT option (Section 5.3.26)
NIT keyword Purpose
To specify the number of iterations for the MINRES factor solution of the smoothed correlation matrix. Format NIT=n
Default NIT=3. Minimum value = 1.
NROOT keyword Purpose
To specify the number of latent roots to be extracted. NROOT must be greater or equal to NFAC. Format NROOT=n
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FACTOR COMMAND Default NFAC.
Related topics
FACTOR command: NFAC keyword
RESIDUAL option Purpose
To request the computation of the residual correlation matrix. This matrix will be computed by the initial correlation matrix minus the final correlation matrix. The residual variance for each item appears in the diagonal of this matrix. Format RESIDUAL
Default
No residual correlation matrix. ROTATE keyword Purpose
To request rotation of the factors. VARIMAX or PROMAX has to be entered (in full) if rotation is required, there is no default. d is the number of leading factors to be rotated, and must be equal to or less than NFAC. e is the constant for PROMAX rotation and must be between 2 and 4, inclusive. Format ROTATE=([VARIMAX/PROMAX],d,e)
Default d=NFAC, e=3.
Related topics
FACTOR command: NFAC keyword
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5 TESTFACT REFERENCE SMOOTH option Purpose
To request the computation of an f-factor positive definite estimate of the latent response process correlation matrix. Format SMOOTH
Note
The SMOOTH option affects only the output of the final smoothed correlation matrix. Initial smoothing of the correlation matrix will take place whether the SMOOTH option is entered or not. The off-diagonal elements 1.0, -1,0, 9.0 or -9.0 in the initial tetrachoric correlation matrix (caused by too small cell or marginal frequencies in a contingency table) will be automatically replaced by a new correlation coefficient estimated by the centroid method. The positive-definite tetrachoric correlation matrix is then produced before the principal factor analysis.
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FRACTILES COMMAND 5.3.10 FRACTILES command (Optional) Purpose
To group scores into fractiles by score boundaries or percentiles. The number of fractiles must be set in the PROBLEM command. Format >FRACTILES
SCORE/PERCENTIL, BOUNDARY=(list);
Examples >FRACTILES
SCORE, BOUNDARY=(15,27,33,40,60);
Fractiles will correspond to the mutually exclusive score bands:
through 15 16 through 27 28 through 33 34 through 40 41 through 60 >FRACTILES
PERCENTIL, BOUNDARY=(20,40,60,80,100);
Each fractile will correspond to 20 percent of the scores. Default
No fractiles. Related topics
PROBLEM command (Section 5.3.17)
BOUNDARY keyword Purpose
To specify the boundaries to be used.
If the SCORES option is selected, the boundaries consist of cumulative upper scores on the test bands. The scores are expressed in integers from 1 to NITEM. If the PERCENTIL option is selected, the boundaries consist of the cumulative upper per-
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5 TESTFACT REFERENCE centages of the score distribution. The percentages are expressed in integers from 1 to 100. Format BOUNDARY= ( n1 , n2 ,..., ns )
Related topics
PROBLEM command: NITEMS keyword (Section 5.3.17) FRACTILES command: SCORE/PERCENTIL option (Section 5.3.10)
SCORE/PERCENTIL option Purpose
To specify the fractiles used.
If SCORE is selected, each fractile corresponds to a band of scores on the main test. If the number of items is small, it is better to use score bands rather than the percentiles to define fractiles. If PERCENTIL is selected, each fractile corresponds to a percentile of scores on the main test.
Format SCORE/PERCENTIL
Default SCORE.
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FULL COMMAND 5.3.11 FULL command (Optional) Purpose
To request full information item factor analysis, starting from the principal factor solution, and the computation of the likelihood ratio χ 2 and change of χ 2 . Format >FULL OMIT=RECODE/MISS/LORD, CYCLES=n, CPARMS=(list), TIME, FREQ=n, QUAD=n;
Note RECODE, MISS and LORD may not be abbreviated in the FULL command.
Example >FULL
CYCLES=20, OMIT=MISS, CPARMS=(0.25(0)10);
Default
No full information analysis. CPARMS keyword Purpose
To specify the value of chance or so-called guessing parameters, j = 1, 2, …, n. Repeated values of n may be coded as n(0)m, where m is the number of repetitions of the values of (0.0 < n < 1.0). TESTFACT regards these parameters as fixed when fitting the guessing model. Format CPARMS= ( n1 , n2 ,.., nn )
Examples CPARMS=(0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1);
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5 TESTFACT REFERENCE CPARMS=(0.1(0)32);
CPARMS=(0.05(0)16,0.1,0.1,0.15(0)14);
Default
0.0. CYCLES keyword Purpose
To specify the maximum number of EM cycles. Format CYCLES=n
Default
15. FREQ keyword Purpose
To list observed and expected response pattern frequencies and their differences. Format FREQ=0 or FREQ=1
Default FREQ=0 (observed and expected response frequency table not written to output file.)
OMIT keyword Purpose
To specify the treatment of omitted items.
If OMIT=RECODE, omits will be recoded as wrong responses. If OMIT=MISSING is selected, omits will not be used at all. If OMIT equals LORD, F.M. Lord’s convention of scoring omits of multiple-choice items as 438
FULL COMMAND fractionally correct will be observed. The fraction is the chance success parameter for the items. It is set to the reciprocal of the number of alternatives in the item. Only factor scores are affected. Format OMIT=RECODE/MISSING/LORD
Default RECODE.
QUAD keyword Purpose
To control the number of quadrature points for the EM estimation of the parameters. Format QUAD=n
Default
When the NOADAPT option is specified on the TECHNICAL command, the default values for QUAD are as follows: Factors Quad points
1
21
2
15
3
5
>3
3
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5 TESTFACT REFERENCE Otherwise Factors Quad points
1
9
2
5
3
4
>3
3
Related topics
TECHNICAL command: NOADAPT option (Section 5.3.26)
TIME option Purpose
To treat omitted responses following the last-responded-to item as not-presented. Format TIME
Default
Omitted responses will be treated as the OMIT keyword specifies. Related topics
FULL command: OMIT keyword
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INPUT COMMAND 5.3.12 INPUT command (Required) Purpose
To begin input data for item analysis or factor analysis. Format >INPUT
NIDCHAR=n, NFMT=n, TRIAL=‘filename’, WEIGHT=(CASE,n)/PATTERN, FILE=‘filename’, SCORES/CORRELAT/FACTORS, FORMAT/UNFORMAT, LIST, REWIND;
Note CASE and PATTERN may not be abbreviated in the INPUT command.
Examples Example 1:
Item response data, unweighted and unformatted, are in the file with the name mydata.dat. There are two groups, coded M and F, one form, and two external criteria. Because these are raw data, the variable format record must be in the command file. End of command file: >INPUT NIDCHAR=4,FILE=‘MYDATA.DAT’; (4A1,3X,A1,2X,10A1,2F5.1) >CONTINUE
The file mydata.dat: 0001 0002 … 2000
M M
1324431223 1332143214
261 193
372 301
F
1334331214
176
280
Example 3:
A 5 × 5 correlation matrix for MINRES factor analysis, possibly including VARIMAX or PROMAX rotation. Data are in the indicated file. >INPUT >STOP
CORRELAT ,FILE= ‘EXAMPL02.COR’;
Data are read as one long string of numbers with decimal points – space-delimited and format-free. They can appear, for example, in the following form:
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5 TESTFACT REFERENCE 1.0000 .6231 1.0000 .5574 .5395 1.0000 .3746 .3952 .4871 1.0000 .3210 .3456 .3863 .5894 1.0000 >CONTINUE
Example 4:
A factor solution for VARIMAX and PROMAX rotation; factor loadings are unformatted output from SAVE command and are in the file factor.dat. The first line of the external file with the factor loadings contains the variable format statement. >INPUT FACTORS, FILE='FACTOR.DAT’,UNF; >CONTINUE
Example 5:
Item response data for full information factor analysis; trial values from a previous principal factor analysis are input as starting values. The trial values and the variable format statement describing the layout are in the file pfact.tri. The raw data are in the external file survey.dat. The corresponding variable format statement is required in the command file. Command file: >INPUT NIDCHAR=10, FILE=SURVEY.DAT’, TRIAL=‘PFACT.TRI’; (10A1,15A1) >CONTINUE
The trial value file pfact.tri: (I3,2X,3F10.5) 01 0.36512 … 15
-0.62143
0.01684
The data file survey.dat: 0001M24 101101110010100 0250F20 111101011010111
Example 6:
Item response data with case weights normalized to 1000; data are formatted in the file survey.dat. Two variable format records are in the command file.
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INPUT COMMAND >INPUT
NIDCHAR=5, NFMT=2, WEIGHT=(CASE,1000.0), FILE=‘SURVEY.DAT’, REWIND; (5A1,A1,T16,4A1,T14,2A1,T8,4A1, T20,10A1,F6.3) >CONTINUE
The file survey.dat: 00001ABDAECAEDBBCAEDEBACDEABC … 01651ABDBEACEDCBBCDABEBDDCEAA
0.632 0.467
Related topics SAVE command (Section 5.3.20)
NIDCHAR keyword Purpose
To specify the number (between 1 and 50) of characters in the subject identification field. Format NIDCHAR=n
Default
1. FILE keyword Purpose
To provide the filename of the data records (of response records, correlations, or factor loadings). It may contain up to 128 characters including path and extension and should be enclosed in single quotes. The drive and directory path should be included if the data file is not in the same folder as the command file. For more information, see Section 3.2.6. Format FILE=
Default
Data are read from the command file.
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5 TESTFACT REFERENCE FORMAT/UNFORMAT option Purpose
To indicate whether or not the input data file is formatted:
If the data file is formatted as described in a variable format statement, the FORMAT option should be used. UNFORMAT is used when the data file is unformatted (binary). To create the records of the unformatted file, the following WRITE statements may be used: SCORES: WRITE(FILE)(ID(I),I=1,NIDCHAR),C,(IR(I),I=1,NITEM), WT,EXTV(I)I=1,EXT),
where C, WT and EXTV(I) are optional. The variable IR is integer; the others are real, single precision. ID is a case identifier, C a class indicator, IR an item response pattern, and WT a weight (for either CASE or PATTERN). CORRELAT: WRITE(FILE)(CORR(I)I=1,NTRI),
where NTRI=(NITEMS x (NITEMS+1))/2 and CORR is real, single precision. FACTOR: WRITE(FILE)(A(I)I=1,NFA),
where NF=(NITEMS x NFAC). Format FORMAT/UNFORMAT
Default FORMAT.
Related topics
INPUT command: SCORES option INPUT command: CASE/PATTERN option
LIST option
To list in output file, for all subjects:
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INPUT COMMAND identification main test score subtest scores (if any).
Format LIST
NFMT keyword Purpose
To specify the number of variable format records (80 characters) describing the data records. The format records must appear in the command file immediately following the INPUT command. Format NFMT=n
Default
1. REWIND option Purpose
This is a program instruction to read the data file from the beginning for a subsequent problem in a stacked command file. The term rewind dates from mainframe days, when data files were commonly read from a tape that needed to be rewound to the start of the file for a fresh reading of the data. Format REWIND
SCORES/CORRELAT/FACTORS option Purpose
To indicate the type of data being read from the specified file as follows: Use the SCORES option to read subject response records. The data should conform to the following specifications:
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5 TESTFACT REFERENCE
a characters of case identification read as an A1 character field: (aA1), where a is the value specified with the NIDCHAR keyword in the INPUT command. Class code if CLASS is more than 0 in the PROBLEM command; read as (A1), (A2), (A3), or (A4) characters. One-character item responses read as (nA1) or, for example, ( n1 A1,10X, n2 A1) where n1 + n2 = n ; n being the value specified with the NITEMS keyword in the PROBLEM com-
mand. Weight, if the WEIGHT keyword is specified in the INPUT command; read as (Fw.d). r external variable values, if the EXTERNAL keyword on the PROBLEM command is set to more than 0; read as (rFw.d), where r is the value specified with the EXTERNAL keyword. End of scores is indicated by one completely blank record. Use the CORRELAT option to use the input correlation matrix for MINRES factor analysis (full information factor analysis requires item response data and cannot be carried out directly on the correlation matrix). Also see Example 3 of the INPUT command. NITEMxNITEM correlation matrix in lower triangular form. The NITEM(1+NITEM)/2 elements up to and including each diagonal element are read as one long string. Use the FACTORS option to read input factor loadings (for rotation only) from the file specified by the FILE keyword. Also see Example 4 of the INPUT command. NITEMxNFAC matrix of factor loadings read by rows. One variable format record precedes the first row of this matrix and describes one row.
Format SCORES/CORRELAT/FACTORS
Default SCORES.
Related topics
FACTOR command: NFAC keyword (Section 5.3.9) INPUT command: FILE and WEIGHT keywords PROBLEM command: CLASS, NITEM, and EXTERNAL keywords (Section 5.3.17)
TRIAL keyword Purpose
To specify the filename for input of trial values for the full information factor analysis. It may contain up to 128 characters including path and extension and must be enclosed in single quotes. Each line of the trial values file must contain the item number followed by the intercept and slope for each factor. The variable format record must appear in the first line of this file. See 446
INPUT COMMAND Example 5 of the INPUT command. Trial values are saved in this form, with the format statement, by the TRIAL option of the SAVE command. If the trial values are in the command file, they must appear immediately after the data format records. Format TRIAL=
Default
No trial values. Related topics
SAVE command: TRIAL option (Section 5.3.20)
WEIGHT keyword Purpose
To specify the type of weight for a weighted analysis. The two options below may be used with this keyword.
(CASE,n): Each record includes a case weight (real, i.e., with decimal points in data records, or read in F-format). Real normalizing constant n must be specified if CASE option is chosen. PATTERN: Each data record consists of an answer pattern with a frequency (integer, i.e., without a decimal point and read in I-format). CASE and PATTERN may not be abbreviated in the INPUT command.
Format WEIGHT=(CASE,n)/PATTERN
Default
No weights.
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5 TESTFACT REFERENCE 5.3.13 KEY command (Required) Purpose
To specify correct-response codes for all the items on the main test, in their original, preselected order. Format >KEY
ccccccccccccccccccccccc;
Notes
Rules for entering the key to correct responses:
The key is a continuous string of correct-answer response codes. Each response code is one character only, a letter or a number. Blanks in the response key string are ignored. If a blank is used as a response code, represent it by a comma (,) in the key string. Codes may continue without break on up to ten continuation lines.
Example >KEY
AABCAEDCEDEACBD125342;
Default
If this command is omitted, the job will abort.
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NAMES COMMAND 5.3.14 NAMES command (Optional) Purpose
To provide brief names for all of the items on the test in their original order. Format >NAMES n1 , n2 ,..., nn ;
Notes
If items are selected and/or reordered using the SELECT command, their item names and the answer key will be selected and/or reordered at the same time. Rules for entering names of items:
Names may be no more than 8 characters each. Names must be separated by commas and should normally start with an alphabetic character. Names must be enclosed in single quotes if they: o do not start with a letter o contain a semicolon o contain a forward slash o contain a blank
Examples >NAMES KNOW1, KNOW2, UNDER1, ANAL1, KNOW3, UNDER2, ANAL2, COMP, ANAL3; >NAMES
‘100’,’A100’,’100B’,’C-8’,’D-9’,’E/10’,’F/20’;
Default
If this command is omitted, every item will be given a blank name.
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5 TESTFACT REFERENCE 5.3.15 PLOT command (Optional) Purpose
To produce a line plot of a measure of item difficulty against discriminating power. Format >PLOT BISERIAL/PBISERIAL, NOCRITERION/CRITERION, FACILITY/DELTA;
Use only one of the items in each pair. Examples >PLOT
BISERIAL,CRITERION,DELTA;
>PLOT
BISERIAL,CRITERION,FACILITY;
>PLOT
PBISERIAL,NOCRITERION,DELTA;
Default
No plot. PLOT command: BISERIAL/PBISERIAL option Purpose
To indicate the choice of discrimination index. The discrimination index may be either of these:
BISERIAL: biserial coefficient PBISERIAL: point biserial coefficient
Format BISERIAL/PBISERIAL
Default BISERIAL.
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PLOT COMMAND FACILITY/DELTA option Purpose
To indicate the frame of reference for the item difficulty. Item difficulty may be plotted in terms of either of these:
FACILITY: item facility (percent correct) DELTA: difficulty index.
Format FACILITY/DELTA
Default FACILITY.
NOCRITERION/CRITERION option Purpose
To define the discriminating power. Discriminating power may be with respect to either of these:
NOCRITERION: internal test or subtest score CRITERION: external criterion score
Format NOCRITERION/CRITERION
Default NOCRITERION
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5 TESTFACT REFERENCE 5.3.16 PRIOR command (Optional) Purpose
To constrain the maximum likelihood estimation of slope and intercept parameters using a beta prior distribution on the uniquenesses and a normal prior distribution on the intercepts. Format >PRIOR
SLOPE=n, INTER=(m,s);
Example >PRIOR
SLOPE=1.5, INTER=(0,4);
Default
None. If the PRIOR command does not appear, the ML estimation will not be constrained. INTER keyword Purpose
To define the mean (m) and standard deviation (s) of the normal distribution for intercept parameters, such that c j ∼ n(m, s). Format INTER=(m,s)
Default m=0, s=2.
SLOPE keyword Purpose
To define the parameter of the beta distribution for uniquenesses, such that u j ∼ β (n,1). Larger values of n correspond to stronger priors.
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PRIOR COMMAND Format SLOPE=n
Default
1.2.
453
5 TESTFACT REFERENCE 5.3.17 PROBLEM command (Required) Purpose
To specify overall characteristics of components included in each problem. Format >PROBLEM
NITEM=n, SELECT=n, RESPONSE=n, SUBTEST=n, CLASS=n, FRACTILES=n,EXTERNAL=n, SKIP=n, NOTPRES;
Examples >PROBLEM NIT=150;
In this case, defaults would be invoked for all remaining parameters. >PROBLEM
NITEMS=50, SELECT=30, RESPONSE=7, SUBTEST=3, CLASS=2, FRACTILES=5, EXTERNAL=0, SKIP=2;
Default
If this command does not appear, the job will abort. CLASS keyword Purpose
To specify the number of classes (n = 1 to 10) into which respondents will be divided. This corresponds to the number of classes identified and named in the CLASS command. Format CLASS=n
Default
0. Related topics
CLASS command (Section 5.3.4)
454
PROBLEM COMMAND EXTERNAL keyword Purpose
To specify the number of external variates (n = 0 to 5). This should equal the number of external variates named in the EXTERNAL command. Format EXTERNAL=n
Default
0. Related topics EXTERNAL command (Section 5.3.8)
FRACTILES keyword Purpose
To specify the number of fractiles (n = 1 to 10) into which scores will be divided. Boundaries of the fractiles are defined in the FRACTILES command. Format FRACTILES=n
Default
1. Related topics
FRACTILES command (Section 5.3.10)
NITEMS keyword Purpose
To specify the total number of test items. This should equal the number of item names specified in the NAMES command.
455
5 TESTFACT REFERENCE Format NITEMS=n
Default
None; must be specified. Related topics NAMES command (Section 5.3.14)
NOTPRES option Purpose
To indicate that one of the response codes identifies “not-presented” items. See the RESPONSE command, discussed in Section 5.3.19. Format NOTPRES
Default
All items are presented to all respondents. Related topics RESPONSE command (Section 5.3.19)
RESPONSE keyword Purpose
To specify the number of response codes (n = 2 to 15). This should equal the number of codes specified in the RESPONSE command. Format RESPONSE=n
Default
2.
456
PROBLEM COMMAND Related topics
RESPONSE command (Section 5.3.19)
SELECT keyword Purpose
To specify the number of items selected for this run (n = 0 to NITEM, the number of items specified in the SELECT command). Format SELECT=n
Default
0; no selection; all items will be used in the original order. Related topics
PROBLEM command: NITEMS keyword SELECT command (Section 5.3.22)
SKIP keyword Purpose
To specify which steps of the analysis should be performed. Format SKIP=n
n=0
Do not skip, perform item analysis and all subsequent steps.
n=1
Skip item analysis and proceed to calculation of tetrachoric correlations immediately after data entry.
n=2
Proceed directly to factor analysis or rotation after input of correlation matrix, factor pattern, or provisional parameter estimates.
Default
Do not skip (n = 0).
457
5 TESTFACT REFERENCE SUBTEST keyword Purpose
To indicate the number of boundaries and subtest names as specified in the SUBTEST command. Format SUBTEST=n
Default
0. Related topics
SUBTEST command (Section 5.3.25)
458
RELIABILITY COMMAND 5.3.18 RELIABILITY command (Optional) Purpose
To specify a measure of internal consistency for the main test (or all subtests). Format >RELIABILITY KR20/ALPHA;
Use only one of the two options:
KR20: The default if the RELIABILITY command is used. The Kuder-Richardson formula
20 is calculated for each subtest (or for the main test when there are no subtests). Omits are not allowed in computing KR20. ALPHA: Coefficient alpha is calculated for each subtest (or for the main test when there are no subtests). Omits are permissible. The computer time required to calculate alpha may be excessive if the number of items and respondents is large.
Example >RELIABILITY KR20;
Default
No reliability measure.
459
5 TESTFACT REFERENCE 5.3.19 RESPONSE command (Required) Purpose
To specify the response codes common to all items on the main test. Format >RESPONSE omit, n1 , n2 ,..., nm − 2 , not-presented;
Notes
Rules for entering response codes:
Codes must be 1 character each. Commas must separate codes. A code must be enclosed in single quotes if it is any non-alphabetic character. The first code must be for “omit”, and is required even if the data contain no omitted responses. The last code must be for “not-presented” if the NOTPRES option is entered in the PROBLEM command. The total number of codes must not exceed 15.
Examples >RESPONSE
‘0’,A,B,C,D;
In this example, there are 5 response codes on the main test (m = 5). >RESPONSE
‘ ‘,’1’,’2’,’3’,’4’,’-‘;
In this example, there are 6 response codes on the main test (m = 6). Omit is blank, items not-presented to respondents are coded “minus”. Default
If this command is omitted, the job will abort. Related topics
PROBLEM command: NOTPRES option (Section 5.3.17)
460
SAVE COMMAND 5.3.20 SAVE command (Optional) Purpose
To save scores and/or item parameters in output files specified by the user. Format >SAVE
SCORE, MAIN, SUBTESTS, CRITERION, CMAIN, CSUB, CCRIT, CORRELAT, SMOOTH, UNROTATE, ROTATE, PARM, FSCORES, TRIAL, SORTED, EXPECTED;
Notes
The saved file for data simulation is described in the SIMULATE command. All results are saved in fixed-column text files; the first record of each file contains the format statement describing the column layout. The saved files will have the jobname as default filename.
Example >SAVE SCORE,MAIN,SUBTESTS,CRITERION,SMOOTH,FSCORES;
Default
Not saved. Related topics
SIMULATE command (Section 5.3.23)
CCRIT option Purpose
To save the class item statistics based upon criterion score in the file <jobname>.ccr: Format Description (A8,1X
Class name
A8,1X
Criterion name
I4,1X
Item number 461
5 TESTFACT REFERENCE
A8,1X
Item name
E10.3
Number of respondents attempting item (weights)
F6.2
Mean criterion score
F6.3
Standard deviation
F6.3
Mean criterion score of respondents giving correct response
F6.3
Facility
F6.2
Difficulty
F6.2
Biserial correlation
F6.2
Point biserial correlation
F6.3
Biserial correlation based on criterion score
F6.3
Point biserial correlation based on criterion score
Format CCRIT
Default
Do not save. CMAIN option Purpose
To save separate estimates for each class based on the main test score in the file <jobname>.cma: Format
Description
(A8,1X
Class name
‘MAIN’,5X
-
I4
Item number
A8,1X
Item name 462
SAVE COMMAND
E10.3
Number of respondents attempting item (weights)
F6.2
Mean score
F6.2
Standard deviation
F6.2
Mean score of respondents giving correct response
F6.3
Facility
F5.2
Difficulty
F6.3
Biserial correlation
F6.3
Point biserial correlation
F6.3
Biserial correlation based on criterion score
Format CMAIN
Default
Do not save. CORRELAT option Purpose
To save the tetrachoric correlation matrix in the file <jobname>.cor. This matrix may not be positive-definite (diagonal and lower triangle only, NITEMxNITEM). Output format
Output is 80-column, space-delimited format-free, in lower triangular form with line wrap. Format CORRELAT
Default
Do not save.
463
5 TESTFACT REFERENCE Related topics
PROBLEM command: NITEMS keyword
CRITERION option Purpose
To save the item statistics based upon criterion score in the file <jobname>.cri: Format Description (A8,1X
Criterion name
I4,1X
Item number
A8,1X
Item name
E10.3
Number of respondents attempting item (weights)
F5.3
Facility
F6.2
Difficulty
F6.3
Biserial correlation
F6.3
Point biserial correlation
F6.2
Mean criterion score
F6.2
Mean criterion score of respondents giving correct response
F6.3
Biserial and point biserial based upon criterion score
Format CRITERION
Default
Do not save.
464
SAVE COMMAND CSUB option Purpose
To save the item statistics of each class based upon subtest scores in the file <jobname>.csu: Format Description (A8,1X
Class name
A8,1X
Subtest name
I4,1X
Item number
A8,1X
Item name
E10.3
Number of respondents attempting item (weights)
F6.2
Mean subtest score
F6.2
Standard deviation
F6.2
Mean score of respondents giving correct response
F6.3
Facility
F5.2
Difficulty
F6.3
Biserial correlation
F6.3
Point biserial correlation
Format CSUB
Default
Do not save. EXPECTED option Purpose
To save the results of the final E-step of the full information item factor analysis in the file <jobname>.exp: 465
5 TESTFACT REFERENCE counter designating quadrature point quadrature point weight for each quadrature dimension (the product of these weights is the weight for the point.) expected number of correct attempts on each item at quadrature point expected number of attempts at quadrature point. If all respondents attempt every item, all of these numbers will be equal. If some items are not attempted by some respondents, these numbers will change from item to item.
Format EXPECTED
Note
This option applies only to the solution (NOADAPT option of the TECHNICAL command). Output format (1X,fI2,n(/,1X,7F10.5)) f: the number of factors, as specified by the NFAC keyword in the FACTOR command. n: number of lines required to print E-step results 2 through 4 above.
Default
Do not save. Related topics
FACTOR command: NFAC keyword (Section 5.3.9) TECHNICAL command: NOADAPT option (Section 5.3.26)
FSCORES option Purpose
To save the factor scores and their posterior standard deviations with subject identification in the file <jobname>.fsc. Output format is given in the first line of the factor score file. Format FSCORES
466
SAVE COMMAND Default
Do not save. MAIN option Purpose
To save the following classical item statistics in the file <jobname>.mai: Format
Description
‘MAIN’,5X
-
I4,1X
Item number
A8,1X
Item name
E10.3
Number of respondents (or weight) for item
F6.2
Mean test score for all respondents
F6.2
Standard deviation of score for all respondents
F6.2
Mean score of respondents answering item correctly
F5.3
Facility
F6.2
Difficulty
F6.3
Biserial correlation
F6.3
Point biserial correlation
Format MAIN
Default
Do not save.
467
5 TESTFACT REFERENCE PARM option Purpose
To save the item numbers, intercepts, factor slopes, and guessing parameters in a form suitable for computing factor scores at a later time in the file <jobname>.par. If VARIMAX or PROMAX is selected, these parameters will be saved after the VARIMAX rotation; otherwise, they will be saved from the principal factor solution. If BIFACTOR is selected, the item number, intercepts, general and specific factor slopes will be saved. For scoring purposes, set the FILE keyword of the SCORE command equal to <jobname>.par. Note that PARM and TRIAL cannot be used in the same BIFACTOR analysis. Output format
Output format is (I3,2X,F8.5,fF8.5), where f is the keyword value for NFAC in the FACTOR command, or f = 2 for the BIFACTOR command. This format is given in the first line of the PARM or TRIAL values file. Format PARM
Default
Do not save. Related topics
FACTOR command: ROTATE keyword (Section 5.3.9) FACTOR command: NFAC keyword BIFACTOR command (Section 5.3.3) SCORE command: FILE keyword (Section 5.3.21) SAVE command: TRIAL option
ROTATE option Purpose
To save the rotated factor loadings (NITEMxNFAC) in the file <jobname>.rot. Output format
The output format is (10F8.5).
468
SAVE COMMAND Format ROTATE
Default
Do not save. Related topics
PROBLEM command: NITEMS keyword (Section 5.3.17) FACTOR command: NFAC keyword (Section 5.3.9)
SCORES option Purpose
To save the following case score information according to the status of WEIGHT in the INPUT command in the file <jobname>.sco:
case identification test form number case weight main test score subtest score criterion score WEIGHT keyNumber Formats used by program word in INPUT of test forms command
yes
1
no
1
yes
>1
(aA1,3X,E10.3,3X,F7.0,3X,pF7.0,F9.2) (aA1,3X,F7.0,3X,pF7.0,F9.2) (aA1,3X,I2,3X,E10.3,3X,F7.0,3X,pF7.0,F9.2) (aA1,3X,I2,3X,F7.0,3X,pF7.0,F9.2)
no
>1
a: the number specified with the NIDCHAR keyword in the INPUT command. p: the number of subtests.
469
5 TESTFACT REFERENCE If p = 1, the main test and subtest score will be identical. If there is no CRITERION command, the criterion field will be null. Format SCORE
Default
Do not save. Related topics
INPUT command: NIDCHAR and WEIGHT keywords (Section 5.3.12) CRITERION command (Section 5.3.7)
SMOOTH option Purpose
To save the “smoothed” NFAC common factor approximation to the correlation matrix in the file <jobname>.smo. Output format
This matrix will be positive-definite (diagonal and lower triangle only, NITEMxNITEM). The output format is (10F8.5). Format SMOOTH
Default
Do not save. Related topics
PROBLEM command: NITEMS keyword (Section 5.3.17)
SORTED option Purpose
To save the sorted file of identity, item responses, and weight in the file <jobname>.sor.
470
SAVE COMMAND This applies only to the non-adaptive solution (NOADAPT option on TECHNICAL command). Format SORTED
Default
Do not save. Related topics
TECHNICAL command: NOADAPT option (Section 5.3.26)
SUBTESTS option Purpose
To save the item subtest parameter estimates as follows in the file <jobname>.sub: Format Description A8,1X
Subtest name
I4,1X
Item number
A8,1X
Item name
E10.3
Number of respondents attempting item (weight)
F6.2
Mean subtest score
F6.2
Standard deviation
F6.2
Mean score of respondents answering item correctly
F5.3
Facility
F6.2
Difficulty
F6.3
Biserial correlation
F6.3
Point biserial correlation
Format SUBTESTS
471
5 TESTFACT REFERENCE Default
Do not save. TRIAL option Purpose
To save the item number intercepts, factor slopes, and guessing parameters in the file <jobname>.tri in a form suitable for performing additional EM parameter estimation cycles at a later time. The trial values are saved at the end of the EM cycles and before reorthogonalization or rotation. In BIFACTOR analysis, TRIAL and PARM are identical. To use the saved trial values parameters as the starting point for continued EM cycles, set the TRIAL keyword of the INPUT command equal to <jobname>.tri. Output format
The output format is (I3,2X,F8.5,fF8.5), where f is the (keyword) value for NFAC in the FACTOR command, or f = 2 for the BIFACTOR command. This format is given in the first line of the PARM or TRIAL values file. Note that PARM and TRIAL cannot be used in the same BIFACTOR analysis. Format TRIAL
Default
Do not save. Related topics
FACTOR command: NFAC and ROTATE keywords (Section 5.3.9) BIFACTOR command (Section 5.3.3) INPUT command: TRIAL keyword (Section 5.3.12) SAVE command: TRIAL option
UNROTATE option Purpose
To save the unrotated (principal) factor loadings (NITEMxNFAC) in the file <jobname>.unr. Use UNROTATE to save BIFACTOR loadings.
472
SAVE COMMAND Output format
The output format is (10F8.5). Format UNROTATE
Default
Do not save. Related topics
PROBLEM command: NITEMS keyword (Section 5.3.17) FACTOR command: NFAC keyword (Section 5.3.9) BIFACTOR command (Section 5.3.3)
473
5 TESTFACT REFERENCE 5.3.21 SCORE command (Optional) Purpose
To obtain factor score estimates (EAP or MAP) and standard error estimates for each case from estimated or supplied item parameters, the EAP score of the general factor of the bifactor model, and estimates of the standard error of the general factor score allowing for conditional dependence introduced by the group factors. Format >SCORE LIST=n, NFAC=n, FILE=‘filename’, MISSING, TIME, CHANCE, LOADINGS, METHOD=n, PARAM=n, SPRECISION=n;
Examples >SCORE LIST=20; >SCORE LIST=10,NFAC=6,FILE=‘NEWTEST.PAR’,MISSING,TIME,CHANCE;
Default
No factor score estimates. CHANCE option Purpose
To specify the use of the guessing model in computing factor scores. When used in conjunction with the SIMULATE command, the item parameter file must include the chance parameters. This option has the same effect as CPARMS in the FULL command. Format CHANCE
Related topics
FULL command: CPARMS keyword (Section 5.3.11) . SIMULATE command (Section 5.3.23)
474
SCORE COMMAND
FILE keyword Purpose
To specify the name (enclosed in single quotes) of the file containing item parameters for scoring. The name may include a path and a filename extension, but the total length may not exceed 128 characters. The drive and directory path should be included if the data file is not in the same folder as the command file. For more information, see Section 3.2.6. This file has the format as trial values produced by the TRIAL option in the SAVE command, i.e., chance value, intercept, and slopes. The layout of this file is as follows: First record:
A variable format statement (in parentheses) describing the item parameter column assignments. Following records:
Without chance parameter: intercept and factor slopes and/or standard difficulty loadings. With chance parameter: intercept or factor slopes, and standard difficulty loadings.
Format FILE=
Default
None. Related topics
SAVE command: TRIAL option (Section 5.3.20)
LIST keyword Purpose
To specify the number of leading cases for which factor scores will be listed in the program output. If FSCORES appears in the SAVE command, factor scores for all cases will be saved in the file with the same name as the command file, but with the extension *.fsc.
475
5 TESTFACT REFERENCE Format LIST=n
Default
Factor scores of all cases will be listed. Related topics
SAVE command: FSCORES option (Section 5.3.20)
LOADINGS keyword Purpose
To specify that the parameter file contains item standard difficulties and factor loadings. Format LOADINGS
Default
The parameter file contains item intercepts and slopes. METHOD keyword Purpose
To specify the method of estimation. Format METHOD=n n = 1: Maximum A Posteriori (MAP) estimation n = 2: Bayes (EAP) estimation
476
SCORE COMMAND MISSING option Purpose
The OMIT keyword on the BIFACTOR command will be automatically set to MISSING if the TIME option has been selected in the TETRACHORIC command. Format MISSING
Related topics
TETRACHORIC command: TIME option (Section 5.3.27)
NFAC keyword Purpose
To specify the number of factors when estimating factor scores from a user-supplied file of parameter values. If the keyword NFAC appears, a parameter file must be designated by the FILE keyword of the SCORE command and available for reading. Format NFAC=n
Default
Factor scores will be computed from parameters in the current specified analysis. Related topics
SCORE command: FILE keyword
PARAM keyword Purpose
To specify the number of parameter values (integer and factor slopes) supplied by the user for estimating factor scores, where n = f + 1, with f being the number of factor loadings. Format PARAM=n
477
5 TESTFACT REFERENCE Note
Required if scale score estimates for each subject are desired, without factor analysis. PARAM must not be used if the FACTOR command or the FULL command is included. If the PARAM keyword is invoked, the parameter file must be designated with the FILE keyword in the SCORE command and available for reading.
Related topics
FACTOR command (Section 5.3.9) FULL command (Section 5.3.11) SCORE command: FILE keyword
SPRECISION keyword Purpose
To control the EAP and MAP precision in the calculation of factor scores. Format SPRECISION=n
Default
0.0001. QUAD keyword Purpose
To specify the number of quadrature points in EAP estimation. Format QUAD=n
Default
1 factor: 10 factors : 5 factors: 3 …
478
SCORE COMMAND TIME option Purpose
To specify that omitted items after the last non-omitted item should be treated as notpresented. Tetrachoric correlation coefficients will be computed with the TIME option, even if TIME has not been specified in the TETRACHORIC command. Format TIME
Related topics
TETRACHORIC command: TIME option (Section 5.3.27)
479
5 TESTFACT REFERENCE 5.3.22 SELECT command (Optional) Purpose
To specify items to be selected and/or reordered for each problem. Requires the SELECT keyword of the PROBLEM command to be set to n '. Format SELECT= (item1 , item2 ,..., itemn' )
Notes
If the SELECT command is used in a given problem, all commands following it in the same problem will pertain only to the selected and/or reordered set of items. Rules for selecting and reordering of items:
Selection is made by listing the original order-numbers of the desired items. For example, from the items 1, 2, 3, 4, 5, 6, 7, 8, 9, the items 1, 4, 5, 7, 8, 9 might be selected. The selected items can be in any order. For example, the items could have been selected in the order 7, 5, 9, 1, 8, 4. If all the items (n) are to be reordered, n ' = n, and the selection list will contain the original n item numbers in the new order. Contiguous items may be entered with a “(1)” between the first and last item numbers. For example, 10(1)34, would select all items numbered 10 through 34. To select every b-th item from a to c, write a(b)c. For example, 1(2)99 will select every odd-numbered item from a 100-item test. Each item’s name and the answer key will be selected and/or reordered at the same time as the item.
Example >SELECT
10,9,11,2(1)5,15,14,13,12;
From an original list of 20 items, 11 items are to be selected (n = 20; n ' = 11). Default
All items will be included in the analysis in their original order.
480
SELECT COMMAND Related topics
PROBLEM command: SELECT keyword (Section 5.3.17)
481
5 TESTFACT REFERENCE 5.3.23 SIMULATE command (Optional) Purpose
To simulate item response records of cases drawn from a multivariate latent distribution of factor scores with user-specified vector mean and fixed correlation matrix. The user must supply standard item difficulties and NFAC factor loadings (or intercepts and factor slopes) for each item. If a model with chance correct responses is specified, the probabilities of correct responses must also be supplied. The factor loadings must be orthogonal, e.g., principal factors. If desired, the means of the factors can be set to arbitrary values to simulate group effects. The default mean value is 0.0. Format >SIMULATE
NFAC=n, NCASES=n, SCORESEED=n, ERRORSEED=n, GUESSSEED=n, CHANCE, LOADINGS/SLOPES, FILE=‘filename’, MEAN=(list), FORM=n, GROUP=n, PARM=n;
Notes
The simulated item responses will be saved in the file with the name <jobname>.sim. The communalities of the factor loadings must be less than 1.0. For simulation, only the TITLE, PROBLEM, SIMULATE, and CONTINUE or STOP commands are required; the NAMES command is optional. There must be no SAVE or INPUT command. Response codes in the simulated data are 1 for correct and 0 for incorrect.
Examples >SIMULATE NFAC=1, NCASES=2500, SLOPES, FILE=‘SIM01.PAR’; >SIMULATE NFAC=3, NCASES=2500, LOADINGS, CHANCE, FILE=‘SIM01C1G.PAR’, MEAN=(0.8,0.5,0.5);
Default
No simulation. Related topics
TITLE command (Section 5.3.28) PROBLEM command (Section 5.3.17) CONTINUE command (Section 5.3.6) STOP command (Section 5.3.24) NAMES command (Section 5.3.14)
482
SIMULATE COMMAND
SAVE command (Section 5.3.20) INPUT command (Section 5.3.12)
CHANCE option Purpose
To indicate that the model allowing for correct responses by chance is assumed. The chance parameters must be present in the parameter file. Format CHANCE
Default
Non-chance model. ERRORSEED keyword Purpose
To provide the seed of the random number for generating the univariate normal independent uniqueness distributions of the items. The random number generator seed may be any number greater than 1 and less than 2147483647. Format ERRORSEED=n
Default
453612. FILE keyword Purpose
To specify the name (enclosed in single quotes) of the file containing item parameters of the simulation model. This name may include a path and filename extension, but the total length may not exceed 128 characters.
483
5 TESTFACT REFERENCE The simulation parameter file must have the following layout when the CHANCE option is not present: First record: variable format statement describing the fixed-column layout of the file. NITEMS following records: Standard difficulty, NFAC factor loadings or standard difficulty, NFAC slopes.
If the CHANCE option is present, the chance probabilities should precede standard difficulties. Format FILE=‘filename’
Default
None. Related topics SIMULATE command: CHANCE option PROBLEM command: NITEMS keyword (Section 5.3.17) FACTOR command: NFAC keyword (Section 5.3.9)
FORM keyword Purpose
To provide, solely for convenience, a test form identification following the case number in the simulation records; n may be set to any natural number. Format FORM=n
Default
1. GROUP keyword Purpose
To provide, solely for convenience, a group identification following the case number in the simulation records; n may be set to any natural number.
484
SIMULATE COMMAND Format GROUP=n
Default
1. GUESSSEED keyword Purpose
To specify the seed of the random number for generating the independent probability of chance success on an item response. The random number generator seed may be any natural number greater than 1 and less than 2147483647. Format GUESSEED=n
Default
543612. LOADINGS/SLOPES option Purpose
To indicate the form in which the item parameters are provided:
Select LOADINGS if the item parameters are supplied in the form of standard item difficulty (i.e., the standard normal deviate corresponding to the population percent CORRECT), followed by the NFAC factor loadings. Select SLOPES if the item parameters are in the form of standard item difficulty, followed by NFAC factor slopes.
Format LOADINGS/SLOPES
Default LOADINGS.
485
5 TESTFACT REFERENCE Related topics FACTOR command: NFAC keyword (Section 5.3.9)
MEAN keyword Purpose
To provide the population means of the factor scores from which the responses are generated. These means will be added to the random standard normal deviates representing the ability of each case of the corresponding factors. The maximum number of factors allowed is 15. Format MEAN= ( n1 , n2 ,..., nm )
Default
None. NCASES keyword Purpose
To specify the number of response records to be generated. Format NCASES=n
Default
1. NFAC keyword Purpose
To specify the number of factors in the model. Format NFAC=n
486
SIMULATE COMMAND Default
1. PARM keyword Purpose
To specify the number of parameter values (intercept and factor loadings) supplied by the user. n = f + 1, where f is the number of factor loadings. Format PARM=n
SCORESEED keyword Purpose
To provide the seed of the random number for generating the multivariate normal ability distribution. The mean and standard deviation for each variate is assumed zero and one, respectively. The random number generator seed may be any natural number greater than 1 and less than 2147483647. Format SCORESEED=n
Default
345261.
487
5 TESTFACT REFERENCE 5.3.24 STOP command (Required) Purpose
To terminate the problem stack. Format >STOP
Note
A semicolon to signal the end of this command is not needed.
488
SUBTEST COMMAND 5.3.25 SUBTEST command (Optional) Purpose
To specify the partition of the main test into subtests and to assign names to the subtests. Format >SUBTEST
BOUNDARY=(list), NAMES=(list);
Examples >SUBTEST
BOUNDARY=(10,20,30);
A test with 30 items will be partitioned into 3 subtests of 10 items each. >SUBTEST
BOUNDARY=(10,20,30), NAME=(BASIC, AVERAGE, ADVANCED);
A test with 30 items will be partitioned into 3 named subtests of 10 items each: Subtest Name Item Boundaries
BASIC
1 through 10
AVERAGE
11 through 20
ADVANCED
21 through 30
Default
No subtests. BOUNDARY keyword Purpose
If the SELECT command is used to reorder items before subtests are partitioned, boundaries are specified by the new order numbers. Format BOUNDARY= ( n1 , n2 ,..., n p )
with n = order number of the last item in each subtest. 489
5 TESTFACT REFERENCE Default
None. Related topics
SELECT command (Section 5.3.22)
NAMES keyword Purpose
To specify the names, of no more than 8 characters, for each subtest. Note that the rules for naming items also apply to naming subtests (see the NAMES command, Section 5.3.14). Format NAMES= ( n1 , n2 ,..., n p )
Default
No names. Related topics
NAMES command (Section 5.3.14)
490
TECHNICAL COMMAND 5.3.26 TECHNICAL command (Optional) Purpose
To change the value of the default constants in the item factor analysis. Format >TECHNICAL
QUAD=n, SCQUAD=n, ITER=(list), PRV=n, FREQ=n, NITER=(list), NSAMPLE=n, MCEMSEED=n, QSCALE=n, QWEIGHT=n, IQUAD=n, ITLIMIT=n, PRECISION=n, NOADAPT, FRACTION, ACCEL=n, NOSORT;
Example >TECHNICAL
QUAD=5, ITER=(20,3,0.001);
Default
All program defaults. ACCEL keyword Purpose
To request acceleration of the full information analysis by three-point extrapolation between EM cycles. n is the proportional step size of the acceleration. Format ACCEL=n
Default
1.0. FRACTION option Purpose
To invoke a three-point quadrature with an 81-point fractional factorial design. This option is only applicable in the case of adaptive quadrature with five factors. Otherwise, the full 243-point design is used.
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5 TESTFACT REFERENCE Format FRACTION
FREQ keyword Purpose
To specify whether to print the pattern frequencies. Format FREQ=n n = 0: Do not print observed and expected response pattern frequency. n = 1: Print observed and expected response pattern frequency.
Default
0. IQUAD keyword Purpose
To specify the type of quadrature:
n = 1: Gauss-Hermite quadrature. n = 2: Gauss-Hermite quadrature; the quadrature points as well as the weights are printed. n = 3: Quadrature using ordinates. n= 4: Quadrature using ordinates; the quadrature points as well as the weights are printed.
Format IQUAD=n
Default n = 4.
ITER keyword Purpose
To specify the parameters for EM cycles.
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TECHNICAL COMMAND Format ITER=(c,d,e) c: maximum number of EM cycles (min = 3). d: maximum number of iterations within M-step. e: convergence criterion in M-step.
Note d and e are used only in non-adaptive quadrature; there is only one M-step per EM-cycle in
adaptive quadrature. Default (c,d,e)=(15,5,0.005).
ITLIMIT keyword Purpose
To specify the number of EM cycles prior to the fixing of conditional distributions. In adaptive quadrature, the means and covariances of the conditional distribution of factor variables for each case is computed only in the first ITLIMIT EM cycles. Thereafter, the conditional distributions are held fixed for each case. Change of the log likelihood between EM cycles is computed and displayed only after fixing has occurred. In Monte Carlo EM, the sampled points are fixed at their values for each case in the ITLIMIT cycle. Format ITLIMIT=n n : The number of EM cycles prior to fixing.
Default
Adaptive: n = 10 Monte Carlo: n = 15. MCEMSEED keyword Purpose
To specify the generation of random multivariate normal variables for Monte Carlo integration in the full information EM solution. n is the number of points sampled for Monte Carlo
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5 TESTFACT REFERENCE EM solution (min = 2; max = 2147483646). If this keyword appears, the quadratures in the E-step of the EM cycles will be performed by Monte Carlo integration; otherwise, fixedpoint quadrature is used. Format MCEMSEED=n
Default
Fixed-point quadrature. NITER keyword Purpose
To specify the parameters for communality improvements. Format NITER=(h,i)
where
h : Maximum number of iterative communality improvements. Must be between 0 and 5,
i : Convergence criterion for communality improvements.
inclusive.
Default (h,i)=(3, 0.001).
NOADAPT option Purpose
To specify that non-adaptive quadrature be performed in the full information solution. Note that this option can only be invoked if there are 5 or fewer factors; with more than 5 factors, this option if present will be ignored and adaptive fractional quadrature will be performed (with 3 points per dimension). If the NOADAPT option is not invoked, all quadrature is adaptive. Format NOADAPT
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TECHNICAL COMMAND
NOSORT option Purpose
To suppress the sorting of response patterns with respect to their number correct scores. In non-adaptive quadrature, such sorting can be used to speed computation. As it has no advantage in adaptive quadrature or Monte Carlo, or in the BIFACTOR solution, NOSORT is always used in these solutions. Format NOSORT
Related topics
BIFACTOR command
NSAMPLE keyword Purpose
To specify the number of points sampled in the latent variable space when numerical integration used in the marginal maximum likelihood procedure is based on a fractional factorial design. For example, if the number of factors equals 4, then a fractional factorial design requires 34 = 81 points. Likewise, when five factors are specified the number of points is 35 = 243.
Format NSAMPLE=n
Default
Set by program, value is written to the output file. Maximum = 243. PRECISION keyword Purpose
To specify the convergence criterion for change between EM cycles. Format PRECISION=n
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Default
One-third of the maximum number of EM cycles (see ITER keyword on TECHNICAL command). Related topics TECHNICAL command: ITER keyword
PRV keyword Purpose
To control the printing of provisional estimates. Format PRV=n
n = 0
No provisional estimates of slope and intercept parameters are printed.
n = 1
Provisional estimates of slope and intercept parameters are printed after each Estep.
n = 2
Provisional estimates of slope and intercept parameters are printed after each Mstep iteration.
n = 3
Provisional estimates of slope and intercept parameters are printed after each Estep and M-step iteration.
n = 4
Provisional estimates of slope and intercept parameters and their corrections are printed as in 3.
Default
0. QSCALE keyword Purpose
To set the value of the extreme points in adaptive quadrature when QUAD or SQUAD equals 3.
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TECHNICAL COMMAND n : Absolute value in the points (-n, 0.0, +n).
Format QSCALE=n
Default
1.2. Related topics
TECHNICAL command: QUAD keyword TECHNICAL command: SQUAD keyword
QUAD keyword Purpose
To specify the number of quadrature points, 1 to 10, per dimension in full information solution. Format QUAD=n
Default
Depends on the number of factors; see program output. QWEIGHT keyword Purpose
To set the value of the weights of extreme points in three-point quadrature. m : The weights (m, 1 – 2m, m) are assigned to the points; n must be fractional.
Format QWEIGHT=m
Default
The weights are normal ordinates at (-n, 0.0, +n).
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SQUAD keyword Purpose
To specify the number of quadrature points for EAP estimation of factor scores. Format SCQUAD=n
Default
Depends on number of factors; see program output.
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TETRACHORIC COMMAND 5.3.27 TETRACHORIC command (Optional) Purpose
To specify how to form the count matrix that is used in calculating tetrachoric correlations. Format >TETRACHORIC
RECODE/PAIRWISE/COMPLETE, TIME, LIST, CROSS, NDEC=n;
Examples >TETRACHORIC COMPLETE, LIST, TIME; >TETRACHORIC PAIRWISE, LIST, TIME; >TETRACHORIC RECODE, NDEC=3, LIST, TIME;
Default
No correlations computed. CROSS option Purpose
To ensure the joint frequencies for each pair of items will appear in the output. Format CROSS
Default
No joint frequencies are listed. LIST option Purpose
To ensure that the matrix of tetrachoric correlations (and possibly warning messages) will appear in the printed output. This correlation matrix may be saved even when it is not listed (see the SAVE command).
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5 TESTFACT REFERENCE Format LIST
Default
No listing of the tetrachoric correlations is provided. Related topics SAVE command (Section 5.3.20)
NDEC keyword Purpose
To specify the number of decimal places printed in tetrachoric correlation coefficients. Format NDEC=n
Default
3. RECODE/PAIRWISE/COMPLETE option Purpose
To specify the treatment of observations that include omits. One of the following options may be selected:
RECODE: Omits will be coded as wrong responses. PAIRWISE: All observations will be used. Wherever an omit response occurs, the item will
be ignored. A pair of responses containing a “not-presented” item will be excluded from the calculation. COMPLETE: Only observations with no omit responses will be used.
Format RECODE/PAIRWISE/COMPLETE
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TETRACHORIC COMMAND Default RECODE.
TIME option Purpose
To specify that omitted items following the last non-omitted item be treated as notpresented. All omitted items prior to the last non-omitted item will be recoded as “wrong” if the guessing mode is not selected. If the guessing mode is selected, these items will be scored “correct” with probability g j and “incorrect” with probability 1 − g j . The TIME option does not affect RECODE, but if TIME is combined with COMPLETE or PAIRWISE, different tetrachoric correlation coefficients will result. Format TIME
Related topics
TETRACHORIC command: RECODE option
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5 TESTFACT REFERENCE 5.3.28 TITLE command (Required) Purpose
To provide a label that will be used throughout the output to identify the problem run. Format >TITLE …text… …text…
Notes
The TITLE command consists of three lines. The first line contains the TITLE command, and is followed by two lines of 80 characters maximum containing the title text. Using only one title line will cause an error condition. If the title does not require two lines, leave the second line blank. A semicolon to end this command is not needed. Example >TITLE ENGLISH LANGUAGE COMPREHENSION TEST ITEM AND TEST STATISTICS >PROBLEM…
Default
No default, title lines are required. 5.3.29 Variable format statement
The data layout must be described in a variable format statement. This statement is entered within parentheses and follows immediately after the INPUT command. When data (labels, raw data, summary statistics) are used in fixed format, a format statement is needed to instruct the program how to read the data. The general form of such a statement is (rCw) or (rCw.d),
where:
r
Repeat count; if omitted, 1 is assumed. 502
VARIABLE FORMAT STATEMENT
C w d
Format code Field width, or number of columns Number of decimal places (for F-format).
The following codes are used to indicate the type of value to be read:
A I F
Code for character values Code for integer values Code for real numbers
The format statement must be enclosed in parentheses. Blanks within the statement are ignored: (rCw.d) is acceptable. The program also ignores anything after the right parenthesis and on the
same line. Thus, comments may be placed after the format statement. Examples of format statements:
The labels HEIGHT, WEIGHT, AGE, and IQ could be read in fixed format as (A6,A6,A3,A2) HEIGHTWEIGHTAGEIQ
Or, with the same result, as (4A6) HEIGHTWEIGHT
AGE
IQ
Note that the first method lets the repeat count default to 1, and that it describes several different fields, separated by commas, with one statement. The following example shows three ways to read five integers, with the same result: (5I1) 12345 (5I2) 1 2 3 4 5 (I1,I2,3I3) 1 2 3 4 5
The F-format requires that the number of decimal places be specified in the field description, so if there are none (and eight columns) specify (F8.0); (F8) is not allowed. However, if a data value contains a decimal point, then this overrides the location of the decimal point as specified by the general field description. If the general field description is given by (F8.5), then 12345678 would result in the real number +123.45678, but the decimal point in –1234.56 would not change. Just a decimal point, or only blanks, will result in the value zero. The plus sign is optional.
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5 TESTFACT REFERENCE It is possible to use the format statement to skip over variables in the data file when they are not needed in the analysis. For example, (F7.4,8X,2F3.2) informs the program that the data file has 21 columns per record. The first value can be found in the first seven columns (and there are four decimal places), then eight columns should be skipped, and a second and third value are in columns 16 – 21, both occupying three columns (with two decimal places). Note that the SELECT command allows selection and reordering of variables. Another possibility is the use of the tabulator format descriptor T, followed by a column number n. For example, (1F8.5,T60,2F5.1) describes three data fields: in columns 1 – 8, with five decimal digits, next in columns 61 – 65 and 66 – 70, both with one decimal digit. If the number n is smaller than the current column position, left tabbing results. A forward slash (/) in an F-format means “skip the rest of this line and continue on the next line” . Thus, (F10.3/5F10.3) instructs the program to read the first variable on the first line, then to skip the remaining variables on that line and to read five variables on the next line. Related topics
SELECT command (Section 5.3.22) INPUT command (Section 5.3.12)
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6 IRT GRAPHICS
6 IRT graphics 6.1 Introduction A new feature included with the IRT programs is the IRT GRAPHICS procedure. Item characteristic curves, item and test information curves, and a histogram of the estimated abilities may be plotted. A matrix plot showing all item characteristic curves simultaneously can also be obtained. This feature is accessed via the Run menu on the main menu bar and becomes available once the analysis has been completed. The plots are based on the contents of the parameter files produced by the respective programs. In this chapter, an overview of the interface and options of this feature is given.
6.2 Main menu The Main window of the IRT GRAPHICS program is used to access the following graphics:
Item characteristic curves through the ICC option Item information curves through the Information option ICC and item information curves on the same page through the ICC and Info option Total information curve through the Total Info option Simultaneous display of all Item Characteristic Curves (ICCs) through the Matrix Plot option Regression of ability on the percentage correct through the Bivariate Plot option Histogram of estimated abilities through the Histogram option.
The graphs displayed may be selected, changed, saved to file, or printed using various options and dialog boxes described in Section 6.3. To exit the program, click the Exit option on the Main menu.
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6.2.1 The ICC option This option provides access to item characteristic curves for all the items in the test. In the image below, the ICC for item 2 is displayed.
As a nominal model was fitted in this case, the high category is displayed in red and a message to this effect is displayed in the Category Legends box at the bottom of the window. This field contains the legend for all categories plotted. The Next button provides access to following items, while the Prev button allows the user to go back to previously viewed Item Characteristic Curves (ICCs). Use the Main Menu button at the bottom left of the window to return to the main menu. The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options menus on the main menu bar. For more on the options available, see Section 6.3.
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Related topics
Manipulating and modifying graphs (see Section 6.3) Item characteristic curves (see Section 6.4)
6.2.2 The Information option
This option provides access to item information curves for all the items in the test. In the image below, the item information curve for the second item is displayed. The Scaling Information box at the bottom of the window contains information on the scaling of the information axis. The item with the most information is indicated here for all items in a test. The Next button provides access to following items, while the Prev button allows the user to go back to previously viewed item information curves. Use the Main Menu button at the bottom left of the window to return to the main menu. The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options options on the main menu bar. For more on the options available, see Section 6.3.
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Related topics
Manipulating and modifying graphs (see Section 6.3) Item information curves (see Section 6.5)
6.2.3 The ICC and Info option When this option is selected from the Main menu, the ICC and item information curve for an item are displayed simultaneously.
As a nominal model was fitted in this case, the high category is displayed in red and a message to this effect is displayed in the Category Legends box at the bottom of the window. This field also contains information on the legend for all other categories plotted. The Next button provides access to following items, while the Prev button allows the user to go back to previously viewed item curves. Use the Main Menu button at the bottom left of the window to return to the main menu. The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options menus on the main menu bar. For more on the options available, see Section 6.3.
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Related topics
Manipulating and modifying graphs (see Section 6.3) Item characteristic curves (see Section 6.4) Item information curves (see Section 6.5)
6.2.4 The Total Info option This option is used to access the test information and standard error curves.
The total test information for a given scale score is read from the axis on the left of the graph and is plotted in blue. The axis to the right of the graph is used for reading the standard error estimate for a given scale score. The measurement error is shown in red. Use the Main Menu button at the bottom left of the window to return to the main menu. The Next and Prev buttons may be used to access similar plots for multiple groups (if any). The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options menus on the main menu bar. For more on the options available, see Section 6.3.
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Related topics
Manipulating and modifying graphs (see Section 6.3) Test information curves (see Section 6.6)
6.2.5 Matrix Plot option This option provides an organized way of simultaneously looking at the item characteristic curves of up to 100 items. In the graph below, the ICCs of 35 items are plotted. As can be seen from the graph, models fitted to the items range from the 1PL model to the nominal, graded and multiple response models. Item 1 is shown in the top left corner of the combined graph, as indicated by the item numbers given to the right of the plots. The gray lines dividing each plot into four quadrants are drawn at a probability of 0.5 (on the y-axis) and ability of 0 (on the x-axis).
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To take a closer look at item 20, to which a nominal response model was fitted, click and drag the right mouse button to select the area for zooming as shown below.
Releasing the mouse button produces a closer look at the graph for item 20 as shown below. Note that any part of the matrix of plots can be selected for zooming, and that the zoom option is also available for already enlarged areas of the matrix such as that shown below. 511
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Note that the high category is shown in red. To reset the image, double-click the right mouse button.
Up to 100 items can be simultaneously displayed. If the test contains more than 100 items, return to the Main Menu and click the Matrix Plot button again for the next set of items. The graphs can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options menus on the main menu bar. For more on the options available, see Section 6.3.
Related topics
Manipulating and modifying graphs (see Section 6.3) Item information curves (see Section 6.5)
6.2.6 The Histogram option The Histogram option provides a histogram of the ability scores. This option is only available if scoring has been requested and the scores have been saved to an external file. As indicated in the legend box at the bottom of the window, abilities are rescaled to a mean of 0 and standard deviation of 1. The area under the bell-shaped curve equals the total area of the histogram.
Use the Main Menu button at the bottom left of the window to return to the main menu. The Next and Prev buttons may be used to access similar plots for multiple groups (if any). The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options options on the main menu bar.
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Related topics
Manipulating and modifying graphs (see Section 6.3)
6.2.7 The Bivariate Plot option The Bivariate Plot option provides a regression of ability on the percentage correct. This option is only available if scoring has been requested and the scores have been saved to an external file.
As with the matrix plots, segments of the plot may be inspected by zooming in. This is done by clicking and dragging the mouse to select the area of interest. A 95% prediction interval for a new examinee is also shown on the plot. Use the Main Menu button at the bottom left of the window to return to the main menu. The graph can be selected, edited, saved, or printed using the File, Edit, Graphs, and Options menus on the main menu bar. For more on the options available, see Section 6.3. If information is available for multiple groups, bivariate plots are available by group and the Next and Prev buttons may be used to access the plots for following groups.
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6.2.8 The Exit option Use this option to exit the application.
6.3 Manipulating and modifying graphs Displayed graphs can be modified, saved and printed by using menus available on the main menu bar of the graph window. 6.3.1 File menu The File menu controls the printing and saving of graphs.
The Save as Metafile option is used to save the selected page or graph as a *.wmf (Windows Metafile) for later use in other applications. Note that an entire page, including legend boxes, may be printed using the Print current page option. Alternatively, the Show Selectors option on the Options menu may be used to select a graph, after which the Print selected graph option of the File menu may be used to print only the selected graph. The Printer Setup and Printing Options options provide access to the standard Windows printing controls.
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Related topics
Options menu
6.3.2 Edit menu The Edit menu is used for copying of graphs or entire pages to the Windows clipboard. To select a graph, the Show Selectors option on the Options menu may be used.
Related topics
Options menu
6.3.3 Options menu The Options menu is used to enable graph selectors and to highlight a selected graph.
In the image below, both options have been enabled
and the selectors for the three areas of the graph below (the ICC, the item information curve and the Category legends box) are displayed at the right of the window. The second graph has been selected, and this entire section of the window is highlighted in dark red. This selected graph may now be saved or printed using options on the File menu.
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6.3.4 Graphs menu The Graphs menu provides access to the Parameters and Fill Page options.
The Fill Page option is used to resize the graph to fill the entire window. The Parameters option is used to change attributes of the graph displayed and is associated with the Graph Parameters dialog box. This dialog box is used to change the position, size, and color of the currently selected graph and its plotting area. The following functions are defined:
The Left, Top, Width, and Height edit controls allow the user to specify a new position and size of the graph (relative to the page window) and of the plotting area (relative to the graph window). The Color drop-down list boxes are used to specify the graph window color and the color of the graph’s plotting area. If the Border check box is checked, the graph will have a border around it. If the Border check box is checked, the Border Attributes button leads to another standard dialog box (the Line Parameters dialog box) that allows specification of the thick-
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6 IRT GRAPHICS ness, color, and style of the borderline.
In addition to the Graphs Parameters dialog box, a number of other dialog boxes may be used to change attributes of graphs. The dialog boxes accessible depend on the type of graph displayed. The dialog boxes are:
Axis Labels dialog box Text Parameters dialog box Bar Graph Parameters dialog box Legend Parameters dialog box Line Parameters dialog box
The user may access any of these dialog boxes by double-clicking in the corresponding section of the graph. For example, double-clicking in the legend area of the graph will activate the Legend Parameters dialog box. Double-clicking on the title of the graph, on the other hand, will provide access to the Text Parameters dialog box. 6.3.5 Axis Labels dialog box This dialog box is used for editing axis labels and is activated by double clicking on the axis of a displayed graph. The following functions are defined:
The Labels Position group box controls the position of the labels relative to the axis or plotting area. The Last Label group box allows manipulation of the last label drawing options. If On is selected, the last label is displayed like the others. If Off is selected, it is not displayed, If Text is selected, the text string entered in the edit box below will be displayed instead of the last numerical label. The format of the numerical labels can be specified using the radio buttons in the Format group box.
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The Date Parameters group box becomes active once the Date radio button is checked. The Date Format box selects the date format to use for labels, while the Date Time Base box selects the time base (minute, hour, day, week, month, year) for the date calculations. The Starting Date drop-down list boxes specify the starting date that corresponds to the axis value of 0. All dates are calculated relative to this value. If the Set Precision check box is not checked, the labels’ precision is determined automatically. If it is checked, the number entered into the #Places field specifies the number of digits after the decimal point. The Text Parameters button provides access to the Text Parameters dialog box (see Section 6.3.10) that controls the font, size, and color of labels.
Related topics
Text Parameters dialog box
6.3.6 Bar Graph Parameters dialog box This dialog box is used for editing the parameters of all bars in a regular bar graph, or a selected group member of grouped bar graphs. It is displayed when a bar in the histogram (Histogram option on the Main menu) is double-clicked.
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It operates as follows:
If the Border check box is checked, the bars have a border around them. In this case, the Border Attributes button leads to the Line Parameters dialog box that controls border thickness, color, and style. The Data button leads to the spreadsheet-style window for editing plotted data points (shown below)
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The Hatch Style drop-down list box allows the user to choose the hatch style for bars. The Bar Color scrolling bars control the bar RGB color. The Position radio buttons control the bar position relative to the independent variable values. The Width string field allows the user to enter the bar width in units of the independent variable.
6.3.7 Legend Parameters dialog box This dialog box allows the editing of legends. It opens when the mouse button is double-clicked while the cursor is anywhere inside the legend box, except over a symbol representing a plotting object.
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This dialog box operates as follows:
The Left, Top, Width, and Height edit controls allow the user to specify a new position and size of the legend-bounding rectangle relative to the graph window. The Color drop-down menu specifies the legend rectangle background color. If the Border check box is checked, the rectangle will have a border. In this case, the Border Attributes button leads to the Line Parameters dialog box that controls border thickness, color, and style of the border line. The multi-line text box in the lower left corner lists and allows editing of each of the legend text strings. The Text Parameters button leads to the Text Parameters dialog box discussed earlier.
Related topics
Text Parameters dialog box
6.3.8 Line Parameters dialog box This dialog box is used for editing lines in the graph. It is accessed via the Plot Parameters dialog box, which is activated when a curve in a graph is double-clicked.
It has the following functions:
The Color drop-down list box controls the line color.
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The Style drop-down list box, visible when activated, allows selection of a line style. The Width control specifies the line width, in window pixels.
Related topics
Plot Parameters dialog box
6.3.9 Plot Parameters dialog box The Plot Parameters dialog box is accessed when a curve is double-clicked.
The type of line to be displayed may be changed using the Type drop-down lost box. To fill the area under the curve, the Fill Area check box may be used. The type of curve fitted (spline or not) is controlled by the Spline check box. The Data button provides direct access to the data used to plot the curve. The Line Attributes button provides access to the Line Parameters dialog box (shown to the right of the Plot Parameters dialog box below). The Line Parameters dialog box is discussed elsewhere in this section.
Related topics
Line Parameters dialog box
6.3.10 Text Parameters dialog box This dialog box is used for editing text strings, labels, titles, etc. It can be called from some of the other dialog boxes controlling graphic features. It may be activated by double clicking on any text in a displayed graph.
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The following functions are defined:
The Text edit control allows the user to edit the text string. The Font drop-down list box allows control of the typeface. The text color can be selected from the Color drop-down menu. The size of the fonts (in points) is controlled by the Size drop-down menu. The Bold, Italic and Underline check boxes control the text style.
6.4 Item characteristic curves The item characteristic curve is a nonlinear function that portrays the regression of the item score on the trait or ability measured in a test. It shows the relationship between the probability of success on the item to the ability measured by the item set or test containing the item. In the case of binary data, a single curve is used to portray this relationship, and the difficulty, discrimination and guessing parameters (where applicable) are indicated on the graph. In polytomous models such as the graded response model and nominal response model, a number of item option curves are plotted. Each curve shows the selection probability of a category of the item as a function of the ability. For a description of the models for which item characteristic curves or item option curves may be obtained, see Binary data:
The one-parameter (1PL, Rasch) model (see Section 7.2) The two-parameter (2PL, Birnbaum) model The three-parameter (3PL, guessing) model
Polytomous data:
Masters’ Partial Credit model (see Section 7.3.3) Thissen and Steinberg’s (1984) multiple response model (see Section 7.4.4) 523
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The nominal model (see Section 7.4.7) Samejima’s (1969) graded model (see Sections 7.3.2) The rating-scale model (see Section 7.3.4)
6.5 Item information curves Item information functions are dependent on ability and provide valuable insight into the differences in the precision of measurement different ability levels. They are of particular interest in test construction, where these curves can be used to ensure the inclusion of different items that maximize the precision of measurement at different levels of θ in the test. In the case of a 1PL model, the item information function is given by (Hambleton & Swaminathan, 1985, Table 6-1)
D 2 {1 + exp[− D(θ − bi )]}−1{1 − (1 + exp[− D(θ − bi )]) −1} The maximum value of the information is constant for the one-parameter model and is at the point bi . For a 2PL model, the item information function is given by (Hambleton & Swaminathan, 1985, Table 6-1)
D 2 ai2 {1 + exp[− D(θ − bi )]}−1{1 − (1 + exp[− D(θ − bi )]) −1} with the maximum value directly proportional to the square of the item discrimination parameter, a. A larger value of a is associated with greater information. The maximum information is obtained at bi . For the three-parameter model, the information function is (Hambleton & Swaminathan, 1985, Table 6-1)
D 2 ai2 {1 + exp[− D(θ − bi )]}−1{1 − (1 + exp[− D(θ − bi )]) −1} {1 − ci }2 The maximum information is reached at bi +
1 ln{1/ 2 + 1/ 2 1 + 8ci } Dai
An increase in information is associated with a decrease in ci . The maximum information is obtained when ci = 0. The slope of the item response function and the conditional variance at each ability level θ play 524
6 IRT GRAPHICS an important role in terms of the information provided by an item. An increase in the slope, together with a decrease in the variance, leads to more information being obtained. This in turn provides a smaller standard error of measurement. By assessing these curves, items with large standard errors of measurement may be identified and discarded. The contributions of both item and test information curves are summarized by Hambleton & Swaminathan (1985) as follows: “The item and test information functions provide viable alternatives to the classical concepts of reliability and standard error. The information functions are defined independently of any specific group of examinees and, moreover, represent the standard error of measurement at any chosen ability level. Thus, the precision of measurement can be determined at any level of ability that is of interest. Furthermore, through the information function, the test constructor can precisely assess the contribution of each item to the precision of the total test and hence choose items in a manner that is not contradictory with other aspects of test construction. ” The item and item information curves for two items to which a 3PL model has been fitted are shown below. The discrimination parameter for item 24 is approximately twice that of item 25, and the effect of this can be seen in the corresponding item information curves. Both item information functions were plotted on the same scale. The item in the test with the most information determines the scale.
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6 IRT GRAPHICS
Related topics
The Information option (see Section 6.2.2) The ICC and Info option (See Section 6.2.3)
6.6 Test information curves The test information function summarizes the information function for a set of items or test. The contribution of each item in the test to the total information is additive, as can be seen from the definition of the test information function n
I (θ ) = ∑ i =1
Pi ' (θ ) 2 Pi (θ )Qi (θ )
where Pi (θ ) denotes the probability of an examinee responding correctly to item i given an ability of θ , and Qi (θ ) = 1 − Pi (θ ) . The function provides information for a set of items at each point on the ability scale and the amount of information is influenced by the quality and number of test items. As was the case for the item information function, the item slope and item variance play an important role. An increase in the slope and a decrease in the item variance both lead to more information being obtained. This in turn provides a smaller standard error of measurement. Also note that the contribution of each test item is independent of the other items in the test. The amount of information provided by a set of test items at an ability level is inversely related to the error associated with ability estimates at the ability level. The standard error of the ability
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6 IRT GRAPHICS estimates at ability level θ can be written as
SE (θ ) =
1 . I (θ )
An example of test information and measurement error curves is shown below. Note that the vertical axis to the left is used to find the information at a given ability while the vertical axis to the right serves a similar purpose for the standard error curve. Related topics
The Total Info option (see Section 6.2.4)
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7 OVERVIEW OF MODELS
7 Overview and models 7.1 Overview of IRT programs In this section, a brief overview of each of four IRT programs published by Scientific Software International, Inc., is given. In subsequent sections, the models available and statistics that are produced by each program are discussed in detail. 7.1.1 BILOG-MG BILOG-MG (Zimowski, Muraki, Mislevy & Bock, 1996) is an extension of the BILOG (Mislevy & Bock, 1990) program that is designed for the efficient analysis of binary items, including multiple-choice or short-answer items scored right, wrong, omitted, or not-presented. The program performs the same analyses as BILOG in the single-group case. The BILOG-MG program implements an extension of Item Response Theory (IRT) to multiple groups of respondents. BILOG-MG is capable of large-scale production applications with unlimited numbers of items or respondents; it can perform item analysis and scoring of any number of subtests or subscales in a single program run. All the program output may be directed to text files for purposes of selecting items or preparing reports of test scores. The program provides 1, 2, and 3 parameter logistic models for binary scored responses and accommodates both nonequivalent groups equating for maintaining the comparability of scale scores as new forms of the test are developed, and vertical equating of test forms across school grades or age groups. Analysis of differential item functioning (DIF) with respect to item difficulty associated with demographic or other group differences may be performed with BILOG-MG, and provision is made for the detection and correction for item parameter trends over time (DRIFT). In addition, the BILOG-MG program provides for “variant items” that are inserted in tests for purposes of estimating item statistics, but are not included in the scores of the examinees. The present version of BILOG-MG includes a fully developed Windows graphical interface. Syntax can be generated or adapted using menus and dialog boxes or, as before, in the format of command files in text format. The interface has menu options that are in the order that the user would most generally use: model specification is followed by data specification and technical specifications, etc. Each of the menu options provides access to a number of dialog boxes on which specifications are entered by the user. 7.1.2 PARSCALE A versatile IRT rating-scale program is PARSCALE, written by Muraki & Bock (1996). PARSCALE is capable of large-scale production applications with unlimited numbers of items or respondents. The program can perform item analysis for both dichotomous and polytomous data, and scoring of any number of subtests or subscales in a single program run. Up to 15 cate528
7 OVERVIEW OF MODELS gories can be accommodate by PARSCALE. The user has the option to use the normal ogive or the logistic response function. This program includes options to make adjustments for differences in rater severity, and to include DIF of rating-scale items. PARSCALE has the ability to mix rating-scale items and multiple-choice with or without guessing, and to handle multiple subtests and weighted combinations of subtest scores. The program also provides the option to use Samejima’s graded response model generalized for rating-scales or Masters’ partial credit model with or without discriminating power coefficients. Program output may be directed to text files for purposes of selecting items or preparing reports of test scores. 7.1.3 MULTILOG The most versatile of the SSI IRT programs is MULTILOG (Thissen, 1991). It applies to both binary and multiple category item scores and makes use of logistic response models, such as Samejima’s (1969) model for graded responses, Bock’s (1972) model for nominal (non-ordered) responses, and Thissen & Steinberg’s (1984) model for multiple-choice items. The commonly used logistic models for binary item response data are also included, because they are special cases of the multiple category models. MULTILOG provides Marginal Maximum Likelihood (MML) item parameter estimates for data in which the latent variable of IRT is random, as well as Maximum Likelihood (ML) estimates for the fixed-effects case. χ 2 indices of the goodnessof-fit of the model are provided. In IRT, the item parameter estimates are the focus of item analysis. MULTILOG also provides scaled scores: ML and Bayes modal (MAP) estimates of the latent variable for each examinee or response pattern. MULTILOG is best suited to the analysis of multiple-alternative items, such as those on multiple-choice tests or Likert-type attitude questionnaires. It is the only widely available program capable of fitting a wide variety of models to these kinds of data using optimal (MML) methods. MULTILOG also facilitates refined model fitting and hypothesis testing through general provisions for imposing equality constraints among the item parameters and for fixing item parameters at a particular value. MULTILOG may also be used to test hypotheses about Differential Item Functioning with either multiple response or binary data, through the use of its facilities to handle data from several populations simultaneously and test hypotheses about the equality of item parameters across groups. It is the only IRT program that handles all the major models: 1, 2, and 3 parameter logistic models, multiple nominal categories, graded rating-scale model, partial credit model, multiple-choice model, and constrained parameter models. In contrast to previous versions, it now analyzes models of any size up to the limit of available memory. 7.1.4 TESTFACT TESTFACT is a factor analysis program for binary scored items. This program, by Bock, Gibbons, Schilling, Muraki, Wilson and Wood, implements all the main procedures of classical item analysis, test scoring, and factor analysis of inter-item tetrachoric correlations, and also modern methods of factor analysis based on IRT. It handles item selection, multiple subtests, multiple 529
7 OVERVIEW OF MODELS groups of examinees and correlation without an external criterion. The user can also compute tetrachoric correlations with or without omitted or not-presented items, perform MINRES principal factor analysis and full information item factor analysis with likelihood ratio test of the number of factors, compute Bayes estimates of factor scores from the multidimensional IRT model, and simulate item response data for the multidimensional model. New features in TESTFACT are all part of Full information Item Factor Analysis (FIFA). The command and procedures of classical item statistics and classical factor analysis of tetrachoric correlation coefficients remain unchanged. The changes to full information item factor analysis consist of a new and improved algorithm for estimating the factor loadings and scores— specifically, new methods of numerical integration are used in the EM solution of the marginal maximum likelihood equations. Four different methods of multidimensional numerical integration for the E-step of the EM algorithm are provided: adaptive quadrature, fractional adaptive quadrature, non-adaptive quadrature, and Monte Carlo integration. In exploratory item factor analysis, these methods make possible the analysis of up to fifteen factors and improve the accuracy of estimation, especially when the number of items is large. The previous non-adaptive method has been retained in the program as a user-selected option (NOADAPT), but the adaptive method is the default. The maximum number of factors with adaptive quadrature is 10; with non-adaptive quadrature, 5; with Monte Carlo integration, 15. Bayes estimates of scores for all factors can be estimated either by the adaptive or non-adaptive method. Estimation of the classical reliability of the factor scores is also included. TESTFACT includes yet another full information method that provides an important form of confirmatory item factor analysis called “bifactor” analysis. The factor pattern in bifactor analysis consists of a general factor on which all items have some loading, plus any number of socalled “group factors” to which non-overlapping subsets of items, assigned by the user, are assumed to belong. The subsets typically represent small numbers of items that pertain to a common stem such as a reading passage or problem-solving exercise. The bifactor solution provides Bayes estimation of scores for the general factor, accompanied by estimated standard errors that properly account for association among responses attributable to the group factors.
7.2 Models in BILOG-MG 7.2.1 Introduction The central concept of item response theory is that of the item response model. These models are mathematical expressions describing the probability of a correct response to a test item as a function of the ability (or proficiency) of the respondent. For binary data, the response functions most often encountered in IRT applications are the normal ogive and the logistic models. These are discussed in Section 7.2.3. Multiple-group applications are considered in Section 7.2.2.
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7 OVERVIEW OF MODELS 7.2.2 Multiple-group analyses3 Background for multiple-group models In the multiple-group case, it is assumed that the response function of any given item is the same for all groups of subjects. In the DIF and DRIFT applications, however, we allow the relative difficulties of the items to differ from one group to another or one occasion to another. In that case, the b j parameters will differ between groups, and we will have to detect and estimate the differences. Even in the presence of DIF and DRIFT, however, it is assumed that the item discriminating powers are the same from one group to another. In the other applications, such as nonequivalent groups equating or two-stage testing, we assume that both the locations and the slope of items common to more than one group are equal. To satisfy this assumption, we would perform a preliminary DIF analysis and not use, in equating, items showing appreciable DIF. The main difference between the single-group and multiple-group case is in the assumption about the latent distribution. In most equation situations, it is reasonable to assume that the respondents in the sample groups are drawn from populations that are normal, but have different means and standard deviations (see Figure 7.1).
Figure 7.1: Normal latent densities in three populations In that case, the item response data can be described completely by estimating the means and standard deviations of the groups along with the item parameters. One must, however, again contend with the arbitrary origin and unit of the latent continuum, and may resolve this indeterminacy either by setting the mean and standard deviation of one of the groups to any arbitrary values, or by setting the overall mean and variance of the combined distributions to arbitrary values. Both options are provided in BILOG-MG. The procedure for simultaneous estimation of item parameters and latent distributions in more than one group are described in Bock & Zimowski (1995) and in Mislevy (1987). In two-stage testing applications, the situation is different. The groups correspond to examinees who have been selected on the basis of a first-stage test to receive second-stage test forms tailored to the provisional estimate of ability based on the first-stage test. Typically, the secondstage groups are determined by cutting points on the θ -scale of the pretest. Because the pretest
3
This section was contributed by Michele Zimowski.
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7 OVERVIEW OF MODELS score is a fallible criterion, the θ distributions of the second-stage groups may overlap to a considerable extent, but they cannot be expected to be normal even when the population from which the examinees originate is normal. More likely in these applications the latent distributions would appear as in Figure 7.2. To accommodate such arbitrary shapes of distributions, one must make use of the empirical estimation procedure (see the section on estimation in the next chapter). As in the single-group case, these empirical distributions can be estimated along with the item parameters by marginal maximum likelihood. Again, the indeterminacy of location and scale must be resolved, either by setting the mean and standard deviation of one of the groups to convenient values, such as zero and one, or setting the overall mean and standard deviation of the combined distributions to similar values. In DIF analysis of ethnic effects, for example, the usual approach is to assign the mean and standard deviation of the reference group, which is usually the majority demographic group.
Figure 7.2: Two-stage testing: latent densities of three second-stage groups In two-stage testing applications, where the groups represent an arbitrary partition of the original sample, assigning the overall mean and standard deviation is more reasonable. In vertical equating and DRIFT analysis, on the other hand, the groups correspond to distinct populations, so the best solution would be to choose a reference group, perhaps the youngest-age group or the firstyear group, and assign the mean and standard deviation arbitrarily in that group. Comparing the estimated means and standard deviations of the remaining groups with the reference group would then show the trends in the mean and variability of test performance in successive age groups or year groups. Equivalent groups equating Equivalent groups equating refers to the equating of parallel test forms by assigning them randomly to examinees drawn from the same population. In educational applications, this type of assignment is easily accomplished by packaging the forms in rotation and distributing them across whatever seating arrangement exists in the classroom. Provided there are fewer forms than students per classroom, it is justifiable to assume that the abilities of the examinees who receive 532
7 OVERVIEW OF MODELS the various forms are similarly distributed in the population. This is the assumption on which the classical equi-percentile method of equating is based, and it applies also to IRT equating. Indeed, the procedure is even simpler in IRT because the latent distribution of ability is invariant with respect to the distribution of item difficulties in the forms (this is not true of the numberright score of classical test theory: the test score distribution in the population of respondents is an artifact of the distribution of item difficulties (see Lord & Novick, 1968, pp. 387-392). The IRT scale scores computed from the various forms are therefore equated whenever their location and scale are set in the same way for all forms. There is no necessity for common items between forms, any more than there is for equi-percentile equating, but neither will they interfere with the equivalent groups equating if present. The method of carrying out equivalent groups equating is somewhat different, however, according to whether common items between forms are or are not present. In both cases, the collection of forms may be treated as if it were one test with length equal to the number of distinct items over all forms. The data records are then subjected to a single-group IRT analysis and scoring. When common items are not present, each form may also be analyzed as an independent test, with the mean and standard deviation of the scale scores of all forms set to the same values during the scoring phase. Equivalent groups equating is especially well suited to matrix-sample educational assessment, where the multiple test forms are created by random assignment of items to forms within each of the content and process categories of the assessment design, and the forms are distributed in rotation in classrooms. Often as many as 30 forms are produced in this way in order to assure high levels of generalizability of the aggregate scores for schools or other large groups of students. Nonequivalent groups equating Nonequivalent groups equating is possible only by IRT procedures and has no counterpart in classical test theory. It makes stronger assumptions than equivalent groups equating, but it remains attractive because of the economy it brings to the updating of test forms in long-term testing programs. Either to satisfy item disclosure regulations or to protect the test from compromise, testing programs must regularly retire and replace some or all of the items with others from the same content and process domains. They then face the problem of equating the reporting scales of the new and old forms so that the scores remain comparable. Although equivalent groups equating will accomplish this, it requires a separate study in which the new and old forms are administered randomly to examinees from the same population. A more economical approach is to provide for a subset of items that are common to the old and new forms, and to employ nonequivalent groups equating to place their scores on the same scale. These common or “link” items are chosen from the old form on the basis of item analysis results. Link items should have relatively high discriminating power, middle range difficulty, and should be free of any appreciable DIF effect. With suitable common items included, the old and new forms can be equated in data from the operational administration of the tests without an additional equating study. Only the BILOG-MG program can perform this type of equating.
533
7 OVERVIEW OF MODELS Although the case records from the current administration of the new form and the earlier administration of the old form are subjected to a single IRT item analysis in nonequivalent equating, the test form is identified on each case record and separate latent distributions are estimated for examinees taking different forms. For typical applications of the procedure to unrestricted samples of examinees, the latent distributions may reasonably be considered normal. In that case, the estimation of the mean and standard deviation of each distribution jointly with the item parameters allows for the nonequivalence of the two equating groups. The common items provide the link between the two samples of data so that we may fix the arbitrary origin and unit of a single reporting scale. Simulation studies have shown that if the sample sizes for the two groups are large enough to ensure highly precise estimation of the item parameters, as few as four anchor items can accurately equate the reporting scales for the test forms (see Lord, 1980). In the BILOG-MG procedure, this method of equating can be extended to nonequivalent groups equating of any number of such forms, provided there are common items linking the forms together in an unbroken chain. An example of a plan for common item linking of a series of test forms is shown in Figure 7.3.
Figure 7.3: An item linking design for test forms updating Variant items If total disclosure of the item content of an educational test is required, a slightly different strategy is followed. Special items, called “variant” items, are included in each test form but not used in scoring the form in the current year. It is not necessary that all test booklets contain the same variant items; subsets of variant items may be assigned in a linked design to different test booklets in order to evaluate a large number of them without unduly increasing the length of a given test booklet. These variant items provide the common items that appear among the operational items of the new form, which itself includes other variant items in anticipation of equating to a later form. The item calibration of the old and new form then includes, in total, the response data in the case records for the operational items of the old form, for the linking variant items that appeared on the old form, and for all operational items from the new form. In this way, all of the items in the current test form can be released as soon as testing is complete. Vertical equating Vertical equating refers to the creation of a single reporting scale extending over a number of school grades or age groups. Because the general level of difficulty of finding items in tests intended for such groups must increase with the grade or age, the forms cannot be identical. There is little difficulty in finding items that are suitable for neighboring grades or age groups, however, and these provide the common items that can be used to link the forms together on a com534
7 OVERVIEW OF MODELS mon scale. Inasmuch as these types of groups necessarily have different latent distributions, nonequivalent groups equating is required. BILOG-MG offers two methods for inputting the response records. In the first method, each case record spans the entire set of items appearing in all the forms, but the columns for the items not appearing in the test booklet of a given respondent are ignored when the data are read by the program. All of the items thus have unique locations in the input records and are selected from each record according to the group code on the record. In the second method, the location of the items in the input records is not unique. An item in one form may occupy the same column as a different item in another form. In this case, the items are selected from the record according to the form and the group codes on the record. These methods of inputting the response records apply in all applications of BILOG-MG. See Chapter 10 for examples of both types of data input. Differential item functioning (DIF) The purpose of differential item functioning analysis is to detect and estimate interactions between item difficulties and various subgroups within the population of respondents (see Thissen, Steinberg, & Wainer, 1993). It is most often applied to interactions with respect to demographic or ethnic groups and to gender, but any classification of the respondents could be investigated in a similar manner. Specifically, it is the interactions of the item location parameters, b j , reflecting the item difficulties, that are in question. DIF includes only the relative differences in difficulties between the groups. Any reduction of the item percent corrects due to the average level of ability in the group, as indicated by the mean of the corresponding latent distribution, we attribute to the “adverse impact” of the test and do not regard it as DIF. Moreover, we assume that the differential item functioning does not extend to the item discriminating powers. The b j parameters for the separate groups are estimated on the assumption that the slope parameters, a j , are homogeneous across groups. (for an alternative form of DIF analysis that includes differential item discriminating power, see Bock, 1993). DIF analysis is similar to nonequivalent groups equating in the sense that different latent distributions are assumed for the groups in question, but it differs because the same form of the test is administered in all of the groups. It also provides a large sample standard error estimate of the effect estimators. In addition, the program provides an overall marginal likelihood ratio test of the presence of differential item functioning in the data. To perform this test, first analyze the data in a single group as if they came form the same population and note the marginal maximum log likelihood of the item parameters in the final iteration (labeled –2 LOG LIKELIHOOD in the output). Then, analyze the data in separate groups using the DIF model and again note the final log likelihood. Under the null hypothesis of no DIF effects on item locations, the difference in these log likelihoods is distributed in large samples as χ 2 with (n − 1)(m − 1) degrees of freedom, where n is the number of items and m is the number of groups. When this χ 2 is significant, there is evidence that differential item effects are present. Their interpretation usually becomes clear when the item content is examined in relation to the direction of the estimated contrasts in the b j parameters, because these contrasts are interactions and must sum to zero (some are positive and others negative).
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7 OVERVIEW OF MODELS Item parameter drift (DRIFT) As defined by Bock, Muraki & Pfiffenberger (1988), DRIFT is a form of DIF in which item difficulty interacts with the time of testing. It can be expected to occur in education tests when the same items appear in forms over a number of years and changes in the curriculum or instructional emphasis interact differentially with the item content (see Goldstein, 1983). Bock, Muraki & Pfiffenberger found numerous examples of DRIFT among the items of a form of the College Board’s Advanced Placement Test in Physics that had been administered annually over a tenyear period (see Figure 7.4). DRIFT is similar to DIF in admitting only the item interaction: changes in the means of the latent distributions of successive cohorts are attributed to changes in the levels of proficiency of the corresponding population cohorts.
Figure 7.4: Drift of the location parameters of two items from a College Board Advanced Placement Examination in Physics DRIFT differs from DIF in that the interaction of item location with time is assumed to be a continuous process that can be modeled by a straight or low degree polynomial regression line. Thus, in place of estimating contrasts between groups, we estimate the coefficients of the linear or polynomial function in time that describes the DRIFT in the b j parameters. The significance of the trends can be judged from the size of the estimated regression coefficient relative to its large sample standard error estimate. The overall presence of DRIFT can be tested in a marginal likelihood ratio test similar to that for DIF. As implemented in BILOG-MG, DRIFT analysis does not require all items to be included in each test form. The DRIFT regression functions are estimated for whatever time points are available for each item. In most DRIFT applications, it is satisfactory to assume that the latent distributions of the yearly cohorts are normal. The corresponding means and standard deviations estimated in the DRIFT analysis describe differences in the proficiencies of the cohorts. Two-stage testing Two-stage testing is a type of adaptive item presentation suitable for group administration. By tailoring the difficulties of the test forms to the abilities of selected groups of examinees, it permits a reduction in test length by a factor of a third or a half without loss of measurement precision. The procedure employs some preliminary estimate of the examinees’ abilities, possibly 536
7 OVERVIEW OF MODELS from a short first-stage test or other evidence of achievement, to classify the examinees into three or four levels of ability. Second-stage test forms in which the item difficulties are optimally chosen are administered to each level. Forms at adjacent levels are linked by common items so that they can be calibrated on a scale extending from the lowest to the highest levels of ability. Simulation studies have shown that two-stage testing with well placed second-stage tests is nearly as efficient as fully adaptive computerized testing when the second-stage test has four levels (see Lord, 1980). The IRT calibration of the second-stage forms is essentially the same as the nonequivalent forms equating described above, except that the latent distributions in the second-stage groups cannot be considered normal. This application therefore requires estimation of the location, spread, and shape of the empirical latent distribution for each group jointly with the estimation of item parameters. During the scoring phase of the analysis, these estimated latent distributions provide for Bayes estimation of ability combining the information from the examinee’s first-stage classification with the information from the second-stage test. Alternatively, the examinees can be scored by the maximum likelihood method, which does not make use of the first-stage information. The BILOG-MG program is capable of performing these analyses for the test as a whole, or separately for each second-stage subtest and its corresponding first-stage test. For an example of an application of two-stage testing in mathematics assessment see Bock & Zimowski (1989). Estimating latent distributions An innovative application of the BILOG-MG program is the estimation, from matrix sampled assessment data, of the latent distributions for schools or other groups of students. Certain matrix sampling designs, such as those employed by the National Assessment of Educational Progress, include in each booklet a number of short scales, consisting of eight or nine items, in several subject-matter areas. These scales have too few items to permit reliable estimation of the proficiencies of individual examinees in each subject matter, but they do allow estimation of the latent distribution of each proficiency at the group-level if the number of respondents is sufficiently large. There is a tradeoff between the number of items for each scale in each test booklet and the number of respondents: the more items, the fewer respondents are needed for accurate estimation of the group latent distribution. If each booklet contains perhaps 48 items, the latent distributions for six content areas could be estimated simultaneously. The results of the assessment could then be reported to the public in terms of the means and standard deviations of the achievement levels of the schools or groups. Alternatively, if achievement standards have been set in terms of IRT scale score levels, the percent of students attaining or exceeding each level can be computed from the latent distribution and reported. The latter form of reporting is often more easily understood than scale-dependent statistics such as the mean and standard deviation. Because the BILOG-MG program allows unlimited numbers of groups as well as unlimited numbers of items and respondents, it is well suited to the estimation of latent distributions for this form of reporting. The shape of the latent distributions may either be assumed normal or estimated empirically.
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7 OVERVIEW OF MODELS 7.2.3 Technical details The normal ogive model A response to a binary test item j is indicated in these expressions by the item score, xj = 1
if the respondent answers correctly, or
xj = 0
if the respondent answers incorrectly.
Let θ denote the ability of the person, and let the probability of a correct response to item j be represented by P ( x j = 1| θ ) = Pj (θ ) ;
and thus, the probability of an incorrect response is given by P ( x j = 0 | θ ) = 1 − Pj (θ ) .
In general, the response function also depends upon one or more parameters characteristic of the item, the values of which must be estimated. The normal ogive model is defined as: 1 Pj (θ ) = 2π
∞
∫
e −[1/ 2]t dt , 2
− (θ −b j ) / σ j
where σ j = 1/ a j is called the item dispersion, a j is the item discriminating power and b j is an item location parameter. The normal ogive model is conventionally represented as Φ j (θ ) . The logistic models for binary scored items At present, the response models most widely used in applied work are the logistic models for binary scored items. The most important of these models are:
The one-parameter (1PL, Rasch) model The two-parameter (2PL, Birnbaum) model The three-parameter (3PL, guessing) model
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7 OVERVIEW OF MODELS The one-parameter (1PL, Rasch) model The one-parameter logistic model is defined as P(1) j (θ ) =
1 1 + exp[− a (θ − b j )]
where
exp(k ) = e k
and
e = 2.718
is the base of the natural logarithm,
a
is a scale constant determining the units of θ , and
bj
is a location parameter related to the difficulty of item j (also referred to as the item “threshold”). Items with larger values of b j are more difficult; those with smaller values are easier.
The two-parameter (2PL, Birnbaum) model
The two-parameter logistic model is defined as P(2) j (θ ) =
1 1 + exp[− a j (θ − b j )]
where a j is the item discriminating power, and b j is an item location parameter as in the 1PL model. The negative of the exponent in this model, z j = a j (θ − b j ), is referred to as a logistic deviate, or logit. The logit can also be written as z j = a jθ + c j where c j = −a j b j . In this form, a j is referred to as the item slope and c j as the item intercept (see Fig-
ure 7.5).
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7 OVERVIEW OF MODELS
Figure 7.5: the two-parameter logistic model
The 2PL model is conventionally represented as Ψ j (θ ) =
1 1+ e
−zj
If all a j are equal, the model is reduced to a one-parameter logistic or Rasch model. The three-parameter (3PL, guessing) model
In the case of multiple-choice items, an examinee who does not know the correct alternative may succeed in responding correctly by randomly guessing. If the examinee’s ability is θ , the probability that the examinee will not know the answer, but guesses correctly (with probability g j ) is g j [1 − Ψ j (θ )] . The probability that the examinee will respond correctly either by knowledge or
by random guessing is therefore P3 j (θ ) = g j [1 − Ψ j (θ )] + Ψ j (θ ) = g j + (1 − g j )Ψ j (θ ),
where g j is the probability of a correct response to a multiple-choice item as a result of guessing. If the correct response alternative is randomly assigned, and all of the examinees guess blindly, the value of g j is equal to 1/A, where A is the number of alternatives of the multiplechoice item. If some of the examinees guess after eliminating one or more of the alternatives, g j
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7 OVERVIEW OF MODELS will be greater than 1/A by some amount that must be determined empirically along with the a j and b j or c j parameters. The parameter g j corresponds to the lower asymptote of the item response function, P3 j (θ ) . This interpretation of g j , as well as that of the other item parameters, is shown in Figure 7.6.
Figure 7.6: Three-parameter logistic model Relationship between normal ogive and logistic models
The logistic item response models are closely related to the normal ogive model. In order to bring the logistic models into close agreement with the normal ogive model, θ is multiplied by the factor D = 1.7. When D = 1.7 is used, the discrepancy between the normal response function and its logistic approximation is never greater than 0.01. When the logit incorporates this factor, as in z j = a j D(θ − b j ) , the models are said to be in the normal metric. Classical item statistics
BILOG-MG computes and uses classical item statistics as starting values for the iterative estimation of the IRT parameters. On the assumption that θ is distributed with zero mean and unit standard deviation in the population of respondents, the normal ogive item parameters are related to the classical item statistics as follows (see Lord & Novick, 1968, Sections 16.9 and 16.10). 541
7 OVERVIEW OF MODELS Reliability index (item-trait correlation):
If one assumes a bivariate normal distribution of the population over the item and criterion variables, Richardson (1936) and Tucker (1946) have shown that
ρ j = a j / 1 + a 2j , 0 ≤ ρ j ≤ 1 where ρ j is the biserial correlation between ability and item j. In classical item analysis, ρ j is estimated by the item-test correlation (the correlation between response to the item scored 1 or 0 and number-right score for the test). We see from the equation above that an item with slope 1 (in the normal metric) has a reliability index equal to 1/ 2 = 0.707. Items with slopes greater than 1 are more reliable (more discriminating measures of the trait represented by the test); those with slopes less than 1 but greater than zero are less reliable. Items with a negative slope are keyed in a direction opposite to that of the other items. The same relationships hold with good approximation for the logistic parameters expressed in the normal metric. Item facility (p-value):
Tucker (1946) has expressed classical item difficulty Pj as a function of the item parameters a j and b j : Pj = Φ (−a j b j / 1 + a 2j ) , that is, p j is the value of the standard normal distribution function at the point −a j b j 1 + a 2j
= −b j ρ j
i.e., the area to the left of the point under the normal curve. From the equations above it follows that
a j =
ρ j 2 1− ρ j
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7 OVERVIEW OF MODELS and
z j b j = ρ
j
= P ( z ≤ z j ). where z j is calculated using the inverse normal distribution with ρ j 7.2.4 Statistical tests
Because BILOG-MG employs maximum likelihood estimation when fitting the IRT model, large-sample statistical tests of alternative models are available, provided one model is nested within each other. Two models are called “nested” when the larger model is formed from the smaller by the addition of terms and parameters. For example, the one-parameter logistic model is nested within the two-parameter model, which is in turn nested within the three-parameter model. Similarly, the single-group model is nested within the two-group model, and so on. The smaller of the nested models is referred to as the “null” model and the larger as the “alternative”. The statistical test of the alternative model vs. the null model is equivalent to a test of the hypothesis that the additional parameters in the alternative are all zero and that no significant improvement in fit is obtained by including them. At the end of the estimation cycles in the calibration phase, BILOG-MG prints the negative of the maximum marginal log likelihood. If the program is run, with the same data, once with the null model and once with the alternative model, the negative of the log likelihood of the former will always be larger than that of the latter. In large-samples, the positive difference of these log likelihoods is distributed as χ 2 on the null hypothesis. Its number of degrees of freedom is equal to the difference in the number of parameters in the null and alternative models. A model with more parameters should be adopted only when this test statistic is clearly significant. Otherwise, fitting of the additional parameters will needlessly reduce precision of estimation. BILOG-MG also provides a large-sample test of the goodness-of-fit of individual test items in the analysis: this requires the test to have 20 or more items. If the test is sufficiently long (more than 20 items), the respondents in a sample of size N can be assigned with good accuracy to intervals on the θ -continuum on the basis of their estimated value of θ (for this purpose, we use the EAP estimate with whatever prior is assumed for item calibration; see the section on test and item information to follow). Then the number of those in each interval who respond correctly to item j can be tallied from their item scores. Finally, a likelihood ratio χ 2 test statistic can be used to compare the resulting frequencies of correct and incorrect responses in the intervals with those expected from the fitted model:
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7 OVERVIEW OF MODELS rhj N h − rhj , + ( N h − rhj ) log e X = 2∑ rhj log e _ _ h =1 N h Pj (θ h )) N h (1 − Pj (θ h )) ng
2 j
where ng is the number of intervals, rhj is the observed frequency of correct responses to item j in interval h, N h is the number of respondents assigned to that interval, and Pj (θ h ) is the value
of the fitted response function for item j at θ h , the average ability of respondents in interval h. Because neither the MML or MAP methods of fitting the response functions actually minimize this χ 2 , the residuals are not under linear constraints and there is no loss of degrees of freedom due to the fitting of the item parameters. The number of degrees of freedom is therefore equal to the number of intervals remaining after neighboring intervals are collapsed if necessary to avoid expected values less than 2.
7.3 Models in PARSCALE4 7.3.1 Introduction
Psychological, sociological, educational and medical data often consist of responses classified in two or more predefined categories. The extra information contained in multiple-category response classifications helps offset the greater cost (compared to machine scored multiple-choice items) of ratings based on human judgments. Provided the readers are able to assign the categories consistently, multiple-category scoring is more informative than binary scoring, because it contains multiple thresholds of difficulty corresponding to the boundaries between the categories. By discriminating among the respondents at more than one level, multiple-category scoring of an extended response has the same advantages as adaptive testing with several binary-scored items at different levels of difficulty. The generalization of IRT to the multiple-category case provides a comprehensive methodology for the analysis and scoring of this type of data. It applies optimal procedures in place of the ad hoc rules traditionally used in quantifying and weighting categorical data. In particular, it solves the hitherto intractable problem of how to combine the information in ratings when different items have varying numbers of differently defined categories. Utilizing maximum likelihood or Bayes estimation derived from multiple-category item response models, the IRT approach employs all the information in the categorical data efficiently to assign quantitative scores to the respondents. Readers familiar with IRT in the binary case will find the generalization to the multiple-category case quite straightforward. The concept of a latent dimension on which response probability
4
This section was contributed by Eiji Muraki.
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7 OVERVIEW OF MODELS functions are defined carries over from the binary case without changes, and parameters of the response functions must still be estimated; the estimated parameters are then used to estimate scores for the respondents. The only new element is the more general form of the response functions and the greater number of parameters per item. The similarity of the two cases is apparent in the parallel structure of the BILOG-MG and PARSCALE programs: both programs have a data input, item calibration and test-scoring phase. Both are designed for efficient use in largescale testing programs based on instruments with many items and possible multiple subtests or scales. The current version of PARSCALE handles data in which the responses to a number of items are classified in a common set of ordered categories. This is perhaps the most common type of data. In the context of attitude measurement, this type of item is often treated as a so-called “Likert” scale, where the categories are arbitrarily assigned successive integer values (Likert, 1932). In contrast, the IRT procedures estimate optimal empirical values to be assigned to the boundaries between the categories. Since all of the items are rated in the same categories, the number of boundaries to be estimated equals one less than the number of categories. The boundaries, item locations, and respondent scores are all represented as points on the latent dimension of measurement. Another common type of data is where each item has its own specific number and definition of categories. The number of boundaries to be estimated is therefore equal to the sum of one less than the number of categories for each item. In this case, the item locations are absorbed in the category boundaries of the items and are not separately estimated. Alternatively, the instrument or test may consist of a mixture of common-category and specificcategory items. PARSCALE handles this case by assigning items to “blocks”, with categories common within blocks and different between blocks. Each specific-category item is its own block. In the case of binary items, i.e., items with only two categories, the categories are common by definition and all belong to the same block. An educational test, for example, may contain open-ended exercises scored in five or six categories in one block, and multiple-choice items in another block. The presence of multiple-choice items introduces the additional problem of guessing effects (which is often absent in rated items). These effects are estimated using a threeparameter model in the binary case. A case not handled by the current version is that of nominal categories. These categories each represent a qualitatively distinct type of response to the stimulus and have no predefined ordinal relationship. A common use of the nominal model is to extract all information in responses to all alternatives of a multiple-choice item, beyond just the contrast of correct and incorrect alternatives. At the present time, only MULTILOG (Thissen, 1991) handles both ordinal and nominal category item response data. But MULTILOG does not allow for Likert-type items with a common set of response categories. The response models in PARSCALE are derived from normal and logistic models in the binary case. In this section Samejima’s Graded Response Model and Masters’ Partial Credit Model are discussed. The scoring function of the Generalized Partial Credit Model, the Rater's-Effect
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7 OVERVIEW OF MODELS model, the DIF model, and the Trend model for dichotomous item response models (see Bock, Muraki & Pfeiffenberger, 1988) are then reviewed. 7.3.2 Samejima’s graded response model
If we define Pjk+ (θ ) and Pj+,k +1 (θ ) as the regression of the binary item score method in which all response categories less than k and k + 1, respectively, are scored 1 for each item j, the operating characteristic (Samejima, 1972) of the graded item scoring for the latent trait variable θ is Pjk (θ ) = Pjk+ (θ ) − Pj+,k +1 (θ ).
Samejima (1969) further defines Pj+0 (θ ) and Pj+,m+1 (θ ) so that Pj+0 (θ ) = 1 Pj+,m +1 (θ ) = 0,
where m is the number of categories minus 1. Therefore, Pj 0 (θ ) = Pj+0 (θ ) − Pj+1 (θ ) = 1 − Pj+1 (θ ) Pj1 (θ ) = Pj+1 (θ ) − Pj+2 (θ ) + + (θ ) − Pj+,m +1 (θ ) = Pjm (θ ), Pjm (θ ) = Pjm
and, in general, Pjk (θ ) = Pjk+ (θ ) − Pj+,k +1 (θ ) ≥ 0.
For the normal ogive model (Samejima, 1974), the formula for Pjk+ (θ ) in the general case, is given by a j (θ −b jk )
Pjk+ (
θ) =
∫
φ (t ) dt ,
−∞
where a j is a common slope parameter and b jk is called an item-category threshold parameter. For each m + 1 category item, there are m category threshold parameters. From this definition, b j1 ≤ b j 2 ≤ ... ≤ b jm .
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7 OVERVIEW OF MODELS An extension of Samejima’s graded item response model suitable for Likert items is a j (θ −b j + ck )
∫
Pjk+ (
θ) =
φ (t ) dt ,
−∞
where b j is now the item-location parameter and ck the category parameter. We refer to this extension as the “rating-scale” model. In the same manner, we can write a j (θ −b j + ck +1 )
∫
Pj+,k +1 (
θ) =
φ (t ) dt.
−∞
If a j > 0, then Pjk+ (θ ) − Pj+,k +1 (θ ) ≥ 0 , and ck − ck +1 ≥ 0 . From these results, we obtain the response function of a graded category under the normal ogive model: a j (θ −b j + ck )
Pjk+ (
θ) =
∫
φ (t ) dt.
a j (θ −b j + ck +1 )
The corresponding logistic form of the graded response model is Pjk (θ ) = =
exp[ Da j (θ − b j + ck )]
−
exp[ Da j (θ − b j + ck +1 )]
1 + exp[ Da j (θ − b j + ck )] 1 + exp[ Da j (θ − b j + ck +1 )] 1 1 − 1 + exp[− Da j (θ − b j + ck )] 1 + exp[− Da j (θ − b j + ck +1 )]
where D = 1.7. Both models in these equations are response functions for items scored in successive categories. A major distinction between the two models is that in the rating-scale model, Samejima’s parameter b jk is resolved into an item location parameter b j and a category parameter ck . If each item has its own response categories, which may differ in number m j (j = 1, 2, …, n), the graded response model is required. Figures 7.7, 7.8 and 7.9 illustrate the meaning of the parameters, a j , b j and ck . All examples are the logistic rating-scale models with four categorical responses. Therefore, the model contains three category parameters, ck .
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7 OVERVIEW OF MODELS
Figure 7.7: Graded response model: a = 1.0, b = 0.0, c = (2.0,0.0,-2.0)
In Figure 7.7, the values of parameters are set as a j = 1.0, b j = 0.0, c1 = 2.0, c2 = 0.0, and c3 = 2.0. Item category trace lines are drawn from left to right as the order of Pj 0 , Pj1 , Pj 2 , and Pj 3 . Since the distances between adjacent category thresholds are equal and the location parameter is zero, the trace lines are symmetric with respect to θ = 0. If the slope a j increases by 0.5 ( a j = 1.5) and the location is changed from b j = 0 to b j = 0.5, then all of the four trace lines become steeper and are shifted to the right of the θ scale, as shown in Figure 7.8. These mechanisms of the function behave the same as the dichotomous item response model.
Figure 7.8: Graded Response Model: a = 1.5, b = 0.5, c = (2.0,0.0,-2.0)
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7 OVERVIEW OF MODELS If the distance between c2 and c3 becomes narrower by 0.5 ( c2 = -0.5) as shown in Figure 7.9, the trace lines of Pj1 and Pj 2 are shifted to the right. In other words, these two categories become more difficult to be endorsed or attained. However, the trace lines of Pj 0 and Pj 3 stay the same since these probabilities do not involve c2 . Since the trace lines of the extreme categories, Pj 0 and Pj 3 , are essentially the cumulative probability function, the slope of these functions change only if the slope parameter, a j , is altered. However, the slope of the middle category is affected by the distance of adjacent categories. Therefore, the trace line of Pj 2 (θ ) is not only shifted, but also becomes flatter.
Figure 7.9: Graded response model: a = 1.5, b = 0.5, c = (2.0,0.5,-2.0)
In the case of dichomotomous item response models, the slope parameter is synonymous with the discriminating power. However, for the polytomous item response model, the discriminating power of a specific categorical response depends on the width of adjacent category thresholds as well as a slope parameter. Because of this property, the simultaneous estimation of the slope parameter and of all m j category parameters is not obtainable. If the model includes the slope parameter for each item j, the location of the category parameters must be fixed. The natural choice is to fix the values of the mean of the category parameters c1 and cm . The program provides the keyword CADJUST on the BLOCK command to set this mean (default is 0.0). The option NOCADJUST causes the program to omit the adjustment during the calibration or scoring phase. The relationships among parameters in the rating-scale model are expressed by a j (θ − b j + ck ) =
aj s
[ sθ − ( sb j − t ) + ( sck − t )]
where s is a scaling factor and t is a location constant. This equation shows that shifting the center of the category metric results in a shift of b j in the same direction by the same units. If the 549
7 OVERVIEW OF MODELS intervals of the category scale are expanded by the factor s and the scale of θ is held constant, the b j will expand and the a j will contract by the same factor. Therefore, if the assumption that more than two subsets of items measure the same ability is met and their ability distributions are constrained to have the same mean and standard deviation, both the scale and location parameters are determinate and estimable. 7.3.3 Masters’ partial credit model
Masters (1982) reformulated Andrich’s polytomous rating response model by utilizing the Rasch dichotomous model, which does not contain a discriminating power parameter. It is quite legitimate, however, to formulate the general model based on the two-parameter logistic response model, following the same operating characteristic that Masters employs. Since the essential mechanism for constructing the general model is shared with Masters’ partial credit model and Andrich’s rating-scale model, the models constructed in this text can simply be called the generalized partial credit model. The generalized partial credit model is formulated based on the assumption that each probability of choosing the k-th category over the (k–1)-th category is governed by the dichotomous response model. To develop the partial credit model, let us denote Pjk as the specific probability of choosing the k-th category from m j + 1 possible categories of item j. In the dichotomous model ( m j + 1 = 2 ),
Pj 0 (θ ) + Pj1 (θ ) = 1. The conditional probability of choosing category 1 given the
probability of choosing categories 0 and 1 is then Pj1|0,1 =
Pj1 (θ ) Pj 0 (θ ) + Pj1 (θ )
= Pj1 (θ ) =
exp[a j (θ − b j1 )] 1 + exp[a j (θ − b j1 )]
.
Therefore, Pj 0|0,1 (θ ) = 1 − Pj1 (θ ) =
1 . 1 + exp[ a j (θ − b j1 )]
In the polytomous response model, in which m j + 1 is more than 3 for item j, mj
Pj 0 (θ ) + Pj1 (θ ) + ... + Pjm j (θ ) = ∑ Pjk (θ ) = 1. k =0
For each of the adjacent categories, the probability of the specific categorical response k over k – 1 is given by the above conditional probability:
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7 OVERVIEW OF MODELS C jk = Pjk |k −1,k (θ ) = =
Pjk (θ ) Pj ,k −1 (θ ) + Pjk (θ ) exp[a j (θ − b jk )] 1 + exp[a j (θ − b jk )]
,
where k = 1, 2, … , m j . Then, Pjk (θ ) =
C jk 1 − C jk
Pj ,k −1 (θ ),
where C jk 1 − C jk
= =
Pjk |k −1,k (θ ) 1 − Pjk |k −1,k (θ ) Pjk |k −1,k (θ ) Pjk −1|k −1,k (θ )
= exp[a j (θ − b jk )].
This equation may be called the operating characteristic for the partial credit model. If we start by determining Pj 0 (θ ) =
1 , G
we obtain the following probabilities by applying the operating characteristic: Pj1 (θ ) = Pj 2 (θ ) =
exp[a j (θ − b j1 )] G exp[a j (θ − b j1 ) + a j (θ − b j 2 )] G
exp[∑ v =1 a j (θ − b jv )] g
Pjg (θ ) =
G
exp[∑ v =j1 a j (θ − b jv )] m
Pjm j (θ ) =
G
,
where g is a subscript for a specific categorical response k = g.
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7 OVERVIEW OF MODELS Since ∑ Pjk (θ ) = 1, mj c G = 1 + ∑ exp ∑ a j (θ − b jv ) . c =1 v =1
Therefore, the partial credit model is given by k exp ∑ a j (θ − b jv ) v =1 Pjk (θ ) = mj c 1 + ∑ exp ∑ a j (θ − b jv ) c =1 v =1 k exp ∑ a j (θ − b jv ) v =0 , = m c j ∑ exp ∑ a j (θ − b jv ) c =0 v =0
where b j 0 ≡ 0. The partial credit model reduces to the dichotomous item response model when m j = 1 and k = 0, 1. Note that b j 0 is arbitrarily defined as 0.0. This quantity is not a location
constant and could be any value because the term containing this parameter cancels from both numerator and denominator: k exp ∑ z jv v =1 Pjk (θ ) = m j c ∑ exp ∑ z jv c =1 v =0
k k exp[ z j 0 (θ )] × exp ∑ z jv (θ ) exp ∑ z jv (θ ) v =1 v =1 , = = mj mj c c exp[ z j 0 (θ )] + ∑ exp z j 0 (θ ) + ∑ z jv (θ ) 1 + ∑ exp ∑ z jv (θ ) c =1 v =1 c =1 v =1
where z jk (θ ) = a j (θ − b jk ). Masters (1980) calls the quantity b jk in this equation an “item step” parameter. It is the intersection point of Pjk (θ ) and Pj ,k +1 (θ ) expressed as the operating characteristic. Thus, assuming a j > 0,
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7 OVERVIEW OF MODELS θ = b jk , then Pjk (θ ) = Pj ,k +1 (θ )
if: θ > b jk , then Pjk (θ ) < Pj ,k +1 (θ ) θ < b jk , then Pjk (θ ) > Pj ,k +1 (θ ).
It should be noted that b jk is not sequentially ordered within item j, because it represents the relative magnitude of the adjacent probabilities Pjk (θ ) and Pj ,k +1 (θ ) . Furthermore, when all probabilities Pjk (θ ) are equal, the values of b jk also become identical. While the item-category threshold parameter, b jk , in the graded response model determines the steepest point of the trace line, the b jk parameter in the partial credit model is the intersection point of Pjk (θ ) and Pj ,k +1 (θ ) . These lines intersect only once anywhere on the θ scale. Figure 7.10 is the graph of the partial credit model with a j = 1.0, b j1 = -2.0, b j 2 = 0.0, and b j 3 = 2.0. If b j1 and b j 2 are brought closer together by changing b j1 = -2.0 to -0.5, then the probability of
completing only the first step would decrease, as illustrated in Figure 7.11. If the slope parameter is changed from 1.0 to 0.7, as shown in Figure 7.12, the intersection points of all trace lines are left unchanged and the curves become flatter.
Figure 7.10: Partial credit model: a = 1.0, b = (-2.0,0.0,2.0)
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7 OVERVIEW OF MODELS
Figure 7.11: Partial credit model: a = 1.0, b = (-0.5,0.0,2.0)
Figure 7.12: Partial credit model: a = 0.7, b = (-0.5,0.0,2.0)
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7 OVERVIEW OF MODELS
Figure 7.13: Partial credit model: a = 0.7, b = (0.5,0.0)
When the second step is made easier than the first ( b j1 > b j 2 ), the trace lines of Pj 2 drop and every person becomes less likely to complete only the first step. This is illustrated in Figure 7.13. The trace line in Figure 7.13 is the partial credit model with three categorical responses. If we add another category ( b j 3 = 2.0) to this model, the trace lines become more complicated. However, the interpretation remains clear. The transition or step from Pj ,k −1 (θ ) to Pjk (θ ) is governed by the item step parameter, b jk . Since the magnitude of b jk s are ordered as b j 3 (= 2.0), b j1 (= 0.5) and b j 2 (= 0.0), the step from b j 2 (θ ) to b j 3 (θ ) is the hardest, next to the step from Pj 0 (θ ) to Pj1 (θ ) . The easiest step is the transition from Pj1 (θ ) to Pj 2 (θ ) . Consequently, the re-
spondent becomes more likely to complete the first category, but less likely to complete the second category. Therefore, as shown in Figure 7.14, the probability of the second categorical response, Pj 2 (θ ) , appears dominant. If all item step parameters are the same value, all trace lines intersect at the same value. Even though the values of item step parameters are not sequentially ordered, the partial credit model expresses the probabilities of ordered response. The subsequent steps can be completed only after the former ones are successfully completed. In other words, the locations of the trace lines can never be interchanged, only their intersection points.
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7 OVERVIEW OF MODELS
Figure 7.14: Partial credit model: a = 0.7, b = (0.5,0.0,2.0)
The Likert version of the partial credit model is the simple extension of the foregoing results, namely, k exp ∑ v =0 a j (θ − b j + cv ) , Pjk (θ ) = m c j ∑ c=0 exp ∑ v=0 a j (θ − b j + cv )
where θ − b j + c0 ≡ 0 and c0 ≡ 0 and the parameter b jk is resolved into two parameters b j and ck ( b jk = b j − ck ). Andrich (1978) first introduced this separation of the item location and the category boundary parameters. In the graded response model, the probability of responding in category k to a specific item is obtained by subtracting the person’s probability of responding in or below category k. Since the probability of the categorical response is determined by the distance between the boundaries of the category, the order of the boundaries is fixed by the order of the categories. In the partial credit model, the probability of responding in a category k to a specific item is expressed by the conditional probability of responding in category k, given the probability of responding in categories k – 1 and k. The models are constructed by recursively applying a dichotomous model to the probability of choosing category k over another adjacent category k – 1 for each pair of binary categories. Therefore, the probability of a specific categorical response is determined by the number of the upper boundaries the person has passed and the combination of their unique parameter values. The values of the item-category parameters, b jk , are not necessarily in successive order on a scale like those of the graded response model. Since the item-category parameters are not necessarily ordered within item j, the category parameters ( ck ) may not be sequentially ordered for k = 1, 2, …, m. The parameter ck is interpreted as the relative diffi556
7 OVERVIEW OF MODELS culty of step k in comparing other steps. 7.3.4 Scoring function of generalized partial credit model
In the normal metric, the sum of z jv (θ ) above can be written as k z +jk (θ ) = Da j k (θ − b j ) + ∑ cv . v =0
The model can then be rewritten as z +jk (θ ) = Da j Tk (θ − b j ) + K k
.
Andrich (1978) calls Tk and K k the scoring function and the category coefficient, respectively. For the partial credit model, the scoring function Tk is a linear integer scoring function, that is T = (1, 2,3,..., m j + 1) where m j + 1 is the number of categories of item j. The log-odds can be expressed by using the scoring function as λ j ,k |k −1 = Da j (Tk − Tk −1 )(θ − b j ) + ck .
This shows that the log-odds is a monotonically increasing function of the latent trait only when the increment scoring is used for successive categorical responses. The higher latent trait value a subject has, the more likely he or she responds in upper categories. In other words, the partial credit model becomes the model for ordered categorical responses only when the scoring function is increasing, that is, Tk > Tk −1 , for any k and a j > 0.
Figure 7.15: Partial credit model: T=(1,2,3,4), a = 1.0, b = 0.0, d = (0.0,2.0,-1.0,-2.0)
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7 OVERVIEW OF MODELS Figure 7.15 is the partial credit model with four categorical responses, where a j = 1, b j = 0, c = (0.0, 2.0, -1.0, -2.0), and T = (1, 2, 3, 4). The trace lines of these Pjk (θ ) s do not change if we use T = (0, 1, 2, 3) or even T = (-3, -2, -1, 0), because the increment rate of both scoring functions is identical, that is Tk − Tk −1 = 1. However, if we multiply the scoring by 2, that is, T ' = (2, 4, 6, 8),
the trace lines become steeper and their intersection points are –1.0, 0.5, and 1.0, respectively, because z +jk (θ ) = Da j Tk' (θ − b j ) + K k K = 2 Da j Tk (θ − b j ) + k . 2
This effect is similar to that of multiplying the slope parameter. The scoring function provides a convenient notation for collapsing or recoding categorical responses. For example, if the number of categorical responses of an item is five, then a scoring function T can be specified as T = (1, 2, 3, 4, 5). If the original response categories are collapsed by combining the first and second categories into one category, the scoring function T ' can be written as T ' = (1, 1, 2, 3, 4). If these modified response categories are recoded by treating the original fourth category as the fifth and the original fifth as the fourth, the scoring function can be further modified as T '' = (1, 1, 2, 4, 3). The generalized partial credit model can be expressed as a form of the nominal response model (Bock, 1972): Pjk (θ ) =
exp Da j Tk (θ − b j ) + K k
∑ c=0 exp Da j Tc (θ − b j ) + Kc mj
.
The partial credit model can be expressed as a form of the nominal response model (Bock, 1972): Pjk (θ ) =
exp Da*jkθ + c*jk
∑ c=0 exp Da*jcθ + c*jc mj
where a*jk = Da jTk and c*jk = − Da j (Tk b j − K k ) . The nominal response model is the model in which the scoring function is constant over response categories, that is T = Tk for any k, and the discrimination power varies for each categorical response. Or, it can be said that the nominal response model is the model whose scoring functions are unknown and treated as the parameters to be estimated. If a common slope parameter is used for all categorical responses, the trace lines become horizontally straight lines since they are 558
7 OVERVIEW OF MODELS independent of θ . Therefore, varied discriminating powers among categorical responses are essential features of the nominal response model. Since the estimates of the discriminating powers or slope parameters contain indeterminacy, the constraint, such that making the sum of those parameters zero, is commonly used (see Thissen & Steinberg, 1986). We have observed that the scoring function determines the orderliness of categorical responses. Thus, if we assign an identical scoring to two response categories, we can construct the partial credit model (PCM) with partially unordered categorical responses. This model can be called a partially unordered partial credit model (PUPCM). The basic difference between the PCM for the collapsed categories and the PUPCM is that the item-category parameters for each of the original categories are estimated for the PUPCM. If the scoring, Tk and Tk ' , are identical, the log-odds of these categorical responses are independent of the latent trait, θ . The log-odds become a function of the difference of the categorical coefficients, that is, λ j ,k |k ' = Da j [ K k − K k ' ] .
These odds are constant along the θ scale and the trace lines never intersect. Figure 7.16 is the partial credit model with four categorical responses. The parameter values are the same as the previous example, but the scoring, T = (1, 2, 2, 3) is used instead. In other words, we impose the assumption that the second and third categories do not have an inherent ordering. Since K 2 = 2.0 is larger than K3 = 1.0, Pj 2 (θ ) is always higher than Pj 3 (θ ) . The positions of these two trace lines are reversed if K 2 < K3 . Notice again that imposing the same scoring on the categories does not mean collapsing of those categories. We just eliminate the assumption about the ordering nature among those categories, in other words, nominalizing the categories.
Figure 7.16: Partial credit model: T=(1,2,2,3), a = 1.0, b = 0.0, d = (0.0,2.0,-1.0,-2.0)
559
7 OVERVIEW OF MODELS 7.3.5 Multiple-group polytomous item response models
Muraki (1993) proposed several variations of polytomous item response models for the multigroup settings: the Rater's-Effect (RE) model, the DIF model, and the Trend model. The DIF and Trend models for the dichotomous item response models were also discussed by Bock, Muraki & Pfeiffenberger (1988). The model for the differential item function (DIF) contains the following deviate Z gjk (θ ) : Z gjk (θ ) = Da j (θ − b j − d gj + c jk )
where d gj is a DIF (or item location deviate) parameter for group g and item j. In a similar manner, the deviate for the Rater's-Effect (RE) model is expressed as Z gjk (θ ) = Da j (θ − b j − d g + c jk )
where d g is a rater effect (or group) parameter for rater or rater group g. Notice that the group parameter d gj for the DIF model is nested within each item. For the DIF model, it is assumed that only the item location parameters differ among groups while the slope and category parameters are common for groups (this restriction can be relaxed in the program). The subgroup identification may be a gender, a year, or some other covariate. On the other hand, in the RE model, the group effect d g is crossed with the item effect. This model is generally referred to as a multifacet model (Linacre & Wright, 1993). The basic difference between the DIF model and the RE model is whether the group parameter is nested within or crossed with the item difficulty facet. For the DIF model, a separate prior distribution is used for each group member, and the prior distribution is updated after each estimation cycle based on the posterior distribution from the previous cycle. For the RE model, a single prior distribution is used for the responses rated by multiple groups of raters. The prior distribution may or may not be updated after each estimation cycle. 7.3.6 Constraints for group parameters
For the DIF model, it is assumed that different groups have different distributions with mean µ g and standard deviation σ g . The distributions are not necessarily normal. These empirical posterior distributions are estimated simultaneously with the estimation of the item parameters. To obtain these estimates, we impose the following constraint for the DIF model:
560
7 OVERVIEW OF MODELS J
J
j =1
j =1
∑ d Rj = ∑ d Fj . This constraint implies that the overall difficulty levels of a test or a set of common items given both the reference group and focal group (indicated by subscripts R and F, respectively) are the same. Therefore, the item difficulty parameters for the focal groups are adjusted. Any overall difference in terms of test difficulty will be assumed to be the difference in ability level for subgroups. The ability level difference among groups can then be estimated by the posterior distributions. The constraint imposed for the group parameters for the RE model is G
∑ d g = 0. g =1
Group weight coefficient
The weight coefficient wg for group g is used only for the RE model. For the case of multiple raters, it may be reasonable to assume that all raters are not equally reliable. If the reliability index for each rater is computed by some method, the index can be used for the coefficients. 7.3.7 Test of goodness-of-fit
The goodness-of-fit of the polytomous item response model can be tested item by item. Summation of the item fit can also be used for the goodness-of-fit for the test as a whole. If a test is sufficiently long, the method used in BILOG-MG (Mislevy and Bock, 1990) can be used with slight modifications. In the method of Mislevy & Bock, the respondents in a sample of size N are assigned to H intervals of the θ -continuum. The expected a posteriori (EAP) is used for the estimator of each respondent’s proficiency score. The EAP estimate is the mean of the posterior distribution of θ , given the observed response pattern xl (Bock & Mislevy, 1982). The EAP score of the response pattern xl is approximated by the quadrature points, X f , and the weights, A( X f ) , that is,
∑ f =1 X f Ll ( X f ) A( X f ) θl = F ∑ f =1 Ll ( X f ) A( X f ) F
where Ll ( X f ) is the probability of observing a particular response pattern xl . The posterior standard deviation (PSD) of the EAP score is approximated by
561
7 OVERVIEW OF MODELS
∑ f =1 ( X f − θl )2 Ll ( X f ) A( X f ) . F ∑ f =1 Ll ( X f ) A( X f ) F
PSD(θ l ) =
After all respondents' EAP scores are assigned to any one of the predetermined H intervals on the θ -continuum, the observed frequency of the k-th categorical responses to item j in interval h, rhjk , and the number of respondents assigned to item j in the h-th interval, N hj , are computed. The estimated θ s are rescaled so that the variance of the sample distribution equals that of the latent distribution on which the MML estimation of the item parameters is based, which is usually set as N (0,1) as a default. Thus, we obtain the H by m j + 1 contingency table for each item j. For each interval, we compute the interval mean, θ h , and the value of the fitted response function, Pjk (θ h ) . Finally, a likelihood ratio χ 2 statistic for each item is computed by G 2j
=2
H j mj
rhjk
h =1 k =0
N hj Pjk (θ h )
∑∑ rhjk ln
−
,
where H j is the number of intervals left after neighboring intervals are merged if necessary to avoid expected values, N hj Pjk (θ h ) , less than 5. The degrees of freedom is equal to the number of intervals, H j , multiplied by m j . The likelihood ratio χ 2 test statistic for the test as a whole is simply the summation of the separate χ 2 test statistics. The number of degrees of freedom is also the summation of the degrees of freedom for each item. These fit statistics are useful in evaluating the fits of the models to the same response data when the models are nested in their parameters. 7.3.8 Initial parameter estimates Point biserial and biserial correlation coefficients
The point biserial correlation, rPB , j for item j is a computationally simplified Pearson’s r between the dichotomously scored item j and the total score x. It is computed as rPB , j =
(µ j − µ x )
pj
σx
qj
,
where µ j is the mean total score among examinees who have responded correctly to item j, µ x is the mean total score for all examinees, p j is the item difficulty index for item j, q j = (1 - p j ), and σ x is the standard deviation of the total score for all examinees.
562
7 OVERVIEW OF MODELS
The biserial correlation coefficient estimates the relationship between the total score and the hypothetical normally distributed score on a continuous scale underlying the dichotomous item. The biserial correlation between an item and the total score can be estimated from the p-value and the point biserial correlation of the item: rB , j = rPB , j
p jq j h( z j )
,
where z j is the z score that cuts off p j proportion of the cases to item j in the standard normal distribution, and h( z j ) is the ordinate of the normal distribution at the point z j . Lord & Novick (1968) show that the slope and threshold parameters of the normal ogive model for the item are functions of the biserial correlation coefficient: aj =
rB , j 1 − rB2, j
and bj = −
zj rB , j
=
ajzj 1 + a 2j
.
Polyserial correlation coefficient
The point polyserial correlation, rPP , is simply the Pearson correlation between equally spaced integers assigned to the successive categories (e.g., Likert scores). The relation between the point polyserial correlation, and the polyserial correlation, rP , is rPP , j = rP , j
1
σj
m j −1
∑ h( z jk )(T j,k +1 − T jk ), k =0
where T jk is the scoring function for item j and category k, σ j is the standard deviation of item scores y for item j, z jk is the z score corresponding to the cumulative proportion, p jk , of the k-th response category to item j. If consecutive integers are used for scoring (that is T jk = 0,1,… , m j ), then the relation expressed by this equation becomes
563
7 OVERVIEW OF MODELS m j −1
rP , j rPP , j =
∑ h( z jk ) k =0
σj
or rP , j =
rPP , jσ j m j −1
.
∑ h( z jk ) k =0
The polyserial correlation becomes the biserial correlation if the number of response categories is two. Olsson, Drasgow, & Dorans (1982) presented three estimation methods for the polyserial correlation coefficient - the maximum likelihood estimator, the two-step estimator and the ad hoc estimator. The latter is obtained by substituting sample statistics into the preceding equation. The sample product-moment correlation of the total score and polytomous item score is the pointpolyserial correlation, rPP , j . h( z jk ) is the normal ordinate corresponding to the proportion p jk of examinees with item scores less than or equal to T jk . Initial slope parameter
From results of a simulation study, Olsson et al. (1982) concluded that the ad hoc estimator was sufficiently unbiased and accurate for applied research. Thus, we compute initial slope values by using the ad hoc estimator aj =
rp , j 1 − rp2, j
This value applies to both the graded model and the generalized partial credit model. To obtain the m j − 1 initial category threshold parameters of the graded model, we compute the item category cumulative proportions from the numbers of examinees m jk responding in the successive categories of item j.
∑ v=0 n jv . p jk = m ∑ v=0 n jv k
j
564
7 OVERVIEW OF MODELS The corresponding deviates are obtained from the inverse normal distribution function: z jk = Φ −1 ( p jk ) .
The threshold values are b jk =
− z jk rp , j
=
−a j z jk 1 + a 2j
.
For the partial credit model, the corresponding parameters are computed from the proportions of examinees in the higher of the two successive categories, p jk =
n jk n j ,k −1 + n jk
.
The quantities z jk and b jk are computed as above, but the latter must be adjusted to reflect the average ability of examinees who respond in these two categories relative to all examinees in the sample. The adjusted value is b'jk = b jk +
m jk − m jT s jT
,
where m jk is the mean test score (computed from items scores 0, 1, …, m-1) of examinees responding in categories k or k – 1, and m jT and s jT are the mean and standard deviation of the test scores of all examinees responding to item j. These initial values are printed for each item in the Phase 1 program output (see Chapter 11 for an example). Note, however, that in the results the category parameters of both models are expressed as deviations about the mean value and therefore sum to zero. The mean value itself is referred to as the item “location” and appears along with the item slope in the listing of items within blocks. For two-category (binary) items, the category parameters are equal in absolute value by opposite in sign, and the location parameter is just the threshold parameter of the normal ogive or 2PL model. Initial category parameters of the rating-scale model
Multiple items that appear within the same block (and thus have the same number of categories) comprise a rating-scale. Each item has a slope and location parameter, the initial values of which are computed as above. But all items have the same category parameters, which are therefore a property of the block. The corresponding initial values are computed, first, by the category response frequencies over the m items in the block to obtain
565
7 OVERVIEW OF MODELS n
pk =
k
∑∑ n j =1 v = 0 m m
∑∑ n j =1 v = 0
jv
jv
or n
pk =
∑n j =1
m
∑ (n j =1
j , k −1
jk
. + n jk )
Then zk = Φ −1 ( pk ),
and for the graded rating-scale model, bk =
− a j zk 2 1 + a j
,
where a j is the geometric mean of the slopes of the items within the bock (i.e., the n-th root of the product of the n slopes). The use of the geometric mean is justified by the assumption that the slopes of the items in the rating-scale domain are log-normally distributed. For the partial credit rating-scale model, the quantities used in computing the category adjustment formula shown above are accumulated over all items in the block; that is, bk' = bk +
mk − mT , sT
where mk = ∑ j =1 m jk is the mean test score of all examinees responding in category k or k – 1 m
for all items, and mT and sT are the mean and standard deviation of the test scores of all examinees responding to all items in the block.
566
7 OVERVIEW OF MODELS
7.4 Models in MULTILOG5 7.4.1 Introduction
In this section we describe the item response (trace line) models available in MULTILOG. There are two general models that can be fitted using MULTILOG: Samejima’s (1969) “graded” model and Thissen and Steinberg’s (1984) multiple response model. Many other (seemingly) different models are available, as constrained subsets of one or the other of these two. For an extended discussion of the relationship among IRT models, see Thissen & Steinberg (1986). 7.4.2 The graded model
Samejima’s (1969) graded model, for ordered responses x = k, k = 1, 2, …, m; where response m reflects the highest θ value is defined as follows: P( x = k ) =
1 1 − 1 + exp[− a(θ − bk −1 )] 1 + exp[− a (θ − bk )] , = P* (k ) − P* (k + 1)
where a is the slope and bk is threshold(k). P* (k ) is the trace line describing the probability that a response is in category k or higher, for each value of θ . For completeness of the model definition, we note that P* (1) = 1 and P* (m + 1) = 0. The value of bk −1 is the point on the θ -axis at which the probability passed 50% that the response is in category k or higher. The properties of the model are extensively described by Samejima (1969). In the MULTILOG output, the parameter a is labelled A, and bk is labelled B(k). This model is obtained by using the GR option on the TEST command in MULTILOG. 7.4.3 The one- and two-parameter logistic models
When there are only two possible responses to each item, as for binary items on a test of proficiency (correct/incorrect) or forced-choice items on a personality measure, the graded model is equivalent to the 2PL model, which is usually written in the following simpler form: P ( x = 2) =
1 . 1 + exp[− a (θ − b)]
For compatibility with the graded model, the key is used to recode the “higher” of the two responses (correct, positive) to have the internal value “2” in MULTILOG. The lower (incorrect, negative) response has the value “1.” The 2PL model has two parameters for each item, a (labelled A in the output), and b (labelled B(1)).
5
This section was contributed by David Thissen.
567
7 OVERVIEW OF MODELS If the additional constraint that a j = a for all items j is imposed, the 2PL model is equivalent to the 1PL model, sometimes referred to as the “Rasch model” (after Rasch, 1960). At times the term “Rasch model” refers simply to the constraint that the slopes are equal for all items in the test, in which case this is the “Rasch model,” using the marginal maximum likelihood estimation algorithm described by Thissen (1982); at other times, the term “Rasch model” refers to both the constraint and another method of parameter estimation (Conditional Maximum Likelihood), which is not implemented in MULTILOG. The output is identical to that for the 2PL model, except that the value of a (labelled A) is the same for all items. Note that we do not include the scale factor 1.7 in the definition of either the 2PL or the 1PL models. This model is obtained by using the L2 option on the TEST command in MULTILOG. 7.4.4 The multiple response model
A modified version of Samejima’s (1979) modification of Bock’s (1972) nominal model, for responses x = 1, 2, ..., m (or m +1), is Thissen and Steinberg’s (1984) multiple response model: P( x = k ) =
h*exp[akθ + ck ] + hd k exp[a1θ + c1 ] m +1
∑ exp[a θ + c ] i
i
i =1
in which two classes of constraints are required:
The parameters ak and ck are not identified with respect to location; either TRIANGLE contrasts define the parameters that are estimated, in which case a1 = c1 = 0 , or DEVIATION or POLYNOMIAL contrasts among these parameters are estimated, in which case ∑ ak = ∑ ck = 0 , or
The parameters represented by d k are proportions, representing the proportion of those who “don’t know” who respond in each category on a multiple-choice item (see Thissen & Steinberg, 1984). Therefore, the constraint that ∑ d k = 1 (where the sum is from k = 1 to 2 for binary data and from 2 to m+1 for m > 2) is required. This is enforced by estimating d k such that dk =
exp d k*
∑ exp d
* k
and contrasts among the d k are the parameters estimated. The parameters h* and h are used to provide several different models, and are calculated by MULTILOG. The value of h* is always 1 for items with m > 2.
568
7 OVERVIEW OF MODELS 7.4.5 The multiple-choice model
When m > 2 and h = 1, the Thissen and Steinberg model described in Section 7.4.4 becomes P( x = k ) =
exp[akθ + ck ] + d k exp[a1θ + c1 ] m +1
∑ exp[a θ + c ] i
i
i =1
which is the “Multiple-choice” model, as described by Thissen and Steinberg (1984). The data should be keyed into categories 2, 3, … m + 1, because category 1 in the program is the “0” or “Don’t Know” latent category (see Section 12.10). MULTILOG prints the values of the parameters ak , ck , and d k , labelled A(K), C(K), and D(K), respectively. The values of the d k contrasts may be fixed at zero using the MULTILOG command language. This produces Samejima’s (1979) version of the model, in which the “guessing proportions” are equal to 1/ m :
P( x = k ) =
exp[akθ + ck ] + m +1
1 exp[a1θ + c1 ] m .
∑ exp[a θ + c ] i
i
i =1
7.4.6 The three-parameter logistic model
For m = 2, h* = 0 for category 1 (incorrect) and h* = 1 for category 2 (correct) this gives a parameterization of the conventional 3PL model (Lord, 1980; see Thissen & Steinberg, 1986, for a description of this conception of the 3PL), in which P ( x = 2) =
exp[a2θ + c2 ] + d 2 exp[a1θ + c1 ] 2
∑ exp[a θ + c ] i
i
i =1
=
exp[a2θ + c2 ] + d 2 exp[a1θ + c1 ] exp[a1θ + c1 ] + exp[a2θ + c2 ]
=
d 2 exp[a1θ + c1 ] exp[a2θ + c2 ] . + exp[a1θ + c1 ] + exp[ a2θ + c2 ] exp[ a1θ + c1 ] + exp[a2θ + c2 ]
The constraints described above require that a1 = −a2 and c1 = −c2 . Thus, the model is: P ( x = 2) =
d 2 exp[−(a2θ + c2 )] exp[a2θ + c2 ] + exp[−(a2θ + c2 )] + exp[a2θ + c2 ] exp[−( a2θ + c2 )] + exp[a2θ + c2 ]
1 1 = d 2 1 + θ exp a c exp a2θ + c2 )] 1 [ 2( )] 1 [ 2( + − + + − 2 2 1 = d 2 + (1 − d 2 ) 1 + exp[−2(a2θ + c2 )]
569
7 OVERVIEW OF MODELS which is a fairly conventional form of the 3PL model. MULTILOG actually estimates the logit of d 2 , and the contrasts between a1 and a2 , and c1 and c2 . These are printed in the output, as well as the “traditional 3PL, normal metric” form of the parameters, labelled A, B, and C from the model when written 1 P ( x = 2) = C + (1 − C) . 1 + exp[−1.7 A(θ − B )]
This model is obtained by using the L3 option on the TEST command in MULTILOG. 7.4.7 The nominal model
When h = 0, the model defined in Section 2.4.3 is equivalent to Bock’s (1972) “nominal model”: P( x = k ) =
exp[akθ + ck ] m
∑ exp[a θ + c ] i
;
i
i =1
in this case, the data are keyed into categories 1, 2, …, m, and the parameters represented by d k are not estimated. MULTILOG prints the values of the parameters ak and ck , labelled A(K) and C(K) respectively. This model is obtained by using the NO option on the TEST command in MULTILOG. 7.4.8 Contrasts
MULTILOG estimates the contrasts between the as, cs, and d *s ; the unconstrained (estimated) parameters are the α s, γ s and δ s [denoted AK, CK, and DK, respectively, in the syntax, and CONTRAST(k) FOR A, CONTRAST(k) FOR C, and CONTRAST(k) FOR D in the MULTILOG output], where a' = α ' Ta ,
c' = γ ' Tc , and d*' = δ ' Td .
The default form of the T matrices consists of deviation contrasts, as suggested by Bock (1972). For varying numbers of response categories, those matrices are printed here, along with the alternative polynomial and triangle contrasts. DEVIATION T-matrices
2 Categories –0.50 0.50
570
7 OVERVIEW OF MODELS 3 Categories –0.33 0.67 –0.33 –0.33 –0.33 0.67 4 Categories –0.25 0.75 –0.25 –0.25 –0.25 –0.25 0.75 –0.25 –0.25 –0.25 –0.25 0.75 5 Categories –0.20 –0.20 –0.20 –0.20
0.80 –0.20 –0.20 –0.20
–0.20 0.80 –0.20 –0.20
–0.20 –0.20 –0.20 –0.20 0.80 –0.20 –0.20 0.80
0.83 –0.17 –0.17 –0.17 –0.17
–0.17 0.83 –0.17 –0.17 –0.17
–0.17 –0.17 0.83 –0.17 –0.17
–0.17 –0.17 –0.17 0.83 –0.17
–0.17 –0.17 –0.17 –0.17 0.83
0.86 –0.14 –0.14 –0.14 –0.14 –0.14
–0.14 0.86 –0.14 –0.14 –0.14 –0.14
–0.14 –0.14 0.86 –0.14 –0.14 –0.14
–0.14 –0.14 –0.14 0.86 –0.14 –0.14
–0.14 –0.14 –0.14 –0.14 0.86 –0.14
–0.14 –0.14 –0.14 –0.14 –0.14 0.86
0.88 –0.13 –0.13 –0.13 –0.13 –0.13 –0.13
–0.13 0.88 –0.13 –0.13 –0.13 –0.13 –0.13
–0.13 –0.13 0.88 –0.13 –0.13 –0.13 –0.13
–0.13 –0.13 –0.13 0.88 –0.13 –0.13 –0.13
–0.13 –0.13 –0.13 –0.13 0.88 –0.13 –0.13
–0.13 –0.13 –0.13 –0.13 –0.13 0.88 –0.13
6 Categories –0.17 –0.17 –0.17 –0.17 –0.17 7 Categories –0.14 –0.14 –0.14 –0.14 –0.14 –0.14 8 Categories –0.13 –0.13 –0.13 –0.13 –0.13 –0.13 –0.13
–0.13 –0.13 –0.13 –0.13 –0.13 –0.13 0.88
571
7 OVERVIEW OF MODELS 9 Categories –0.11 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11
0.89 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11
–0.11 0.89 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11
–0.11 –0.11 0.89 –0.11 –0.11 –0.11 –0.11 –0.11
–0.11 –0.11 –0.11 0.89 –0.11 –0.11 –0.11 –0.11
–0.11 –0.11 –0.11 –0.11 0.89 –0.11 –0.11 –0.11
–0.11 –0.11 –0.11 –0.11 –0.11 0.89 –0.11 –0.11
–0.11 –0.11 –0.11 –0.11 –0.11 –0.11 0.89 –0.11
–0.11 –0.11 –0.11 –0.11 –0.11 –0.11 –0.11 0.89
POLYNOMIAL T-matrices
2 Categories –0.50 0.50 3 Categories –1.00 0.00 1.00 0.58 –1.15 0.58 4 Categories –1.50 –0.50 0.50 1.50 1.12 –1.12 –1.12 1.12 –0.50 1.50 –1.50 0.50 5 Categories –2.00 1.69 –1.00 0.38
–1.00 –0.85 2.00 –1.51
0.00 –1.69 0.00 2.27
1.00 –0.85 –2.00 –1.51
2.00 1.69 1.00 0.38
–1.50 –0.46 2.18 –2.37 1.32
–0.50 –1.83 1.25 1.58 –2.64
0.50 –1.83 –1.25 1.58 2.64
1.50 –0.46 –2.18 –2.37 –1.32
6 Categories –2.50 2.28 –1.56 0.79 –0.26
2.50 2.28 1.56 0.79 0.26
572
7 OVERVIEW OF MODELS 7 Categories –3.00 2.89 –2.16 1.28 –0.58 0.17
–2.00 0.00 2.16 –2.98 2.31 –1.04
–1.00 –1.73 2.16 0.43 –2.89 2.61
0.00 –2.31 0.00 2.56 0.00 –3.48
1.00 –1.73 –2.16 0.43 2.89 2.61
2.00 0.00 –2.16 –2.98 –2.31 –1.04
3.00 2.89 2.16 1.28 0.58 0.17
–2.50 0.50 1.99 –3.39 3.19 –1.99 0.77
–1.50 –1.50 2.79 –0.78 –2.36 3.59 –2.32
–0.50 –2.50 1.20 2.35 –2.08 –1.99 3.87
0.50 –2.50 –1.20 2.35 2.08 –1.99 –3.87
1.50 –1.50 –2.79 –0.78 2.36 3.59 2.32
2.50 0.50 –1.99 –3.39 –3.19 –1.99 –0.77
3.50 3.50 2.79 1.83 0.97 0.40 0.11
–3.00 1.03 1.72 –3.64 3.94 –2.96 1.59 –0.55
–2.00 –1.18 3.20 –1.90 –1.43 3.83 –3.70 1.91
–1.00 –2.50 2.22 1.56 –3.22 0.17 3.70 –3.82
0.00 –2.94 0.00 3.12 0.00 –3.48 –0.00 4.78
1.00 –2.50 –2.22 1.56 3.22 0.17 –3.70 –3.82
2.00 –1.18 –3.20 –1.90 1.43 3.83 3.70 1.91
3.00 1.03 –1.72 –3.64 –3.94 –2.96 –1.59 –0.55
(When used for the vector c, the constraint
∑c = 0
8 Categories –3.50 3.50 –2.79 1.83 –0.97 0.40 –0.11 9 Categories –4.00 4.12 –3.45 2.42 –1.43 0.70 –0.26 0.07
4.00 4.12 3.45 2.42 1.43 0.70 0.26 0.07
TRIANGLE T-matrices
is replaced with the constraint c1 = 0 ).
2 Categories 0.00
–1.00
3 Categories 0.00 0.00
–1.00 –1.00 0.00 –1.00
573
7 OVERVIEW OF MODELS 4 Categories 0.00 0.00 0.00
–1.00 –1.00 –1.00 0.00 –1.00 –1.00 0.00 0.00 –1.00
5 Categories 0.00 0.00 0.00 0.00
–1.00 0.00 0.00 0.00
–1.00 –1.00 0.00 0.00
–1.00 –1.00 –1.00 0.00
–1.00 –1.00 –1.00 –1.00
–1.00 0.00 0.00 0.00 0.00
–1.00 –1.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00
–1.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00
–1.00 0.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00 0.00
6 Categories 0.00 0.00 0.00 0.00 0.00 7 Categories 0.00 0.00 0.00 0.00 0.00 0.00 8 Categories 0.00 0.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00 –1.00
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7 OVERVIEW OF MODELS 9 Categories 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
–1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 0.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 0.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 0.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 0.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00 0.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00 –1.00 0.00
–1.00 –1.00 –1.00 –1.00 –1.00 –1.00 –1.00 –1.00
7.4.9 Equality constraints and fixed parameters
MULTILOG permits any subset of the parameters to be constrained to be equal, or to retain fixed values. The approach is very similar to that used in LISREL (Jöreskog & Sörbom, 1996). Using these facilities, a wide variety of item response models may be specified. Some uses of equality constraints are described in the examples given in Chapter 12.
7.5 Options and statistics in TESTFACT 7.5.1 Introduction
The TESTFACT program implements all the main procedures of classical item analysis, test scoring, and factor analysis of inter-item tetrachoric correlations, and also modern methods of item factor analysis based on item response theory (IRT). In addition, the program includes a facility for simulating responses to test items having difficulties and factor loadings specified by the user. This section reviews the mathematical and statistical backgrounds of these procedures. 7.5.2 Classical item analysis and test scoring6
Classical item analysis aims at inferring the expected characteristics of responses of persons in the population to whom the test will be administered. The data to be analyzed are assumed to come from a sample of respondents from that population. The various sample indices—item difficulties based on item p-values, test reliability expressed as a ratio of true-score variances to test-score variances, and test validity measured by the correlation of the test score with an external criterion of performance—are all estimates of population statistics. Classical test theory employs such estimates to predict how the test will perform when administered to members of the population in question. Classical methods also give useful details about the discriminating power of the items. They provide various measures of the item-by-test score correlation, supplemented by tabulations of the
6
This section was contributed by Robert Wood.
575
7 OVERVIEW OF MODELS frequencies of responses to multiple-choice item alternatives at selected fractiles of the test-score distribution. The latter show how the distractors function in discriminating among respondents of differing levels of ability. This information, together with plots of item discriminating power vs. item difficulty, guides the test constructor in choosing items that will be informative throughout the population. The TESTFACT program computes these statistics from the sample of item-response data and display them in tables and plots for ready interpretation. It also allows the user to specify subtests of the main test and to analyze each subtest separately. Similarly, the user can assign respondents to groups (by age, grade, or class, for example) and can analyze the groups independently. In addition, TESTFACT provides a powerful data analytic tool in the form of item factor analysis. As a preliminary to test construction, or in preparation for latent trait analysis with programs such as BILOG (Mislevy & Bock, 1990), BILOG-MG (Zimowski, Muraki, Mislevy & Bock, 1996), or MULTILOG (Thissen, 1988), item factor analysis permits a more comprehensive and detailed examination of item dimensionality than is currently available with any other procedure. Because they are based on Thurstone’s multiple factor model, the results of the analysis—factor loadings, orthogonal and oblique rotations, factor correlations, and factor scores—are familiar to most users. Since the model is fitted by Bock & Aitkin’s (1981) marginal maximum likelihood (MML) method, the analysis provides a rigorous test of the statistical significance of factors added successively to the model. Item factor analysis has other interesting uses beside those of test construction and exploration of test dimensionality. The existence of more than one statistically significant factor implies a similar number of distinguishable profiles of individual differences in the population of respondents. By calling attention to the common features of items that participate in these distinctions, item factor analysis gives clues to the cognitive basis for the item responses. It serves as a “discovery procedure” revealing often-unsuspected cognitive components of the test task. If the sample size is large, factor analysis of item responses is often more productive than factor analysis of test scores, because data for many distinct items is easier to obtain than data for a comparable number of tests. The factor analysis procedure in TESTFACT makes exploration of any type of binary scored characteristics dependable and informative. The potential distortions of chance successes, notreached items, and Heywood cases are effectively controlled. Principal factor, VARIMAX and PROMAX patterns are provided; Bayes estimates of scores for orthogonal factors can be computed for each respondent. The main features and statistical principles of the TESTFACT program are described in the remainder of this section. 7.5.3 Classical descriptive statistics
Each item has a set of responses: right, wrong, omitted, or not-presented. For item j, the response of person i can be written as 576
7 OVERVIEW OF MODELS
xij = 1
if response is correct
xij = 0
if response is incorrect.
At the user’s option, omitted items can be considered either wrong or not-presented. For a test of n items, the total main test score X i for person i would be n
X i = ∑ xij . j =1
If the main test is divided into K subtests of nk items each, the subtest scores are nk
X ik = ∑ xijk
k = 1, , K .
j =1
Given scores X i or X ik , the program provides estimates of means, standard deviations, and correlations - whether the group of respondents is taken as a whole or split into classes. In addition, histograms of main test and subtest scores are supplied to enable the user to check the nature of the dispersion of each score. Product-moment correlations between the main and subtest scores and external variates (where applicable) are also provided. 7.5.4 Item statistics
The most important item parameters for test construction are those that measure item difficulty and discriminating power. Difficulty
The number of respondents who answer item j correctly is called the item facility and is expressed as p j (a proportion of the total number attempting the item). For a standard measure of item difficulty, the delta statistic ( ∆ ) is available. Delta is a non-linear transformation of the proportion correct arranged to have a mean of 13 and a standard deviation of 4. Its effective range is 1 to 25. The formula is
∆ = −4Φ −1 ( p) + 13, where p is the proportion correct (or item facility) and Φ −1 is the inverse normal transformation. (For details, see Henryssen, 1971, p. 139-140).
577
7 OVERVIEW OF MODELS A transformation based upon proportions or percentages that fall on a non-linear scale can cause misleading judgments about relative differences in difficulty. The difference in difficulty between items with facilities of .40 and .50 would be quite small, but the difference between items with facilities of .10 and .20 would be quite large. The delta scale, on the other hand, is assumed to be linear. The difference in difficulty between items with deltas of 13 and 14 is assumed to be the same as the difference in difficulty between items with deltas of 17 and 18. Figure 7.17 shows a delta of 13 (i.e., Φ −1 = 0) corresponds to a facility of 0.50.
Figure 7.17: Relationship between delta and facility Discriminating power
According to Marshall & Hales (1972), more than 60 different indices for measuring item discriminating power have been proposed. TESTFACT provides two classical indices, the point biserial and the biserial correlations. Both call for calculation of correlation between the score (1 or 0) on the item and the score on the test as a whole. The higher the correlation between these two scores, the more effective the item is in separating the test scores of the respondents. Naturally, this relationship is relative: a given item could have a higher item-test correlation when included in one test than when included in a different test. The point biserial correlation, rpbis , is a product-moment correlation between two variates, when one of the variates is binary (the item score) and the other is the complete test or subtest, con-
578
7 OVERVIEW OF MODELS tinuously distributed. The formula for the sample point biserial correlation can be written as
rpbis =
Mp −M S
×
p , 1− p
where: Mp
is the mean score on the test for those subjects who get the item correct
M
is the mean score on the test for the entire group
S
is the standard deviation of test scores for the entire group
p
is the proportion that gets the item right (the item facility)
Evidently, rpbis serves as a measure of separation through the action of the term, Mp −M S
.
Note that rpbis is also a function of item facility. In principle, values of the point biserial lie between -1 and +1. But, as Wilmut (1975, p. 30) has demonstrated, in item analysis it is unlikely ever to exceed 0.75 or to fall below -0.10. This should be kept in mind when interpreting output. Of the many classical discrimination indices, the only serious rival to the point biserial is the biserial correlation. Unlike the point biserial, the biserial is not a product-moment correlation; rather, it should be thought of as a measure of association between performance on the item and performance on the test (or some other criterion). The biserial is less influenced by item difficulty and tends to be invariant from one testing situation to another—advantages the point biserial does not possess (see below). Also distinguishing it from its rival is the biserial correlation’s assumption that a normally distributed latent variable underlies the right/wrong dichotomy imposed in scoring an item. This variable may be thought of as representing the trait that determines success or failure on the item. The formula for calculating the sample biserial correlation coefficient, rbis , is rbis =
Mp −M S
×
p . h( p )
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7 OVERVIEW OF MODELS Except for h( p ) , the terms are as before; h( p ) stands for the ordinate or elevation of the normal curve at the point where it cuts off a proportion p of the area under the curve. As might be expected, h( p ) enters into the formula because of the assumption of a normally distributed underlying variable. The relationship between the biserial and point biserial formulas is straightforward: rpbis = rbis ×
h( p ) . p (1 − p )
The point biserial is equal to the biserial multiplied by a factor that depends only on an item difficulty, so the point biserial will always be less than the biserial. In theory, the biserial can take any value between -1 and +1, but values greater than 0.75 are rare, although the biserial can even exceed 1 in exceptional circumstances - usually resulting from some peculiarity in the test score or criterion distribution (Glass & Stanley, 1970, p.171). In practice, negative values usually indicate that the wrong answer has been keyed. Lord & Novick (1968, p. 340) show that the point biserial can never attain a value as high as 0.80 of the biserial, and they present a table showing how the fraction varies according to item difficulty (see also Bowers, 1972). They remark that the extent of biserial invariance is necessarily a matter for empirical investigation, but present some results in support of the conclusion that “biserial correlations tend to be more stable from group to group than point biserials”. Bowers (1972) observes that as long as a markedly non-normal distribution of the criterion variable is not anticipated, substantially the same items are selected or rejected no matter which statistic is used to evaluate discrimination. It is true that the point biserial is more dependent on the level of item difficulty, but this is not serious, as it only leads to rejection of very easy or very difficult items, which would be rejected anyway. Users who have not made up their minds on this issue are advised to fasten on to one or another statistic, learn about its behavior, and stay with it. Switching from one to the other or trying to interpret both simultaneously is likely to be confusing. Note, however, that in the factor analysis procedure, factor loadings of the items serve as discrimination indices. 7.5.5 Fractile tables
Although point biserial and biserial correlations are useful guides to the discriminating power of an item, they cannot describe how respondents of differing levels of achievement or ability respond to specific items. By defining fractiles of the distribution of test scores, and classifying item responses according to membership in these fractiles, the user can observe the behavior of items across the ability range and, in particular, keep an open eye for malfunctioning distractors. Items may:
Fail to differentiate between respondents in the lower, and sometimes in the middle, fractile bands; 580
7 OVERVIEW OF MODELS
Function well over lower fractiles, but give little or no information about respondents in the higher fractiles; Discriminate in a way that fluctuates wildly over fractiles.
By way of illustration, consider Table 7.1. The item that produced the data belonged to a 50-item external examination in chemistry taken by 319 candidates. In many cases, of course, the sample of candidates would be much larger than this. The correct (starred) answer was option A, chosen by 146 candidates or, as the number underneath indicates, by 0.46 of the sample. The facility of this item is therefore 0.46 and the difficulty ( ∆ ) is 13.42. Of the distractors, E was most popular (endorsed by 82 candidates, or 0.26 of the sample), followed by options C, D, and B. Only two candidates omitted the item. Table 7.1: Item responses classified by fractiles SCORE
Response Frequencies & Proportions for Options
Total
A*
B
C
D
E
O
146*
13
54
22
82
2
319
0.46
0.04
0.17
0.07
0.26
0.01
1.00
0-18
9
6
18
7
21
2
63
18-22
16
5
16
8
19
0
64
22-29
30
1
7
7
19
0
64
29-35
42
1
8
0
13
0
64
35-47
49
0
5
0
10
0
64
Mean criterion
30.8
18.8
22.0
19.2
23.5
12.5
26.02
Turning to the body of the table, we see an evident pattern. Under the correct answer A, the count increases as these score level rises. Under the distractors (excepting D, where the trend is unclear), the gradient runs in the opposite direction. This is what we should see if the item is discriminating in terms of total test score. The pattern we should not see is one in which the counts under A are relatively equal, or worse, that all the counts in the table tend to equality. As it is, the distribution of the responses tells us quite a lot. Relatively speaking, options B and C are much more popular in the lowest score fractile, and in that fractile the correct answer was 581
7 OVERVIEW OF MODELS barely more popular than B or D. In the higher score fractiles, however, B and D are almost totally rejected. In all, Table 7.1 supports the view that wrong answers are seldom, if ever, equally distributed across the distractors, either in the sample as a whole or in fractiles. Nor is there any evidence of blind guessing, an indication of which would be an inflated number in the cell for option A in the 0-18 score group - the one containing a 9 - which could cause the gradient to flatten out at low score levels, or even to go in the other direction. In Table 7.1, the five fractiles (any number can be defined, but five is enough for a first look at an item) have been constructed so as to contain equal or nearly equal numbers of candidates. This means that, unless the distribution of scores is rectangular, the score intervals will always be unequal. However, there is no reason why fractiles cannot be defined in terms of equal score intervals or according to some assumption about the underlying score distribution. If, for example, the user believes that the underlying score distribution is normal, the fractiles might be constructed so as to have greater numbers in the middle fractiles and smaller numbers in the outer fractiles. The only problem with this strategy is that, given small numbers of respondents, any untoward behavior in the tails of the distribution would be amplified or distorted. Also, interpretation of the table might be prone to error because of the varying number of fractiles. 7.5.6 Plots
TESTFACT provides line plots of item difficulties (or facilities) vs. the point biserial (or biserial) correlations. It is often the case that high item difficulty corresponds to low biserial values, and vice versa. When evaluating item statistics, plotting item difficulty (or facility) against point biserial (or biserial) correlations should enable the user to see which items need attention (but see Section 7.5.5, above). The user can specify either measure of difficulty and either measure of discrimination in the PLOT command. 7.5.7 Correction for guessing
Following the argument of Wood (1977), the initial part of TESTFACT does not correct for guessing in the case of omitted items. In the factor analysis part of the program, the user may elect to proceed under the 3-parameter multidimensional normal ogive model, which will provide for the effects of guessing. TESTFACT does not estimate guessing parameters, but does allow the user to specify these values, either a priori or as estimated by a program such as BILOG (see Section 7.5.11, below). 7.5.8 Internal consistency
TESTFACT also provides measures of internal test consistency. It is important to understand that internal consistency is not the same as homogeneity: a test may be internally consistent—an empirical, statistical fact—even though it includes items that are patently dissimilar in content (see Green, Lissitz & Mulik, 1977). A measure of the internal consistency is the intra-class correlation coefficient of the test or subtest. The correlation is commonly known as coefficient α . 582
7 OVERVIEW OF MODELS For an n-item test,
σ2 α= 2 , σ + σ ε2 / n where σ 2 is the variance component due to respondents, and σ ε2 is the residual or error variance. Unlike many other programs, the calculation of α in TESTFACT allows for omits—technically it is a variance components analysis in the unbalanced case (Harvey, 1970). Users should be aware that the time taken to compute α is prohibitive for a large number of items or respondents. We have, therefore, provided a simpler alternative, the Kuder-Richardson (KR20) coefficient: n
KR20 =
n ⋅ n −1
S 2 − ∑ p j (1 − p j ) j =1
S2
,
where n is the number of items in the test, p j is the facility of item j, and S 2 is the variance of the test scores. Note: if large numbers of respondents omit items, this can affect the estimate of KR20. 7.5.9 Tetrachoric correlations and factor analysis
The response to any particular item can be thought of in terms of an item threshold on the trait continuum being assessed. Respondents with a response process greater than or equal to the threshold will give the correct answer; otherwise, they will give a wrong answer. By assuming that the processes are normally distributed, and knowing the proportion of cases that respond correctly to both items in any pair, we can estimate tetrachoric correlations for all distinct n(n − 1) / 2 pairs of items. To calculate these correlations, TESTFACT uses Digvi’s (1979) method. If all respondents get either or both of the items correct, the tetrachoric correlation becomes ±1. Because the presence of such values causes difficulties for the MINRES factor analysis of the correlation matrix, TESTFACT uses a one-factor version of Thurstone’s (1947) centroid method to estimate admissible values for these correlations. As the final phase of item analysis, the matrix of tetrachoric correlations can be subjected to principal factor analysis with communality iterations. This is equivalent to unweighted leastsquares (ULS) or MINRES factor analysis based on Thurstone’s (1947) multiple-factor model (see Harman, 1976). The resulting principal factor pattern can be rotated orthogonally to the varimax criterion (Kaiser, 1958). With the varimax solution as a target, the pattern can then be rotated obliquely by the promax method of Hendrickson & White (1964). The latter pattern is especially appropriate for item analysis, because it tends to identify clusters of items that form unidimensional subsets within a heterogeneous collection of items.
583
7 OVERVIEW OF MODELS In general, item tetrachoric correlation matrices are not positive-definite. This means that they often cannot be used in any of the many statistical procedures that require positive-definiteness, such as computing partial correlations among some of the items while holding others fixed. In TESTFACT, this inconvenience can be avoided by listing and saving a smoothed positive definite matrix of the item correlations. The smoothed matrix is computed from all the positive roots (renormed to sum to n) of the original tetrachoric matrix. After the number of factors has been determined, the smoothed matrix is reproduced from the MINRES factor solution. 7.5.10 IRT based item factor analysis7
Classical item analysis makes use of only that information in the examinees’ responses available in the sample correct and incorrect occurrence frequencies for each item, together with the joint correct and incorrect occurrence frequencies for all possible pairs of items. The estimation procedures of classical item statistics, including the MINRES factor loadings, are therefore referred to as “partial” information methods. IRT estimation procedures, on the other hand, make use of all of the information in each examinee’s pattern of correct and incorrect responses to the test items (which is equivalent to the information in all possible occurrence and joint occurrence frequencies of all orders, i.e., of item pairs, item triples, item quadruples, etc.) The IRT procedures are therefore called “full” information methods. In the TESTFACT program, IRT-based full information estimation procedures are only needed in, and only applied to, item factor analysis. Their main advantage is that they are not affected by the occurrence of zero- or 100-percent joint occurrence frequencies, for which tetrachoric correlations cannot be estimated. Items with zero or 100-percent of correct responses in the sample, which correspond to infinitely negative or positive item thresholds, do disturb IRT procedures, however. They should therefore be eliminated from the response patterns before factor analysis is attempted. For this reason, it is advisable to perform a preliminary classical item analysis before proceeding with the item factor analysis. The full information procedure in TESTFACT maximizes the likelihood of the item factor loadings and standardized difficulties given the observed patterns of correct and incorrect responses. It solves the corresponding likelihood equations by integrating over the latent distribution of factor scores assumed for the population of examinees (the so-called θ distribution). Because this type of integration is called “marginalization” in the statistical literature, the estimation method is called “marginal maximum likelihood” or MML. The definite integrals involved in this method are computed numerically in a procedure referred to, for historical reasons, as “quadrature”. This version of TESTFACT makes use of recently developed innovations in quadrature to make MML estimation feasible for fitting item response models in high-dimensional factor spaces. It also includes, in addition to the preceding exploratory factor analysis, a confirmatory factor analysis based on the bifactor model.
7
This section was contributed by R. Darrell Bock and Stephen G. Schilling.
584
7 OVERVIEW OF MODELS 7.5.11 Full information factor analysis
Bock & Aitkin (1981) introduced the marginal maximum likelihood (MML) or estimating item parameters of the 1- and 2-parameter normal ogive item response models. Their iterative solution of these likelihood equations was based on the EM algorithm of Dempster, Laird & Rubin (1977). This method can be applied straightforwardly to the estimation problem of item parameters in the item response model with a guessing term and with more than one latent dimension of ability, θ . Details are given in Bock, Gibbons & Muraki (1988). In the multidimensional case, the normal ogive item response model with guessing is given by Prob( xij = 1| θ i ) = g j + (1 − g j )Φ[ z j (θ i )] = g j + (1 − g j )
1 2π
∫
∞
− z j (θ j )
exp(−t 2 / 2) dt ,
where z j (θ i ) = c j + a j1θ j1 + a j 2θ j 2 + … + a jmθ jm . The MML estimates of the factor loadings α jk , k = 1, 2,… , m , and standard difficulty, δ ξ are then calculated from the estimates of the slope parameter, a jk , and the intercept parameter, c j , as follows:
α jk =
a jk dj
, δj =
cj dj
k = 1, 2,… , m,
where d j = 1 + a 2j1 + a 2j 2 + … + a 2jm . Chance or guessing parameters, g j , are treated as known constants in TESTFACT. If the chance parameter is not included in the item-response model, g j is set to zero for all items. Otherwise, values of these parameters must be supplied as a part of the input data. When the guessing model is invoked, the tetrachoric correlation coefficients are computed according to Carroll’s (1945) correction chances/successes. Guessing parameters are the ordinates of the asymptote of the response function in the direction of low ability. As such, they do not depend upon the form of the response curve at higher abilities. For this reason, guessing parameters can be satisfactorily estimated by a one-dimensional item response model such as that used in the BILOG program of Mislevy & Bock (1990). Otherwise, the a priori value equal to 1 divided by the number of response alternatives can be used.
585
7 OVERVIEW OF MODELS 7.5.12 Bifactor analysis
Prior to Thurstone’s development of the multiple-factor model, Holzinger & Swineford (1937) introduced the bifactor model to extend the Spearman (1904) one-factor model for intelligence tests to include so-called “group” factors. By including these mutually uncorrelated factors they were able to explain departures from one common factor when distinguishable items, such as spatial or number series items, appeared in the tests. Their model also applies to educational achievement tests containing more than one subject-matter content area - for example, a mathematics test containing an algebra and a geometry section. Such tests are often scored for general mathematics achievement effects, but the multiple content areas may induce group factors. The bifactor model has special relevance for IRT, because it accounts for departures from conditional independence of responses to groups of items that depend on a common stimulus such as a reading passage or problem-solving task. This type of item has been called a “testlet” (see Wainer, 1995). The presence of these items violates the assumption of conditional independence and leads to under-estimation of the standard error of the test score. Taking advantage of the fact that a common factor and uncorrelated group factors imply only two non-zero factors per item, Gibbons & Hedeker (1992) showed that MML estimation for the bifactor model requires quadratures in only two dimensions. This means that the conditional dependence problem can be solved in a way that is computationally practical and easily extendable to large numbers of testlets. Standard errors for scores on the common factor after integrating over the group factor dimensions then correctly account for the presence of conditional dependence within the item groups. Comparing marginal maximum likelihoods of the bifactor solution and a one-factor exploratory solution also provides a statistical test for failure of conditional independence. Analysis based on the bifactor model is included in TESTFACT. 7.5.13 Not-reached items in factor analysis
Item factor analysis should be applied only to power tests. If the time limits of such a test are too short, a substantial proportion of the respondents may not reach later items in the test. In appraising ability, such items might be scored as incorrect. But to do so in the item factor analysis would introduce a spurious factor associated with item position. To minimize these effects in the factor analysis, TESTFACT provides an option called TIME in the SCORE, FULL, and TETRACHORIC commands. When this option is invoked (for each respondent), all items omitted after the last-responded-to item are scores as “not-presented”. Omitted items prior to the last-responded-to item are scored “incorrect”, unless the guessing mode is selected (CPARMS in the FULL command or CHANCE option in the SCORE command). In that case, the latter items would be scored “correct” with the probability of chance success g j and “incorrect” with probability 1 − g j . 7.5.14 Constraints on item parameter estimates
Unless otherwise constrained, maximum-likelihood factor analysis may encounter one or more 586
7 OVERVIEW OF MODELS so-called Heywood cases (i.e., items for which the unique variance goes to zero). In these cases, the iterative MML solution will not converge. When that happens, the user has the option of suppressing Heywood cases by placing a stochastic constraint, in the form of a prior Beta distribution, on the uniqueness (1 – communality) of the items. The default values of the parameters of the Beta distribution have been chosen so that the effect of the prior on the estimated factor loadings will be comparatively mild. The uniqueness will not become zero or negative, factor loadings will not go to ±1, and loadings of smaller absolute value will not be much affected. TESTFACT also permits a normal prior with specified mean and variance to be placed on the intercept parameter of the response function. This protects the maximum likelihood analysis from excessively large or small item intercepts, corresponding to one hundred percent or zero percent item facility. 7.5.15 Statistical test of the number of factors
If the sample size is sufficiently large that all 2n possible response patterns have expected values greater than one or two, the χ 2 approximation for the likelihood ratio test of fit of the model relative to the general multinomial alternative is 2n
G = 2∑ rl ln 2
l =1
rl , l NP
l is computed from the maximum likelihood estimates of where rl is the frequency of pattern l, P the item parameters, and N = ∑ rl is the number of cases. The number of degrees of freedom is 2n − 1 − n(m + 1) + m(m − 1) / 2, where n is the number of items and m is the number of factors. In this case, the goodness-of-fit test can be carried out after performing repeated full information analyses, adding one factor at a time. When G 2 falls to insignificance, no further factors are required to explain association between item responses in the sample. The degrees of freedom above do not apply to bifactor analysis. With that model, the number of degrees of freedom is 2n − 1 − 2n − the number of items assigned to group factors Note that the term, 2n − 1 , in the above formulas applies only to the case where all 2n possible patterns are represented in the data. It can only be used in situations where the patterns are entered with frequency weights, some of which can be zero to account for patterns not appearing in the data (See Section 13.2).
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7 OVERVIEW OF MODELS In situations where the data are entered as individual observations, the program replaces 2n − 1 with N − 1 , the number of cases in the sample minus 1. This is a rather arbitrary expedient, however, since the degrees of freedom are determined by the number of distinct patterns, and any given pattern may occur more than once even when there are a large number of items. Also, the restriction that the probabilities of the patterns must sum to one, which eliminates one degree of freedom, does not apply unless all possible patterns are represented in the data. When the number of possible patterns is much larger than the sample size, many patterns will have zero frequency in the data and many of their expected frequencies will be very small. The above χ 2 or other approximation to the probability of the likelihood ratio statistics on the null hypothesis will then be too inaccurate to be relied on as a goodness-of-fit test. Haberman (1977) has shown, however, that the difference in these statistics for alternative models is distributed in large samples as χ 2 , with degrees of freedom equal to the difference of respective degrees of freedom, even when the frequency table is sparse. Thus, the contribution of the last factor added to the model can be judged significant if the corresponding change of χ 2 value is statistically significant, even when there are many patterns that do not occur in the sample. Since the term N − 1 subtracts out of the difference in degrees of freedom when two models with different number of factors are analyzed with the same data, the degrees of freedom printed by the program can be subtracted to obtain the degrees of freedom for the difference of the corresponding χ 2 values. These statistics should be interpreted with caution, however: in large-scale studies based on respondents from different sites, cluster effects may inflate the χ 2 statistics. To be conservative about the number of factors that are identifiable in such studies, it is advisable to divide the χ 2 by a design factor of 2 or 3 before assessing its probability. Factors in large-scale studies that do not show a significant χ 2 by this criterion are usually uninterpretable. 7.5.16 Factor scores
In TESTFACT, factor scores for the respondents can be computed by the Bayes/EAP (expected a posteriori) method suggested by Bock & Aitkin (1981) (see also Muraki & Engelhard, 1985). The posterior standard deviation (PSD) measuring the precision of each factor score estimate is also computed. The factor scores are computed only for orthogonal solutions (principal factor or varimax). Transformation to oblique factor scores (by the promax transformation, for example) could be carried out subsequently, but there is no provision for that in the present version of the program. Factor scores may be computed either from standard difficulties and factor loadings estimated within the program, or from standard difficulties and loadings supplied by the user from external sources. Alternatively, item intercepts and slopes may be supplied. If the guessing model is selected, chance success parameters must also be supplied. The factor scores in TESTFACT are Bayes estimates computed on the assumption that the corresponding ability factors are normally distributed in the population from which the sample of 588
7 OVERVIEW OF MODELS examinees was drawn. That is, the score for each factor is the mean of the posterior (conditional) distribution of ability, given the item response pattern of the examinee in question. The standard deviation of the posterior distribution is also calculated and is interpreted as the standard error of measurement for the score. The user can request the factor scores and corresponding standard errors to be printed in the output listing and/or saved in an ASCII (plain text) file. The name of that file will be the command filename with the extension *.fsc. Following estimation of factor scores, the program will list their sample mean and variances, together with the mean-square and root-mean-square of the measurement errors. From the score variance and the mean-square measurement error, the empirical reliability of the test in the particular sample of examinees is calculated and listed in the output. The expected value of each factor score variance and the corresponding mean-square error is unity. If the sum of these listed sample quantities varies widely from 1.0, it may be an indication of poor convergence or of the presence of a near-Heywood case (see Bock & Zimowski, 1999, for details). 7.5.17 Number of quadrature points
The computations of MML item factor analysis can be time consuming when the number of items and number of examinees are large, because the program must then evaluate the posterior probability of each examinee’s response pattern at each point in the quadrature space. In conventional multidimensional quadrature, i.e., “product” quadrature, the total number of points in the full space is equal to the number of points in one dimension raised to the power of the number of dimensions. To avoid excessively long computing times in earlier versions of TESTFACT, the total number of points was limited to 243. This meant that with three points per dimension, the largest number of factors that could be accommodated in full information analysis was five (while this is not true of classical partial information MINRES analysis, programming restrictions allow only up to 15 factors in MINRES analysis). With large numbers of items, perhaps 30 or more, the dispersion of the posterior distribution for a given examinee can become small relative to that of the examinee population distribution in the fill factor space. In that situation, the quadratures tend to become inaccurate with only 243 points in the full space, because too few of the points fall in the neighborhood of the location of the posterior. Rather than increase the total number of points to avoid this problem, TESTFACT now employs a form of quadrature that adapts the placement of the points to the region of the factor space occupied by the posterior distribution corresponding to each pattern. With this method, three points per dimension are quite adequate for accurate estimation of the factor loadings and factor scores. It also makes possible a form of “fractional” quadrature based on a subset of points in product space. The method of choosing these points is described below. For the integrations by adaptive quadrature, the user has the options of fill or fractional quadrature for five factors. For one to four factors, the quadratures use all points in the product space; for six to ten factors, the program uses successive one-third fractions of the points. Thus, the number of points actually involved in the quadrature never exceeds 243. The points and weights in these quadratures may be those for rectangular or for Gauss-Hermite quadrature, at the user’s option. (Rectangular is the program default.) The program implementation of adaptive quadrature is based on mathematical and statistical results of Naylor & Smith (1982), Schilling (1993), 589
7 OVERVIEW OF MODELS Meng & Schilling (1996), Bock & Schilling (1997) and Schilling & Bock (1999). In addition to increasing to 10 the program limit on the number of factors, adaptive quadrature improves accuracy of estimation, especially when the number of items is large. To allow for comparison between the two methods of quadrature, TESTFACT now includes a technical option, NOADAPT, to invoke the non-adaptive procedure for up to five factors. In fractional quadrature, a subset of the full set of points in product quadrature is selected in a way that retains estimability of the parameters of the multiple factor model. Since factor analysis is equivalent to determining the mean and covariance matrix of the latent factors (or an arbitrary orthogonal transformation of the covariance matrix), any subset of points that allows means and covariances to be estimated will be suitable for quadrature in MML item factor analysis. Designs that have this property have been found for the formally equivalent problem of factorial experiments in which main effects and two-way interactions must be estimable, but higher-way interactions may be assumed null. Such designs exist for factorial experiments in 5 or more treatment variables each with three equally spaced levels of treatment. Because these designs reduced the total number of points by the one-third as an additional treatment is included, the total number of treatment combinations remains fixed at 243 from six-treatment variable onward. With 5 treatment variables, the one-third fraction contains 81 combinations. The employment of these fractional factorial designs for multidimensional quadrature requires only the choice of values corresponding to the treatment levels. Simulation studies by Schilling & Bock (1998) have shown that - on the assumption that the latent factor score distribution is multivariate normal - near optimum values are: -1.2
0.0
1.2
These are the default values in TESTFACT, but they can be altered by the user if desired. Whatever their values, the corresponding quadrature weights are the respective normal ordinates (densities) constrained to sum to unity by dividing by their unconstrained total weight. In Monte Carlo integration with importance sampling, the adaptation is carried out in a similar way, but the points are drawn randomly from the provisionally estimated posterior distribution corresponding to each distinct response pattern, which is assumed multivariate normal. In both fractional quadrature and Monte Carlo integration, the factor scores for each examinee’s response pattern are estimated by the Bayes modal method (also called “maximum a posteriori”, or MAP estimation). In this method, the multivariate mode of the posterior distribution serves as the estimate of the examinee’s factor scores, and the inverse of the Fisher information at the mode (i.e., the curvature of the posterior density at the mode) serves to estimate the variances and covariances of modal values. In the adaptive solution, the MAP estimates are recomputed during each of the earlier EM cycles. After a number of cycles set by the ITLIMIT keyword of the TECHNICAL command (default equals one-third of the number of cycles set by the CYCLES keyword of the FULL command) the posterior modes and information matrix for each examinee remain fixed for the rest of the EM cycles.
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7 OVERVIEW OF MODELS 7.5.18 Monte Carlo integration
The program now provides the option of Monte Carlo integration in the EM solution of the marginal maximum likelihood equations. The user may choose the number of random deviates to be sampled from the assumed multivariate normal distributions corresponding to each response pattern in the data, and choose also the seed for their generation. In principle, this method of solving the likelihood equations applies to any number of factors, but programming restrictions in the implementation limit the number to 15. 7.5.19 Applications
Interesting applications of the TESTFACT program are described in Muraki & Engelhard (1985), Zimowski (1985), Zwick (1987), Bock, Gibbons & Muraki (1988), and Zimowski & Bock (1987).
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8 Estimation 8.1 Introduction8 Item Response Theory (IRT) has only recently come into widespread application in the fields of educational and psychological measurement. However, the theory is not really new. Its roots can be found in the work of Thurstone (1925), Thorndike (1927), Symonds (1927), Horst (1933), and others. Its beginnings are in the pioneering efforts of Lawley (1943) and Lord (1952). In contrast to traditional test theory, as presented in Gulliksen’s (1950) landmark text and elsewhere, IRT provides the following features:
Respondents may be scored on the same scale, even if they do not respond to the same set of items. Respondents may be comparably scored on two or more forms of the same test. Short forms, long forms, easy forms, hard forms, parallel forms, and other alternate forms are all treated in the same way. Tests can be tailored to proficiency, with easy questions for those who show low proficiency and difficult questions for those who exhibit higher proficiency.
The magic of IRT arises in placing all of the test scores on the same scale after all of these machinations, even if the respondents answer different sets of questions. IRT also permits the use of all of the information included in an examinee’s response to a question or test item, even if that response may be in one of three or more graded categories (as on a rating scale) or in one of several strictly nominal categories (as among the four or five choices of a conventional multiplechoice item). Responses on attitude measures are frequently graded, and on multiple-choice proficiency tests some distractors are usually “wronger” than others. IRT permits the use of the information in any choice of an item response to be used to estimate the value of that respondent’s trait or proficiency. The power of IRT is associated primarily with the phrase “estimate the value of the trait”. Loosely speaking, we say that a test is “scored”. But strictly speaking, the test is not scored; one does not simply count the positive responses, as is done in traditional test theory. One “estimates the value of the trait” using the inferred relationships between the item responses and the trait being measured. In the process, one finds that there is no longer an idea of “reliability” in many cases; instead, there is information. An understanding of this estimation process and the idea of information in the technical sense (after Fisher, 1925) are crucial for an appreciation of the theory. Both are discussed in the following sections.
8
This section was contributed by David Thissen.
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8 ESTIMATION 8.1.1 Trait estimation with Item Response Theory Item response theory is concerned with the probabilistic relationship between the response to some test item and the respondent’s attribute that the test item is intended to measure. Test items may be problems on a proficiency test, questions on an attitude scale, or behaviors on a behavior checklist. The attribute of the person may be a cognitive proficiency, an attitude, or a personality construct (either “trait” or “state”). The attribute being measured by the test is usually called θ and is usually arbitrarily placed on a z-score scale, so zero is average and θ -values range, in practice, roughly from -3 to +3. Item response theory is used to convert item responses into an estimate of θ , as well as to examine the properties of the items in item analysis. In its simplest form, item response theory is concerned with the relationship between binary test items (correct/incorrect, agree/disagree, yes/no) and θ , however it may be conceived. In a useful binary test item, the relationship between the probability of a positive response and θ must be more or less like the function in the top panel of Figure 8.1. As is illustrated there, the probability of a positive response (on the y-axis) is plotted against θ : it is an increasing, S-shaped function, indicating a low probability of a positive response among persons of low θ , moderate probability for individuals of average θ , and a high probability of positive response for persons of high θ.
Figure 8.1: Probabilities and joint relative likelihood of sequence of binary items
Top panel: A trace line for a binary test item (referred to as item 1); that is, the probability of a positive response plotted against the trait value ( θ ). Center panel: The probability of a negative response to a second item (referred to as item 2). Lower panel: The joint relative likelihood of the response sequence {positive, negative} as a function of θ .
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8 ESTIMATION Computational aspects of item response theory usually require that the function in Figure 8.1 have some specified mathematical form; the normal ogive and logistic functions have frequently been used (see Lawley, 1943; Lord, 1952; Rasch, 1960; Birnbaum, 1968). In either case, each binary item has a curve like that in the top panel of Figure 8.1, sometimes called an Item Characteristic Curve (ICC) or “trace line” (Lazarsfeld, 1950), which is defined by its “location” and “slope”. The latter terminology will be used here. In some of the simpler IRT models, the location parameter of the trace line is the point on the θ scale at which the curve crosses P = 0.5. So persons whose trait value exceeds the location parameter of the item have greater than a 50% chance of a positive response, while persons whose θ values lie below that location have less than 50% chance of a positive response. In the context of proficiency tests, the location of an item corresponds to its difficulty: the higher the location parameter, the more proficiency is required before the examinee has a 50% chance of a correct response. The slope of a trace line reflects the rate at which the probability of a positive response changes as θ increases. This is the classical discrimination parameter. The trace line for item 2 in Figure 8.1 (for a negative or incorrect response, since it decreases over θ ) changes more quickly as θ changes than does the trace line for item 1. The item 2 curve drops from about 0.9 to about 0.1 between 0 and 2, while it takes the range from -2 to +2 for the trace line for item 1 to climb from 0.1 to 0.9. Item 2 is an item with a higher slope than item 1. The location of item 2 is also higher at θ =1. If the trace lines for items 1 and 2 are known, or, more precisely, if their parameters are known, and an examinee responds positively to item 1 and negatively to item 2, that information may be used to estimate the θ -value for that person. One way to make such an estimate uses the principle of Maximum Likelihood (ML). If the item responses are independent (conditional on θ ), then the joint likelihood of the sequence {positive response, negative response} ({right, wrong}, {agree, disagree}, and so on) at any value of θ is the product of the item 1 and item 2 probability values at that level of θ in Figure 8.1. That product has been computed and is labeled “Total” at the bottom of Figure 8.1. The total likelihood is low for low values of θ , because it is unlikely that a person there would respond positively to item 1, and it is low for high values of θ , because it is unlikely that a person there would respond negatively to item 2. The total likelihood of the sequence {positive, negative} is highest at about θ = 0.4 so that is the Maximum Likelihood Estimate (MLE) for θ , called MLE[ θ ]. So a person who responds {positive, negative} might be assigned a trait value of 0.4 as a “test score” or measurement. The MLE is the mode of the total likelihood in Figure 8.1. If desired, the average, called EAP[ θ ] for Expected a Posteriori, or some other estimate may be used. The point estimate of location provides a very limited summary of the total likelihood. In a subsequent section on test information, we will consider the addition of indices of spread or width of that likelihood around its location. At this point, it is sufficient to understand that “estimation” of θ uses the relative likelihood of the observed response sequence as a function of θ , and consists of summarizing the distribution of the likelihood, like the “Total” in Figure 8.1, by one or more numbers - the first of
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8 ESTIMATION which is always its location. The procedure is easily extended to more items. Figure 8.2 shows the trace lines associated with a five-item test, for the sequence {negative, negative, positive, positive, positive}. Again, the total at the bottom is a plot of the likelihood of that particular sequence over values of θ . There is almost no likelihood above zero, and MLE[ θ ] for this sequence of responses to this very easy test is about -1.3. So the examinees that responded with this sequence to these five items might be assigned -1.3 as a point estimate of their trait values. If items 1 and 2 of Figure 8.1 and items 1 to 5 of Figure 8.2 came from a pool of items which measured the same trait ( θ ) and their item parameters were known, the ML estimates of θ in the two cases would be on the same scale and thus directly comparable. This is true even though the examinee represented by Figure 8.1 responded to only two items and the examinee represented by Figure 8.2 responded to five (different) items. This feature of item response theory allows tests made up of different sets of items (like tests with missing data, short and long forms, alternate forms, and so on) to be used to assign comparable trait estimates to examinees. The ML estimation of θ for each person takes into account the properties of each item in constructing the total likelihood of the observed responses. Thus, the estimate of θ which as the value that has the highest likelihood of producing those responses, is the same regardless of the set of items.
Figure 8.2: Trace lines and total relative likelihood for sequence of 5 binary items
Top five panels: Trace lines for five binary items, in sequence {negative, negative, positive, positive, positive}. Lower panel: Total relative likelihood for that sequence as a function of θ .
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8 ESTIMATION Item responses need not be binary. Samejima (1969) (see Sections 2.3.2 and 2.4.2) has developed item response models for graded items with 3, 4, or more ordered categories of response (like disagree, moderately disagree, moderately agree, and agree). The trace lines for the highest and lowest of the graded responses are like those for positive and negative binary responses; for binary items, the graded models become identical to binary models using the same functions. For more than two graded responses, the intermediate responses have increasing, then decreasing trace lines: intermediate responses must be more likely at some moderate value of θ . Figure 8.3 shows trace lines for a test of three graded items, with the probability of responses 2 on item 1, 3 on item 2, and 1 on item 3 plotted. Samejima’s graded model has one slope parameter for each item (very high for item 3, moderate for item 2, and low for item 1 in Figure 8.3). There are several location parameters, called thresholds: one less than the number of response categories. Each location parameter specifies the point on the θ scale at which a person has a 50% chance of responding in some higher category than the one to which the threshold belongs. As illustrated in Figure 8.3, there is still a total likelihood over θ for any given response pattern, even when all (or some) of the items have more than two possible responses. θ may still be estimated as the maximum of the total likelihood, it is -1.2 in the example in Figure 8.3.
Figure 8.3: Trace lines and joint relative likelihood of response sequence of 3-category items
Top panel: Trace line for a response in category 2 of a 3-category graded item. Second panel: Trace line for a response in category 3 of a 3-category item. Third panel: Trace line for a response of 1 on another 3-category item. Fourth panel: The joint relative likelihood of the response sequence as a function of θ .
There are sometimes test items that permit multiple responses that have no obvious order. Multiple-choice vocabulary questions are such items. While one response may be “correct”, the others may not be obviously graded with respect to “correctness” although they may be chosen dif596
8 ESTIMATION ferentially by examinees of different proficiency. Bock (1972) has proposed a logistic response model for just such items. The parameters of that model are contrasts among the slopes and intercepts of the trace lines. The model produces trace lines for each response category, which can be combined with other trace lines and used in the ML estimation of θ . Thissen and Steinberg (1984, see Section 7.4.4) describe an extended version of this model which is included in MULTILOG. Frequently, prior information is available or can be assumed about the distribution of θ in the population of persons responding to a test. Such information, based on population membership, is numerically equivalent to a test item to which all of the members of the population respond identically. A N[0,1] prior – assuming that the examinees are drawn from a standard normal distribution – is equivalent to a trace line for a Gaussian distribution. The curve marked “Pop.” in Figure 8.4 is a density for such a normal prior. Prior information of this sort may be combined with the item responses just as though it represented an item on the test. The implicit test item is “Do you belong in this population?” Yes = mean of population, No = missing data. Item response theory is a flexible system for scoring tests and thus providing measurement of individual differences. Alternative models are available for many types of item response data. The reader is advised to examine the technical literature in this field or obtain competent advice before applying any model (or set of models). Once the parameters are estimated, item response theory should provide a satisfactory solution to any problem of psychological measurement. 8.1.2 Information In the preceding section, we have made use of point estimates of the location of total likelihoods as estimates of the unobserved trait value θ . When the total likelihood over θ derived from the item responses is relatively flat, as it is for the five item responses in Figure 8.2, an unmitigated version of this procedure should generate some discomfort in the reader. In Figure 8.2, there is a substantial likelihood for the observed response pattern {00111} for all θ -values between –3.0 and 0.0. Under these circumstances, the point estimate MLE[ θ ] = –1.3 may apply too much precision. An estimate of the width or spread of the total likelihood may be used to specify the precision with which the MLE[ θ ] estimates θ . Since the form of the distribution of total likelihood is roughly Gaussian, an estimate of the standard deviation of that distribution is a useful and widely comprehensible index of spread. For the situation in which the item parameters are taken to be fixed and known and the only parameter to be estimated is the trait-value θ for a particular examinee, the distribution is the sampling distribution of θ and its standard deviation is the standard error of MLE[ θ ].
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Figure 8.4: Trace lines and joint relative likelihood for five binary items
Top five panels: Trace lines for five binary test items {00100}. Sixth panel: N[0,1] population density. Lowest panel: The joint relative likelihood (posterior) as a function of θ .
It is possible to employ any variety of methods to estimate the spread of distributions such as the total likelihood in Figure 8.2. One method, which is extremely convenient in the context of ML estimation, makes use of the fact that the negative inverse of the expected value of the second derivative of the log likelihood is approximately equal to the variance of the estimator (Fisher, 1925; Kendall & Stuart, 1961, p.10). While the term used to describe that number is unpleasantly long, that value is a routine by-product of ML estimation. It is frequently used as an estimate of the standard error to describe the spread of the total likelihood in terms that are interpretable in a roughly Gaussian sense, i.e. a 95% confidence interval is MLE[ θ ] ± 2 standard errors. The standard error estimated in this way for MLE[ θ ] = –1.3 from the likelihood at the bottom of Figure 8.2 is 1.3. So the central (Gaussian) 68% confidence interval for θ would run from -1.3 ± 1.3 = –2.6 to 0.0. Examination of Figure 8.2 reveals that, although the total likelihood is not strictly Gaussian, the inflection points are very nearly at –2.6 and 0.0, as would be expected if the distribution were Gaussian and 1.3 were the standard deviation. Standard errors estimated in this way are different for different response patterns for the same test. The likelihoods may be broad or narrow, depending on the relative locations of θ for the individual and the item parameters. Since the standard error is the width of the total likelihood, it varies. With some exceptions, there is a pattern to the standard errors: they are small for θ -locations near clusters of discriminating items and large far away, usually at the edges of the range of the test. This variation is at odds with the concept of reliability ( ρ ), which is based on a model in which all the estimates have the same error of estimate, equal to 1 − ρ for standardized tests.
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8 ESTIMATION So reliability is frequently not a useful characteristic of a test scored in this way. No single number characterizes the precision of the entire set of IRT trait-estimates made from a test. Instead, the pattern of precision over the range of the test may be plotted. A plot of the standard error against θ would serve this purpose, but the variable conventionally plotted is Information, which is approximately equal to 1/(standard error)2 . This definition, due to Fisher (1925) and therefore sometimes called Fisherian information, uses the word “information” in an intuitively obvious way: if the standard error reflects our lack of knowledge about the parameter, then its inverse is information. Information is used primarily because it is additive: each test item produces a fixed quantity of information at each level of θ . The information function for a test is simply the sum of the item information functions. This allows easy computation of information functions for tests of varying compositions.
8.2 Estimation in BILOG-MG9 8.2.1 Item calibration To make use of IRT for test scoring, the parameters of the model for each item of the test must be estimated. Estimating the item parameter and checking the fit of the models is referred to as item calibration. The calibration process requires item data from a sample of respondent who have been administered the test under exactly the same conditions as those in which the test will be used in practice. After a preliminary pilot study to select suitable items, the main item calibration can be performed on data obtained in the first operational use of the test. Replacement of items in subsequent administrations can then be carried out by one of the methods described in the section on test equating. Marginal Maximum Likelihood estimation (MML) An approach to item parameter estimation that applies to all types of item response models and is efficient for short and long tests is the method of marginal maximum likelihood (MML). (See Bock & Aitkin, 1981; Harwell, Baker & Zwarts, 1988). Except in special cases, the MML method assumes the conditional independence of responses to different items by persons of the same ability θ . Because the joint probability of independent events is the product of the probabilities of the separate events, this assumption makes it possible to calculate the probability of observing a particular pattern of item scores, x = ( x1 , x2 ,… , xn ) ,
in the responses of a person with ability θ .
9
This section was contributed by R. Darrell Bock.
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8 ESTIMATION This probability may be expressed as n
P (x | θ ) = ∏ [ Pj (θ )] j [1 −Pj (θ )]
Equation Section 8
x
1− x j
(8.1)
j =1
that is, as the continued product of Pj (θ ) or 1 − Pj (θ ) according as the person response correctly or incorrectly to item j. This quantity is the probability of the pattern x , conditional on θ . It is to be distinguished from the probability of observing the pattern x from a person of unknown ability drawn at random from a population in which θ is distributed with a continuous density g (θ ) . The latter is the unconditional probability given by the definite integral, ∞
P (x) =
∫ P(x | θ )g (θ ) dθ .
(8.2)
−∞
This quantity is also referred to as the marginal probability of x . Because the ability, θ , has been integrated out, this quantity is a function of the item parameters only. In IRT applications, the integral in (8.2) cannot generally be expressed in closed form, but the marginal probability can be evaluated as accurately as required by the Gaussian quadrature formula _
q
Px ≈ ∑ P (x | X k ) A( X k ),
(8.3)
k =1
where X is a quadrature point and A( X k ) is a positive weight corresponding to the density function, g ( X ) . Tables giving quadrature points and corresponding weights are available for various choices of g (θ ) (see Stroud & Sechrest, 1966). We recommend 2 x the square root of the number of items as the maximum number of quadrature points. In the MML method, values for the item parameters are chosen so as to maximize the logarithm of the marginal maximum likelihood function, defined as S
_
log LM = ∑ rl log e P ( xl ),
(8.4)
l =1
where rl is the frequency with which the pattern x l is observed in a sample of N respondents, and S is the number of distinct patterns observed. A necessary condition on the maximum of (8.4) for the 3PL model of item j is given by the likelihood equations
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8 ESTIMATION 0 r jk − N jk Pj ( X k ) ∂Pj ( X k ) ∑ P ( X )[1 − P ( X )] c = 0 , k j k k =1 j j 0 ∂ a j g j q
(8.5)
where S
r jk = ∑ rl xlj P (x l | X k ) A( X k ) / P xl l
(8.6)
and S
N k = ∑ rl P (x l | X k ) A( X k ) / P xl l
are, respectively, the posterior expectation of the number-correct and of the number of attempts at point X k . ( xlj is the 0,1 score for item j in pattern l). The so-called EM algorithm and Newton-Gauss (Fisher-scoring) methods are used to solve these implicit equations. Details may be found in Bock and Aitkin (1981) and Thissen (1982). Standard errors and correlations of the parameter estimators are obtained by inverting the information matrix in the Fisher-scoring solution. Marginal Maximum A Posteriori estimation (MMAP) MML estimation for the two- and three-parameter models is essentially a one-dimensional item factor analysis. As such, it is subject to so-called Heywood cases in which a unique variance goes to zero. The symptom of such a case is an indefinitely increasing slope during the EM and Newton iterations of the maximization. Because all items are fallible to some degree, zero unique variance is untenable. It is therefore reasonable to avoid Heywood cases by placing a stochastic constraint on the item slopes to prevent them from becoming indefinitely large. This may be done by adopting a Bayes procedure called “marginal maximum a posteriori” (MMAP) estimation. In one form of this procedure, the slopes (which must be positive) are assumed to have a log normal distribution in the domain from which the items are drawn. Values for the item parameters are then chosen so as to maximize the logarithm of the product of the likelihood of the sample and the assumed log normal “prior” distribution of the slopes. The parameters of this log normal distribution for slopes can be either specified as a priori—the Bayes solution—or estimated from the data at hand—an empirical Bayes solution. This amounts to finding the maximum of the posterior distribution of the slopes, given the data. For the three-parameter model, a similar procedure is needed to keep the lower asymptote parameter, g j , in the open interval from 0 to 1. The beta distribution may be used for this purpose. The intercept parameter can also be constrained to a plausible region, although this is less im601
8 ESTIMATION portant than constraining the slope and asymptote. (See Mislevy, 1986, and Tsutakawa & Lin, 1986, for details). Estimation of the latent distribution It is possible to estimate the distribution of θ by MML. To do so it is necessary to solve the indeterminacy of location and scale that is inherent in the item response models. This indeterminacy arises because, in the logit, z j = a j (θ − b j ) ,
any change in the origin of θ can be absorbed in b j , and any change in the unit of θ can be absorbed in a j . A widely accepted convention is to fix location by setting the mean of the latent distribution (of θ ) to 0 and to fix scale by setting the standard deviation of the distribution to 1. The parameters are then said to be in the “0, 1” metric. To set the mean and standard deviation to some other values, m and s, say, it is only necessary to change b j to b*j = sb j + m
(8.7)
a*j = a j / s.
(8.8)
and
If the logit is parameterized as z j = a jθ + c j , then the change of c j is c*j = c j − a j m / s.
(8.9)
The asymptote parameter, g j , is not affected by these changes. A convenient way to characterize an arbitrary latent distribution with finite mean and variance is to compute the probability density at a finite number of suitably chosen values of θ and to normalize the densities by dividing by their total. The result is a so-called “discrete distribution on a finite number of points” (Mislevy, 1984). These normalized densities can be used as the weights, A( X k ) in quadrature formulas such as (8.3). This discrete representation of the latent distribution can be readily estimated from the item response model. The expected frequency at point X k , given the item data from a sample of N respondents, is N k , the expected number of attempts defined by (8.6) above.
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8 ESTIMATION The estimated densities are these values divided by their total: q
A* ( X k ) = N k / ∑ N h . (8.10) h
They are called empirical or “posterior” weights, as distinguished from the theoretical or “prior” weights, A( X k ) , assumed before the data are in hand. Testing the goodness-of-fit of the IRT model If data from a large sample of respondents is available, the fit of the model may be tested, either for the test as a whole, or item by item. The method of examining fit depends upon the number of items in the test. (i) Very short tests (10 or fewer items) If nearly all of the 2n possible response patterns for an n-item test appear in the data, the overall goodness-of-fit of the model can be tested directly. The distinct response patterns must be counted to obtain the pattern frequencies r1 , r2 ,…, r2 . n
If a few of these frequencies are zero, ½ may be substituted for each and the sum of these substitutions subtracted from the largest frequency. Then the likelihood ratio χ 2 statistic for the test of fit is 2n
G 2 = 2∑ rl log e l =1
rl , N P ( xl )
(8.11)
where P( xl ) is the marginal probability of pattern x l given by (8.3). This χ 2 has degrees of freedom 2n − kn − 1 , where k is the number of item parameters in the model. Significantly large values of the statistic indicate a failure of fit of one or more of the response models for the n items. (ii) Short tests (11 to 20 items) No dependable, formal test of fit yet exists for all of this range. But useful information about the fit of individual items may be obtained by inspecting standardized differences between the posterior probability of correct response at selected values of θ and the probabilities at those points computed from the corresponding fitted response model. These differences are called “standardized posterior residuals”. In terms of quantities defined above, the posterior probability of a correct response is computed for item j at the point X k is the ratio r jk / N k , the terms of which are defined above.
603
8 ESTIMATION The corresponding standardized posterior residual can be expressed as follows: S
δ jk =
∑Wlk [ xlj − P( X k )] l
S
(8.12)
{∑Wlk [ xlj − P ( X k )]}
1/ 2
l
where Wlk =
rl P (x l | X k ) . P (x l )
(8.13)
Values of this residual greater than, say, 2.0 may be taken to indicate some failure of fit of the model at the corresponding point. In interpreting such deviates, it is advisable to take into consideration the posterior weight, A* ( X k ) , at the point, since a discrepancy in a region of θ with very little probability in the population will have little effect on the performance of the model. As an overall index of fit, we suggest the population root-mean-square of the posterior deviates. Its formula is 1/ 2
q q RMS (δ j ) = ∑ N k δ 2jk / ∑ N k k k
.
Unfortunately, the posterior residuals seem to be too highly correlated to be successfully combined into a χ 2 statistic for the item. Neither do they take into account the sampling variance of Pj ( X k ) due to estimation of its item parameters, but this source of variation is presumably small. (iii) Long tests (more than 20 items) If the test is sufficiently long, the respondents in a sample of size N can be assigned with good accuracy to intervals on the θ -continuum on the basis of their estimated value of θ . For this purpose, we use the EAP estimate with whatever prior is assumed for item calibration. The estimated θ ‘s are rescaled so that the variance of the sample distribution equals that of the latent distribution on which the MML estimation of the item parameters is based. The number of respondents in each interval who respond correctly to item j can be tallied from their item scores. Finally, a likelihood ratio χ 2 statistic may be used to compare the resulting frequencies of correct and incorrect responses in the intervals with those expected from the fitted model at the interval mean, θ h :
604
8 ESTIMATION ng rhj N h − rhj G 2j = 2∑ rhj log e + ( N h − rhj ) log e , − N P N P ( θ ) [1 ( θ )] h h h =1 h j h j
(8.14)
where ng is the number of intervals, rhj is the observed frequency of correct response to item j in interval h, N h is the number of respondents assigned to that interval, and Pj (θ h ) is the value of the fitted response function for item j at θ h , the average ability of respondents in interval h. Because neither the MML nor the MMAP method of fitting the response functions actually minimizes this χ 2 , the residuals are not under linear constraints and there is no loss of degrees of freedom due to the fitting of the item parameters. The number of degrees of freedom is therefore equal to the number of intervals remaining after neighboring intervals are merged if necessary to avoid expected values less than 5. To diagnose cases of poor fit, one can inspect a plot of rhj / N h compared to Pj (θ h ) . Ninety-five percent tolerance intervals on these points are ±2 Pj (θ h )[1 − Pj (θ h ) / N h .
When the number of items is small, the standardized posterior deviates should be plotted instead. 8.2.2 Test scoring Unlike classical test theory, IRT does not in general base the estimate of the respondent’s ability (or other attribute) on the number-correct score. The only exception is the one-parameter logistic model, in which the estimate is a non-linear function of that score. To distinguish IRT scores from their classical counterparts, we refer to them as “scale” scores. The main advantages of scale scores are that they
remain comparable when items are added to or deleted from the tests, weight the individual items optimally according to their discriminating powers, have more accurate standard errors, provide more flexible and robust adjustments for guessing than the classical corrections, and are on the same continuum as the item locations.
There are three types of IRT scale score estimation methods now in general use:
Maximum likelihood estimation Bayes estimation Bayes modal estimation.
The three types of IRT scale score estimation methods are discussed in the sections to follow. 605
8 ESTIMATION Maximum likelihood estimation The maximum likelihood (ML) estimate of the scale score of respondent i is the value of θ that maximizes n
log Li (θ ) = ∑ {xij log e Pj (θ ) + (1 −xij ) log e [1 − Pj (θ )]},
(8.15)
j =1
where Pj (θ ) is the fitted response function for item j. The implicit likelihood equation to be solved is
xij − Pj (θ ) ∂Pj (θ ) ∂ log Li (θ ) n =∑ ⋅ = 0. ∂θ ∂θ j =1 Pj (θ )[1 − Pj (θ )] The ML estimate, θ , is conveniently calculated by the Fisher-scoring method, which depends on the so-called “Fisher information”, n
I (θ ) = ∑ a 2j Pj (θ )[1 − Pj (θ )],
(8.16)
j =1
in the case of the two-parameter logistic model. Similar formulas are available for the other models. The iterations of the Fisher-scoring solution are
∂ log Li (θ ) . ∂θ
θ t +1 = θ t + I −1 (θ )
The standard error of the ML estimator is the square root reciprocal of the information at θ : S.E.(θ ) = 1/ I (θ ).
(8.17)
Unlike the classical standard error of measurement, which is a constant, the IRT standard error varies across the scale-score continuum. It is typically smaller towards the center of the scale where more items are located and larger at the extremes where there are fewer items. A disadvantage of the ML estimate is that it is not defined for the response patterns in which all items are correct or all items are incorrect (and occasionally for other unfavorable patterns near the chance level when the three-parameter model is used). These problems do not arise in the other two methods of estimation.
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8 ESTIMATION Bayes estimation
The Bayes estimate is the mean of the posterior distribution of θ , given the observed response pattern xi (Bock & Mislevy, 1982). It can be approximated as accurately as required by the Gaussian quadrature (see the section on MML estimation): q
θi ≅
∑X k =1 q
k
P(xi | X k ) A( X k )
∑ P(x k =1
. i
| X k ) A( X k )
This function of the response pattern xi has also been called the expected a posteriori (EAP) estimator. A measure of its precision is the posterior standard deviation (PSD) approximated by q
PSD(θ i ) ≅
∑(X k =1
k
− θ i ) 2 P(xi | X k ) A( X k ) .
q
∑ P(x k =1
i
| X k ) A( X k )
The weights, A( X k ) , in these formulas depend on the assumed distribution of θ . Theoretical weights, empirical weights, A* ( X k ) , or subjective weights are possibilities.
The EAP estimator exists for any answer pattern and has a smaller average error in the population than any other estimator, including the ML estimator. It is in general biased toward the population mean, but the bias is small within ±3 σ of the mean when the PSD is small (e.g., less than 0.2 σ ). Although the sample mean of EAP estimates is an unbiased estimator of the mean of the latent population, the sample standard deviation is in general smaller than that of the latent population. This is not a serious problem if all the respondents are measured within the same PSD. But it could be a problem if respondents are compared using alternative test forms that have much different PSDs. The same problem occurs, of course, when number-right scores from alternative test forms with differing reliabilities are used to compare respondents. Tests administrators should avoid making comparisons between respondents who have taken alternative forms that differed appreciably in their psychometric properties. A further implication is that, if EAP estimates are used in computerized adaptive testing, the trials should not terminate after a fixed number of items, but should continue until a prespecified PSD is reached. Bayes modal estimation Similar to the Bayes estimator, but with a somewhat larger average error, is the Bayes modal or so-called maximum a posteriori (MAP) estimator. It is the value of θ that maximizes
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8 ESTIMATION n
P(θ | xi ) = ∑ {xij log e Pi (θ ) + (1 −xij ) log e [1 − Pi (θ )]} + log e g (θ ), j =1
where g (θ ) is the density function of a continuous population distribution of θ . The likelihood equation is xij − Pj (θ )
n
∑ P (θ )[1 − P (θ )] ⋅ j =1
j
j
∂Pj (θ ) ∂θ
+
∂ log e g (θ ) = 0. ∂θ
Analogous to the maximum likelihood estimate, the MAP estimate is calculated by Fisher scoring, employing the posterior information, J (θ ) = I (θ ) +
∂ 2 log e g (θ ) , ∂θ 2
where the right-most term is the second derivative of the population log density of θ . In the case of the 2PL model and a normal distribution of θ with variance σ 2 , the posterior information is n
I (θ ) = ∑ a 2j Pj (θ )[1 − Pj (θ )] + j =1
1
σ2
.
The PSD of the MAP estimate, θ , is approximated by ∧
PSD(θ ) = 1/ I (θ ). Like the EAP estimator, the MAP estimator exists for all response patterns, but is generally biased toward the population mean. 8.2.3 Test and item information
According to classical theory, the standard error of measurement (SEM) is a function only of the test reliability and the variance of the score distribution. But this is an oversimplification. Actually, the error standard deviation of a score on a test of finite length—whether the classical number-right score or an IRT scale score—also depends upon the level of the score itself. When the maximum likelihood estimator is used to obtain an IRT scale score, the SEMs of the three logistic models expressed in the normal metric are as follows:
608
8 ESTIMATION 1PL: 1
n 2 S.E.(1) (θ ) = 1/ D 2 a 2 ∑ P(1) j (θ )[1 − P(1) j (θ )] j =1
(8.18)
2PL: 1
n 2 S.E.(2) (θ ) = 1/ D 2 ∑ a 2j P(2) j (θ )[1 − P(2) j (θ )] j =1
(8.19)
3PL: n 1 − P(3) j (θ ) P(3) j (θ ) − g j 2 2 ⋅ S.E.(3) (θ ) = 1/ D ∑ a j ) 1− g j P ( θ 1 = j (3) j
1
2
2
(8.20)
Although these formulas are more realistic than the classical standard error of measurement, they are nevertheless approximations. Strictly speaking, they are exact only as the number of items becomes indefinitely large. But in general, they are good approximations for tests with as few as ten or twenty items. Although they neglect the errors due to estimating the item parameters, these errors are inconsequential if the calibration sample is large. Because the terms that are summed in the information functions (8.18), (8.19), and (8.20) can be regarded as the information functions of the items, they show how the SEM depends upon the item slopes, locations and lower asymptotes. By plotting the item information functions of the items against the test information, the test constructor can see which items are contributing most to increasing the test information in relevant regions of the scale, and thus to decreasing the SEM. The plots show where additional items are needed to improve the precision of measurement locally. Generally, the aim is to produce a test item function that is high and flat over the range of θ in which accurate measurement is required. It is evident in the information functions for the logistic models that, as Pj (θ ) goes to 1 or 0 (or to g j for the 3PL model), the information goes to zero and the standard error to infinity. Thus, the ML estimator is effective only over a finite range. As a result, it is necessary to set some limit, perhaps ±5 standard deviations of the latent distribution, as upper and lower bounds of θ . The posterior information for the Bayes modal (MAP) estimator has properties similar to those of the Fisher information of the ML estimator except that, when the prior is suitably chosen (e.g., normal) the posterior information does not go to zero as θ becomes extreme. Rather, for a normal prior, the posterior information goes to 1/ σ 2 , and the SEM goes to the population standard
609
8 ESTIMATION deviation, σ , which means that nothing is known about θ except that it is very large or very small, depending on the sign of θ . The squared inverse posterior standard deviation (PSD) of the Bayes (EAP) estimator does not have the convenient additive property of the Fisher and posterior information. But because of the equivalence of the EAP and MAP estimators as the number of items becomes large, ML information analysis of items can be applied to the EAP estimation for most practical purposes of test construction. 8.2.4 Effects of guessing
Guessing in response to multiple-choice items has a deleterious effect on any estimator of ability, classic or IRT. For the three-parameter model, the average effect of guessing, and thus the size of the asymptote parameter, g j , can be reduced by instructing the examinees to omit the item rather than make a blind guess. But when the three-parameter model is used in scoring, it does not distinguish between those examinees who omit and those who ignore the instructions, and guess. Two methods of improving the accuracy of scale score estimation in the presence of mixed omitting and guessing have been proposed. One method is to assign to the omitted responses a probability equal to the asymptote parameter, g j , or to 1/A, where A is the number of alternatives of the multiple-choice item (Lord, 1980, p. 229). In effect, the omitted responses are replaced by guessed responses and scored fractionally correct. The other method is to score omits as incorrect, but suppress the effects of guessing by giving reduced weight to unlikely correct responses in the response pattern. A technique of robust data analysis, called “biweighting”, has been proposed for this purpose (Mislevy & Bock, 1982). Simulation studies have shown that such robustifying procedures improve the accuracy of estimating ability in the presence of chance successes in response to multiple-choice items. 8.2.5 Aggregate-level IRT models
In some forms of educational assessment, scores are required for populations of groups and students (schools, for example) rather than for individual students (Mislevy, 1983). In these applications, IRT scale scores for the groups can be estimated directly from matrix sampling data if the following conditions are met:
The assessment instrument consists of 15 or more randomly parallel forms, each of which contain exactly one item from each content element to be measured. The forms are assigned in rotation to students in the groups being assessed and administered under identical conditions.
On these conditions, it may be reasonable to assume that the ability measured by each scale is normally distributed within the groups. In that case, the proportion of students in the groups who respond correctly to each item of a scaled element will be well approximated by a logistic model 610
8 ESTIMATION in which the ability parameter, θ , is the mean ability of the group. Because each item of the element appears on a different form, these responses will be experimentally independent. An aggregate-level IRT model can therefore be used to analyze data for the groups summarized as the number of attempted responses, N hj , and the number of correct responses, rhj , to item j in group h. The probability of these response frequencies for the n items of the element, given the mean ability of the group, θ h , is then N hj !
n
P(rh | N h ,θ h ) = ∏ j =1
( N hj − rhj )!rhj !
⋅ [Ψ j (θ h )] hj [1 − Ψ j (θ h )] r
N hj − rhj
.
(8.21)
Using (8.21) in place of the individual MML estimator
P(x | θ ) = ∏ j =1[ Pj (θ )] j [1 −Pj (θ )] n
x
1− x j
we can carry out MML estimation of item parameters for the aggregate-level IRT model in the same manner as estimation for the individual-level model. Scale scoring of the pattern of frequencies of attempts and correct is performed by a similar substitution in (8.15), (8.16) or (8.17). All other aspects of the IRT analysis are unchanged. Unlike the individual-level analysis, the aggregate-level permits a rigorous test of fit of the response pattern for the group. Because the response frequencies for the items of a scaled element are binomially distributed and independent, a likelihood ratio or Pearsonian χ 2 test statistic may be computed to test the fit of the model within each group. The starting values computed in the input phase and used in item parameter estimation in the calibration phase in BILOG-MG are generally too high for aggregate-level models. The user should reduce these values by substituting other starting values in the TEST command.
8.3 Estimation in PARSCALE10 PARSCALE estimates the parameters of the response models by marginal maximum likelihood assuming either a normal or empirically estimated latent distribution with mean zero and standard deviation one (see Muraki, 1990). The EM algorithm is used in the solution of the likelihood equations starting from the initial values described previously. The current version includes the Newton cycles used in BILOG-MG to improve the EM results. Because of the potentially wide spacing of category boundary locations on the latent dimension, it is advisable to use a greater number of quadrature points than in BILOG-MG. Thirty points is
10
This section was contributed by Eiji Muraki.
611
8 ESTIMATION the default. Simulation studies show that with smaller numbers of points the item slopes are increasingly underestimated. The effect tends to be proportional, however, and is hardly apparent in the test scores when they are rescaled to an assigned standard deviation in the sample. Despite the greater number of parameters in the multiple-category models as opposed to the binary, the greater information in the data allows stable estimation in similarly sized samples. Sample sizes around 250 are marginally acceptable in research applications, but 500 or 1000 should be required in operational use. Beyond 1000, the additional precision may not justify the additional computing time. 8.3.1 Prior densities for item parameters For a slope parameter, we assume that the natural logarithm of the parameter, ln(a j ) , is distributed as N ( µ a ,σ a2 ) : f (a j ) =
1
σ a (2π )1/ 2
−(ln a j − µ a ) 2 exp . 2σ a2
We assume that a threshold parameter b j is distributed N ( µb ,σ b2 ) : f (b j ) =
1
σ b (2π )1/ 2
−(ln b j − µb ) 2 exp . 2σ b2
For a guessing or lower asymptote parameter g j , a beta density is used: f (g j ) =
g αj −1 (1 − g j ) β −1 B(α , β )
where B (α , β ) is the beta function. 8.3.2 Rescaling the parameters The graded response model and the partial credit model contain the element z jk (θ ) = Da j (θ − b jk ) = Da j (θ − b j + ck ).
Let us change the location and scaling of the original θ scale by θ * = Aθ − B,
612
8 ESTIMATION where θ ∼ N (m, s 2 ) and θ * ∼ N (m* , s*2 ) ; then A = s* / s and B = Am − m* . Then, the element z jk (θ * ) on the new θ * scale is expressed by z jk (θ * ) = Da*j (θ * − b*jk ) = Da*j (θ −
b*jk + B A
),
where D is the adjustment for a normal metric. We then obtain the following relations: a*j =
aj A
and b*jk = Ab jk − B.
We define b jk = b j − ck and constrain the category parameters by mj
mj
k =0
k =0
∑ ck =∑ ck* = 0. Therefore, the location shift, B, is absorbed by the item location parameter, b j . Consequently, we obtain b*j = Ab j − B and ck* = Ack . 8.3.3 The information function The item information function, I j (θ ) , is the information contributed by a specific item j. The item information for the polytomous item response model as proposed by Samejima (1974) is I j (θ ) =
mj
∑ Ajk (θ ) k =0
∂ Pjk (θ ) mj ∂ θ = θ ( ) P jk k =0
2
∑
where A jk (θ ) is called the basic function of the item response model.
613
8 ESTIMATION For the normal ogive form of the graded response model, the basic function A jk (θ ) is written as A jk (θ ) = D 2 a j
[ϕ jk (θ ) − ϕ j ,k +1 (θ )]2 A jk (θ )
where ϕ jk (θ ) is the normal ordinate for Pjk+ (θ ) . For the logistic graded response model, the basic function becomes A jk (θ ) = D 2 a 2j
[ Pjk+ (θ )[1 − Pjk+ (θ )] − Pj+,k +1 (θ )[1 − Pj+,k +1 (θ )]]2 Pjk (θ )
.
The item information for the partial credit model is I j (θ ) =
D 2 a 2j
2 mj mj T 2 P (θ ) − T P (θ ) . c jc c jc c =0 c =0
∑
∑
For the case of dichotomous item responses, the equation simplifies to I j (θ ) = D 2 a 2j (T0 − T1 )2 Pj 0 (θ ) Pj1 (θ ),
where Pj1 (θ ) = 1 − Pj 0 (θ ) . Bock (1972) proposed the information due to the response in category k of item j as the partition of the item information, that is, I jk (θ ) = Pjk (θ ) I j (θ ).
This result may be called the item’s response information function, according to Samejima’s term, although she formulated it slightly differently. The item information function may also be expressed by the summation of the response information functions: I j (θ ) =
mj
∑ I jk (θ ). k =0
614
8 ESTIMATION Finally, the test information function is defined as the summation of item information functions: I (θ ) =
n
∑ I j (θ ). j =1
8.3.4 Warm’s weighted ML estimation of ability parameters Warm’s (1989) weighted maximum likelihood (WML) estimator is obtained by maximizing the likelihood weighted by a square root of the test information function. The likelihood of a particular response vector ( U jk ) given θ is L[(U jk ) | θ ] =
∏ j =1∏ k =0[ Pjk (θ )] n
mj
U jk
.
The weighted likelihood is given by L*[(U jk ) | θ ] = f (θ ) L[(U jk ) | θ ].
Taking a natural logarithm of the weighted likelihood above yields ln L*[(U jk ) | θ ] = ln f (θ ) + ln L[(U jk ) | θ ].
A weighted maximum likelihood estimator WML( θ ) is the value that maximizes the weighted likelihood above. If f (θ ) is a positive constant, WML( θ ) is a maximum likelihood estimate of θ . If f (θ ) is a square root of the test information function I (θ ) , it is called Warm’s weighted maximum likelihood estimate, WML( θ ). This is not a Bayesian estimator of a latent trait since f (θ ) is not a prior probability, but a reciprocal of the standard error of MLE( θ ). The logarithm of the weighted likelihood for WML( θ ) becomes m 1 n j ln L*[(U jk ) | θ ] = ln Pjk (θ ) I jk (θ ) + 2 j =1 k =0
∑∑
n
mj
∑∑U ijk ln[ Pjk (θ )]. j =1 k = 0
The Newton-Raphson technique is used to obtain the MLE( θ ) or WLE( θ ) via an iterative procedure. The Newton-Raphson estimation equation is given by ∂ 2 ln L*[(U jk ) | (θ )] ∂ ln L*[(U jk ) | (θ )] θ q +1 = θ q − − . ∂θ ∂θ 2 ∧
∧
The PARSCALE program can also compute EAP( θ ) scores in addition to MLE( θ ) and WML( θ ).
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8 ESTIMATION
8.4 Estimation in MULTILOG 8.4.1 Item parameter estimation In the discussion in Section 3.1, we have assumed that the item parameters were known. Usually, they are not. Current practice requires estimation of the item parameters using empirical data. Usually it is desirable to use a sample sufficiently large that the standard errors of the estimated item parameters are small, and can be ignored in future use of the parameters. Such a sample is called a calibration sample. The sample size required for useful item calibration varies widely, depending on the format of the response and the strength of the relationship between the item responses and the trait. Constraints of the item parameters, in the form of equality constraints or prior information incorporated using Bayes' rule, facilitate estimation with relatively small samples. Large numbers of unconstrained item parameters require relatively large samples. An example of a highly constrained model is the Rasch (1960) 1PL model. Only a few hundred examinees may serve to calibrate a test under this model. On the other hand, a model with three unconstrained parameters per item (as in the 3PL: slope, location and “pseudo-guessing” parameters) may require tens of thousands of examinees to calibrate successfully (Thissen and Wainer, 1982). The relationship of the item responses to the trait is crucial: the parameters of items, which are strongly influenced by the trait, may be estimated precisely with few observations, while weakly related items may require more. So no real guidelines are available in each case for the standard errors of the parameters to determine if the precision of the estimation is satisfactory. Item parameter calibration problems fall into two broad categories: those in which θ is considered fixed and those in which it is considered random. The random- θ problem is the situation most commonly encountered in psychological measurement, but the fixed- θ case is simpler, so we will discuss that first. If θ is assumed to have fixed values, two more alternative conditions arise: the fixed values of θ may also be taken to be known, or the values of θ may be taken to be unknown parameters. In the former case, item parameter calibration is simply a problem in nonlinear regression of the item responses on θ . An example of IRT item calibration in this sort of problem is provided in the Roche, Wainer and Thissen (1975) measurement of skeletal age. The Roche et al. system makes use of graded indicators of skeletal maturity that are observable on radiographs. Thus, each indicator is like a test item, and skeletal age is estimated as was θ above. To calibrate the indicators, or items, Roche et al. defined skeletal age to be linearly related to chronological age in the population from birth to maturity. Then the parameters of the graded response model were estimated by nonlinear regression of the observed indicator grades on the ages of those measured. This procedure depends on the existence of an observed variable linearly related to the trait being measured, called a criterion. Estimation is complicated somewhat if θ is taken to have fixed but unknown values for several pre-defined groups of examinees. Bock (1983) considered such a problem in an item calibration context very similar to the skeletal age problem, in which the items to be calibrated were 96 questions from the Stanford-Binet and the examinees were again classified by age. Each age 616
8 ESTIMATION group was assumed to have some fixed mean developmental age ( θ ), which was estimated simultaneously with the item parameters. Bock (1976) and Kolakowski and Bock (1981) discuss an algorithm for the simultaneous estimation of the fixed values of θ and the item parameters in general, for any item response model. This procedure depends on the existence of a division of the examinees into homogeneous groups with respect to the trait being measured. An example, amounting to a very simple fixed effects, unknown- θ -calibration of a single “item”, is given in Chapter 5. When θ is assumed to be an unobserved random variable, the only fixed parameters to be estimated are the item parameters, but their estimation is numerically complex. For categorical item response models, Bock and Lieberman (1970) provided a theoretically satisfying but impractical algorithm; Bock and Aitkin (1981) provided the workable algorithm used in MULTILOG. Its workings are explained there, and by Thissen (1982) for the 1PL model and Thissen & Steinberg (1984) for the multiple-choice model. MULTILOG reports the closest approximation to reliability available in the context of IRT, the so-called marginal reliability. This value is (effectively) an average reliability over levels of θ ; it is an accurate characterization of the precision of measurement only if the test information is relatively uniform.
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9 USES OF ITEM RESPONSE THEORY
9 Uses of Item Response Theory11 9.1 Introduction The development of item response theory (IRT) has reached a point where testing applications, whether in educational or psychological testing programs or in research, can be carried out entirely with IRT methods. These methods have significant advantages over those of classical test theory: they improve the quality of the tests and scales produced, handle a wider range of response modes, facilitate test equating, allow adaptive test administration to reduce testing time, and offer important economies in labor and cost of test construction and maintenance. The more limited methods that grew from classical theory were strongly conditioned by the rudimentary data processing capabilities available in the formative years from 1910 to 1950. The more flexible and efficient, but computationally intensive, IRT methods could not develop and find practical use until electronic computation became widely accessible. Although classical and IRT methods now exist side-by-side in computer implemented form, IRT uses the power of computers in more varied and effective ways. For the benefit of readers who have studied and worked with classical methods prior to or along with IRT, this chapter contrasts these approaches to item response data in various areas of application. In terms of present uses of tests and scales, the following five areas perhaps cover most possibilities:
Selection testing Qualification testing Program evaluation and assessment testing Clinical testing Measurement methods and research
These five areas are discussed in Sections 9.2 to 9.6. In Section 9.7 various approaches to analysis of item response data are considered.
9.2 Selection testing Selection tests are administered to persons competing for a limited number of positions in some organization or program. Examples are college entrance examinations, employment tests, civil service examinations, military enlistment tests, etc. A test as an aid to selection is valuable if it predicts with acceptable accuracy some criterion of the person’s later performance. First-year college grades point average, success in a job training program, and on-the-job productivity are typical examples of performance criteria. By suitable choice of item content and operating characteristics, tests can be constructed to maximize correlation between the test scores and some measure of the criterion. In most applications, prediction is further improved by use of multiple
11
This section was contributed by R. Darrell Bock.
618
9 USES OF ITEM RESPONSE THEORY regression procedures to combine the test score with other information about the applicant. An major economic role of selection tests is to reduce losses incurred when a person selected for a position proves untrainable or unable to perform work assignments satisfactorily.
9.3 Qualification testing Qualification tests results are used in connection with education or training as evidence that a person has attained an acceptable level of knowledge or skills. Examples are tests required in school promotion or graduation, licensing examinations of persons going into professions such as law or medicine, pre-service testing of public school teachers, etc. In these applications a clear-cut criterion of later performance rarely exists. The rational justification of the test is that it samples the domain of competence. The percent of items on the test that the examinee responds to satisfactorily is assumed to estimate the percent of mastery of the domain, which must be sufficiently high to “pass” the test. Because qualification tests often have high-stakes for persons taking them, they must be carefully constructed to ensure that they represent fairly the domain of competence and give consistent results from one test form to another.
9.4 Program evaluation and assessment testing Evaluation tests are administered to persons in some program of education or training, not for individual qualification, but to evaluate whether the program, or institution conducting the program, is achieving its instructional goals. In the evaluation of schools or school systems, this testing is now referred to as assessment. The objective of assessment is to stimulate and guide change and improvements in instruction when they are needed. An important requirement of assessment is, therefore, that it include measures of outcomes in all main areas of instruction; otherwise, under pressure to obtain favorable results, schools may concentrate instruction on areas that are tested at the expense of those that are not. Because assessment programs are often carried out in very large scale at state or national levels, and are intended to measure achievement trends over a period of years, attention to the efficiency of the test forms to deliver accurate and stable results is of the greatest importance.
9.5 Clinical testing Clinical tests in fields such as counseling psychology, pediatrics, and psychiatry help in the identification of learning difficulties and behavioral disorders. The Binet and Wechsler I.Q. scales are well-known examples of tests administered to children to determine whether they are learning and reasoning at the level expected for their chronological age. The Minnesota Multiphasic Personality Inventory (MMPI) is the leading self-report device for obtaining information about personal adjustment problems and neuroses. Clinical tests are administered and interpreted only by qualified professionals, usually on a one-to-one basis with the client. Ideally, they produce a profile of scores exhibiting patterns that aid diagnosis of the behavioral problem. Because these tests are limited to controlled clinical settings, they are in little danger from overexposure or compromise and can remain in the same form over a period of years.
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9.6 Measurement methods and research In fields devoted to the study of human behavior—including psychology, education, sociology, political science, and market research—tests and scales are instrumental in making behavioral phenomena accessible to quantitative analysis. In so doing, they serve to operationalize constructs derived from some theory. Measurement methodology becomes relevant when the instruments consist of multiple items, responses to which must be combined into a score or profile of scores for each observational unit (usually a person). Responses of a sample of persons to items that can be interpreted in isolation—for example, opinion about a particular public issue, or preference for a certain consumer product—can be analyzed by statistical methods for aggregatelevel categorical data and do not require classical test theory or IRT. If there are many alternative items that could represent the construct, however, the question will arise as to how well the particular items in a test or scale serve in this role. The fundamental question is whether the items represent one construct or more than one construct, and if the latter, how many and how characterized. The item factor analysis procedures discussed in Section 7.5 provide some relevant answers. Once a construct is identified and items representing it are in hand, the questions focus on the measurement characteristics of the resulting test or scale:
can the full range of variation in the population of potential respondents be measured with acceptable precision? what is the measurement error variance at various points on the scale? are scores obtained at different sites of application, or on different occasions in time, stable and consistent? if ratings based on human judgments are involved in scoring, are results sufficiently reproducible between judges or between teams of judges recruited and trained at different sites and times?
Classical test theory, especially generalizability theory, answers these questions in an average sense, while IRT test information analysis gives a more detailed account of measurement error throughout the range of scores. In addition, IRT test equating facilitates the construction of parallel test forms measuring the construct, and these in turn can serve as “multiple indicators” for structural equation modeling to validate the construct through its relationships with external variables.
9.7 Approaches to analysis of item response data Once the test has been administered to persons in some population of interest and the item responses are in hand, certain analysis operations must be performed to put the information in the data in usable form. Many testing programs and research organizations still perform these operations with procedures based entirely on classical test theory, others rely on a mixture of classical and IRT methods, and a few others use IRT methods exclusively.
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To give some idea of how day-to-day work of data analysis may change in a shift from classical to IRT procedures, this section compares the two approaches in the areas of application detailed previously. It also serves as guide to the references for further reading at the end of the subsections. Although no two persons would likely agree on which or how many such aspects of data analysis deserve attention, twelve topics frequently appearing in the current literature are discussed in the following sections. They are test scoring, test generalizability, item analysis, estimating the population distribution, differential item functioning, forms equating, vertical equating, construct definition, analysis and scoring of rated responses, matrix sampling, estimating domain scores, and adaptive testing. 9.7.1 Test scoring A given of classical test theory is that the score on a test in which the responses are marked correct or incorrect is the number correct or percent correct. Minor variations are the score on a multiple-choice test corrected for guessing (number correct minus the quantity (number incorrect divided by the number of choice alternatives less one) or arbitrary scoring formulas in which some items count for more than others. It is an interesting fact that the number correct score was not part of the first rationally developed standardized test—the Binet-Simon Intelligence Scale, first published in 1909. The test consisted of an age-graded series of tasks and questions presented successively to a child by a test administrator. The score on the test, called the child’s “mental age”, corresponded to the highest of several age-graded items that the administrator found the child could complete successfully. The child’s “I.Q.” was defined as this mental age divided by chronological age. There is a sense then in which measurement methodology has come full circle, for, except in some special cases, number-correct is not a summary statistic used by IRT in computing the score of a person taking the test. IRT uses instead the person’s total pattern of correct and incorrect responses to the test items to estimate a score on the construct scale. The result is referred to as a “full information” estimate: it makes use of all information in the answer pattern, not just that in the number-correct count. Finding this IRT “scale score” is much like locating the test taker’s position on the Binet-Simon scale, except that the continuum on which it is expressed is not an external variable, such as age, but is a construct inferred from the internal consistency of item responses within the sample data. During the early period when IRT was oriented primarily toward selection testing, this construct was called “ability”. Later, as qualification and program evaluation became more prominent in testing, the term “proficiency” was introduced. In other areas of application—consumer research, for example—“preference” or “propensity” would be apposite. “Proficiency” is used in the present writing. The process of inferring a proficiency continuum is based on a mathematical model expressing the probability of a correct response to an item as a function of the scale position of the person taking the test and the values of certain parameters specific to the item. In effect, the continuum is constructed so that the patterns of correct and incorrect responses in the sample data are best accounted for by the item models. The models most commonly used for this purpose are the socalled normal ogive model, based on the normal distribution function, and the very similar but mathematically simpler logistic function.
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9 USES OF ITEM RESPONSE THEORY These models have so-called threshold parameters that are related to the difficulty of the item and determine where the item is located on the inferred scale. They also have slope parameters, related to the discriminating power of the items, that determine how much each will influence estimation of the proficiency scores. The score on the test is that point on the scale where, when the person’s score is substituted in the item response models, the person’s pattern of correct and incorrect responses is best accounted for. Scores determined in this way can be represented, along with the item thresholds, on the proficiency scale in the same manner that mental ages and age-graded items appear on the Binet intelligence scale. The IRT method of extracting information about the person from the item responses, although more intricate than the simple number-correct count, has several important advantages in testing practice. First, the person’s scale score is little affected by adding or deleting items from the test. The precision with which the scale point is located may change, but the meaning of the scale and its units of measurement remain the same. This is not true of the number-correct score or even the percent-correct score: they vary depending on the difficulties of the items added or removed; if the average difficulties of the items change, the difficulty of the test as a whole is altered. This does not happen with the IRT scale score because differences in item difficulty are accounted for by the threshold parameters of the item response model. Second, the IRT scale scores have smaller relative measurement error than number-right scores because the influence of the items on the estimate is adjusted by the discriminating power parameters to minimize error. Finally, the IRT scale-score concept generalizes in a direct and consistent way to other response modes—for example, extended responses to open-ended items scored in graded categories by raters. Classical test theory has no comparable capability; it merely resorts to arbitrary assignment of numerical values to the various grades and summing the values to provide a score. Apart from some remarks and references to examples in connection with item analysis and analysis of rated responses, technical particulars of how item parameters and scale scores are estimated from item response data are beyond the scope of this section. Computer programs that implement the IRT methods of estimation are described in the chapters to follow. 9.7.2 Test generalizability A fundamental concept of both classical test theory and IRT is that the items of a test are a sample from some larger domain of items or tasks, any of which might equally well have been presented to the test taker. A score from any such test therefore raises the essential question of sampling—namely, how much error variation in the test score must be attributed to the sampling process. In classical test theory, this question is posed in terms of the so-called true score model, in which the observed test score is assumed to be the sum of a true score component and error component. The two components are defined to be statistically independent, such that the variance of the test score in the population of persons to be tested equals the sum of the variances of the components. These variances can be estimated in test data by giving correct responses a score of 1 and incorrect responses a score of 0, and carrying out a person-by-items analysis of variance. On the assumptions underlying this model, the square root of the estimated variance of the error component is the standard error of measurement of the test. It can be used, for example, to 622
9 USES OF ITEM RESPONSE THEORY place an approximate 90 percent confidence interval on the true score (i.e., observed true score plus or minus 2 times the standard error). The variance estimates can also be used to calculate a generalizability index for the test as the ratio of the true score variance to the sum of the true score variance and the error variance. This index is variously referred to as coefficient α , KuderRichardson reliability, or test reliability. It can be modified to predict the coefficient of generalizability of a test in which the number of items sampled is increased n times merely by dividing the error variance component in the ratio by that factor. The resulting formula is equivalent to the Spearman-Brown prophecy formula of classical test theory. A more penetrating treatment of the classical model, called strong true score theory, shows the preceding results to be an oversimplification. The standard error of a test score is not constant but varies with the value of the test score. IRT results take this into account by providing, not just one error estimate, but an error function, computable from the item parameters, that yields an estimate of the error variance specific to every point on the scale score. This function typically shows the test to have the highest precision in the region of the scale where the item locations are most dense. For a test in which the greater part of the item set is in the middle range of difficulty, the error function tends to be “U” shaped. A related, very useful concept is that of the test information function, which is the reciprocal of the error function. The information function shows the relative precision of the test at every point on the scale. High values of the information correspond to high precision of the scale score and low values to low precision. The important property of the test information function is that it is the sum of corresponding information functions of the items. Item information functions depend on both the item location and its discriminating power. The maximum of the function occurs for the normal and logistic models, for example, at the location of the item threshold, and the height of the function increases and decreases with item discriminating power. The test information thus shows in quantitative detail how the measurement precision of a test can be adapted to a particular application by the placement of items of differing difficulty and discriminating power. A test can be made highly informative in a narrow score range by concentrating items in that interval, or made uniformly informative over a wide range by spacing items evenly over the range. Incorporating effects of both item thresholds and discriminating powers, plots of item information functions play the same role in IRT that plots of item difficulty vs. part-whole correlation play in classical test theory. 9.7.3 Item analysis In classical test theory, the estimation of item difficulties, part-whole correlations and other characteristics, such as distractor use in multiple-choice items, is referred to as item analysis. The corresponding operations of IRT theory are called item calibration. For the normal ogive or logistic model calibration involves the estimation of the item thresholds and discriminating powers from samples of item response data. If the test contains multiple-choice items, then a modification of these models that accounts for chance successes may be used. These so-called threeparameter normal and logistic models require estimation of item-specific probabilities of correct response due to guessing in addition to the threshold and slope parameters. In most instances, only the more difficult items of a test, with their greater frequency of wrong responses, require a three-parameter model. 623
9 USES OF ITEM RESPONSE THEORY An important purpose of item analysis, both in classical and IRT methodology, is to check the extent to which each item belonging to some larger set represents the construct that the set is intended to measure. The classical item statistic that conveys this information is the part-whole correlation (computed as the biserial or point biserial correlation between the item 0, 1 score and the test score). Of course, this correlation succeeds in this role only if the preponderate majority of the items in the test are validly construct-related. When this condition is satisfied and a small minority of items depart from the construct or are in some way ambiguous, their part-whole correlations will be low. The IRT statistic that functions in a similar way is the slope parameter of the item response model—high slopes correspond to high part-whole correlations, and vice versa. In fact, the slope statistics can be converted into a correlation index, similar to part-whole correlation, that measures the relationship between the item response and the inferred construct. An operational difference between classical and IRT procedures, however, is that during score estimation the presence of a very low slope parameter will automatically nullify the influence of the item, whereas the item must be specifically excluded from the classical number-correct score. The other essential statistic of classical item analysis is item difficulty or, more accurately item facility—namely, the percent or proportion of correct responses to the item when the test is administered to a sample of persons representing the relevant population. It is well known from classical and IRT theory that an item is most informative about a particular person when that person’s probability of responding correctly is in the neighborhood of one-half. Although this probability will differ considerably among persons in the population, it is advisable from the standpoint of minimizing the average measurement error that test items be selected so that an appreciable proportion of persons has an intermediate probability of correct response. Near zero or 100 percent chances of correct response across the population as a whole are of no help in measurement. The IRT statistic that measures item difficulty is the item-threshold parameter, located at or near the point on the scale where a person with that scale score will have probability onehalf of responding correctly. This parameter is also sometimes referred to as the item location. It is not related in a simple way to the percent of persons in the population expected to respond correctly to the item, but if the origin or units of the IRT scale are chosen suitably, the threshold parameter conveys similar information. The appropriate scaling convention for this purpose is to set respectively values 0 and 1, to the mean and standard deviation of the distribution of scale values in the sample data. If, as is often the case, this distribution is approximately normal, the item thresholds are on a scale in which their values correspond to normal deviates in the population of persons. At the core of any IRT item analysis is the algorithm for estimating parameters of the response models from a sample—preferably a large sample—of data obtained by administering the test to persons in some population of interest. Fitting models to such data is referred to as item calibration. The most general and robust procedures for this purpose, applicable to any well-identified, twice-differentiable model, are based on the statistical techniques of maximum marginal likelihood or Bayes estimation. These methods give a single best estimate of each parameter of each item, and also an interval estimate indicating the effect of sampling variation. They also provide for statistical tests of the improvement of fit to the data when additional parameters are included. With the multiple-group IRT models discussed below, more general forms of these methods also estimate the proficiency distributions of the populations corresponding to the groups.
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9 USES OF ITEM RESPONSE THEORY As mentioned above, IRT theory gives more precision to item selection criteria by combining the information in the item slopes and thresholds into item information functions, that accumulate additively to form the test information function. A plot of item and test information functions on the same scale as the sample score distribution conveys clearly how the items or tests will perform in the population of interest. The same approach applies to a classical statistic pertaining to multiple-choice items—namely, the percent of responses in the sample falling into each of the alternatives of the multiple-choice item. In IRT, the nominal categories model gives the probability of the correct response and of each of the distractors as a function of the scale score. It is easy to identify in these plots the distractors that are not functioning as desired in various regions of the range. In addition, the analysis under this model shows the amount of construct related information in the distractors as well as in the correct response. In many cases, plausible distractors contain information that can improve the precision of estimated scale scores and can be recovered by the IRT scoring procedure based on the model. This model is implemented in the MULTILOG program. 9.7.4 Estimating the population distribution For purposes of norming test results, it is necessary to estimate the distribution of test scores in the population of interest. This presents a problem for classical test theory for two reasons. First, the number-correct test scores contain both true score variation and measurement error variation; since the measurement error variance is a function of test length, the variance of the score distribution therefore depends on an arbitrary choice in test construction. Second, the shape of the test score distribution depends arbitrarily upon the distribution of item difficulties in the test; tests with severely skewed distributions of item difficulties will produce skewed distributions of test scores in the population. Classical test theory sidesteps these problems by expressing norms as population percentiles, which are invariant with respect to the spread or shape of the score distribution. Further analysis of the test scores by statistical methods that assume a normal distribution may still be affected, however. IRT theory is more favorable in this respect in that the shape of the observed scale score distribution is relatively little influenced by the distribution of item difficulties. If the true score distribution is approximately normal, for example, the scale score distribution will be also. The variance of the latter is still increased by measurement error, but as is also true of test scores, the effect can be largely suppressed independent of test length by computing so-called “regressed” or “shrunken” estimates as a function of test reliability. The Bayes (EAP) regressed and Bayes modal (MAP) scores provided by the programs are regressed estimates. IRT can handle this problem more rigorously, however, by estimating an inferred latent distribution of proficiency scores. The shape of the latent distribution is estimated directly from the patterns of correct and incorrect responses to the test items and does not involve the test scores. If there is only one sample group in the analysis, the location and dispersion of the latent distribution are indeterminant and must be set arbitrarily (e.g. to 0 and 1). If there are multiple sample groups in the analysis, locations and dispersions of their latent distributions can be set relative to a designated reference group or relative to arbitrarily set values in the combined groups. Multiple-group analysis is implemented in the BILOG-MG and MULTILOG programs.
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9 USES OF ITEM RESPONSE THEORY 9.7.5 Differential item functioning Almost any population of potential test takers will consist of identifiable subpopulations— different age groups, the two sexes, urban or rural residents, education levels, ethnic and language groups, etc. Relevant information on group membership may be available from background questionnaires administered along with the test. If so, the data will allow investigation of whether persons in one such group experience differences in item difficulty or discriminating power relative to those in other groups when all groups have equal mean scores on the test as a whole. When this is the case, the test is said to exhibit differential item functioning (DIF). DIF is essentially item by group interaction in item difficulty or discriminating power. If at the same time, the groups show unequal mean test scores, the test is said to have adverse impact on the groups that perform more poorly. Adverse impact can, of course, also occur in the absence of DIF. Since DIF in effect alters the substantive meaning of the test score from one group to another, it is undesirable and should be eliminated if possible. An English language vocabulary test with words of Latin or Germanic origin, for example, will tend to show DIF with respect to English or Spanish as first language acquired. If only a few items of the test exhibit DIF, they usually can be removed without impairing measurement of the intended construct. The problem for the data analyst is how to detect DIF in tests that may also show adverse impact. There are both classical and IRT approaches to this problem. The classical methods look for differences in item difficulty among persons from different background groups whose tests scores are equal or fall in a narrow score interval. A summary statistic for these differences over the scores or score intervals provides a measure of DIF; an associated statistical test establishes its presence. Based on a log-linear model of item by group interaction, a similar analysis can be carried out with the so-called Mantel-Haenszel statistic. The IRT treatment of DIF is an example of multiple-group analysis in which item thresholds or discriminating power are estimated separately in each group and jointly with the group latent distributions, under the restriction that the means of the item thresholds must be equal in all groups. The item guessing parameters, if any, are also restricted to be equal among groups. IRT estimation of DIF effects includes standard errors that can be used to assess statistical significance of effects for individual items. In addition, a test of DIF in all items jointly is provided by comparison of the goodness-of-fit of the response model when different thresholds or discriminating power are assumed vs. the fit when a single set of thresholds or discriminating power is estimated in the combined data. The IRT method of analyzing DIF is in general more sensitive than its classical counterparts, especially with shorter tests, because IRT better defines the latent construct measured by the test. DIF in item difficulty is implemented in BILOG-MG, PARSCALE and MULTILOG. DIF in discriminating power is implemented in PARSCALE and MULTILOG. 9.7.6 Forms equating Many testing programs must update their test at regular intervals to prevent overexposure and compromise of the item content. This creates the problem of keeping the reporting scores for successive forms comparable so that a person is neither advantaged nor disadvantaged by taking one form rather than another. Somehow, the reported results must allow for the differences in 626
9 USES OF ITEM RESPONSE THEORY overall difficulty of the forms that inevitably occur when items are changed. Classical test theory solves this problem by equivalent-groups equating. This method requires the alternative forms to be assigned randomly to persons in some large sample. The randomization ensures that persons taking different forms will have essentially the same true score distribution (provided that the successor forms are of the same length as the preceding form and have similar distributions of item difficulties and discriminating powers). If these conditions are met, the test scores for the new forms can be expressed on the same scale as the old forms by assigning them to the corresponding points of their respective observed score distributions. This is the equipercentile method of keeping the score reports comparable to one another through successive generations of test forms. If the distribution of item difficulties within the forms is more or less normally distributed and well centered for the population, the test score distributions will be approximately normal. In that case, a nearly equivalent equating can be obtained merely by standardizing the scores of the various forms—that is, by setting the mean and standard deviations of their respective distributions to any convenient fixed values. This method is called linear equating. Since IRT scale scores are much more likely to approximate normal distribution than numberright scores, equipercentile equating is less needed in IRT applications. Linear equating suffices, and it happens automatically if the origin and unit of the IRT scale is set so that the scale scores have a specified mean and standard deviation in the sample. In addition, IRT is unique in allowing the equating forms administered to non-equivalent groups—i.e., groups with different true score distributions. This type of equating requires, however, that the test forms share a certain number of common “linking” items. Provided the linking items do not exhibit DIF with respect to the groups, multiple-group IRT analysis of all forms together automatically produces a single IRT scale on which the reported scores are comparable. The multiple-group procedure estimates separate latent distributions of the groups jointly with the item parameters of all the forms. The advantage of this method is that it does not require a separate administration of the forms to some group of persons for purposes of equivalent groups equating. Forms can, for example, be updated in the course of operational administrations of the test in which a certain proportion of items from the previous year’s forms is carried over to the current year’s forms. A random sample of examinees from the previous year’s operational testing provides data for one of the groups, and a similar sample from the current year provides data for the other. The resulting scale scores are linearly equated to those of the previous year by setting the mean and standard deviations of the latent distribution of the first group to its previous year’s values. Estimates of change in the mean and standard deviation between years are a by-product of the equating. If desired, nonequivalent groups equating can be carried back more than one year, provided linking items exist between at least adjacent pairs of forms. Multiple-groups forms equating is implemented in BILOG-MG and MULTILOG. 9.7.7 Vertical equating In school systems with a unified primary and secondary curriculum, there is often interest in monitoring individual children’s growth in achievement from Kindergarten through eighth grade. A number of test publishers have produced articulated series of tests covering this range for subject matter such as reading, mathematics, language skills, and, more recently, science. The tests are scored on a single scale so that each child’s gains in these subjects can be measured. The ana-
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9 USES OF ITEM RESPONSE THEORY lytical procedure for placing results from the grade-specific test forms on a common scale for this purpose is referred to as vertical equating. The most widely used classical method of vertical equating is the transformation of test scores into so-called grade equivalents. In essence, the number-correct scores for each year are scaled in such a way that the mean score for the age group is equal to the numerical values of the grades zero through eight. This convention permits a child’s performance on any test in the series to be described in language similar to that used with the Binet mental age scale. One may say of a child whose reading score exceeds the grade mean, for example, that he or she is “reading above grade level”. For IRT, vertical equating is merely another application of non-equivalent groups equating in which the children administered particular grade-specific tests correspond to the groups. As in the equating of updated forms mentioned above, linking items between at least consecutive forms in the series are required. They must be provided in each subject matter included in the graded series. (Note that grade-equivalent scaling does not require linking items.) The two methods produce quite different scales. Grade equivalents are of course linear in school grade. They treat the average gain between first and second grade, for example, as if it were equal to that between seventh and eighth. On this scale, the amount of variation between children’s scores appears to increase as the cohort moves through the grades, and there is a corresponding positive correlation between a child’s average score level over the years and the child’s average gain. In other words, children who begin at a lower level appear to gain less overall than those who begin at a higher level. This so-called “fan-spread effect” is regularly seen in all subject matters. On an IRT vertically equated scale, average gains are generally greatest at the earlier grade levels and decrease with increasing grade. Within grade, standard deviations are fairly uniform, and the correlation between children’s average score levels and their gains are small, or even slightly negative in some subject matters. Unfortunately, there is no objective basis for deciding which of these scales better represents a child’s true course of growth in knowledge and skills during the school years. Different IRT models assuming other transformations of the proficiency scale could be made to fit the item response data equally well and yet exhibit much different appearing relationships between grade level and average score or average gain. Extrinsic considerations would have to be brought to bear on the question to determine a preferred scale. For example, if one wished to compare annual average gains in test performance of children in different classrooms when assignment to classrooms is non-random, the scale that showed zero correlation between level and gain would be most advantageous. IRT vertical equating comes much closer to this ideal than grade equivalents, but might require some further transformation, possibly subject matter and site specific, to attain complete independence of level and gain. (See Bock, Wolfe & Fisher, 1996, for a discussion of this topic).
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9 USES OF ITEM RESPONSE THEORY 9.7.8 Construct definition The discussion up to this point assumes that all items in the test measure the same underlying construct. When it is not clear that the item set is homogeneous in this sense, steps must be taken to explore the construct dimensionality of the set. The classical approach to this problem is to perform, in a large sample of test data, a multiple factor analysis of the matrix of tetrachoric correlations between all pairs of items. The more familiar Pearson product-moment correlation of item responses assigned different numerical values if correct or incorrect (phi coefficient) is not generally satisfactory for this purpose because variation in item difficulties introduces spurious factors in the results. Random guessing on multiple-choice items has a similar effect that must also be allowed for. Tetrachoric correlations with corrections for guessing are largely free of these problems, but they have others of their own. One of these is computational instability that appears when the correlations have large positive or negative values and the item difficulties are very low or high; in these cases, it is often necessary to replace the correlation in the matrix with an attributed default value. The other problem is that factor analysis of tetrachoric correlation matrices almost always produces a certain number of small, unreal factors that are meaningless and must be discarded. IRT improves on this procedure by a method of full information item factor analysis that operates directly on the patterns of correct and incorrect responses without intervening computation of correlation coefficients. In effect, this method fits a multidimensional item response model to the patterns in the sample data. Full information item factor analysis is robust in the presence of omitted or not-presented items and is free of the artifacts of the tetrachoric method. It also provides a statistical test of the number of factors that can be detected in the data. The objective of both classical and IRT item factor analysis is the identification of items with similar profiles of factor loadings—an indication that they arise from the same cognitive or affective sources underlying the responses of persons taking the test. Objective methods of rotating the factor structure, such as orthogonal varimax rotation and non-orthogonal promax rotation, are especially effective in picking out clusters of items that identify these implicit constructs. The presence of significant multiple factors in the data means that there are corresponding dimensions of variation in the population of persons. In some cases, actual subgroups in the population associated with particular factors can be identified by including demographic variables in the analysis. Alternatively, they may be found by conventional multiple regression analysis of factor scores for the persons, which are also provided by IRT full information item factor analysis. Full information item factor analysis is implemented in the TESTFACT program. 9.7.9 Analysis and scoring of rated responses When tests contain items or exercises that cannot be scored mechanically, the responses are often rated on a graded scale that indicates quality or degree of correctness. For individually administered intelligence tests the grading is done by the test administrator at the time the response is recorded. For group administered open-ended exercises and essay questions, written responses are graded later by trained raters. In both cases, the additional information conveyed, beyond that provided by correct-incorrect scoring, provides better justification of the considerable cost of graded scoring. 629
9 USES OF ITEM RESPONSE THEORY In addition to problems that may arise in preparing the rating protocol and training the raters for graded scoring, analysis of the resulting data presents other difficulties not encountered with correct-incorrect scoring. How to combine the ratings into an overall score in a rational way is not at all clear in classical test theory—especially so if the test also includes multiple-choice items. The classical approach never goes much beyond mere assignment of arbitrary numerical values to the scale categories and summing these values to obtain the test score. The arbitrariness of this method, and the fact that items with different numbers of rating categories receive different weights in the sum, has always proved troublesome. In this respect, IRT methods are a very considerable advance. Item response models now exist that express the probability of a response falling in a given graded category as function of 1) the respondent’s position on the IRT scale, 2) parameters for the spacing of the categories, and 3) the difficulty and discriminating power of the item. Models for items with different numbers of rating categories and models for dichotomously scored responses can be mixed in any order when analyzing items or scoring tests; arbitrary assignments of score points are not required. The IRT test scoring based on these models makes use of the information in the pattern of ratings in a way that is internally consistent in the data and minimizes measurement error. The IRT approach to graded data allows tests to have more interesting and varied item formats and makes them accessible to IRT methods of construction and forms equating. Provision for graded scores is included in PARSCALE and MULTILOG. 9.7.10 Matrix sampling Testing at the state and national level plays a part not only in counseling or qualification of individual students, but also in evaluating the effectiveness of instructional programs, schools, or school systems. The objective is to compare instructional programs and schools with respect to their strength and weaknesses in promoting student achievement in various categories of the curriculum. Testing used in this way is referred to as assessment to distinguish it from studentoriented achievement testing. Educational assessment is typically carried out in large-scale surveys, often on a sampling basis rather than a total census of schools and students. The sampling approach consists of drawing a probability sample of schools and, within these schools, testing a random sample of students. To minimize the burden on schools and students alike there is an attempt to test as many curricular categories as possible in a limited amount of time, usually one class period. This is accomplished by assigning randomly to the selected students one of 20 or 30 different test forms each containing only a small number of the items representing a category. Usually the categories are main topic areas within subject matters. The total sampling design can be laid out as a table in which the rows correspond to schools and students tested and the columns correspond to items sampled for the test forms and the categories within forms. This arrangement is referred to as a matrix sample. In the original conception of a matrix-sampled assessment, the score to be assigned to programs, schools, states, or demographic groups is the average aggregated percent-correct for the items in each subject topic. As an aid to interpretation of differences between groups or between assessment years, statistical theory provides formulas for standard errors for these average scores under the assumptions of matrix sampling. This treatment of the data is within the framework of the number-correct score concept of classical test theory, although no explicit scores for individual 630
9 USES OF ITEM RESPONSE THEORY students are computed. A problem with average-percent correct reporting occurs, however, if the assessment aims at monitoring trends in average achievement over successive years. When the time comes to update the items of the assessment instrument, new items substituted for old inevitably introduce changes in average scores at higher levels of aggregation—changes which may be larger than the expected differences between year or programs or schools. Although scores on the successive instruments can be made comparable by equivalent groups equating, very large sample groups are required to bring the equating errors below the size of the smallest difference that would have policy implications. IRT nonequivalent groups equating, which can be done in the full operational samples, is much more cost effective in this situation. It requires only that a certain proportion of items from the previous assessment be carried over into the update to serve as links between successive forms. Typically, one-third of the items are retained as links. A large random sample of cases from the two assessments are then analyzed in a multiple-group IRT calibration that estimates the latent distributions for the two assessment samples jointly with the new set of item parameters. The link items serve to set the origin and unit of scale equal to those of the previous assessment. Paralleling the average percent-correct approach, IRT can also estimate scores at the group-level without intervening score estimation for individual students. This can be done in one of two ways. If the interest is only in comparing mean scores among schools or higher level aggregates, these quantities can be estimated directly from counts of the number of times each item is presented to a student in the group, and of these, the proportion correct. The group means are estimated on a scale that is standardized by setting the mean of the estimated group means weighted by the numbers of persons testing in the respective groups, and the standard deviation of the estimated group means calculated in a similar weighted form. Standard errors with respect to the sampling of students within schools are available for the estimated school means, and the higherorder aggregate means. If it is also of interest, however, to know something about the distribution of student achievement within the aggregate groups, multiple-group IRT analysis can be used to estimate the latent distributions within the groups directly, without estimating scores for individual respondents. The procedure is more efficient for a definite form of latent distribution, such as the normal or other distribution that depends on a relatively small number of parameters. If a completely general form is assumed, a nonparametric procedure, possibly involving computer simulations, may be necessary. 9.7.11 Estimating domain scores Both classical test theory and item response theory have to contend with the arbitrary nature of test scores as measurements. As mentioned above, the classical number-correct score, and even the length-independent percent-correct score, depend arbitrarily upon the difficulties of the items selected for the test. The IRT scale score, although relatively free of that problem, is nevertheless expressed on a scale of arbitrary origin and unit. The earliest and still most widely used method of removing this arbitrariness is to scale the scores relative to their distribution in some large 631
9 USES OF ITEM RESPONSE THEORY sample of persons taking the test. This is most commonly done by expressing the scores as percentiles of the distribution or as standardized scores, i.e., subtracting the mean of the distribution from the observed score and dividing by the distribution standard deviation. This approach to reporting test scores is called norm referencing; it assumes that comparisons between persons is the object of the testing, which undeniably it is in selection testing. In the context of qualification testing, however, a more relevant objective is whether a person taking the test shows evidence of having learned or mastered a satisfactory proportion of the knowledge and skills required for qualification. Similarly, in program evaluation the objective is whether a sufficient proportion of students in a program has reached a satisfactory level of learning or mastery. Reporting test results in these terms is referred to as domain referencing, or in a somewhat similar usage, criterion referencing. For domain referencing to be realizable in practice, some reasonably large pool of items or exercises must exist to define the domain operationally. Particular tests containing items or exercises from the pool may then be selected for purposes of estimating domain scores. The classical method of domain score estimation is to assume that items of the test are a random sample of the pool. In that case, the test percent-correct directly estimates the domain percentcorrect, and its standard error can be computed from the test’s generalizability coefficient. IRT can improve upon this estimate if response models for items in the pool have been calibrated in data from a relevant population of examinees. With this information available, the items selected for a particular test do not need to be a random sample of the pool. They need only be link items in tests calibrated by non-equivalent groups equating. In that case, one estimates the domain score by first estimating the person’s IRT scale score, then substituting the score in the model for each test item to compute the person’s corresponding probability of correct response: the IRT estimated domain score is the sum of these probabilities divided by the number of items on the test. Domain scores estimated in this way are more accurate than classical estimates because they take into account the varying difficulty and discriminating power of the items making up the test. These methods of estimation can be carried out with multidimensional as well as unidimensional response models. Domain scores are implemented in the BILOG-MG program. 9.7.12 Adaptive testing Adaptive testing is a method of test administration in which items are chosen that are maximally informative for each individual examinee. Among items with acceptable discriminating power, those selected are at a level of difficulty that affords the examinee a roughly 50 percent probability of correct response. This corresponds to minimum a priori knowledge of the response, and thus maximum information gain from its observation. The two main forms of adaptive test administration are two-stage testing and sequential item testing. In the two-stage method, which is suitable for group administration, a brief first-stage test is administered in order to obtain a rough provisional estimate of each examinee’s proficiency level. At a later time, a longer second-stage test form is administered at a level of difficulty adapted to the provisional score of each examinee. In sequential adaptive testing, usually carried out by computer, a new provisional estimate of the examinee’s proficiency is calculated after each item presentation, and a most informative next item is chosen based on that estimate. 632
9 USES OF ITEM RESPONSE THEORY The presentation sequence begins with an item of median difficulty in the population from which the examinee is drawn. Depending on whether the response to that item is correct or incorrect, the second item chosen is harder or easier. The presentations continue in this manner until the successive provisional estimates of proficiency narrow-in on a final value with acceptably small measurement error. Unlike two-stage testing, this method of administration requires the adaptive process to be carried out during the testing session. For this reason computer administration is possible only if the items are machine scorable. When IRT scale scores are used to obtain the provisional estimates of proficiency in computerized adaptive testing, the presented items must be calibrated beforehand in data obtained nonadaptively. Once the system is in operation, however, items required for routine updating can be calibrated “on line”. For this purpose, new items that are not part of the adaptive process must be presented to examinees at random, usually in the early presentations. Responses to all items in the sequence are then saved and assembled from all testing sites and sessions. A special type of IRT calibration called variant item analysis is applied in which parameters are estimated for the new “variant” items only; parameters of the old items are kept at the values used in the adaptive testing. Because IRT calibration as well as scoring can be carried out on different arbitrary subsets of item presented to respondents, the parameters of the variant items are correctly estimated in the calibration even though the old items have been presented non-randomly in the adaptive process. Variant item analysis is implemented in the BILOG-MG program. With different examinees presented items of differing difficulty in adaptive testing, the numbercorrect score is not appropriate for comparing proficiency levels among examinees. For this reason, no treatment of adaptive testing appeared within classical test theory, and hardly any discussion of the topic arose until item response theory made it possible to estimate comparable scores from arbitrary item subsets. That development, combined with the availability of computer terminals and microcomputers, has made sequential testing a practical possibility. Significant applications of computerized adaptive testing have followed, particularly in the area of selection testing. Apart from its logistical and operational convenience, the primary benefit of this method of test administration is in reducing testing time. As little as one-third of the time required for a non-adaptive test suffices for a fully adaptive sequential test of equal precision.
633
10 BILOG-MG EXAMPLES
10 BILOG-MG examples 10.1 Conventional single-group IRT analysis This example illustrates how the BILOG-MG program can be used for traditional IRT analyses. The data are responses to 15 multiple-choice mathematics items that were administered to a sample of eighth-grade students. The answer key and the omitted response key are in files called exampl01.key and exampl01.omt, respectively (defined on the INPUT command). The data lines, of which the first few lines are shown below, contain 15 item responses. This is the simplest form in which raw data can be read from file: there is one line of data for each examinee, and the response to item 1, for example, can always be found in column 6. All items are used on the single subtest. Item responses start in column 6 as reflected in the format statement (4A1,1X,15A1). 1 2 3 4
242311431435242 243323413213131 142212441212312 341211323253521
Exampl01.key contains a single line: KEY
341421323441413
With such a short test (15 items), item chi-squares are not reliable. For illustration purposes the minimum number of items needed for chi-square computations has been reduced from the default of 20 to the number of items in this test, using the CHI keyword on the CALIB command. With the item chi-squares computed, the PLOT=1 specification can now be used to plot all the item response functions. Note that the ICCs produced with the IRTPLOT program in the Windows version of BILOGMG display the χ 2 -test statistics, degrees of freedom, and probability, as well as the observed response probabilities only for those items that have a significance level below the value specified with the PLOT keyword. The scoring phase includes an information analysis (INFO=2) with expected information indices for a normal population (POP). Rescaling of the scores and item parameters to mean 0 and standard deviation 1 in the estimated latent distribution has been requested (RSC=4). Printing of the students' scores on the screen is suppressed (NOPRINT), because that information is saved in the exampl01.sco file. EXAMPL01.BLM - TRADITIONAL IRT ANALYSIS OF A FIFTEEN-ITEM PRETEST FROM A TWO-STAGE TEST OF MATHEMATICS AT THE EIGHTH-GRADE LEVEL >GLOBAL DFNAME='EXAMPL01.DAT', NPARM=3, SAVE; >SAVE PARM='EXAMPL01.PAR', SCORE='EXAMPL01.SCO'; >LENGTH NITEMS=15; >INPUT NTOTAL=15, NALT=5, NIDCHAR=4, KFNAME='EXAMPL01.KEY', OFNAME='EXAMPL01.OMT';
634
10 BILOG-MG EXAMPLES >ITEMS INAMES=(MATH01(1)MATH15); >TEST1 TNAME='PRETEST', INUMBER=(1(1)15); (4A1,1X,15A1) >CALIB NQPT=31, CYCLES=25, NEWTON=10, CRIT=0.001, ACCEL=0.0, CHI=15, PLOT=1; >SCORE NOPRINT, RSCTYPE=4, INFO=2, POP;
Phase 1 output This is a standard 3-parameter, one-form, single-group analysis of a 15 item test. The Phase 1 classical item statistics for the first 5 items are as follows. ITEM STATISTICS FOR SUBTEST PRETEST ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL -----------------------------------------------------------------------1 MATH01 1000.0 844.0 84.4 -0.99 0.274 0.415 2 MATH02 1000.0 972.0 97.2 -2.09 0.112 0.285 3 MATH03 1000.0 696.0 69.6 -0.49 0.356 0.468 4 MATH04 1000.0 503.0 50.3 -0.01 0.442 0.553 5 MATH05 1000.0 594.0 59.4 -0.22 0.477 0.603 ------------------------------------------------------------------------
Phase 2 output No new features are illustrated in the Phase 2 analysis, except that the plot criterion has been set to include all items. >CALIB
NQPT=31, CYCLES=25, NEWTON=10, CRIT=0.001, ACCEL=0.0, CHI=15, PLOT=1;
The first and last item response function plots are shown below. The first item is extremely easy and the last extremely difficult. These plots were produced using the IRTGRAPH procedure, which is accessed via the Plot option on the Run menu after completion of the analysis. Note that the Phase 2 output file also contains similar line plots.
635
10 BILOG-MG EXAMPLES
Phase 3 output With this short, wide range test, ten quadrature points are sufficient for scoring. The item pa636
10 BILOG-MG EXAMPLES rameters are rescaled so that the scores have mean zero and standard deviation one in the latent distribution estimated from the full sample of 1000 examinees. Population characteristics of the score information, including the IRT estimate of test reliability (equal to [score variance– 1/average information] / score variance) are shown with the information plot. >SCORE, NOPRINT, RSCTYPE = 4, INFO = 2, POP; QUAD RESCALING CONSTANTS TEST NAME POINTS SCALE LOCATION ------------------------------------------1 PRETEST 10 1.000 0.000 ------------------------------------------ITEM INFORMATION STATISTICS FOR TEST PRETEST FOR A NORMAL POPULATION WITH MEAN = 0.000
FORM 1 AND S.D. =
1.000
MAXIMUM POINT OF MAXIMUM AVERAGE INFORMATION MAX INFORMATION EFFECTIVENESS INFORMATION ITEM STANDARD STANDARD POINT OF INDEX OF ERROR * ERROR * MAX EFFECTIVENESS * RELIABILITY ------------------------------------------------------------------------MATH01| 0.2142 | -1.3703 | 0.0587 | 0.1206 | 0.0579* | 0.2226* | -0.5284* | 0.1076* (Similar output omitted) MATH15| 1.0608 | 2.5110 | 0.0375 | 0.0476 | 0.8638* | 0.3388* | -1.9279* | 0.0454* -------------------------------------------------------------------------
Using the Plot option on the Run menu to access the IRTGRAPH program, the following plot of test information is obtained:
637
10 BILOG-MG EXAMPLES
10.2 Differential item functioning This example is based on an example in Thissen, Steinberg & Wainer (1993). The data are drawn from a 100 word spelling test administered by tape recorder to psychology students at a large university. The words for the test were randomly selected from a popular word book for secretaries. Students were asked to write the words as used in a sentence on the tape recording. Responses were scored 1 if spelled correctly and 0 if spelled incorrectly. Because the items are scored 1,0, an answer key is not required. A complete description of these data are given in Section 2.4.1. The groups in this example are the two sexes and this is indicated by the NGROUP keyword on the INPUT command. The same four items are presented to both groups on a single test form. The format statement following the second GROUP command describes the position and order of data in exampl01.dat. The group indicator is found in column 3 of the data records and is read in integer format. A form indicator is not required in the data records because there is only one form. The data have been sorted into answer patterns, and the frequencies are found in columns 10-11 of the data (2A1). These frequencies serve as case weights in the analysis. The TYPE=2 and NWGHT=3 keywords describe this type of data. The value assigned to the keyword NWGHT requests the use of weighting in both the statistics and calibration (by default, no weights would be applied). A 1-parameter logistic model is requested using the NPARM keyword on the GLOBAL command. The LOGISTIC option on the GLOBAL command indicates that the natural metric of the logistic response function will be assumed in all calculations. If this keyword is not present, the logit is, by default, multiplied by 1.7 to obtain the metric of the normal response function. The SAVE option on the GLOBAL command indicates that a SAVE command will follow directly after the GLOBAL command. On the SAVE command, the item parameter estimates are saved to an external file exampl02.par and the DIF analysis results are written to an external file exampl02.dif. The total number of unique items is described using the NTOTAL keyword on the INPUT command while the NITEMS keyword on the LENGTH command is set to 4 to indicate that all 4 items are to be used in the single subtest. The ITEMS command lists the four items in the order that they will be read from the data records. The INAMES and INUMBERS keywords assign each item a name and a corresponding number. Because there is only one form, the NFORM keyword is not required in the INPUT command and a FORM command is not required. Because examinees in both groups are presented all the items listed in the ITEMS command, the TEST and GROUP commands need contain only the test name and the group names, respectively. A DIF analysis is requested through the use of the DIF option on the INPUT command. The REFERENCE=1 keyword on the CALIB command designates males as the reference group. The convergence criterion is set to 0.005 instead of the default 0.01 using the CRIT keyword. 638
10 BILOG-MG EXAMPLES When NGROUP >1, 20 quadrature points will be used for each group. Setting the NQPT keyword to 10 implies that 10 points will be used for each group, as fewer points are needed when the number of items is small. No SCORE command is included in the command file, as DIF models cannot be scored. EXAMPL02.BLM - MALE VS FEMALE DIFFERENTIAL ITEM FUNCTIONING SPELLING, GIRDER ITEM 4, OTHER 3 ITEMS 1-3 >GLOBAL NPARM=1, LOGISTIC, SAVE, NWGHT=3, DFNAME='EXAMPL02.DAT'; >SAVE PARM='EXAMPL02.PAR', DIF='EXAMPL02.DIF'; >LENGTH NITEMS=4; >INPUT NTOTAL=4, NGROUPS=2, DIF, NIDCHAR=2, TYPE=2; >ITEMS INAMES=(SP1(1)SP4), INUMBERS=(1(1)4); >TEST TNAME=SPELL; >GROUP1 GNAME=MALES; >GROUP2 GNAME=FEMALES; (2A1,I1,T10,F2.0,T5,4A1) >CALIB NQPT=10, CYCLES=15, CRIT=0.005, NEWTON=2, REFERENCE=1, PLOT=1;
Phase 1 output The title and additional comments (if the optional COMMENT command has been used) are echoed to the output file. Immediately after that, Phase 1 commands and specifications of the analysis are given. Under FILE ASSIGNMENT, relevant information as read in from the GLOBAL, SAVE, LENGTH, and TEST command are listed. EXAMPLE 02: MALE VS FEMALE DIFFERENTIAL ITEM FUNCTIONING SPELLING, GIRDER ITEM 4, OTHER 3 ITEMS 1-3 >GLOBAL NPARM=1,LOGISTIC,SAVE,NWGHT=3, DFNAME=’EXAMPL02.DAT’; FILE ASSIGNMENT AND DISPOSITION =============================== SUBJECT DATA INPUT FILE BILOG-MG MASTER DATA FILE CALIBRATION DATA FILE ITEM PARAMETERS FILE CASE SCALE-SCORE FILE CASE WEIGHTING
EXAMPL02.DAT MF.DAT CF.DAT IF.DAT SF.DAT
FOR SUBJECT STATISTICS AND ITEM CALIBRATION 1 PARAMETER LOGISTIC LOGIT METRIC (I.E., D = 1.0)
ITEM RESPONSE MODEL
>SAVE
WILL BE CREATED FROM DATA FILE WILL BE CREATED FROM DATA FILE WILL BE CREATED THIS RUN
PARM='EXAMPL02.PAR',DIF='EXAMPL02.DIF';
BILOG-MG SAVE FILES [OUTPUT FILES] ITEM PARAMETERS FILE DIF PARAMETER FILE >LENGTH
EXAMPL02.PAR EXAMPL02.DIF
NITEMS=4;
639
10 BILOG-MG EXAMPLES TEST LENGTH SPECIFICATIONS ========================== MAIN TEST LENGTHS: >INPUT
4
NTOTAL=4,NGROUP=2,DIF,NIDCHAR=2,TYPE=2;
Specification of input related keywords are echoed in the next section. The data are entered as item-score patterns (right = 1, wrong = 0) and frequencies (case weights). DATA INPUT SPECIFICATIONS ========================= NUMBER OF FORMAT LINES 1 NUMBER OF ITEMS IN INPUT STREAM 4 NUMBER OF RESPONSE ALTERNATIVES 1000 NUMBER OF SUBJECT ID CHARACTERS 2 NUMBER OF GROUPS 2 NUMBER OF TEST FORMS 1 TYPE OF DATA SINGLE-SUBJECT DATA, CASE WEIGHTS MAXIMUM SAMPLE SIZE FOR ITEM CALIBRATION 10000000 ALL SUBJECTS INCLUDED IN RUN >ITEMS
INAMES=(SP1(1)SP4),INUMBERS=(1(1)4);
TEST SPECIFICATIONS =================== >TEST
TNAME=SPELL;
The following lines indicate the assignment of items to the single subtest, utilizing the information on both the TEST and ITEMS commands. TEST NUMBER: 1 NUMBER OF ITEMS:
TEST NAME: SPELL 4
ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM NUMBER NAME NUMBER NAME NUMBER NAME NUMBER NAME --------------------------------------------------------------------1 SP1 2 SP2 3 SP3 4 SP4 ---------------------------------------------------------------------
Information on the forms and groups is given next. The definition of the male and female groups, and the use of the same four items for both groups are reflected below. It is also noted that a DIF model is to be employed in this analysis. FORM SPECIFICATIONS =================== ITEMS READ ACCORDING TO SPECIFICATIONS ON THE ITEMS COMMAND >GROUP1 >GROUP2
GNAME=MALES; GNAME=FEMALES;
640
10 BILOG-MG EXAMPLES MULTIPLE GROUP SPECIFICATIONS ============================= DIFFERENTIAL ITEM FUNCTIONING MODEL IS EMPLOYED. GROUP NUMBER:
1 1 NUMBER OF ITEMS:
GROUP NAME: MALES TEST NAME: SPELL 4
TEST NUMBER:
ITEM ITEM NUMBER NAME -----------------1 SP1 2 SP2 3 SP3 4 SP4 -----------------GROUP NUMBER: 2 TEST NUMBER: 1 NUMBER OF ITEMS:
GROUP NAME: FEMALES TEST NAME: SPELL 4
ITEM ITEM NUMBER NAME -----------------1 SP1 2 SP2 3 SP3 4 SP4 ------------------
Following is the format statement used in reading the data and the answer, omit, and not-present keys (if any). Data for this example are item scores and they are complete; keys are not required. The case ID is read in the first 2 columns (2A1), followed by the group indicator (I1). After reading the weights (F2.0), the 4 item responses are read (4A1). FORMAT FOR DATA INPUT IS: (2A1,I1,T10,F2.0,T5,4A1)
The first two cases are echoed to the output file so that the user can verify the input. OBSERVATION #
1
SUBTEST #: GROUP #:
SPELL MALES
TRIED 4.000 ITEM TRIED RIGHT
1 1
WEIGHT:
22.0000
ID :
1
RIGHT 0.000 1 1.0 0.0
2 1.0 0.0
3 1.0 0.0
4 1.0 0.0
641
10 BILOG-MG EXAMPLES OBSERVATION #
2
SUBTEST #: GROUP #:
SPELL MALES
TRIED 4.000 ITEM TRIED RIGHT
1 1
WEIGHT:
10.0000
ID :
2
RIGHT 1.000 1 1.0 0.0
2 1.0 0.0
3 1.0 0.0
4 1.0 1.0
Classical item statistics for the total sample and each group sample follow. #TRIED designates the number of examinees responding to the item. For completeness, both the Pearson and biserial item-test correlations are shown. The latter has smaller bias when the percent right is extreme. The item statistics are given by group and then for the total group. Item means, initial slope estimates, and Pearson and polyserial item-test correlations are given in the next table. Pearson The point biserial correlation rPB , j for item j is a computationally simplified Pearson’s r between the dichotomously scored item j and the total score x. It is computed as
rPB , j =
(µ j − µ x )
pj
σx
qj
where µ j is the mean total score among examinees who have responded correctly to item j, µ x is the mean total score for all examinees, p j is the item difficulty index for item j, q j is 1 − p j , and σ x is the standard deviation of the total scores for all examinees. Polyserial correlation
The polyserial correlation rP , j can be expressed in terms of the point polyserial correlation as
rP , j =
rPP , jσ j
∑
m −1 k =1
h( z jk )
where
z jk is the scoring corresponding to the cumulative proportion, p jk of the k-th response category to item j, σ j is the standard deviation of items scores y for item j, and rPP , j is the point642
10 BILOG-MG EXAMPLES polyserial correlation. The biserial correlation estimates the relationship between the total score and the hypothetical score on the continuous scale underlying the (dichotomous) item. The biserial correlation also assumes a normal distribution of the hypothetical scores. The reason for reporting these correlations separately for each group is that the appearance of large discrepancies between groups for a given item would suggest that the assumption of a common slope is untenable. Note that, if a biserial correlation more negative than –0.15 is detected by the program during this phase of the analysis, the item in question will be assumed miskeyed and will be omitted in the Phase 2 analysis. ITEM STATISTICS FOR GROUP:
1
MALES
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT PEARSON BISERIAL ----------------------------------------------------------------------1 SP1 285.0 215.0 0.754 -1.12 0.243 0.332 2 SP2 285.0 181.0 0.635 -0.55 0.351 0.450 3 SP3 285.0 91.0 0.319 0.76 0.364 0.474 4 SP4 285.0 179.0 0.628 -0.52 0.360 0.461 ---------------------------------------------------------------------ITEM STATISTICS FOR GROUP:
2
FEMALES
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT PEARSON BISERIAL ----------------------------------------------------------------------1 SP1 374.0 305.0 0.816 -1.49 0.254 0.370 2 SP2 374.0 230.0 0.615 -0.47 0.295 0.376 3 SP3 374.0 109.0 0.291 0.89 0.231 0.307 4 SP4 374.0 171.0 0.457 0.17 0.306 0.385 ----------------------------------------------------------------------ITEM STATISTICS FOR MULTIPLE GROUPS
SPELL
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT PEARSON BISERIAL ----------------------------------------------------------------------1 SP1 659.0 520.0 0.789 -1.32 0.239 0.337 2 SP2 659.0 411.0 0.624 -0.51 0.320 0.409 3 SP3 659.0 200.0 0.303 0.83 0.291 0.383 4 SP4 659.0 350.0 0.531 -0.12 0.324 0.406 -----------------------------------------------------------------------
Phase 2 output
During calibration, a logistic item response function is fitted to each item of each subscale. In this example, a 1-parameter logistic response function is fitted (NPARM =1 on GLOBAL). Echoing of the Phase 2 commands and specification of the analysis starts the listing of Phase 2 output. >CALIB
NQPT=10,CYCLES=15,CRIT=0.005,NEWTON=2,REFERENCE=1;
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10 BILOG-MG EXAMPLES Under CALIBRATION PARAMETERS, the definitions of calibration related keywords for this analysis are given: CALIBRATION PARAMETERS ====================== MAXIMUM NUMBER OF EM CYCLES: MAXIMUM NUMBER OF NEWTON CYCLES: CONVERGENCE CRITERION: ACCELERATION CONSTANT:
15 2 0.0050 0.5000
LATENT DISTRIBUTION:
PLOT EMPIRICAL VS. FITTED ICC’s: DATA HANDLING: CONSTRAINT DISTRIBUTION ON SLOPES: CONSTRAINT DISTRIBUTION ON THRESHOLDS:
EMPIRICAL PRIOR FOR EACH GROUP ESTIMATED CONCURRENTLY WITH ITEM PARAMETERS REFERENCE GROUP: 1 YES, FOR ITEMS WITH FIT PROBABILITY LESS THAN 1.00000 DATA ON SCRATCH FILE NO NO
MML estimation is used when tests of three or more items are specified. The solution assumes that the respondents are drawn randomly from a population or populations of abilities, which is assumed to have a normal distribution. The empirical distribution of ability is represented as a discrete distribution on a finite number of points. The quadrature points and weights used for MML estimation of the item parameters for the two groups are printed next. METHOD OF SOLUTION: EM CYCLES (MAXIMUM OF 15) FOLLOWED BY NEWTON-RAPHSON STEPS (MAXIMUM OF
2)
QUADRATURE POINTS AND PRIOR WEIGHTS: GROUP 1 MALES 1 2 3 4 5 POINT -0.4000E+01 -0.3111E+01 -0.2222E+01 -0.1333E+01 -0.4444E+00 WEIGHT 0.1190E-03 0.2805E-02 0.3002E-01 0.1458E+00 0.3213E+00
POINT WEIGHT
6 0.4444E+00 0.3213E+00
7 0.1333E+01 0.1458E+00
8 0.2222E+01 0.3002E-01
9 0.3111E+01 0.2805E-02
10 0.4000E+01 0.1190E-03
QUADRATURE POINTS AND PRIOR WEIGHTS: GROUP 2 FEMALES 1 2 3 4 5 POINT -0.4000E+01 -0.3111E+01 -0.2222E+01 -0.1333E+01 -0.4444E+00 WEIGHT 0.1190E-03 0.2805E-02 0.3002E-01 0.1458E+00 0.3213E+00
POINT WEIGHT
6 0.4444E+00 0.3213E+00
7 0.1333E+01 0.1458E+00
8 0.2222E+01 0.3002E-01
9 0.3111E+01 0.2805E-02
10 0.4000E+01 0.1190E-03
The MML solution employs both the EM method and Newton-Gauss iterations to solve the marginal likelihood equations. On the CALIB command, a maximum of 15 EM cycles and 2 NewtonGauss iterations were requested. Results for each iteration are displayed so that the extent of convergence can be judged.
644
10 BILOG-MG EXAMPLES In the case of nested models on the same data, the –2 log likelihood values at convergence can be used to evaluate the fit of the models. Refitting this example, for instance as a single-group analysis will allow the comparison of non-DIF and DIF models for these data. In that way, it can be determined whether differential item functioning effects are present. [E-M CYCLES] -2 LOG LIKELIHOOD =
3152.375
CYCLE 1; LARGEST CHANGE= 0.17572 -2 LOG LIKELIHOOD = 3128.806 ... CYCLE
8;
LARGEST CHANGE=
0.00486
The information matrix for all item parameters is approximated during each Newton step and then used at convergence to provide large-sample standard errors of estimation on the item parameter estimates. [FULL NEWTON CYCLES] -2 LOG LIKELIHOOD: 3110.3990 CYCLE 9; LARGEST CHANGE= 0.00416
In Phase 2, when there is a single group, the unit and origin of the scale on which the parameters are expressed is based on the assumption that the latent ability distribution has zero mean and unit variance (the so-called “0,1” metric). In the case of multiple groups, the program provides the option of setting the mean and standard deviation of one group to 0,1 as shown here. The user may set the mean and standard deviation of the combined estimated distribution of the groups to 0 and 1 by setting the REFERENCE keyword on the CALIB command to zero. The parameter estimates can be rescaled in Phase 3 according to scale conventions selected by the user (using the RSCTYPE, SCALE and LOCATION keywords on the SCORE command). In a DIF model, no scoring is done, so use of the REFERENCE=0 specification is not pursued here. Estimated item parameters for the two groups are given next. The INTERCEPT column contains the estimates of the item intercepts, which are defined as the product of each item’s slope and threshold. This is followed by the slope or discrimination parameters and the item threshold or location parameters. The LOADING column represents the one-factor item factor loadings given by the expression
slope 1.0 + slope 2
.
For a 1PL model, no asymptotes or guessing parameters are estimated. In a 1PL model, all slopes are equal. In DIF analyses, the assumption is made that slopes are equal over the groups. This implies that items will discriminate equally well in all groups. Note that, in this example, the slopes of all items for both groups are constrained to 1.285.
645
10 BILOG-MG EXAMPLES GROUP
1
MALES
;
ITEM PARAMETERS AFTER CYCLE
9
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------SP1 | 1.489 | 1.285 | -1.159 | 0.789 | 0.000 | 2.7 7.0 | 0.168* | 0.096* | 0.130* | 0.059* | 0.000* | (0.9146) | | | | | | SP2 | 0.749 | 1.285 | -0.583 | 0.789 | 0.000 | 20.0 7.0 | 0.152* | 0.096* | 0.119* | 0.059* | 0.000* | (0.0056) | | | | | | SP3 | -1.008 | 1.285 | 0.784 | 0.789 | 0.000 | 36.2 5.0 | 0.109* | 0.096* | 0.085* | 0.059* | 0.000* | (0.0000) | | | | | | SP4 | 0.709 | 1.285 | -0.552 | 0.789 | 0.000 | 26.6 7.0 | 0.150* | 0.096* | 0.117* | 0.059* | 0.000* | (0.0004) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE = 0.004158 95.3 12.0 (0.0000) GROUP 2 FEMALES ; ITEM PARAMETERS AFTER CYCLE 9 ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------SP1 | 1.887 | 1.285 | -1.468 | 0.789 | 0.000 | 11.3 7.0 | 0.168* | 0.096* | 0.131* | 0.059* | 0.000* | (0.1261) | | | | | | SP2 | 0.617 | 1.285 | -0.480 | 0.789 | 0.000 | 34.6 7.0 | 0.136* | 0.096* | 0.106* | 0.059* | 0.000* | (0.0000) | | | | | | SP3 | -1.113 | 1.285 | 0.866 | 0.789 | 0.000 | 24.5 7.0 | 0.144* | 0.096* | 0.112* | 0.059* | 0.000* | (0.0009) | | | | | | SP4 | -0.203 | 1.285 | 0.158 | 0.789 | 0.000 | 43.0 7.0 | 0.133* | 0.096* | 0.104* | 0.059* | 0.000* | (0.0000) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE = 0.004158 95.3 12.0 (0.0000) NOTE: ITEM FIT CHI-SQUARES AND THEIR SUMS MAY BE UNRELIABLE FOR TESTS WITH LESS THAN 20 ITEMS
The item parameter estimates for each group are followed by the averages for the group thresholds. The mean threshold of the female group (Group 2) is 0.146 above that of the male or reference group. DIF is item by group interaction under the constraint that the mean thresholds of the groups are equal. The threshold adjustment sets the mean of the reference group’s threshold to 1, and the mean threshold for the females is accordingly adjusted to 0.148. The unadjusted and adjusted mean thresholds for the two groups form the next section of the Phase 2 output file. PARAMETER MEAN STN DEV ----------------------------------GROUP: 1 NUMBER OF ITEMS: 4 THRESHOLD -0.377 0.823 GROUP: 2 NUMBER OF ITEMS: 4 THRESHOLD -0.231 0.991 -----------------------------------
646
10 BILOG-MG EXAMPLES THRESHOLD MEANS GROUP ADJUSTMENT -----------------------1 0.000 2 0.146 -----------------------MODEL FOR GROUP DIFFERENTIAL ITEM FUNCTIONING: ADJUSTED THRESHOLD VALUES ITEM GROUP 1 2 ---------------------------------SP1 | -1.159 | -1.614 | 0.130* | 0.131* | | SP2 | -0.583 | -0.626 | 0.119* | 0.106* | | SP3 | 0.784 | 0.720 | 0.085* | 0.112* | | SP4 | -0.552 | 0.012 | 0.117* | 0.104* ---------------------------------*STANDARD ERROR
The adjusted threshold values are followed by the group differences of the constrained values. The standard errors for the differences are computed as
s.e.G 2−G1 = var(G 2) + var(G1). ITEM
GROUP 2 - 1 ----------------------SP1 | -0.455 | 0.185* | SP2 | -0.043 | 0.159* | SP3 | -0.065 | 0.141* | SP4 | 0.564 | 0.156* ----------------------*STANDARD ERROR
The estimated latent distributions of the groups are given next; with the origin and unit of scale set so that the mean of the reference group is 0 and the standard deviation is 1. GROUP:
1
POINT POSTERIOR
MALES
QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.:
1 2 3 4 5 -0.3578E+01 -0.2788E+01 -0.1998E+01 -0.1208E+01 -0.4180E+00 0.1972E-03 0.4485E-02 0.4394E-01 0.1737E+00 0.2780E+00
647
10 BILOG-MG EXAMPLES POINT POSTERIOR
6 0.3720E+00 0.2647E+00
MEAN S.E.
0.00000 0.00000
S.D. S.E.
1.00000 0.00000
GROUP:
2
POINT POSTERIOR
POINT POSTERIOR
FEMALES
7 0.1162E+01 0.1724E+00
8 0.1952E+01 0.5526E-01
9 0.2742E+01 0.7020E-02
10 0.3532E+01 0.3483E-03
QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.:
1 2 3 4 5 -0.3724E+01 -0.2934E+01 -0.2144E+01 -0.1354E+01 -0.5642E+00 0.2099E-03 0.4246E-02 0.3608E-01 0.1456E+00 0.3067E+00 6 0.2258E+00 0.3161E+00
MEAN S.E.
-0.16191 0.06907
S.D. S.E.
0.89707 0.00845
7 0.1016E+01 0.1525E+00
8 0.1806E+01 0.3473E-01
9 0.2596E+01 0.3598E-02
10 0.3386E+01 0.1624E-03
A plot of the two estimated latent distributions are shown below. The solid line represents the estimated distribution of the male group.
BILOG-MG is also capable of producing graphic representations of a number of item and test characteristics. Using the PLOT keyword on the CALIB command, it is possible to obtain plots of the item-response functions with a significance level below the value assigned to the PLOT key648
10 BILOG-MG EXAMPLES word. By default, PLOT=0 and no plots are produced. On the other hand, setting PLOT to 1.0 will lead to the display of all item response functions in the output file. One such plot, for the fourth item administered to the female group, is shown below. The plot also shows 95% tolerance intervals for the observed percent correct among respondents in corresponding EAP groups, assuming the percent-correct predicted by the model is correct. Note that similar plots may be obtained using the IRTGRAPH program accessible from the Run menu. Similar graphs may be obtained through the IRTGRAPH program, accessed via the Plot option on the Run menu in BILOG-MG for Windows. GROUP: 2 FEMALES SUBTEST: SPELL ITEM:
SP4 CHISQ = 43.0 DF = 7.0 PROB< 0.0000 1.00+------------------------------------------------------------+ | | | | 0.90| | | ...| | ... | 0.80| ... | | X .. | | | .. | 0.70| | .. | | |.. | | .| | 0.60| .. | | | .. | | | . | 0.50| | .. | | | .. | | |.. | 0.40| | | | ..| | | .. | | 0.30| | .. X | | | ... | | | .. | 0.20| .|. | | ... | | | .... | | 0.10| ...... X | |.. | | | 0.00| | +--+-----+-----+-----+-----+-----+-----+-----+-----+-----+---+ THETA -1.86 -1.48 -1.11 -0.73 -0.36 0.02 0.40 0.77 1.15 1.53
By saving the estimated parameter estimates to an external file, the estimates can also be used in external packages to produce additional plots. Below, the item response functions for both groups are plotted by item.
649
10 BILOG-MG EXAMPLES
10.3 Differential item functioning The data from example 2 are analyzed here as a single group. Thus no NGROUP keyword is provided on the INPUT command and, by default, the program assumes there is only one group. No GROUP commands follow the TEST command, and the group indicator has been removed from the variable format statement. The acceleration factor on the CALIB command has been set to its default value of 0.5 (ACCEL=0.5). The difference in the log likelihoods from the two-group and single-group solutions can be examined to determine if differential item functioning effects are present. The item parameter file obtained in the previous section is specified in the GLOBAL command to provide starting values for parameter estimation in Phase 2.
650
10 BILOG-MG EXAMPLES
EXAMPL03.BLM - MALE VS. FEMALE DIFFERENTIAL ITEM FUNCTIONING SPELLING, GIRDER ITEM 4, OTHER 3 ITEMS 1-3 >GLOBAL NPARM=1, NWGHT=3, LOGISTIC, IFNAME='EXAMPL02.PAR', DFNAME='EXAMPL02.DAT'; >LENGTH NITEMS=4; >INPUT NTOTAL=4, NIDCHAR=2, TYPE=2; >ITEMS INAME=(SP01,SP02,SP03,SP04), INUMBERS=(1(1)4); >TEST TNAME=SPELL; (2A1,T10,F2.0,T5,4A1) >CALIB EMPIRICAL, NQPT=31, CRIT=0.005, ACCEL=0.5;
Phase 1 output EXAMPLE 02: MALE VS. FEMALE DIFFERENTIAL ITEM FUNCTIONING SPELLING, GIRDER ITEM 4, OTHER 3 ITEMS 1-3
The Phase 1 output for this example is the same as that obtained in Section 10.2, except that classical item statistics are computed only for the total sample. Phase 2 output
The main interest in this example is the comparison of the log likelihood of the fit of the DIF and non-DIF models. The difference, 3138.4122 – 3110.3990 = 28.0132, distributed as χ 2 on four degrees of freedom, indicates significantly better fit of the DIF model. -2 LOG LIKELIHOOD: 3138.4122 CYCLE 4; LARGEST CHANGE= 0.00439 SUBTEST SPELL
;
ITEM PARAMETERS AFTER CYCLE
4
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------SP01 | 1.688 | 1.234 | -1.368 | 0.777 | 0.000 | 15.4 3.0 | 0.124* | 0.094* | 0.101* | 0.059* | 0.000* | (0.0015) | | | | | | SP02 | 0.662 | 1.234 | -0.536 | 0.777 | 0.000 | 33.6 3.0 | 0.105* | 0.094* | 0.085* | 0.059* | 0.000* | (0.0000) | | | | | | SP03 | -1.069 | 1.234 | 0.866 | 0.777 | 0.000 | 17.9 3.0 | 0.111* | 0.094* | 0.090* | 0.059* | 0.000* | (0.0005) | | | | | | SP04 | 0.169 | 1.234 | -0.137 | 0.777 | 0.000 | 32.2 3.0 | 0.102* | 0.094* | 0.082* | 0.059* | 0.000* | (0.0000) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE =
0.004551
99.0 12.0 (0.0000)
NOTE: ITEM FIT CHI-SQUARES AND THEIR SUMS MAY BE UNRELIABLE FOR TESTS WITH LESS THAN 20 ITEMS
651
10 BILOG-MG EXAMPLES
10.4 Equivalent groups equating This example illustrates the equating of equivalent groups with the BILOG-MG program. Two parallel test forms of 20 multiple-choice items were administered to two equivalent samples of 200 examinees drawn from the same population. There are no common items between the forms. Because the samples were drawn from the same population, GROUP commands are not required. The FORM1 command lists the order of the items in Form 1 and the FORM2 command lists the order of the items in Form 2. These commands follow directly after the TEST command as indicated by the NFORM=2 keyword on the INPUT command. As only one test is used, the vector of items per subtest given by the NITEMS keyword on the LENGTH command contains only one entry. The SAVE option on the GLOBAL command is used in combination with the SAVE command to save item parameter estimates and scores to the external files exampl04.par and exampl04.sco respectively. In this example, 40 unique item responses are given in the data file. The first few lines of the data file are shown below. The first record shown after the answer keys for the two forms, which should always appear first and in the same format as the data, contains responses to items 1 through 20 in the second line associated with this examinee. In the case of the data shown for another examinee who responded to the second form, responses in the same positions in the data file correspond to items 21 through 40. Keep in mind that the number of items read by the format statement is the total number of items in the form, when NFORM=1 and the total number of items in the longest form when NFORM>1. 1 2 1 1 1 1 … 2 2 2
001 002 003 004
11111111111111111111 11111111111111111111 11111111122212122111 11222212221222222112 12121221222222221222 11212212222222212222
198 11112211111222212211 199 21122222222222222122 200 11111111111111221111
The FLOAT option is used on the CALIB command to request the estimation of the means of the prior distributions of item parameters along with the parameters. This option should not be used when the data set is small and items few. Means of the item parameters may drift indefinitely during estimation cycles under these conditions. In the CALIB command, the FIXED option is also required to keep the prior distributions of ability fixed during the EM cycles of this example. In multiple-group analysis, the default is “not fixed”. ML estimates of ability are rescaled to a mean of 250 and standard deviation of 50 in Phase 3 (METHOD=1, RSCTYPE=3, LOCATION=250, SCALE=50). By settting INFO to 1 on the SCORE command, the printing of test information curves to the phase 3 output file is requested. To request the calculation of expected information for the population, the POP option may be added to this command. In the case of multiple subtests, the further addition of the YCOMMON option will re-
652
10 BILOG-MG EXAMPLES quest the expression of test information curves for the subtests in comparable units. EXAMPL04.BLM - EQUIVALENT GROUPS EQUATING SIMULATED RESPONSES TO TWO 20-ITEM PARALLEL TEST FORMS >GLOBAL DFNAME='EXAMPL04.DAT', NPARM=2, SAVE; >SAVE SCORE='EXAMPL04.SCO', PARM='EXAMPL04.PAR'; >LENGTH NITEMS=40; >INPUT NTOT=40, NFORM=2, KFNAME='EXAMPL04.DAT', NALT=5, ,NIDCHAR=5; >ITEMS INUM=(1(1)40), INAME=(T01(1)T40); >TEST TNAME=SIM; >FORM1 LENGTH=20, INUM=(1(1)20); >FORM2 LENGTH=20, INUM=(21(1)40); (5A1,T1,I1,T7,20A1) >CALIB FIXED, FLOAT, NQPT=31, TPRIOR, PLOT=.05; >SCORE METHOD=1, RSCTYPE=3, LOCATION=250, SCALE=50, NOPRINT, INFO=1;
Phase 1 output
Because all examples are drawn from the same population, all responses are combined in the results. Since there are no common items between forms, the number tried for each item is 200. If there had been common items, their number tried would be 400. Results for the first 5 items are shown below. 400 OBSERVATIONS READ FROM FILE: 400 OBSERVATIONS WRITTEN TO FILE:
EXAMPL04.DAT MF.DAT
ITEM STATISTICS FOR SUBTEST SIM ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ---------------------------------------------------------------------1 T01 200.0 165.0 82.5 -0.91 0.446 0.658 2 T02 200.0 171.0 85.5 -1.04 0.416 0.642 3 T03 200.0 150.0 75.0 -0.65 0.524 0.715 4 T04 200.0 138.0 69.0 -0.47 0.448 0.588 5 T05 200.0 149.0 74.5 -0.63 0.391 0.531
Phase 2 output
Item parameter estimation assumes a common latent distribution for the random equivalent groups administered the respective test forms. Empirical prior distributions are assumed for the slope and threshold parameters. The means of these priors are estimated concurrently with the item parameters. CALIBRATION PARAMETERS ====================== MAXIMUM NUMBER OF EM CYCLES: MAXIMUM NUMBER OF NEWTON CYCLES: CONVERGENCE CRITERION: ACCELERATION CONSTANT: LATENT DISTRIBUTION: PLOT EMPIRICAL VS. FITTED ICC’s:
DATA HANDLING:
20 2 0.0100 1.0000 NORMAL PRIOR FOR EACH GROUP YES, FOR ITEMS WITH FIT PROBABILITY LESS THAN 0.05000 DATA ON SCRATCH FILE
653
10 BILOG-MG EXAMPLES CONSTRAINT DISTRIBUTION ON SLOPES: CONSTRAINT DISTRIBUTION ON THRESHOLDS: SOURCE OF ITEM CONSTRAINT DISTRIBUTION MEANS AND STANDARD DEVIATIONS:
YES YES PROGRAM DEFAULTS ITEM CONSTRAINTS IF PRESENT WILL BE UPDATED EACH CYCLE
Final iterations of the solutions and some of the results are as follows. Indeterminacy of the origin and unit of the ability scale is resolved in Phase 2 by setting the mean and standard deviation of the latent distribution to zero and one, respectively. -2 LOG LIKELIHOOD =
8297.415
UPDATED PRIOR ON LOG SLOPES; MEAN & SD = UPDATED PRIOR ON THRESHOLDS; MEAN & SD = CYCLE
5;
LARGEST CHANGE=
-0.23882 -0.01801
0.50000 2.00000
-0.23457 -0.01751
0.50000 2.00000
0.00752
[NEWTON CYCLES] UPDATED PRIOR ON LOG SLOPES; MEAN & SD = UPDATED PRIOR ON THRESHOLDS; MEAN & SD = -2 LOG LIKELIHOOD: CYCLE
6;
8297.4560
LARGEST CHANGE=
0.00489
After assigning cases to the intervals (shown below) on the basis of the EAP estimates of their scale scores, the program computes the expected number of correct responses in the interval by multiplying these counts by the response model probability at the indicated θ . The χ 2 is computed in the usual way from the differences between the observed and expected counts. The counts are displayed so that the user can judge whether there are enough cases in each group to justify computing a χ 2 statistic. If not, the user should reset the number of intervals. INTERVAL COUNTS FOR COMPUTATION OF ITEM CHI-SQUARES ----------------------------------------------------------------------15. 30. 36. 52. 70. 69. 48. 36. 44. ----------------------------------------------------------------------INTERVAL AVERAGE THETAS -----------------------------------------------------------------------2.000 -1.520 -1.076 -0.648 -0.191 0.235 0.620 1.100 1.724 ----------------------------------------------------------------------SUBTEST SIM ; ITEM PARAMETERS AFTER CYCLE 6 ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------T01 | 1.339 | 1.000 | -1.338 | 0.707 | 0.000 | 2.3 5.0 | 0.192* | 0.206* | 0.194* | 0.146* | 0.000* | (0.8044) T02 | 1.488 | 0.961 | -1.549 | 0.693 | 0.000 | 4.4 6.0 | 0.211* | 0.199* | 0.218* | 0.144* | 0.000* | (0.6179)
654
10 BILOG-MG EXAMPLES (Similar output omitted) | | | | T39 | 0.508 | 0.911 | -0.557 | 0.673 | 0.000 | 1.8 6.0 | 0.119* | 0.172* | 0.126* | 0.127* | 0.000* | (0.9334) T40 | 0.525 | 0.675 | -0.777 | 0.559 | 0.000 | 5.4 7.0 | 0.107* | 0.130* | 0.175* | 0.108* | 0.000* | (0.6055) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE =
0.004890
176.3 243.0 (0.9996)
PARAMETER MEAN STN DEV ----------------------------------SLOPE 0.809 0.153 LOG(SLOPE) -0.230 0.189 THRESHOLD -0.019 0.975
Phase 3 output
For purposes of reporting test scores, the ability scale is set so that the mean score distribution in the sample of examinees is 250 and the standard deviation is 50. The item parameters are rescaled accordingly. >SCORE METHOD = 1, RSCTYPE = 3,LOCATION = 250, SCALE = 50,NOPRINT, INFO = 1; PARAMETERS FOR SCORING, RESCALING, AND TEST AND ITEM INFORMATION METHOD OF SCORING SUBJECTS: MAXIMUM LIKELIHOOD SCORES WRITTEN TO FILE EXAMPL04.SCO TYPE OF RESCALING: IN THE SAMPLE DISTRIBUTION REFERENCE GROUP FOR RESCALING: GROUP: 0
Before rescaling, the sample mean score is essentially the same as that in the Phase 2 latent distribution. The standard deviation is larger, however, because the score distribution includes measurement error variance. Summary statistics for each group include the following.
The correlation matrix of the test scores (when there is more than one test). The mean, standard deviation and variance of the θ score estimates: Maximum Likelihood (ML) estimate Bayes Model (Maximum A Posteriori, MAP) estimate Bayes (Expected, EAP) estimate
The summary of the error variation depends on the type of estimate:
Maximum Likelihood – Harmonic Root-Mean-Square standard errors: The error variance for each case is the reciprocal of the Fisher information at the likelihood maximum for the case. The standard error is the reciprocal square root of the average of these variances. MAP – Root-Mean-Square posterior standard deviation: The error variance for each case is the posterior information at the maximum of the posterior probability density of θ , 655
10 BILOG-MG EXAMPLES
given the response pattern of the case. The standard error is the square root of the average of these variances. EAP – Root-Mean-Square posterior standard deviation: The error variance for each case is the variance of the posterior distribution of theta, given the response pattern of the case. The standard error is the square root of the average of these variances.
The empirical reliability of the test is the θ score variance divided by the sum of that variance and the error variance. Note:
The expected value of the sum of the θ score variance and the error variance is the variance of the latent distribution of the group. The sum of the corresponding sample variances should tend to that value as the sample size increases. SUMMARY STATISTICS FOR SCORE ESTIMATES ====================================== CORRELATIONS AMONG TEST SCORES SIM 1.0000
SIM
MEANS, STANDARD DEVIATIONS, AND VARIANCES OF SCORE ESTIMATES TEST: MEAN: S.D.: VARIANCE:
SIM 0.0057 1.1426 1.3054
HARMONIC ROOT-MEAN-SQUARE STANDARD ERRORS OF THE ML ESTIMATES TEST: RMS: VARIANCE:
SIM 0.4203 0.1767
EMPIRICAL RELIABILITY:
0.8647
RESCALING WITH RESPECT TO SAMPLE DISTRIBUTION ---------------------------------------------------
TEST SIM
RESCALING SCALE 43.762
CONSTANTS LOCATION 249.749
The scaled scores are saved on an external file and their printing is suppressed in all but the first two cases.
656
10 BILOG-MG EXAMPLES
GROUP SUBJECT IDENTIFICATION WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. ---------------------------------------------------------------1 1 | | 1.00 SIM 20 14 70.00 | 282.5091 17.5097 | 1 1 | | 1.00 SIM 20 6 30.00 | 217.0505 16.8979 | ----------------------------------------------------------------
The magnitudes of the rescaled item parameters reflect the new origin and unit of the scale. The thresholds center around 250 and the slopes are smaller by a factor of about 50. The slopes are printed here to only three decimal places but appear accurately in the saved items parameter file. If saved parameters are used to score other examinees, the results will be determined in the present sample. TEST ITEM
SIM
; RESCALED ITEM PARAMETERS INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE S.E. S.E. S.E. S.E. S.E. --------------------------------------------------------------T01 | -4.371 | 0.023 | 191.189 | 0.707 | 0.000 | 1.190* | 0.005* | 8.501* | 0.146* | 0.000* | | | | | T02 | -3.994 | 0.022 | 181.956 | 0.693 | 0.000 | 1.157* | 0.005* | 9.357* | 0.144* | 0.000* | | | | | (Similar output omitted) | | | | | T39 | -4.691 | 0.021 | 225.362 | 0.673 | 0.000 | 0.988* | 0.004* | 5.524* | 0.127* | 0.000* | | | | | T40 | -3.327 | 0.015 | 215.726 | 0.559 | 0.000 | 0.748* | 0.003* | 7.651* | 0.108* | 0.000* --------------------------------------------------------------PARAMETER MEAN STN DEV ----------------------------SLOPE 0.018 0.003 LOG(SLOPE) -4.009 0.189 THRESHOLD 248.921 42.657 MEAN & SD OF SCORE ESTIMATES AFTER RESCALING:
250.000
50.000
Results of the information analysis are depicted in the following line printer plot. Points indicated by + and * represent the information and measurement error functions, respectively. This plot applies to all 40 items and not to the separate test forms. Because the item thresholds are normally distributed with mean standard similar to that of the score distribution, the precision of the item set is greatest toward the middle of the scale.
657
10 BILOG-MG EXAMPLES TEST INFORMATION CURVE FOR TEST: SIM FORM: 1 STANDARD INFORERROR MATION -----------------------------------------------------------------1.48| * +++++ * | .1341 | + ++ | 1.41| ++ + | 6.7774 | * * | 1.33| + + | 6.4207 | + + | 1.26| * + * | 6.0640 | + | 1.19| + * | 5.7073 | * + | 1.11| + * | 5.3506 | * + | 1.04| + * | 4.9939 | * + | 0.96| * | 4.6372 | * + + * | 0.89| | 4.2805 | * + + * | 0.82| * | 3.9237 | * + + | 0.74| * | 3.5670 | * + * | 0.67| * + * | 3.2103 | * + * | 0.59| + + | 2.8536 | * ** | 0.52| + * * + | 2.4969 | ** ** + | 0.44| + ** ** | 2.1402 | + **** **** + | 0.37| *********** + | 1.7835 | + + | 0.30| + + | 1.4268 | + + | 0.22| + + | 1.0701 | + ++ | 0.15| ++ + | 0.7134 | ++ +++ | 0.07|+++ +| 0.3567 | | 0.00| | 0.0000 -+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+ -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00
10.5 Vertical equating Two hundred students at each of three grade levels, grades four, six, and eight, were given gradeappropriate versions of a 20-item arithmetic examination. Items 19 and 20 appear in the grade 4 and 6 forms; items 37 and 38 appear in the grade 6 and 8 forms. Because each item is assigned a unique column in the data records, a FORM command is not required. The data file contains, the answer key, not-presented key and raw data. Two lines of information are given per examinee as shown below. The answer key contains 56 entries, each equal to 1. If 658
10 BILOG-MG EXAMPLES an item has not been presented, its presence in the data will be indicated with ‘ ‘. KEY NOT 001 002 003 004 005
11111111111111111111111111111111111111111111111111111111 1 1 1 1 1
11111112221211222212 21121211121111121212 11112112211222212212 11111112121111111211 21111112221212121222
No items are assigned to the TEST command using the INAMES or INUMBERS keywords. By default, it is assumed that all items are assigned to the test. Although the test name (TNAME=MATH) is not enclosed in single quotes, the group names are as these names contain blanks as part of the name. The distributions of ability are assumed to be normal at each grade level (NORMAL on the CALIB command). Grade 6 serves as the reference group in the calibration of the items (REFERENCE=2). EAP estimates of ability are calculated using the information in the posterior distributions from Phase 2. The ability estimates are rescaled to a mean of 0 and standard deviation of 1 by specifying RSCTYPE=3 on the SCORE command. EXAMPL05.BLM - VERTICAL EQUATING OF TEST FORMS OVER THREE GRADE LEVELS >GLOBAL >SAVE >LENGTH >INPUT
DFNAME='EXAMPL05.DAT', NPARM=2, SAVE; SCORE='EXAMPL05.SCO', PARM='EXAMPL05.PAR'; NITEMS=56; NTOT=56, NGROUPS=3, NIDCH=3, KFNAME='EXAMPL05.DAT', NFNAME='EXAMPL05.DAT'; >ITEMS INUM=(1(1)56), INAME=(M01(1)M56); >TEST TNAME=MATH; >GROUP1 GNAME='GRADE 4', LENGTH=20, INUM=(1(1)20); >GROUP2 GNAME='GRADE 6', LENGTH=20, INUM=(19(1)38); >GROUP3 GNAME='GRADE 8', LENGTH=20, INUM=(37(1)56); (3A1,1X,I1,1X,56A1) >CALIB NQPT=51, NORMAL, CYCLE=30, TPRIOR, REFERENCE=2; >SCORE METHOD=2, IDIST=3, NOPRINT, RSCTYPE=3;
Phase 1 output
In this example, items assigned to the three groups of examinees are selected from the following set. The items are selected in such a way that two items are common to groups 1 and 2 and two other items are common to groups 2 and 3. The groups, corresponding to school grades four, six, and eight are non-equivalent and require separate classical item statistics. The fact that classical item statistics are not invariant with respect to sampling from different populations is illustrated by the different results for common items in different groups. MULTIPLE GROUP SPECIFICATIONS ============================= MULTIPLE GROUPS ARE DEFINED, BUT NEITHER DIF MODEL NOR PARAMETER DRIFT MODEL IS EMPLOYED.
659
10 BILOG-MG EXAMPLES GROUP NUMBER: 1 TEST NUMBER: 1 NUMBER OF ITEMS:
GROUP NAME: GRADE 4 TEST NAME: MATH 20
ITEM ITEM NUMBER NAME -----------------1 M01 2 M02 … 20 M20 -----------------GROUP NUMBER: 2 TEST NUMBER: 1 NUMBER OF ITEMS:
GROUP NAME: GRADE 6 TEST NAME: MATH 20
ITEM ITEM NUMBER NAME -----------------19 M19 20 M20 … 38 M38 -----------------GROUP NUMBER: 3 TEST NUMBER: 1 NUMBER OF ITEMS:
GROUP NAME: GRADE 8 TEST NAME: MATH 20
ITEM ITEM NUMBER NAME -----------------37 M37 … 56 M56 -----------------600 OBSERVATIONS READ FROM FILE: 600 OBSERVATIONS WRITTEN TO FILE: SUBTEST GROUP GROUP GROUP
1 1 2 3
MATH GRADE 4 GRADE 6 GRADE 8
EXAMPL05.DAT MF.DAT
200 OBSERVATIONS 200 OBSERVATIONS 200 OBSERVATIONS
Item statistics for the first 5 items of each subtest are shown below. Similar output is produced for grades 6 to 8, and for multiple groups MATH which, in this case, contains the statistics for all the grades.
660
10 BILOG-MG EXAMPLES SUBTEST 1 MATH ITEM STATISTICS FOR GROUP:
1
GRADE 4
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ----------------------------------------------------------------------1 M01 200.0 138.0 0.690 -0.47 0.470 0.616 ... 19 M19 200.0 95.0 0.475 0.06 0.520 0.652 20 M20 200.0 67.0 0.335 0.40 0.475 0.615 ---------------------------------------------------------------------ITEM STATISTICS FOR GROUP:
2
GRADE 6
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ---------------------------------------------------------------------19 M19 200.0 138.0 0.690 -0.47 0.431 0.565 20 M20 200.0 106.0 0.530 -0.07 0.512 0.643 ... 37 M37 200.0 104.0 0.520 -0.05 0.379 0.475 38 M38 200.0 62.0 0.310 0.47 0.497 0.651 -----------------------------------------------------------------------ITEM STATISTICS FOR GROUP:
3
GRADE 8
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL -----------------------------------------------------------------------37 M37 200.0 135.0 0.675 -0.43 0.420 0.546 38 M38 200.0 96.0 0.480 0.05 0.594 0.745 ... 55 M55 200.0 90.0 0.450 0.12 0.471 0.592 56 M56 200.0 111.0 0.555 -0.13 0.529 0.665 ------------------------------------------------------------------------
Phase 2 output
In vertical equating over a range of age levels, the ability distributions of the groups may be widely spaced. For that reason, it is desirable to use a large number of quadrature points – in this case, 51. The origins and unit of the ability distribution can be fixed in the calibration either by setting the mean and standard deviation of a reference group to zero and one, respectively, or, similarly, setting the mean and standard deviation of the combined groups. In this example, group 2 is selected as the reference group. >CALIB
NQPT=51, NORMAL, CYCLE=30, TPRIOR, REFERENCE=2;
CALIBRATION PARAMETERS ====================== MAXIMUM NUMBER OF EM CYCLES: MAXIMUM NUMBER OF NEWTON CYCLES: CONVERGENCE CRITERION: ACCELERATION CONSTANT: LATENT DISTRIBUTION:
30 2 0.0100 1.0000 NORMAL PRIOR FOR EACH GROUP GROUP MEANS AND SDS ESTIMATED CONCURRENTLY WITH ITEM PARAMETERS REFERENCE GROUP: 2
661
10 BILOG-MG EXAMPLES PLOT EMPIRICAL VS. FITTED ICC'S: DATA HANDLING:
NO DATA ON SCRATCH FILE
… MEANS AND STANDARD DEVIATIONS:
PROGRAM DEFAULTS ITEM CONSTRAINTS IF PRESENT WILL BE UPDATED EACH CYCLE
The iterative estimation procedures typically converge more slowly in nonequivalent group data than in one group or equivalent groups data. The last few iterations are shown here along with some of the resulting parameter estimates. The means of the prior distributions on item thresholds and slopes are also listed. CYCLE 19; LARGEST CHANGE= 0.02538 -2 LOG LIKELIHOOD = 13246.111 UPDATED PRIOR ON LOG SLOPES; MEAN & SD = UPDATED PRIOR ON THRESHOLDS; MEAN & SD = CYCLE
20;
LARGEST CHANGE=
21;
LARGEST CHANGE=
0.50000 2.00000
0.00812
[NEWTON CYCLES] UPDATED PRIOR ON LOG SLOPES; MEAN & SD = UPDATED PRIOR ON THRESHOLDS; MEAN & SD = -2 LOG LIKELIHOOD: 13245.9542 CYCLE
-0.23806 0.08303
-0.23533 0.08308
0.50000 2.00000
0.00699
INTERVAL COUNTS FOR COMPUTATION OF ITEM CHI-SQUARES ------------------------------------------------------------------------19. 32. 56. 83. 93. 109. 82. 60. 66. ------------------------------------------------------------------------INTERVAL AVERAGE THETAS -------------------------------------------------------------------------2.695 –1.942 -1.448 -0.866 -0.356 0.145 0.607 1.193 1.989 ------------------------------------------------------------------------SUBTEST MATH ; ITEM PARAMETERS AFTER CYCLE 21 ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------M01 | 1.218 | 0.805 | -1.512 | 0.627 | 0.000 | 3.2 5.0 | 0.194* | 0.155* | 0.163* | 0.121* | 0.000* | (0.6741) | | | | | | M02 | 1.149 | 0.707 | -1.626 | 0.577 | 0.000 | 4.4 6.0 | 0.169* | 0.129* | 0.186* | 0.105* | 0.000* | (0.6249) | | | | | | [Similar output omitted] M55 | -0.584 | 0.707 | 0.826 | 0.577 | 0.000 | 3.9 6.0 | 0.129* | 0.126* | 0.143* | 0.103* | 0.000* | (0.6847) | | | | | | M56 | -0.319 | 0.849 | 0.376 | 0.647 | 0.000 | 1.1 5.0 | 0.127* | 0.144* | 0.125* | 0.110* | 0.000* | (0.9547) ------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE =
0.007897
188.0 296.0 (1.0000)
662
10 BILOG-MG EXAMPLES PARAMETER MEAN STN DEV ----------------------------------SLOPE 0.802 0.138 LOG(SLOPE) -0.235 0.172 THRESHOLD 0.083 0.775
The within-group latent distributions are assumed normal. Their means and standard deviations are estimated relative to the reference group. In these data, the means increase over the grades (0722, 0.000, 0.569), but the standard deviations are relatively constant (1.069, 1.00, 1.126). GROUP:
1
POINT POSTERIOR
GRADE 4 QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.: 1 2 3 4 5 -0.4275E+01 -0.4105E+01 -0.3935E+01 -0.3765E+01 -0.3594E+01 0.4299E-03 0.7062E-03 0.1119E-02 0.1717E-01 0.2558E-02
[Similar output omitted]
POINT POSTERIOR
47 0.3552E+01 0.1899E-04
MEAN S.E.
-0.72298 0.11260
S.D. S.E.
1.06880 0.12631
GROUP:
2
POINT POSTERIOR
48 0.3722E+01 0.9879E-05
49 0.3892E+01 0.3535E-05
50 0.4062E+01 0.1816E-05
51 0.4232E+01 0.9055E-06
GRADE 6 QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.: 1 2 3 4 5 -0.4275E+01 -0.4105E+01 -0.3935E+01 -0.3765E+01 -0.3594E+01 0.1136E-04 0.2278E-04 0.4596E-04 0.8712E-04 0.1599E-03
[Similar output omitted]
POINT POSTERIOR
47 0.3552E+01 0.1172E-03
MEAN S.E.
0.00000 0.00000
S.D. S.E.
1.00000 0.00000
GROUP:
3
POINT POSTERIOR
48 0.3722E+01 0.6346E-04
49 0.3892E+01 0.3291E-04
50 0.4062E+01 0.1689E-04
51 0.4232E+01 0.8409E-05
GRADE 8 QUADRATURE POINTS, POSTERIOR WEIGHTS, MEAN AND S.D.: 1 2 3 4 5 -0.4275E+01 -0.4105E+01 -0.3935E+01 -0.3765E+01 -0.3594E+01 0.4219E-05 0.7809E-05 0.1793E-04 0.3292E-04 0.5918E-04
[Similar output omitted]
663
10 BILOG-MG EXAMPLES POINT POSTERIOR
47 0.3552E+01 0.1837E-02
MEAN S.E.
0.56861 0.11855
S.D. S.E.
1.12577 0.14026
48 0.3722E+01 0.1230E-02
49 0.3892E+01 0.8192E-03
50 0.4062E+01 0.5316E-03
51 0.4232E+01 0.3268E-03
Phase 3 output
With nonequivalent groups, Bayes (EAP) and Bayes Modal (MAP) estimation of test scores should be carried out with respect to the Phase 2 latent distribution to which the examinee belongs. Specify IDIST=3 on the SCORE command. >SCORE
METHOD=2,IDIST=3,NOPRINT, RSCTYPE=3;
PARAMETERS FOR SCORING, RESCALING, AND TEST AND ITEM INFORMATION METHOD OF SCORING SUBJECTS: EXPECTATION A POSTERIORI (EAP; BAYES ESTIMATION) TYPE OF PRIOR: EMPIRICAL, FROM ITEM CALIBRATION TYPE OF RESCALING: IN THE SAMPLE DISTRIBUTION REFERENCE GROUP FOR RESCALING: GROUP: 2
QUAD TEST NAME GROUP POINTS --------------------------1 MATH 1 51 1 MATH 2 51 1 MATH 3 51 --------------------------RESCALING CONSTANTS TEST NAME SCALE LOCATION -----------------------------------1 MATH 1.000 0.000 ------------------------------------
In this example, the scores are rescaled so that their mean and standard deviation in the total sample are zero and one, respectively. The parameter estimates are rescaled accordingly. RESCALING WITH RESPECT TO SAMPLE DISTRIBUTION ---------------------------------------------------
TEST MATH
RESCALING SCALE 1.066
CONSTANTS LOCATION 0.003
664
10 BILOG-MG EXAMPLES
GROUP SUBJECT IDENTIFICATION MARGINAL WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. PROB ------------------------------------------------------------------------1 1 | | 1.00 MATH 20 11 55.00 | -0.3055 0.3598 | 0.000000 1 1 | | 1.00 MATH 20 13 65.00 | -0.0653 0.3620 | 0.000000 ------------------------------------------------------------------------TEST
MATH
;
RESCALED ITEM PARAMETERS
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE S.E. S.E. S.E. S.E. S.E. --------------------------------------------------------------M01 | 1.216 | 0.755 | -1.610 | 0.627 | 0.000 | 0.194* | 0.145* | 0.173* | 0.121* | 0.000* | | | | | M02 | 1.148 | 0.663 | -1.732 | 0.577 | 0.000 | 0.169* | 0.121* | 0.198* | 0.105* | 0.000* | | | | | [Similar output omitted] | | | | | M55 | -0.566 | 0.670 | 0.845 | 0.581 | 0.000 | 0.127* | 0.120* | 0.151* | 0.104* | 0.000* | | | | | M56 | -0.298 | 0.805 | 0.370 | 0.651 | 0.000 | 0.125* | 0.136* | 0.132* | 0.110* | 0.000* --------------------------------------------------------------PARAMETER MEAN STN DEV ----------------------------SLOPE 0.752 0.130 LOG(SLOPE) -0.299 0.172 THRESHOLD 0.092 0.806 MEAN & SD OF SCORE ESTIMATES AFTER RESCALING GROUP MEAN SD ----------------------------1 -0.776 1.067 2 0.000 1.000 3 0.608 1.118 ----------------------------MEAN & SD OF LATENT DISTRIBUTIONS AFTER RESCALING GROUP MEAN SD ----------------------------1 -0.776 1.149 2 0.000 1.074 3 0.608 1.201 -----------------------------
665
10 BILOG-MG EXAMPLES
10.6 Multiple matrix sampling data This example illustrates the use of the TYPE=3 specification on the INPUT command to analyze aggregate-level, multiple-matrix sampling data. The data in exampl06.dat are numbers tried and numbers correct for items from eight forms of a matrix sampled assessment instrument. The groups are selected 8th grade students from 32 public schools. The first record for each school contains the data for the items of a Number Concepts scale, NUMCON, and the second record contains the data for items of an Algebra Concepts scale, ALGCON. Data for the first two schools are shown below. SCHOOL SCHOOL SCHOOL SCHOOL
1 1 2 2
NUM ALG NUM ALG
1 1 5 5
0 0 3 2
3 3 4 4
2 1 4 2
2 2 3 3
1 0 2 2
4 3 3 3
4 2 3 2
3 3 2 2
2 2 2 2
2 2 4 4
1 1 3 2
4 4 4 4
3 1 3 2
4 4 5 5
1 0 3 3
An answer key is not required for aggregate-level data in number-tried, number-right summary form. Note the format statement for reading the two sets of eight number-tried, number-right observations. For more information on how to set up the variable format statement for this type of data, see 2.6.18. The items are multiple-choice and fairly difficult, so the 3PL model is needed. Because aggregate-level data are always more informative than individual-level item responses, it is worthwhile in the CALIB command to increase the number of quadrature points (NQPT), to set a stricter criterion for convergence (CRIT), and to increase the CYCLES limit. A prior on the thresholds (TPRIOR) and a ridge constant of 0.8 (RIDGE) are required for convergence with the exceptionally difficult ALGCON subtest. Aggregate-level data typically have smaller slopes in the 0,1 metric than do person-level data. For this reason, the mean of the prior for the log slopes has been set to 0.5 by use of the READPRIOR option of the CALIB command and the following PRIOR commands. The aggregate scores for the schools are estimated by the EAP method using the empirical distributions from Phase 2. The number of quadrature points is set the same as in Phase 2. The scores are rescaled to a mean of 250 and a standard deviation of 50 in the latent distribution of schools (IDIST=3, LOCATION=250, SCALE=50). The fit of the data to the group-level model is tested for each school (FIT). The NUMCON items have fairly homogeneous slopes and might be favorable for a one-parameter model. EXAMPL06.BLM - MULTIPLE-MATRIX SAMPLING DATA AGGREGATE-LEVEL MODEL >GLOBAL NPARM=3, NTEST=2, DFNAME='EXAMPL06.DAT'; >LENGTH NITEMS=(8,8); >INPUT NTOTAL=16, NALT=5, NIDCHAR=9, TYPE=3; >ITEMS INUM=(1(1)16), INAMES=(N1(1)N8,A1(1)A8); >TEST1 TNAME=NUMCON, INUM=(1(1)8); >TEST2 TNAME=ALGCON, INUM=(9(1)16); (9A1,T15,8(2F3.0)/T15,8(2F3.0)) >CALIB NQPT=51, CYCLES=50, NEWTON=10, CRIT=0.005, TPRIOR, READPRIOR, NOFLOAT, RIDGE=(2,0.8,2.0), CHI=8, PLOT=1; >PRIORS1 SMU=(0.5(0)8); >PRIORS2 SMU=(0.5(0)8);
666
10 BILOG-MG EXAMPLES >SCORE
NQPT=(12,12), IDIST=3, RSCTYPE=4, LOCATION=(250.0,250.0), SCALE=(50.0,50.0), FIT;
Phase 1 output
Group-level data consist of number-tried and number-right frequencies for each item in each group. The program reads them as values rather than characters and conversion to item scores is not required. OBSERVATION # SUBTEST #: GROUP #:
1 1 1
WEIGHT:
2 1
3 2.0 1.0
1
4 4.0 4.0
5 3.0 2.0
6 2.0 1.0
7 4.0 3.0
8 4.0 1.0
4 3.0 2.0
5 3.0 2.0
6 2.0 1.0
7 4.0 1.0
8 4.0 0.0
ALGCON
TRIED RIGHT 22.000 7.000 ITEM 1 2 TRIED 1.0 3.0 RIGHT 0.0 1.0
3 2.0 0.0
OBSERVATION #
2
WEIGHT:
SUBTEST #: GROUP #:
NUMCON
1 1
TRIED RIGHT 30.000 23.000 ITEM 1 2 TRIED 5.0 4.0 RIGHT 3.0 4.0 SUBTEST #: GROUP #:
ID : SCHOOL
NUMCON
TRIED RIGHT 23.000 14.000 ITEM 1 2 TRIED 1.0 3.0 RIGHT 0.0 2.0 SUBTEST #: GROUP #:
1.0000
2 1
3 3.0 2.0
1.0000
ID : SCHOOL
4 3.0 3.0
5 2.0 2.0
6 4.0 3.0
7 4.0 3.0
8 5.0 3.0
4 3.0 2.0
5 2.0 2.0
6 4.0 2.0
7 4.0 2.0
8 5.0 3.0
2
TRIED RIGHT 30.000 17.000 ITEM 1 2 TRIED 5.0 4.0 RIGHT 2.0 2.0
ALGCON
3 3.0 2.0
Classical item statistics are computed for each subtest. Biserial correlations cannot be computed with group-level data.
667
10 BILOG-MG EXAMPLES
ITEM STATISTICS FOR SUBTEST NUMCON ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ------------------------------------------------------------------------1 N1 260.0 160.0 61.5 -0.28 0.637 0.000 2 N2 268.0 162.0 60.4 -0.25 0.682 0.000 3 N3 260.0 163.0 62.7 -0.31 0.663 0.000 4 N4 261.0 137.0 52.5 -0.06 0.637 0.000 5 N5 271.0 129.0 47.6 0.06 0.699 0.000 6 N6 271.0 154.0 56.8 -0.16 0.656 0.000 7 N7 270.0 157.0 58.1 -0.19 0.656 0.000 8 N8 266.0 170.0 63.9 -0.34 0.781 0.000 ------------------------------------------------------------------------ITEM STATISTICS FOR SUBTEST ALGCON ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ------------------------------------------------------------------------1 A1 259.0 120.0 46.3 0.09 0.636 0.000 2 A2 267.0 81.0 30.3 0.49 0.606 0.000 3 A3 241.0 94.0 39.0 0.26 0.669 0.000 4 A4 245.0 121.0 49.4 0.01 0.687 0.000 5 A5 263.0 96.0 36.5 0.33 0.669 0.000 6 A6 263.0 166.0 63.1 -0.32 0.746 0.000 7 A7 267.0 71.0 26.6 0.60 0.667 0.000 8 A8 262.0 90.0 34.4 0.38 0.683 0.000 -------------------------------------------------------------------------
Phase 2 output
The set-up for group-level item calibration differs somewhat from examinee-level analysis: more quadrature points and more iterations for the solution are required. Prior distributions for all parameters are necessary, the means should be kept fixed (default = NOFLOAT), and the mean of the priors for slopes should be set lower than the examinee-level default. >PRIORS1 SMU = (0.5000(0)8); CONSTRAINT DISTRIBUTIONS ON ITEM PARAMETERS (THRESHOLDS, NORMAL; SLOPES, LOG-NORMAL; GUESSING, BETA) THRESHOLDS SLOPES ASYMPTOTES ITEM MU SIGMA MU SIGMA ALPHA BETA ---------------------------------------------------------------------N1 0.000 2.000 0.500 1.649 5.00 17.00 N2 0.000 2.000 0.500 1.649 5.00 17.00 N3 0.000 2.000 0.500 1.649 5.00 17.00 N4 0.000 2.000 0.500 1.649 5.00 17.00 N5 0.000 2.000 0.500 1.649 5.00 17.00 N6 0.000 2.000 0.500 1.649 5.00 17.00 N7 0.000 2.000 0.500 1.649 5.00 17.00 N8 0.000 2.000 0.500 1.649 5.00 17.00 ----------------------------------------------------------------------
668
10 BILOG-MG EXAMPLES Group-level item parameter estimates for the first 3 items in subtest NUMCON are as follows. SUBTEST NUMCON
;
ITEM PARAMETERS AFTER CYCLE
12
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------N1 | 0.030 | 0.190 | -0.156 | 0.186 | 0.232 | 5.7 6.0 | 0.194* | 0.066* | 1.026* | 0.065* | 0.094* | (0.4521) | | | | | | N2 | 0.046 | 0.279 | -0.163 | 0.268 | 0.218 | 3.8 6.0 | 0.222* | 0.107* | 0.801* | 0.103* | 0.093* | (0.7025) | | | | | | N3 | 0.126 | 0.313 | -0.404 | 0.299 | 0.212 | 3.2 5.0 | 0.224* | 0.120* | 0.735* | 0.115* | 0.091* | (0.6638) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE =
0.003146
42.8 53.0 (0.8397)
NOTE: ITEM FIT CHI-SQUARES AND THEIR SUMS MAY BE UNRELIABLE FOR TESTS WITH LESS THAN 20 ITEMS PARAMETER MEAN STN DEV ----------------------------------ASYMPTOTE 0.210 0.041 SLOPE 0.306 0.099 LOG(SLOPE) -1.223 0.290 THRESHOLD 2.241 1.515
Phase 3 output
Computing scores at the group-level is essentially the same as at the examinee level. Note that the selection of EAP estimations based on the empirical latent distribution from Phase 2 overrides the choice here of number of quadrature points. Because of the small number of items, the standard deviation of the estimated scores is considerably smaller than that of the latent distribution. Portions of the Phase 3 output are listed below. >SCORE NQPT = (12, 12),IDIST = 3,RSCTYPE = 4, LOCATION = (250.0000, 250.0000), SCALE = (50.0000, 50.0000),FIT; PARAMETERS FOR SCORING, RESCALING, AND TEST AND ITEM INFORMATION METHOD OF SCORING SUBJECTS: EXPECTATION A POSTERIORI (EAP; BAYES ESTIMATION) TYPE OF PRIOR: EMPIRICAL, FROM ITEM CALIBRATION SUBJECT FIT PROBABILITIES: YES TYPE OF RESCALING: IN THE ESTIMATED LATENT DISTRIBUTION REFERENCE GROUP FOR RESCALING: GROUP: 1 QUAD RESCALING CONSTANTS TEST NAME POINTS SCALE LOCATION ------------------------------------------1 NUMCON 51 50.000 250.000 2 ALGCON 51 50.000 250.000 --------------------------------------------
669
10 BILOG-MG EXAMPLES The scores are rescaled so that the mean and standard deviation of the Phase 3 latent distribution are 250 and 50, respectively. Scores for all 32 schools are computed and printed. Because the data are binomial rather than binary, a χ 2 index of fit on 8 degrees of freedom can be calculated for each school. The corresponding probabilities are shown in the output. RESCALING WITH RESPECT TO LATENT DISTRIBUTION --------------------------------------------------
TEST NUMCON ALGCON
RESCALING SCALE 58.462 56.462
CONSTANTS LOCATION 251.342 251.127
GROUP SUBJECT IDENTIFICATION FIT MARGINAL WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. PROB PROB ------------------------------------------------------------------------1 SCHOOL 1 | | 1.00 NUMCON 23 14 60.87 | 246.5104 43.5894 | 0.1539 0.0000 1 SCHOOL 1 | | 1.00 ALGCON 22 7 31.82 | 243.1547 47.4683 | 0.3197 0.0000 [Similar output omitted] 1 SCHOOL 32 | | 1.00 NUMCON 181 100 55.25 | 221.6762 21.9655 | 0.0166 0.0000 1 SCHOOL 32 | | 1.00 ALGCON 179 77 43.02 | 273.1747 21.8821 | 0.5242 0.0000 ------------------------------------------------------------------------MEAN & SD OF SCORE ESTIMATES AFTER RESCALING: 250.000 31.149 MEAN & SD OF LATENT DISTRIBUTION AFTER RESCALING: 250.000 50.000
10.7 Analysis of variant items In this example, responses to 50 items are read from those of 100 items in the data file using the format statement (10A1,T38,25A1,1X,25A1).
The first few lines of the data file are shown below. In contrast to previous examples, each position in the item response fields for each examinee corresponds to the same item. In the earlier examples, the association between response and item depended on the group/form membership of an examinee. The answer key (KFNAME keyword on the INPUT command) is given first, and is given in the first two lines of the raw data file in the same format as the item responses. KEY 0102111900 0104112200 0105121900
00000000000000000000000000000000000000000000000000000000000000000000000000… 00000401020100002001101002024030005001000000000233004002014062000012000100… 10101200210100000100010230110030013000000100103021014000002042001012001000… 11012041110200000010002230131010122101000000013123000002001042101012001300…
From the 50, 20 are selected as Main Test items and 4 as Variant Test items. This is indicated by setting NITEMS to 24 and NVARIANT to 4 on the LENGTH command. Items for the main test are 670
10 BILOG-MG EXAMPLES selected by name in the TESTM command; items for the variant test are selected by name in the TESTV command. The item names correspond to the sequence numbers in the original set of 100 items. Here the short form of naming and numbering is used – the set of items forms an arithmetic progression of integer or decimal numbers allowing use of the short form (first (increment) last). A similar abbreviation may be used for consecutive item names (INAMES keyword on the ITEMS command). The analysis is performed on a sample of 200 students randomly drawn from the original sample of 660 (SAMPLE=200 on the INPUT command). The EAP scale scores of Phase 3 are computed from the responses to items in the main test. EXAMPL07.BLM - ANALYSIS OF VARIANT ITEMS IN A SPELLING TEST OF RANDOMLY SELECTED WORDS; SUBJECTS: 660 UNDERGRADUATE STUDENTS; 2PL MODEL. >GLOBAL DFNAME='EXAMPL07.DAT', NTEST=1, NVTEST=1, NPARM=2, SAVE; >SAVE PARM='EXAMPL07.PAR', SCORE='EXAMPL07.SCO'; >LENGTH NITEM=24, NVARIANT=4; >INPUT NTOTAL=50, KFNAME='EXAMPL07.DAT', SAMPLE=200, NIDCHAR=10; >ITEMS INUMBERS=(1(1)50), INAME=(I26(1)I75); >TESTM TNAME=MAINTEST, INAMES=(I26,I27,I28,I29,I31,I33,I34, I35,I36,I38,I39,I47,I48,I49,I50,I54,I60,I64,I68,I72); >TESTV TNAME=VARIANT, INAMES=(I53,I59,I69,I73); (10A1,T38,25A1,1X,25A1) >CALIB NQPT=31, CRIT=.005, CYCLES=10, NEWTON=2, FLOAT, ACCEL=0.5; >SCORE METHOD=2, NOPRINT;
Phase 1 output
Phase 1 lists the test specifications and the assignment of items to the main test and the variants. >ITEMS
INUMBERS=(1(1)50), INAME=(I26(1)I75);
TEST SPECIFICATIONS =================== >TESTM
TNAME=MAINTEST, INAMES=(I26,I27,I28,I29,I31,I33,I34, I35,I36,I38,I39,I47,I48,I49,I50,I54,I60,I64,I68,I72);
TEST NUMBER: 1 NUMBER OF ITEMS:
TEST NAME: MAINTEST 20
ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM NUMBER NAME NUMBER NAME NUMBER NAME NUMBER NAME ----------------------------------------------------------------------1 I26 9 I34 23 I48 43 I68 2 I27 10 I35 24 I49 47 I72 3 I28 11 I36 25 I50 4 I29 13 I38 29 I54 6 I31 14 I39 35 I60 8 I33 22 I47 39 I64 ---------------------------------------------------------------------->TESTV
TNAME=VARIANT, INAMES=(I53,I59,I69,I73);
671
10 BILOG-MG EXAMPLES TEST NUMBER: 2 NUMBER OF ITEMS:
TEST NAME: VARIANT 4
ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM NUMBER NAME NUMBER NAME NUMBER NAME NUMBER NAME ----------------------------------------------------------------------28 I53 34 I59 44 I69 48 I73 -----------------------------------------------------------------------
Responses of 660 examinees are read from the data records, but only 200 randomly sampled cases are included in the Phase 1 and Phase 2 analysis. The classical item statistics are shown separately for main and variant items. The test scores for the item-test correlations are based on the test scores from the main test items only. 660 OBSERVATIONS READ FROM FILE: 660 OBSERVATIONS WRITTEN TO FILE: REPORT ON SUBJECT SAMPLING: LEVEL OF SAMPLING = 0.3030 660 SUBJECTS READ FROM FILE: 200 SUBJECTS WRITTEN TO FILE:
EXAMPL07.DAT MF.DAT
MF.DAT CF.DAT
ITEM STATISTICS FOR SUBTEST MAINTEST ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL -----------------------------------------------------------------------1 I26 200.0 134.0 67.0 -0.42 0.188 0.244 2 I27 200.0 102.0 51.0 -0.02 0.421 0.527 3 I28 200.0 78.0 39.0 0.26 0.294 0.374 4 I29 200.0 147.0 73.5 -0.60 0.444 0.598 ... -----------------------------------------------------------------------ITEM STATISTICS FOR SUBTEST VARIANT ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL ------------------------------------------------------------------------1 I53 200.0 139.0 69.5 -0.48 0.454 0.596 2 I59 200.0 135.0 67.5 -0.43 0.456 0.594 3 I69 200.0 53.0 26.5 0.60 0.379 0.510 4 I73 200.0 50.0 25.0 0.65 0.069 0.094 -------------------------------------------------------------------------
Phase 2 output
Calibration of the main test items is computed as in the other examples. Without altering the item parameter estimates of those items, parameter estimates for the variants are computed with respect to the latent dimension determined by the main items.
672
10 BILOG-MG EXAMPLES SUBTEST MAINTEST;
ITEM PARAMETERS AFTER CYCLE
6
ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------I26| 0.451 | 0.360 | -1.254 | 0.339 | 0.000 | 8.6 8.0 | 0.648* | 0.088* | 1.775* | 0.083* | 0.000* | (0.3784) | | | | | | I27| 0.028 | 0.753 | -0.037 | 0.602 | 0.000 | 3.2 6.0 | 0.691* | 0.152* | 0.918* | 0.121* | 0.000* | (0.7857) | | | | | | (Similar output omitted) I72| -0.018 | 0.726 | 0.025 | 0.587 | 0.000 | 13.6 6.0 | 0.684* | 0.149* | 0.942* | 0.121* | 0.000* | (0.0347) ------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE =
0.002542
106.4 131.0 (0.9439)
PARAMETER MEAN STN DEV ----------------------------------SLOPE 0.616 0.220 LOG(SLOPE) -0.548 0.368 THRESHOLD 0.143 1.256 ****************************** CALIBRATION OF VARIANT ITEMS VARIANT ****************************** -2 LOG LIKELIHOOD =
4545.542
ITEM INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------I53| 0.587 | 0.613 | -0.957 | 0.523 | 0.000 | 0.0 0.0 | 0.104* | 0.111* | 0.202* | 0.094* | 0.000* | (1.0000) | | | | | | I59| 0.519 | 0.603 | -0.860 | 0.517 | 0.000 | 0.0 0.0 | 0.101* | 0.109* | 0.195* | 0.093* | 0.000* | (1.0000) | | | | | | I69| -0.702 | 0.549 | 1.280 | 0.481 | 0.000 | 0.0 0.0 | 0.107* | 0.109* | 0.263* | 0.095* | 0.000* | (1.0000) | | | | | | I73| -0.668 | 0.231 | 2.886 | 0.225 | 0.000 | 0.0 0.0 | 0.098* | 0.064* | 0.857* | 0.062* | 0.000* | (1.0000) -------------------------------------------------------------------------
Phase 3 output
In Phase 3, scores for all 660 examinees are computed from the main test item response and saved to an external file. Printing of the scores is suppressed, except for the first three cases. The latent distribution estimated from all 660 cases is computed and printed. Scores are based on the unrescaled Phase 2 parameters, which are then saved to an external file. >SCORE
METHOD=2,NOPRINT;
673
10 BILOG-MG EXAMPLES SCORES WILL NOT BE COMPUTED FOR VARIANT ITEM SUBTESTS PARAMETERS FOR SCORING, RESCALING, AND TEST AND ITEM INFORMATION METHOD OF SCORING SUBJECTS: TYPE OF PRIOR: SCORES WRITTEN TO FILE
EXPECTATION A POSTERIORI (EAP; BAYES ESTIMATION) NORMAL EXAMPL07.SCO
GROUP SUBJECT IDENTIFICATION MARGINAL WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. PROB ------------------------------------------------------------------------1 0102111900 | | 1.00 MAINTEST 20 8 40.00 | -0.4065 0.3645 | 0.000000 1 0104112200 | | 1.00 MAINTEST 20 8 40.00 | -0.4091 0.3641 | 0.000000 1 0105121900 | | 1.00 MAINTEST 20 3 15.00 | -1.2316 0.4637 | 0.000000 ------------------------------------------------------------------------SUMMARY STATISTICS FOR SCORE ESTIMATES ====================================== CORRELATIONS AMONG TEST SCORES
MAINTEST
MAINTEST 1.0000
MEANS, STANDARD DEVIATIONS, AND VARIANCES OF SCORE ESTIMATES TEST: MEAN: S.D.: VARIANCE:
MAINTEST 0.0915 0.8940 0.7992
ROOT-MEAN-SQUARE POSTERIOR STANDARD DEVIATIONS TEST: RMS: VARIANCE:
MAINTEST 0.4493 0.2019
EMPIRICAL RELIABILITY:
0.7984
MARGINAL LATENT DISTRIBUTION(S) =============================== MARGINAL LATENT DISTRIBUTION FOR TEST MAINTEST MEAN = 0.092 S.D. = 0.974
10.8 Group-wise adaptive testing This example illustrates the use of BILOG-MG with multiple groups and multiple subtests. It is designed to illustrate some of the more complicated features of the program, including userspecified priors on the latent distributions and priors on the item parameters. 674
10 BILOG-MG EXAMPLES Based on previous test performance, examinees are assigned to two groups for adaptive testing. Out of a set of 45 items, group 1 is assigned items 1 through 25, and group 2 is assigned items 21 through 45. Thus, there are 5 items linking the test forms administered to the groups. Twenty of the 25 items presented to group 1 belong to subtest 1 (items 1-15 and 21-25); twenty items also belong to subtest 2 (items 6-25). Of the 25 items presented to group 2, 20 belong to subtest 1 (items 21-40) and 20 to subtest 2 (items 21-25 and 31-45). In all, there are 35 items from the set of 45 assigned to each subtest. (This extent of item overlap between subtests is not realistic, but it illustrates that more than one subtest can be scored adaptively provided they each contain link items between the test forms.) This example also illustrates how user-supplied priors for the latent distributions are specified with IDIST=1 on the CALIB command. The points and weights for these distributions are supplied in the QUAD commands. Note that with IDIST=1, there are separate QUAD commands for each group for each subtest. Within each subtest the points are the same for each group. This is a requirement of the program. But as the example shows, the points for the groups may differ by subtest. If IDIST has been set to 2, sets of weights have to be supplied by group. The set of points then applies to all subtests. The PRIOR command for each subtest is placed after the QUAD commands for that subtest. The presence of the PRIOR command is indicated using the READPRIOR option on the CALIB command. In this example, only the prior for the standard deviation of the thresholds is supplied on the PRIOR command. Default values are used for the other prior distributions. The means of the distributions are kept fixed at their specified values by using the NOFLOAT option on the CALIB command. The score distribution in the respondent population is estimated in the form of a discrete distribution on NQPT=16 points by adding the EMPIRICAL option to the CALIB command. This discrete distribution will be used in the place of the prior in MML estimate of the item parameters. When NGROUP>1, separate score distributions will be estimated for the groups. The first group serves as the reference group (REFERENCE=1). If the REFERENCE keyword is omitted, the first group will by default be used as the reference group. When NGROUP>1, the FLOAT option is the default. By using NOFLOAT here, the means of the prior distributions on item parameters are kept fixed at the specified values during estimation. In the scoring phase, the empirical prior from phase 2 is used as prior distribution for the scale scores (IDIST=3). Rescaling of scores to the scale and location in the sample of scale score estimates is requested by setting RSCTYPE to 3. The presence of the INFO keyword indicates that information output is required. In this case INFO=1 and test information curves will be printed to the phase 3 output file. In combination with the YCOMMON and POP options, the test information curves will be expressed in comparable units and an estimate of the classical reliability coefficient, amongst other information, will be calculated for each subtest.
675
10 BILOG-MG EXAMPLES EXAMPL08.BLM GROUP-WISE ADAPTIVE TESTING WITH TWO SUBTESTS DFNAME='EXAMPL08.DAT', NPARM=2, NTEST=2, SAVE; SCORE='EXAMPL08.SCO'; NITEMS=(35,35); NTOT=45, SAMPLE=2000, NGROUP=2, KFNAME='EXAMPL08.DAT', NALT=5, NFORMS=2, NIDCH=5; >ITEMS INUM=(1(1)45), INAME=(C01(1)C45); >TEST1 TNAME=SUBTEST1, INAME=(C01(1)C15,C21(1)C40); >TEST2 TNAME=SUBTEST2, INAME=(C06(1)C25,C31(1)C45); >FORM1 LENGTH=25, INUM=(1(1)25); >FORM2 LENGTH=25, INUM=(21(1)45); >GROUP1 GNAME=POP1, LENGTH=25, INUM=(1(1)25); >GROUP2 GNAME=POP2, LENGTH=25, INUM=(21(1)45); (5A1,T1,I1,T1,I1,T7,25A1) >CALIB IDIST=1, READPRIOR, EMPIRICAL, NQPT=31, CYCLE=25, TPRIOR, NEWTON=5, CRITERION=0.01, REFERENCE=1, NOFLOAT; >QUAD1 POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01,-0.4453E+00, 0.5930E+00, 0.1631E+01, 0.2670E+01, 0.3708E+01, 0.4746E+01), WEIGHTS=(0.2464E-05, 0.4435E-03, 0.1724E-01, 0.1682E+00, 0.3229E+00, 0.3679E+00, 0.1059E+00, 0.1685E-01, 0.6475E-03, 0.8673E-05); >QUAD2 POINTS=(-0.4598E+01,-0.3560E+01,-0.2522E+01,-0.1484E+01,-0.4453E+00, 0.5930E+00, 0.1631E+01, 0.2670E+01, 0.3708E+01, 0.4746E+01), WEIGHTS=(0.2996E-04, 0.1300E-02, 0.1474E-01, 0.1127E+00, 0.3251E+00, 0.3417E+00, 0.1816E+00, 0.2149E-01, 0.1307E-02, 0.3154E-04); >PRIOR TSIGMA=(1.5(0)35); >QUAD1 POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01,-0.4444E+00, 0.4444E+00, 0.1333E+01, 0.2222E+01, 0.3111E+01, 0.4000E+01), WEIGHTS=(0.1190E-03, 0.2805E-02, 0.3002E-01, 0.1458E+00, 0.3213E+00, 0.3213E+00, 0.1458E+00, 0.3002E-01, 0.2805E-02, 0.1190E-03); >QUAD2 POINTS=(-0.4000E+01,-0.3111E+01,-0.2222E+01,-0.1333E+01,-0.4444E+00, 0.4444E+00, 0.1333E+01, 0.2222E+01, 0.3111E+01, 0.4000E+01), WEIGHTS=(0.1190E-03, 0.2805E-02, 0.3002E-01, 0.1458E+00, 0.3213E+00, 0.3213E+00, 0.1458E+00, 0.3002E-01, 0.2805E-02, 0.1190E-03); >PRIOR TSIGMA=(1.5(0)35); >SCORE IDIST=3, RSCTYPE=3, INFO=1, YCOMMON, POP, NOPRINT; >GLOBAL >SAVE >LENGTH >INPUT
Phase 1 output
Phase 1 echoes the assignment of items to subtests, forms, and groups. Classical item statistics are computed for each subtest in each group. Output for subtest 1 and group 1 (POP1) is given below. SUBTEST GROUP GROUP
1 1 2
SUBTEST1 POP1 POP2
200 OBSERVATIONS 200 OBSERVATIONS
SUBTEST GROUP GROUP
2 1 2
SUBTEST2 POP1 POP2
200 OBSERVATIONS 200 OBSERVATIONS
676
10 BILOG-MG EXAMPLES
SUBTEST 1 SUBTEST1 ITEM STATISTICS FOR GROUP:
1
POP1
ITEM*TEST CORRELATION ITEM NAME #TRIED #RIGHT PCT LOGIT/1.7 PEARSON BISERIAL -----------------------------------------------------------------------1 C01 200.0 170.0 0.850 -1.02 0.408 0.625 2 C02 200.0 164.0 0.820 -0.89 0.396 0.580 3 C03 200.0 154.0 0.770 -0.71 0.451 0.626 4 C04 200.0 143.0 0.715 -0.54 0.400 0.532 5 C05 200.0 140.0 0.700 -0.50 0.586 0.772 6 C06 200.0 135.0 0.675 -0.43 0.441 0.574 ... 19 C24 200.0 83.0 0.415 0.20 0.590 0.746 20 C25 200.0 76.0 0.380 0.29 0.558 0.711 ------------------------------------------------------------------------
Phase 2 output
Phase 2 estimates empirical latent distributions for each group and item parameters for each subtest. The arbitrary mean and standard deviation of reference group 1 determine the origin and unit of the ability scales. >CALIB
IDIST=1, READPRIOR, EMPIRICAL, NQPT=31, CYCLE=25, TPRIOR, NEWTON=5, CRITERION=0.01, REFERENCE=1, NOFLOAT;
ITEM
INTERCEPT SLOPE THRESHOLD LOADING ASYMPTOTE CHISQ DF S.E. S.E. S.E. S.E. S.E. (PROB) ------------------------------------------------------------------------------C01 | 1.435 | 0.930 | -1.542 | 0.681 | 0.000 | 8.5 6.0 | 0.196* | 0.187* | 0.211* | 0.137* | 0.000* | (0.2037) | | | | | | C02 | 1.196 | 0.823 | -1.453 | 0.635 | 0.000 | 7.7 6.0 | 0.162* | 0.163* | 0.215* | 0.126* | 0.000* | (0.2580) | | | | | | C03 | 1.028 | 0.922 | -1.115 | 0.678 | 0.000 | 5.8 6.0 | 0.160* | 0.169* | 0.153* | 0.124* | 0.000* | (0.4441) ... | | | | | | C38 | -0.962 | 1.098 | 0.876 | 0.739 | 0.000 | 6.7 6.0 | 0.164* | 0.182* | 0.115* | 0.122* | 0.000* | (0.3520) | | | | | | C39 | -1.144 | 0.879 | 1.302 | 0.660 | 0.000 | 1.7 5.0 | 0.173* | 0.170* | 0.169* | 0.128* | 0.000* | (0.8927) | | | | | | C40 | -1.044 | 0.632 | 1.652 | 0.534 | 0.000 | 3.0 6.0 | 0.133* | 0.123* | 0.268* | 0.104* | 0.000* | (0.8143) ------------------------------------------------------------------------------* STANDARD ERROR LARGEST CHANGE = 0.008756 171.9 233.0 (0.9990) PARAMETER MEAN STN DEV ----------------------------------SLOPE 0.862 0.154 LOG(SLOPE) -0.165 0.180 THRESHOLD -0.164 0.908
677
10 BILOG-MG EXAMPLES Phase 3 output
The only new feature in Phase 3 is the use of the YCOMMON option to place the information plots for the subtests on the same scale. This permits visual comparison of the relative precision of the subtests according to the heights of the information curves. To illustrate, the ICC for subtest 1, form is given below. The POP option also provides IRT estimated reliability for each subtest. TEST INFORMATION CURVE FOR TEST: SUBTEST1 FORM: 1 STANDARD INFORERROR MATION -----------------------------------------------------------------4.41| |10.0923 | | 4.19| | 9.5876 | | 3.97| ++++ | 9.0830 | + + | 3.75| + + | 8.5784 | + + | 3.53| + + | 8.0738 | | 3.31| + + | 7.5692 | | 3.08| + + | 7.0646 | | 2.86| + + | 6.5600 | | 2.64| + | 6.0554 | + | 2.42| + | 5.5507 | + | 2.20| + *| 5.0461 | + * | 1.98|* + | 4.5415 | + + * | 1.76| * * | 4.0369 | * + + | 1.54| * * | 3.5323 | + + * | 1.32| * * | 3.0277 | ** + + * | 1.10| * + + * | 2.5231 | * + ** | 0.88| ** + + * | 2.0185 | +* +* | 0.66| + ** ** + | 1.5138 | + *** *** + | 0.44| + ****** ***** ++ | 1.0092 | ++ **************** ++ | 0.22| +++ +++ | 0.5046 |+++ ++++| 0.00| | 0.0000 -+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+---+ -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 MAXIMUM INFORMATION APPROXIMATELY 0.9046D+01 AT 0.0000 FOR A NORMAL POPULATION WITH MEAN 0.000 AND S.D. 1.000 AVERAGE INFORMATION= 0.7232D+01 AND RELIABILITY INDEX= 0.879
678
10 BILOG-MG EXAMPLES
10.9 Two-stage spelling test This example is based on a study by Bock and Zimowski (1998). The full document is available on the Internet from the American Institutes for Research. As a small computing example, we simulated two-stage testing in data for the “One-Hundred Word Spelling Test” previously analyzed by Bock, Thissen, and Zimowski (1997). A complete description of these data are given in Section 2.4.1. On the basis of item parameters they report, we selected 12 first-stage items and 12 items for each of three levels of the second-stage test. Because of the limited number of items in the pool, we could not meet exactly the requirements of the prototype design, but the resulting test illustrates well enough the main features of the analysis. The item numbers in this and a later example correspond to the words presented in Bock, Thissen, and Zimowski’s (1977) Table 1 in the NAEP report. All computations in the analysis were carried out with the BILOG-MG program of Zimowski, Muraki, Mislevy and Bock (1996). The program command files as well as the data file (with N = 660) are included in the twostage folder of the BILOG-MG installation folder. For assigning the cases in the data to second-stage levels under conditions that would apply in an operational assessment, we re-estimated the parameters for the 12 first-stage items, computed Bayes estimates of proficiency scale scores, and rescaled the scores to mean 0 and standard deviation 1 in the sample. The command file step0.blm, shown below, contains the necessary commands. STEP0.BLM - A SIMULATED TWO-STAGE SPELLING TEST - Prototype 1 computing example. Estimation of the 12 first-stage item parameters. >COMMENTS From: "Feasibility Studies of Two-Stage Testing in Large-Scale Educational Assessment: Implications for NAEP" by R. Darrel Bock and Michele F. Zimowski, May 1998, American Institutes for Research. Based on the 100-word spelling test data. N = 1000 (See Bock, Thissen and Zimowski, 1997). According to page 35 of the NAEP study, we first establish group membership by recalibrating the parameters for the 12 first-stage items and compute EAP estimates of the proficiency scale scores, rescaled to mean 0 and standard deviation 1 in the sample of 1000. Next, we assign group membership based on scores at or below -0.67 (group 1), at or above +0.67 (group 3), and the remaining scores (group 2). The resulting score file was manipulated per these instructions(see result in the STEP0.EAP file) and the assigned group membership added to the original data file as column 12 (before empty). The resulting split is: group 1 236, group 2 531, group 3 233. >GLOBAL >SAVE >LENGTH >INPUT >ITEMS
NPARM=2, DFNAME='SPELL1.DAT', SAVE; PARM='STEP0.PAR', SCORE='STEP0.SCO'; NITEMS=12; NTOTAL=100, NIDCH=11, TYPE=1, SAMPLE=1000, KFNAME='SPELL1.DAT'; INUM=(1(1)100), INAME=(SPELL001(1)SPELL100);
679
10 BILOG-MG EXAMPLES >TEST TNAME=SPELLING, INUM=(1,4,8,10,23,25,28,29,39,47,59,87); (11A1,1X,25A1,1X,25A1/12X,25A1,1X,25A1) >CALIB NQPT=20, CRIT=0.001, CYCLES=100, NEWTON=2, NOFLOAT; >SCORE IDIST=3, METHOD=2, NOPRINT, INFO=1, POP;
Cases with scores at or below -0.67 were assigned to group 1. Those at or above +0.67 were assigned to group 3, and the remainder to group 2. Of the 1000 cases in the original study, 274, 451, and 275 were assigned to groups 1, 2, and 3, respectively. With these assignment codes inserted in the case records, the latent distributions were estimated using the command file for the first-stage analysis shown below (step1.blm in the twostage folder). STEP1.BLM - ANALYSIS 1: A SIMULATED TWO-STAGE SPELLING TEST Estimation of first-stage item parameters and latent distributions. >GLOBAL DFNAME='SPELL2.DAT', NPARM=2, SAVE; >SAVE SCORE='STEP1.SCO', PARM='STEP1.PAR'; >LENGTH NITEMS=12; >INPUT NTOT=100, SAMPLE=1000, NGROUP=3, KFNAME='SPELL2.DAT', NIDCHAR=11, TYPE=1; >ITEMS INUMBERS=(1(1)100), INAMES=(SPELL001(1)SPELL100); >TEST TNAME=SPELLING, INUM=(1,4,8,10,23,25,28,29,39,47,59,87); >GROUP1 GNAME=GROUP1, LENGTH=12, INUM=(1,4,8,10,23,25,28,29,39,47,59,87); >GROUP2 GNAME=GROUP2, LENGTH=12, INUM=(1,4,8,10,23,25,28,29,39,47,59,87); >GROUP3 GNAME=GROUP3, LENGTH=12, INUM=(1,4,8,10,23,25,28,29,39,47,59,87); (11A1,I1,25A1,1X,25A1,/T13,25A1,1X,25A1) >CALIB FIX, NOFLOAT, NQPT=20, CYCLE=35, SPRIOR, NEWTON=2, CRIT=0.001, REF=0; >SCORE IDIST=3, METHOD=2, NOPRINT, INFO=1, POP;
For the second-stage analysis, we used the latent distributions estimated in the first-stage analysis as the prior distributions for maximum marginal likelihood analysis of the combined first- and second-stage data. The points and weights representing the distributions are shown in the corresponding BILOG-MG command file. Inasmuch as there are no second-stage link items in this example, we use the first-stage items as an anchor test. The six easiest of these items provide the links between levels 1 and 2; the six most difficult provide the links between levels 2 and 3. The syntax for this analysis is given in step2.blm, as shown below. STEP2.BLM - ANALYSIS 2: A SIMULATED TWO-STAGE SPELLING TEST. Estimated link and second-stage item parameters, and latent distributions. >COMMENTS The points and weights are the posterior estimates from STEP1.PH2. >GLOBAL >SAVE >LENGTH >INPUT >ITEMS >TEST
>GROUP1
DFNAME='SPELL2.DAT', NPARM=2, SAVE; SCORE='STEP2.SCO', PARM='STEP2.PAR'; NITEMS=48; NTOT=100, SAMPLE=1000, NGROUP=3, KFNAME='SPELL2.DAT', NIDCHAR=11, TYPE=1; INUM=(1(1)100), INAME=(SPELL001(1)SPELL100); TNAME=SPELLING, INUM=( 1, 4, 5, 6, 7, 8, 9,10,12,14,15,17,20,23,24,25, 26,27,28,29,33,34,35,38,39,46,47,48,49,50,53,54, 59,60,64,68,69,72,73,77,78,84,85,86,87,90,95,97); GNAME=GROUP1, LENGTH=18, INUM=( 1, 4, 5,14,24,26,29,38,39,46,53,59,68,78,85,87,90,95);
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10 BILOG-MG EXAMPLES >GROUP2
GNAME=GROUP2, LENGTH=24, INUM=( 1, 4, 8, 9,10,15,20,23,25,27,28,29,33,34,39,47,48,49, 50,54,59,64,72,87); >GROUP3 GNAME=GROUP3, LENGTH=18, INUM=( 6, 7, 8,10,12,17,23,25,28,35,47,60,69,73,77,84,86,97); (11A1,I1,25A1,1X,25A1,/T13,25A1,1X,25A1) >CALIB IDIST=1, FIX, NOFLOAT, CYCLE=35, SPRIOR, NEWTON=2, CRIT=0.001, NQPT=20, REF=0, PLOT=1.0, ACC=0.0; >QUAD1 POINT=(-0.4081E+01, -0.3652E+01, -0.3222E+01, -0.2792E+01, -0.2363E+01, -0.1933E+01, -0.1504E+01, -0.1074E+01, -0.6443E+00, -0.2147E+00, 0.2150E+00, 0.6446E+00, 0.1074E+01, 0.1504E+01, 0.1933E+01, 0.2363E+01, 0.2793E+01, 0.3222E+01, 0.3652E+01, 0.4082E+01), WEIGHT= (0.2345E-03, 0.1159E-02, 0.4738E-02, 0.1624E-01, 0.4605E-01, 0.1077E+00, 0.2023E+00, 0.2785E+00, 0.2311E+00, 0.9390E-01, 0.1678E-01, 0.1320E-02, 0.4924E-04, 0.9717E-06, 0.8556E-12, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00); >QUAD2 POINT=(-0.4081E+01, -0.3652E+01, -0.3222E+01, -0.2792E+01, -0.2363E+01, -0.1933E+01, -0.1504E+01, -0.1074E+01, -0.6443E+00, -0.2147E+00, 0.2150E+00, 0.6446E+00, 0.1074E+01, 0.1504E+01, 0.1933E+01, 0.2363E+01, 0.2793E+01, 0.3222E+01, 0.3652E+01, 0.4082E+01), WEIGHT=(0.0000E+00, 0.0000E+00, 0.0000E+00, 0.3055E-05, 0.7882E-04, 0.1170E-02, 0.1119E-01, 0.6218E-01, 0.1820E+00, 0.2791E+00, 0.2502E+00, 0.1451E+00, 0.5407E-01, 0.1271E-01, 0.1945E-02, 0.2046E-03, 0.8579E-06, 0.0000E+00, 0.0000E+00, 0.0000E+00); >QUAD3 POINT=(-0.4081E+01, -0.3652E+01, -0.3222E+01, -0.2792E+01, -0.2363E+01, -0.1933E+01, -0.1504E+01, -0.1074E+01, -0.6443E+00, -0.2147E+00, 0.2150E+00, 0.6446E+00, 0.1074E+01, 0.1504E+01, 0.1933E+01, 0.2363E+01, 0.2793E+01, 0.3222E+01, 0.3652E+01, 0.4082E+01), WEIGHT= (0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.0000E+00, 0.3914E-11, 0.1006E-05, 0.5966E-04, 0.1650E-02, 0.1943E-01, 0.9720E-01, 0.2237E+00, 0.2717E+00, 0.2051E+00, 0.1111E+00, 0.4735E-01, 0.1652E-01, 0.4763E-02, 0.1128E-02, 0.2324E-03); >SCORE IDIST=3, METHOD=2, NOPRINT, INFO=1, POP;
Since the spelling data contain responses of all cases to all items, we can examine the comparative accuracy of the estimates based on the 24 items per case in the two-stage data with those based on 48 items per case in a conventional one-stage test. Syntax is as given in step3.blm, shown below. STEP3 - ANALYSIS 2: A SIMULATED TWO-STAGE SPELLING TEST Estimation of 48 one-stage item parameters, and latent distributions. >GLOBAL DFNAME='SPELL2.DAT', NPARM=2, SAVE; >SAVE SCORE='STEP3.SCO', PARM='STEP3.PAR'; >LENGTH NITEMS=48; >INPUT NTOTAL=100, SAMPLE=1000, KFNAME='SPELL2.DAT', NIDCHAR=11, TYPE=1; >ITEMS INUMBERS=(1(1)100), INAMES=(SPELL001(1)SPELL100); >TEST TNAME=SPELLING, INUM=(1,4,5,6,7,8,9,10,12,14,15,17,20,23,24,25, 26,27,28,29,33,34,35,38,39,46,47,48,49,50,53,54,59,60,64,68,69,72,73, 77,78,84,85,86,87,90,95,97); (11A1,1X,25A1,1X,25A1,/T13,25A1,1X,25A1) >CALIB IDIST=0, FIX, NOFLOAT, CYCLE=35, SPRIOR, NEWTON=2, CRIT=0.001, NQPT=20, REF=0, PLOT=1.0, ACC=0.0; >SCORE IDIST=3, METHOD=2, NOPRINT, INFO=1, POP;
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10 BILOG-MG EXAMPLES The latter estimates are also shown in Table 10.1. Despite the small number of items and relatively small sample size in this computing example, the agreement between the estimates is reasonably good for the majority of items. There are notable exceptions, however, among the second-stage items: of these, items 6, 7, 77, and 84 show discrepancies in both slope and threshold; all of these are from level 3 and have extremely high thresholds in the one-stage analysis, well beyond the +1.5 maximum we are assuming for second-stage items. Items 12 and 17 from level 3 are discrepant only in slope, as are items 26 and 38 from level 2, and items 50 and 64 from level 1. Table 10.1: Comparison of two-stage and one-stage item parameter estimates in the spelling data (shown for first 10 items) Item
Two-stage Slope (S.E.)
One-stage
Threshold Slope (S.E.) (S.E.)
Threshold (S.E.)
SPELL001 0.74191 -0.22896 0.10040 0.07910
0.84646 -0.32964 0.08642 0.06612
SPELL004 0.64140 -0.45195 0.08831 0.09150
0.71193 -0.54128 0.07347 0.08305
SPELL005 0.68036 -1.47582 0.19351 0.16286
0.69276 -1.40895 0.07525 0.13561
SPELL006 0.87969 1.51254 0.24184 0.13287
0.29534 2.15957 0.04648 0.37184
SPELL007 0.78362 2.59105 0.24146 0.37885
0.32823 3.76009 0.06116 0.67776
SPELL008 0.51257 0.52107 0.07726 0.11154
0.54531 0.59135 0.06226 0.10754
SPELL009 0.98121 -0.28826 0.19997 0.08066
0.68981 -0.25449 0.06884 0.07895
SPELL010 0.94877 0.45341 0.10159 0.06703
0.91421 0.50198 0.08021 0.06909
SPELL012 0.87810 1.41514 0.23453 0.11948
0.78199 1.41415 0.09203 0.13032
SPELL014 1.00579 -1.99060 0.28436 0.20872
0.72159 -1.94803 0.10121 0.20793 682
10 BILOG-MG EXAMPLES In all cases the two-stage slope is larger than the one-stage slope. This effect is balanced however, by the tendency of the first-stage items, 1, 4, 8, 10, 23, 25, 28, 29, 39, 47, 59, and 87, to show smaller slopes in the two-stage analysis. As a result, the average slope in the two-stage results is only slightly larger than the one-stage average. The average thresholds also show only a small difference. In principle, the parameters of a twoparameter logistic response function can be calculated from probabilities at any two distinct, finite values on the measurement continuum. Similarly, those of the three-parameter model can be calculated from three such points. This suggests that in fallible data estimation must improve, even in the two-stage case, as sample size increases. Some preliminary simulations we have attempted suggest that with sample sizes in the order of 5 or 10 thousand, and better placing of the items, the discrepancies we see in the prototype 1 results largely disappear. The latent distributions estimated with items from both stages are depicted in Figure 10.1. The distributions for the three assignment groups are shown normalized to unity. The estimated population distribution, which is the sum of the distributions for the individual groups weighted proportional to sample size, is constrained to mean 0 and standard deviation 1 during estimation of the component distribution. It is essentially normal and almost identical to the population distribution estimated in the one-stage analysis.
Figure 10.1. Prototype 1: estimated latent distributions from two-stage and one-stage spelling data
One may infer the measurement properties of the simulated two-stage spelling test from the information and efficiency calculations shown in Figure 10.2 and Figure 10.3, respectively. When interpreting information curves, the following rules of thumb are helpful. An information value 683
10 BILOG-MG EXAMPLES of 5 corresponds to a measurement error variance of 1/5 = 0.2. In a population in which the score variance is set to unity, the reliability of a score with this error variance is 1.0 - 0.2 = 0.8. Similarly, the reliability corresponding to an information value of 10 is 0.9. In the context of lowstakes score reporting, we are aiming for reliabilities anywhere between these figures. As is apparent in Figure 10.2, this range of reliability is achieved in the two-stage results for spelling over much of the latent distribution.
Figure 10.2. Prototype 1: two-stage spelling test
Figure 10.3. Prototype 1: efficiencies of the two-stage spelling tests
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10 BILOG-MG EXAMPLES Finally, the efficiency curves in Figure 10.3 for the three levels show us the saving of test length and administration time, including both first- and second-stage testing, due specifically to the two-stage procedure in comparison with a one-stage test of the same length and item content. In this case we hope to see efficiencies greater than 2.0, at least away from the population mean where conventional tests with peaked centers typically have reduced precision. The prototype 1 design and analysis meet this criterion. To increase generalizability of group-level mean scores in assessment applications of the prototype 1 design, the second-stage tests will of course have to exist in multiple stratified randomly parallel forms. As with matrix sampling designs, these forms will be administered in random rotation to the examinees in each second-stage level. The sample data will then be suitable for equivalent-groups equating of the second-stage forms.
10.10 Estimating and scoring tests of increasing length In this example, commands for estimating item parameters and computing score means, standard deviations, variances, average standard errors, error variances, and inverse information reliabilities of maximum likelihood estimates of ability, are illustrated. Note: to obtain the same results for EAP estimation, set METHOD=2 in the SCORE command; for MAP estimation, set METHOD=3. EXAMPL10.BLM - MML estimation of item parameters ML estimation of case scores >GLOBAL DFNAME=‘SIM01C0.SIM',NPARM=2,NTEST=6,SAVE; >SAVE SCORE='MLEVAL1.SCO'; >LENGTH NITEMS=(4,8,16,32,64,128); >INPUT NTOTAL=128,NIDCH=5,SAMPLE=3000; >ITEMS INUMBERS=(1(1)128),INAME=(ITEM001(1)ITEM128); >TEST1 TNAME=LENGTH4,INUMBERS=(1(1)4); >TEST2 TNAME=LENGTH8,INUMBERS=(1(1)8); >TEST3 TNAME=LENGTH16,INUMBERS=(1(1)16); >TEST4 TNAME=LENGTH32,INUMBERS=(1(1)32); >TEST5 TNAME=LENGTH64,INUMBERS=(1(1)64); >TEST5 TNAME=LENG128,INUMBERS=(1(1)128); (11A1,1X,128A1) >CALIB NQPT=40,CYCLE=25,TPRIOR,NEWTON=3,CRIT=0.001,NOSPRIOR,NOADJUST; >SCORE METHOD=1,INFO=1,YCOMMON,POP,NOPRINT;
Related topics
SCORE command: METHOD keyword
10.11 Commands for parallel-form correlations This example contains the syntax used for computing parallel form correlations and between test correlations for tests of different lengths. Set METHOD equal to 1, 2, or 3 in the SCORE command to obtain correlations for ML, EAP, and MAP estimated abilities respectively.
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10 BILOG-MG EXAMPLES EXAMPL11.BLM - Correlation of independent ML estimates >GLOBAL DFNAME=‘SIM01C0.SIM',NPARM=2,NTEST=12,SAVE; SCORE='MAPCOR1.SCO'; >SAVE >LENGTH NITEMS=(4,4,8,8,16,16,32,32,64,64,128,128); >INPUT NTOTAL=504,NIDCH=5,SAMPLE=3000; >ITEMS INUMBERS=(1(1)504),INAME=(ITEM001(1)ITEM504); >TEST1 TNAME=LENGTH4a,INUMBERS=(1(1)4); >TEST2 TNAME=LENGTH4b,INUMBERS=(5(1)8); >TEST3 TNAME=LENGTH8a,INUMBERS=(9(1)16); >TEST4 TNAME=LENGTH8b,INUMBERS=(17(1)24); >TEST5 TNAME=LEN16a, INUMBERS=(25(1)40); >TEST6 TNAME=LEN16b, INUMBERS=(41(1)56); >TEST7 TNAME=LEN32a, INUMBERS=(57(1)88); >TEST8 TNAME=LEN32b, INUMBERS=(93(1)120); >TEST9 TNAME=LEN64a, INUMBERS=(121(1)184); >TEST10 TNAME=LEN64b, INUMBERS=(185(1)248); >TEST11 TNAME=LEN128a ,INUMBERS=(249(1)376); >TEST12 TNAME=LEN128b, INUMBERS=(377(1)504); (11A1,1X,504A1) >CALIB NQPT=40,CYCLE=25,NEWTON=3,CRIT=0.001,NOSPRIOR,NOADJUST; >SCORE METHOD=1,INFO=1,YCOMMON,POP,NOPRINT;
Related topics
SCORE command: METHOD keyword
10.12 EAP scoring of the NAEP forms and state main and variant tests The syntax in this example was used to score NAEP forms and state main and variant tests. It is included here as an example of a more complicated analysis and contains numerous TEST and FORMS commands. The use of the INUMBERS keyword on the FORM commands to assign items to the various forms is of interest, as is the naming convention used with the INAMES keyword on the ITEMS command. Finally, note that none of the tests are calibrated (SELECT=0 for all tests on the CALIB command). Scoring is done according to a previously generated item parameter file gr4fin.par read with the IFNAME keyword on the GLOBAL command. EXAMPLE12.BLM - Scoring of main and variant tests Grade 4 Reading >COMMENTS ****************************************************************************** This example is for illustration purposes only. The actual data to run the command file is not available. ****************************************************************************** The syntax in this example was used to score NAEP forms and state main and variant tests. It is included here as an example of a more complicated analysis and contains numerous TEST and FORMS commands. The use of the INUMBERS keyword on the FORM commands to assign items to the various forms is of interest, as is the naming convention used with the INAMES keyword on the ITEMS command. Finally, note that none of the tests are cali-
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10 BILOG-MG EXAMPLES brated (SELECT=0 for all tests on the CALIB command). Scoring is done according to a previously generated item parameter file (gr4fin.par) read with the IFNAME keyword on the GLOBAL command. The variant items in a test are not intended to be scored as a test. They are included in the analysis to obtain preliminary information on there item characteristics with respect to the latent variable measured by the main test. >GLOBAL NPARM=3, NTEST=6, NVTEST=6, DFNAME='GR4FIN.DAT', IFNAME='GR4FIN.PAR', SAVE; >SAVE SCORE='GR4FIN.SCO'; >LENGTH NITEMS=(82,20,56,82,25,47), NVARIANT=(82,20,56,82,25,47); >INPUT NTOTAL=230, SAMPLE=3000, NIDCH=10, NFORM=16, KFNAME='GR4FIN.DAT', OFNAME='GR4FIN.DAT', NFNAME='GR4FIN.DAT'; >ITEMS INUMBERS=(1(1)230), INAME= (MC01,MC02,MC03, OEtw04,OETW05,OEtw06, OEth07,OEth08, OEfo09, MC10(1)MC14, OEtw15(1)OEtw20, OEfo21, MC22(1)MC28, OEtw29(1)OEtw31, OEfo32, MC33(1)MC37, OEtw38(1)OEtw41, OEfo42, MC43(1)MC46, OEtw47(1)OEtw51, OEfo52, MC53(1)MC55, OEth56(1)OEth60, OEfo61, MC62(1)MC64, OEtw65, OEth66(1)OEth69, OEfo70, MC71(1)MC76, OEtw77(1)OEtw81, OEfo82, READ07,READ08,READ16,READ17,READ24,READ25, LIST04,LIST05,LIST06,LIST08,LIST09,LIST12,LIST16, LIST17,LIST18,LIST19,LIST20, WSAM01,WSAM02, DRP01(1)DRP56, READ01,READ02,READ03,READ04,READ05,READ06,READ09,READ10, READ11(1)READ15,READ18(1)READ23, LIST01,LIST02,LIST03,LIST07,LIST10,LIST11,LIST13(1)LIST15, WRIT01(1)WRIT45); >TEST01 TNAME=NAEP1, INUMB=(1(1)82); >TEST02 TNAME=LISTV, INAME=(LIST01(1)LIST20); >TEST03 TNAME=LISTM, INAME=(LIST01(1)LIST20); >TEST04 TNAME=NAEP2, INUMB=(1(1)82); >TEST05 TNAME=DRPV, INAME=(DRP01(1)DRP56); >TEST06 TNAME=DRPM, INAME=(DRP01(1)DRP56); >TEST07 TNAME=NAEP3, INUMB=(1(1)82); >TEST08 TNAME=READV, INAME=(READ01(1)READ25); >TEST09 TNAME=READM, INAME=(READ01(1)READ25); >TEST10 TNAME=NAEP4, INUMB=(1(1)82); >TEST11 TNAME=WRITEV, INAMB=(WRIT01(1)WRIT45,WSAM01,WSAM02); >TEST12 TNAME=WRITEM, INAME=(WRIT01(1)WRIT45,WSAM01,WSAM02); >FORM01 LEN=169, INUM=(83(1)230,10(1)14, 1(1)3,15(1)20, 4(1)6, 7, 8, 21,9); >FORM02 LEN=168, INUM=(83(1)230, 1(1)3,22(1)28, 4(1)6,29(1)31, 7, 8, 9,32); >FORM03 LEN=168, INUM=(83(1)230,22(1)28,62(1)64,29(1)31,65, 66(1)69,32,70); >FORM04 LEN=169, INUM=(83(1)230,62(1)64,10(1)14,65, 15(1)20,66(1)69,70,21); >FORM05 LEN=171, INUM=(83(1)230,10(1)14,22(1)28, 15(1)20,29(1)31,21,32); >FORM06 LEN=166, INUM=(83(1)230, 1(1)3,62(1)64, 4(1)6,65, 7, 8,66(1)69,9,70); >FORM07 LEN=170, INUM=(83(1)230,33(1)37,71(1)76,38(1)41,77(1)81, 42,82); >FORM08 LEN=170, INUM=(83(1)230,71(1)76,43(1)46,77(1)81,47(1)51, 82,52); >FORM09 LEN=167, INUM=(83(1)230,43(1)46,53(1)55,47(1)51,56(1)60, 52,61); >FORM10 LEN=167, INUM=(83(1)230,53(1)55,33(1)37,38(1)41,56(1)60, 61,42); >FORM11 LEN=168, INUM=(83(1)230,33(1)37,43(1)46,38(1)41,47(1)51, 42,52); >FORM12 LEN=169, INUM=(83(1)230,71(1)76,53(1)55,77(1)81,56(1)60, 82,61); >FORM13 LEN=170, INUM=(83(1)230,43(1)46,10(1)14,47(1)51,15(1)20, 52,21); >FORM14 LEN=166, INUM=(83(1)230,53(1)55, 1(1)3, 4(1)6, 56(1)60, 7, 8,61, 9); >FORM15 LEN=169, INUM=(83(1)230,22(1)28,33(1)37,29(1)31,38(1)41,32,42); >FORM16 LEN=169, INUM=(83(1)230,62(1)64,71(1)76,65,77(1)81,66(1)69,70,82); (10A1,2X,I2/15X,19A1,1X,129A1/15X,23A1)
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10 BILOG-MG EXAMPLES >CALIB SELECT=(0(0)6); >SCORE METHOD=2, NOPRINT, NQPT=(25(0)6);
Related topics
CALIB command: SELECT keyword GLOBAL command: IFNAME keyword FORM command FORM command: INUMBERS keyword ITEMS command: INUMBERS keyword TEST command
10.13 Domain scores This is an attempt to reconstruct the domain scores demonstration application reported in “The Domain Score Concept and IRT: Implications for Standards Setting” by Bock, Thissen & Zimowski (2001). We use the dataset spell.dat as included with the TESTFACT program (see Chapter 13). All 100 items of the 100-word spelling test seem to be there, but there are only 660 records (instead of the 1,000 that Bock et. al. report). In a first run (spell1.blm), we calibrate all 100 items and save the parameters in an external file. The syntax is shown below. SPELL1.BLM - CALIBRATION OF THE 100 WORD SPELLING TEST TWO-PARAMETER MODEL >COMMENTS We are trying first to reproduce the table with slope and location parameters for the 100 words as Bock et al. report in "The Domain Score Concept and IRT: Implications for Standards Setting." The SCORE command is included to obtain the percent correct for each examinee (= the true domain scores). >GLOBAL DFNAME='SPELL.DAT', NPARM=2, SAVE; >SAVE PARM='SPELL1.PAR'; >LENGTH NITEMS=(100); >INPUT NTOTAL=100, NIDCHAR=10, KFNAME='SPELL.DAT'; >ITEMS INAMES=(S001(1)S100); >TEST1 TNAME='SPELLING', INUMBERS=(1(1)100); (10A1,1X,25A1,1X,25A1,1X,25A1,1X,25A1) >CALIB NQPT=31, CYCLES=100, CRIT=0.001, NOFLOAT; >SCORE;
The item parameters of the first 5 items, as reported in the item parameter file step1blm.par, are shown in Table 10.2.
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10 BILOG-MG EXAMPLES Table 10.2: Selected item parameters from step1blm.par
Item
Slope
S.E.
Threshold S.E.
S001 0.79494 0.07978 -0.34466
0.06899
S002 0.38723 0.07299 -3.53823
0.61667
S003 0.24041 0.04784 -3.04033
0.61821
S004 0.72020 0.07353 -0.54159
0.08115
S005 0.69253 0.07367 -1.41137
0.13523
The parameter values are in close agreement with Table 1 from Bock. et al. (results for the first 5 items shown in Table 10.3 below), showing also that we have a correct dataset, with the items in the right order (of the table), albeit not all records. Table 10.3: Selected item parameters from Bock et. al.
Item
Slope Threshold
S001 0.843 -0.339 S002 0.351 -3.623 S003 0.239 -3.073 S004 0.785 0.727 S005 0.269 2.273 In a second run (spell2.blm), we let the program compute the expected domain scores for all 660 examinees from the saved parameter file. The DOMAIN and FILE keywords on the SCORE command are used. We skip the calibration phase with the SELECT keyword on the CALIB command. The scores are saved to file by using the SCORE keyword on the SAVE command. The contents of spell2.blm are shown below. All the command files and data discussed here are available to the user in the domscore subfolder of the BILOG-MG installation folder. SPELL2.BLM - CALIBRATION OF THE 100 WORD SPELLING TEST TWO-PARAMETER MODEL >COMMENTS In a second step, we test the "DOMAIN" keyword on the score command. The item parameter file from the SPELL1.BLM analysis has been edited and saved as SPELL2.PAR in accordance with the FILE keyword format requirements. We save the score file.
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10 BILOG-MG EXAMPLES >GLOBAL DFNAME='SPELL.DAT', NPARM=2, SAVE; >SAVE SCORE='SPELL2.SCO'; >LENGTH NITEMS=(100); >INPUT NTOTAL=100, NIDCHAR=10, KFNAME='SPELL.DAT'; >ITEMS INAMES=(S001(1)S100); >TEST1 TNAME='SPELLING', INUMBERS=(1(1)100); (10A1,1X,25A1,1X,25A1,1X,25A1,1X,25A1) >CALIB SELECT=(0); >SCORE DOMAIN=100, FILE='SPELL2.PAR', METHOD=2;
The parameter file that we read in through the FILE keyword on the SCORE command had to be created from the saved parameter file (spell1blm.par) in the spell1.blm run. First we deleted everything before the first line with parameter estimates. Then we deleted all the columns that were not slope, threshold, or guessing parameters, leaving just those three columns and in that order. Then, we added a column with weights as the first column, in the same format. We used 1.0000, because we want all items weighed equally. We then added the variable format statement (4F10.5) as the first line in the file and renamed it to spell1.par. The estimated domain scores from spell2.blm are fairly well recovered as spell2.ph3 shows. Here are the results for the first five examinees: GROUP SUBJECT IDENTIFICATION DOMAIN SCORE S.E. MARGINAL WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. PROB ------------------------------------------------------------------------1 01021119001 64.89 4.92 | 1.00 * SPELLING 100 65 65.00 | -0.1501 0.4187 | 0.000000 1 01041122001 57.14 5.43 | 1.00 * SPELLING 100 56 56.00 | -0.7839 0.4321 | 0.000000 1 01051219001 54.25 5.40 | 1.00 * SPELLING 100 57 57.00 | -1.0132 0.4269 | 0.000000 1 01061219001 71.52 1.80 | 1.00 * SPELLING 100 69 69.00 | 0.4499 0.1768 | 0.000000 1 01071219001 80.77 2.68 | 1.00 * SPELLING 100 81 81.00 | 1.5475 0.4000 | 0.000000 -------------------------------------------------------------------------
If the estimated expected domain scores are not close, something is probably wrong, so this is a good test. In a third and final step (step3.blm), we take a random sample of 20 items, adapt the parameter file (spell3.par as described previously) and produce a new score file (spell3.sco). The contents of spell3.blm are as follows: SPELL3.BLM - CALIBRATION OF THE 100 WORD SPELLING TEST TWO-PARAMETER MODEL >COMMENTS In this third step we use a random sample of 20 items from the 100-word spelling test to score the examinees with the item parameters from the first step. The score file is saved. >GLOBAL DFNAME='SPELL.DAT', NPARM=2, SAVE; >SAVE SCORE='SPELL3.SCO'; >LENGTH NITEMS=20;
690
10 BILOG-MG EXAMPLES >INPUT >ITEMS >TEST1
NTOTAL=100, NIDCHAR=10, KFNAME='SPELL.DAT'; INAMES=(S001(1)S100); TNAME='SPELLING', INUMBERS=(4, 9, 10, 13, 22, 26, 36, 51, 55, 65, 69, 73, 74, 82, 83, 88, 89, 91, 94, 97); (10A1,1X,25A1,1X,25A1,1X,25A1,1X,25A1) >CALIB SELECT=(0); >SCORE DOMAIN=20, FILE='SPELL3.PAR', METHOD=2;
These are the results for the first five examinees: GROUP SUBJECT IDENTIFICATION DOMAIN SCORE S.E. MARGINAL WEIGHT TEST TRIED RIGHT PERCENT ABILITY S.E. PROB ------------------------------------------------------------------------1 01021119001 72.51 6.95 | 1.00 * SPELLING 20 14 70.00 | 0.4109 0.7214 | 0.000000 1 01041122001 62.76 7.94 | 1.00 * SPELLING 20 12 60.00 | -0.4985 0.6850 | 0.000000 1 01051219001 63.42 7.90 | 1.00 * SPELLING 20 12 60.00 | -0.4414 0.6870 | 0.000000 1 01061219001 74.25 6.61 | 1.00 * SPELLING 20 14 70.00 | 0.5971 0.7300 | 0.000000 1 01071219001 79.14 5.32 | 1.00 * SPELLING 20 17 85.00 | 1.2047 0.7604 | 0.000000 -------------------------------------------------------------------------
As can be seen, a decent recovery of the “population domain scores” with the random sample of only 20 items. Related topics
CALIB command: SELECT keyword SAVE command: SCORE keyword SCORE command: DOMAIN keyword SCORE command: FILE keyword
691
11 PARSCALE EXAMPLES
11 PARSCALE examples 11.1 Item calibration and examinee Bayes scoring with the rating-scale graded model This example illustrates calibration and scoring of a test or scale containing 20 multiple category items. The simulated data represent responses of 1000 examinees drawn randomly from a population with a mean trait score of 0.0 and standard deviation of 1.0. Data are read from the file exampl01.dat in the examples folder using the DFNAME keyword on the FILES command. The first few lines of the data file are shown below. The generating trait value of each examinee is the second column of information in the data file. The case ID, given at the beginning of each line, is 4 characters long and is indicated as such using the NIDCHAR keyword on the INPUT command. It is also reflected in the format statement as 4A1. 0001 0002 0003 0004 0005
.44739 -.93465 -.56465 -.58622 -.35223
42444232223343433332 12221121122324121432 32212212213342314121 13222111113224221111 21211122313132312131
All 20 items are used in a single test (NTEST=1 on INPUT command, with LENGTH=20). All 20 items have common categories and are assigned to the same BLOCK (NBLOCK=1 on TEST; NITEMS=20 on BLOCK). All items have four categories (NCAT=4 on BLOCK command) and varying difficulties and discriminating powers. The graded model is assumed (GRADED on CALIB command); and a logistic response model (LOGISTIC on CALIB command) is requested. The choice between a logistic or normal response function metric is effective only if the graded response model is used. The response function of the graded model can be either the normal ogive or its logistic approximation. Graded is the default. If logistic is selected, the item parameters can be in the natural or the logistic metric. Natural is the default. For the normal metric, set SCALE equal to 1.7. Neither LOGISTIC nor SCALE is needed when PARTIAL is selected. Because the generalized model allows for varying item discriminating powers, both a slope and threshold are estimated for each item. The CADJUST keyword on the BLOCK command is used to set the mean of the category parameters to 0 as simultaneous estimation of slope parameters and all category parameters is not obtainable. The ITEMFIT keyword is used to set the number of frequency score groups for the computation of item fit statistics to 10. Note that there is no default value for the ITEMFIT keyword. The CYCLES keyword specifies 25 EM iterations, with maximum 2 inner EM iterations for the item and category parameter estimation. Five Newton-Gauss iterations are requested (NEWTON=5 on CALIB). A convergence criterion of 0.005 is specified by using the CRIT keyword on CALIB. 30 quadrature points are to be used in the EM and Newton estimation instead of the default of 10 for cases where LENGTH less or equal to 50 in the INPUT command. The calibration procedure 692
11 PARSCALE EXAMPLES depends on the evaluation of integrals using Gauss-Hermite quadrature. In general, the accuracy of numerical integration increases with the number of quadrature points used. The score estimation method is specified (EAP option on SCORE command). Scale scores for each subtest are estimated by the Bayes (EAP) method, and their posterior standard deviations serve as standard errors. The scores, which are rescaled to zero mean and unit standard deviation in the sample (SMEAN and SSD on SCORE), are saved in the file exampl01.sco using the SCORE keyword on the SAVE command. The PFQ keyword is specified. This keyword is usually used to make ML scores more computable but would also improve the EAP estimates somewhat. In addition, the estimated item parameters are saved in the file exampl01.par (PARM keyword on the SAVE command). The command file is shown below, with comments omitted. EXAMPL01.PSL: ARTIFICIAL EXAMPLE: MONTE CARLO DATA GRADED RATING SCALE MODEL, NORMAL RESPONSE FUNCTION: EAP SCALE SCORES >FILES DFNAME='EXAMPL01.DAT',SAVE; >SAVE PARM='EXAMPL01.PAR',SCORE='EXAMPL01.SCO'; >INPUT NIDCHAR=4,NTOTAL=20,NTEST=1,LENGTH=(20),NFMT=1; (4A1,10X,20A1) >TEST1 TNAME=SCALE1,ITEM=(1(1)20),NBLOCK=1; >BLOCK1 BNAME=SBLOCK1,NITEMS=20,NCAT=4, CADJUST=0.0; >CAL GRADED,NQPTS=30,CYCLE=(25,2,2,2,2), NEWTON=5,CRIT=0.005,ITEMFIT=10; >SCORE EAP,NQPTS=30,SMEAN=0.0,SSD=1.0,NAME=EAP,PFQ=5;
Phase 0 output At the beginning of the output for Phase 0, the command file is echoed. Information on the number of tests, items, and type of model to be fitted as interpreted by PARSCALE is also given. SINGLE MAIN TEST IS USED. NUMBER OF ITEMS: 20 FORMAT OF DATA INPUT IS (4A1,10X,20A1) >TEST1
TNAME=SCALE1,ITEM=(1(1)20),NBLOCK=1;
BLOCK CARD: 1 >BLOCK1 BNAME=SBLOCK1,NITEMS=20,NCAT=4,CADJ=0.0; >CAL GRADED,LOGISTIC,SCALE=1.7,NQPTS=30,CYCLE=(25,2,2,2,2), NEWTON=5,CRIT=0.005,ITEMFIT=10; MODEL SPECIFICATIONS ====================== LOGISTIC - GRADED ITEM RESPONSE MODEL IS SPECIFIED. SCALE CONSTANT 1.70 FOR SLOPE PARAMETERS.
693
11 PARSCALE EXAMPLES This section of the output file contains information on the settings to be used during the item parameter estimation in Phase 2. CALIBRATION PARAMETERS ====================== MAXIMUM NUMBER OF EM CYCLES: MAXIMUM INNER EM CYCLES: MAXIMUM CATEGORY ESTIMATION CYCLES: MAXIMUM ITEM PARAMETER ESTIMATION CYCLES: MAXIMUM NUMBER OF NEWTON CYCLES: CONVERGENCE CRITERION FOR EM CYCLES: CONVERGENCE CRITERION FOR SLOPE: CONVERGENCE CRITERION FOR THRESHOLD: CONVERGENCE CRITERION FOR CATEGORY: CONVERGENCE CRITERION FOR GEUSSING: ORDER OF INNER EM CYCLES: ESTIMATION ACCELERATOR: RIDGE METHOD:
25 2 2 2 2 0.0050 0.0050 0.0050 0.0050 0.0050 CATEGORY - ITEM PARAMETERS NO (DEFAULT) NO (DEFAULT)
No prior distribution was requested in the CALIB command, and consequently the default prior, a normal distribution on equally spaced points, will be used (DIST=2 on CALIB). The number of quadrature points to be used during item parameter estimation was set to 30 (NQPT on CALIB). The program-generated quadrature points and weights are printed to the Phase 0 output file, as shown below. THE FIXED PRIOR DISTRIBUTION FOR LATENT TRAITS MEAN S.D.
: :
0.0000 1.0000
QUADRATURE POINTS AND PRIOR WEIGHTS (PROGRAM-GENERATED NORMAL APPROXIMATION):
POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3724E+01 -0.3448E+01 -0.3172E+01 -0.2897E+01 0.3692E-04 0.1071E-03 0.2881E-03 0.7181E-03 0.1659E-02
POINT WEIGHT
6 7 8 9 10 -0.2621E+01 -0.2345E+01 -0.2069E+01 -0.1793E+01 -0.1517E+01 0.3550E-02 0.7042E-02 0.1294E-01 0.2205E-01 0.3481E-01
POINT WEIGHT
11 12 13 14 15 -0.1241E+01 -0.9655E+00 -0.6897E+00 -0.4138E+00 -0.1379E+00 0.5093E-01 0.6905E-01 0.8676E-01 0.1010E+00 0.1090E+00
POINT WEIGHT
16 0.1379E+00 0.1090E+00
17 0.4138E+00 0.1010E+00
18 0.6897E+00 0.8676E-01
19 0.9655E+00 0.6905E-01
20 0.1241E+01 0.5093E-01
POINT WEIGHT
21 0.1517E+01 0.3481E-01
22 0.1793E+01 0.2205E-01
23 0.2069E+01 0.1294E-01
24 0.2345E+01 0.7042E-02
25 0.2621E+01 0.3550E-02
694
11 PARSCALE EXAMPLES POINT WEIGHT
26 0.2897E+01 0.1659E-02
27 0.3172E+01 0.7181E-03
28 0.3448E+01 0.2881E-03
29 0.3724E+01 0.1071E-03
30 0.4000E+01 0.3692E-04
TOTAL WEIGHT: 1.00000 MEAN : 0.00000 S.D. : 0.99970
The control settings to be used during calibration are followed by settings to be used during the scoring phase (Phase 3). The EAP method of scoring is requested (EAP option) and, as in the calibration phase, 30 quadrature points were requested. Since no prior distribution was requested using the DIST keyword, by default a normal distribution on equally spaced points will be used (DIST=2 on SCORE). Note that the DIST keyword applies only when EAP scoring has been selected. >SCORE
EAP,NQPTS=30,SMEAN=0.0,SSD=1.0,NAME=EAP,PFQ=5;
PARAMETERS FOR SCORING AND TEST AND ITEM INFORMATION ==================================================== METHOD OF SCORING SUBJECTS:
EXPECTATION A POSTERIORI (EAP; BAYES ESTIMATES)
TYPE OF PRIOR:
NORMAL APPROXIMATION
NUMBER OF QUADRATURE POINTS SCORES WRITTEN TO FILE
30 EXAMPL01.SCO
QUADRATURE POINTS AND PRIOR WEIGHTS (PROGRAM-GENERATED NORMAL APPROXIMATION):
POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3724E+01 -0.3448E+01 -0.3172E+01 -0.2897E+01 0.3692E-04 0.1071E-03 0.2881E-03 0.7181E-03 0.1659E-02
POINT WEIGHT
6 7 8 9 10 -0.2621E+01 -0.2345E+01 -0.2069E+01 -0.1793E+01 -0.1517E+01 0.3550E-02 0.7042E-02 0.1294E-01 0.2205E-01 0.3481E-01
POINT WEIGHT
11 12 13 14 15 -0.1241E+01 -0.9655E+00 -0.6897E+00 -0.4138E+00 -0.1379E+00 0.5093E-01 0.6905E-01 0.8676E-01 0.1010E+00 0.1090E+00
POINT WEIGHT
16 0.1379E+00 0.1090E+00
17 0.4138E+00 0.1010E+00
18 0.6897E+00 0.8676E-01
19 0.9655E+00 0.6905E-01
20 0.1241E+01 0.5093E-01
POINT WEIGHT
21 0.1517E+01 0.3481E-01
22 0.1793E+01 0.2205E-01
23 0.2069E+01 0.1294E-01
24 0.2345E+01 0.7042E-02
25 0.2621E+01 0.3550E-02
POINT WEIGHT
26 0.2897E+01 0.1659E-02
27 0.3172E+01 0.7181E-03
28 0.3448E+01 0.2881E-03
29 0.3724E+01 0.1071E-03
30 0.4000E+01 0.3692E-04
695
11 PARSCALE EXAMPLES TOTAL WEIGHT: 1.00000 MEAN : 0.00000 S.D. : 0.99970
The values assigned to the rescaling constants SMEAN and SSD in the SCORE command are shown: SET NUMBER : 1 SCORE NAME : EAP NUMBER OF ITEMS : 20 RESCALE CONSTANT: MEAN = ITEMS
:
1 11
0.00 2 12
3 13
4 14
S.D. = 5 15
1.00 6 16
7 17
8 18
9 19
10 20
0001 0002 0003 0004 0005 0006 0007 0008 0009 0010 0011 0012 0013 0014 0015 0016 0017 0018 0019 0020
Input and output files as requested with the DFNAME keyword on the FILES command and the PARM and SCORE keywords on the SAVE command are listed: FILE ASSIGNMENTS AND DISPOSITIONS ================================= [INPUT FILES] SUBJECT DATA INPUT FILE
EXAMPL01.DAT SINGLE-SUBJECT DATA NO CASE WEIGHTS
[OUTPUT FILES] ITEM PARAMETERS FILE SUBJECT SCALE-SCORE FILE
EXAMPL01.PAR EXAMPL01.SCO
[SCRATCH FILES] PARSCALE SYSTEM BINARY DATA FILE TEMPORARY FILE TEMPORARY FILE TEMPORARY FILE TEMPORARY FILE
Exampl01.MFL Exampl01.T99 Exampl01.T98 Exampl01.T97 Exampl01.T96
To allow the user to verify that data have been read in correctly from the raw data file, the first two records from the data file are echoed in the output. The INPUT RESPONSES fields give the original responses while the RECODED RESPONSES reflect any recoding of the responses. Recoding of responses is controlled by the ORIGINAL and MODIFIED keywords on the BLOCK command. INPUT AND RECODED RESPONSE OF FIRST AND SECOND OBSERVATIONS OBSERVATION # GROUP: 1 ID: 0001
1
696
11 PARSCALE EXAMPLES INPUT RESPONSES: 4 RECODED RESPONSES:4
2 2
4 4
4 4
4 4
2 2
3 3
2 2
2 2
2 2
3 3
3 3
4 4
3 3
4 4
3 3
3 3
3 3
3 3
2 2
OBSERVATION # 2 GROUP: 1 ID: 0002 INPUT RESPONSES: 1 2 RECODED RESPONSES:1 2
2 2
2 2
1 1
1 1
2 2
1 1
1 1
2 2
2 2
3 3
2 2
4 4
1 1
2 2
1 1
4 4
3 3
2 2
Finally, the number of observations to be used in the analysis is recorded. By default, all observations will be used. The number of observations to be used can be manipulated using the SAMPLE or TAKE keywords on the INPUT command. 1000 OBSERVATIONS READ FROM FILE: 1000 OBSERVATIONS WRITTEN TO FILE:
PSLDAT\EXAMPL01.DAT Exampl01.MFL
Phase 1 output The title given in the TITLE command and name assigned to the test in the TEST command in the command file are echoed in the output file. EXAMPLE 1: ARTIFICIAL EXAMPLE: MONTE CARLO DATA GRADED MODEL, NORMAL METRIC: EAP SCALE SCORES MAINTEST: SCALE1
The master file created during Phase 0 is used as input. Note that the master file exampl01.mfl may be saved using the MASTER keyword on the SAVE command for use as input in a subsequent analysis (MFNAME keyword on the FILES command). The keywords TAKE and SAMPLE on the INPUT command control the number of records read from the raw data file. As the default value of SAMPLE is 100%, neither keyword was used and all data were used by default. 1000 OBS.(WEIGHTS:
1000.000) WERE READ FROM Exampl01.MFL
Summary item statistics for the 20 items are given next. Since no not-represented (NFNAME on FILES) or omit key (OFNAME on FILES) was used, no frequencies or percentages are reported under the “NOT PRESENT” or “OMIT” headings. Under the “CATEGORIES” heading, frequencies and percentages of responses for each of the 4 categories are given item-by-item. Cumulative frequencies and percentages for the categories over all items are given at the end of the table. Note that, if empty categories are encountered, the user has to recode the corresponding items on which this occurs before proceeding with the analysis. BLOCK NO.: 1 NAME: SBLOCK1 --------------------------------------------------------------ITEM | TOTAL NOT OMIT | CATEGORIES | PRESENT | | | 1 2 3 4 --------------------------------------------------------------0001 | | FREQ.| 1000 0 0| 194 303 313 190 PERC.| 0.0 0.0| 19.4 30.3 31.3 19.0
697
11 PARSCALE EXAMPLES | | 0002 | | FREQ.| 1000 0 0| 204 284 310 202 PERC.| 0.0 0.0| 20.4 28.4 31.0 20.2 … 0020 | | FREQ.| 1000 0 0| 305 211 212 272 PERC.| 0.0 0.0| 30.5 21.1 21.2 27.2 | | --------------------------------------------------------------CUMMUL.| | FREQ.| | 4844 5186 5204 4766 PERC.| | 24.2 25.9 26.0 23.8 ---------------------------------------------------------------
Item means, initial slope estimates, and Pearson and polyserial item-test correlations are shown in the next table. Pearson The sample product-moment correlation of the test score, J
ti = ∑ sij , j =1
and m-category polytomous item score, sij = 1, 2,..., m , is the point polyserial correlation rPP , j , where
rPP , j =
∑
n
t s − nts j
i =1 i ij
(∑ i =1 ti2 − nt 2 )(∑ i =1 sij2 − ns 2j ) n
n
where n is the sample size, t is the mean test score and s j , the mean item score. In this example
n = 1000. For item 1,
∑s
i1
= (1×194) + (2 × 303) + (3 × 313) + (4 ×190) ,
so that s1 =
∑s
i1
n
=
2502 = 2.502. 1000
Also
∑s
2 i1
= (12 ×194) + (22 × 303) + (32 × 313) + (42 × 190) = 7263
698
11 PARSCALE EXAMPLES so that
7263 − (1000 × 2.5022 ) 1000 1.0015
S .D.(item 1) =
Polyserial correlation
The polyserial correlation rP can be expressed in terms of the point polyserial correlation as
rP , j =
rPP , jσ j
∑
m −1 k =1
h( z jk )
where
z jk is the scoring corresponding to the cumulative proportion, p jk of the k-th response category to item j (for item 1, for example, the cumulative proportions are 0.194, 0.497, and 0.81 for categories 1,2, and 3), σ j is the standard deviation of item scores for item j (1.0015 for item 1), and rPP , j is the point-polyserial correlation.
h( z jk ) is the ordinate of the normal distribution at the point z jk ; that is h( z jk ) =
1 1 exp(− z 2jk ). 2 2π
Initial slopes and location
The polyserial correlation estimates the item factor loading, α j , say. If the arbitrary scale of the item latent variable, y j , is chosen so that the variance y j equals 1, then y j = α j (θ − b jk ) + ε j , where θ is the factor score with mean 0 and variance 1, and the error, ε j , has mean 0 and variance 1 − rp2, j . For purposes of MML parameter estimation in IRT, it is convenient to rescale the item latent variable so that the error variance equals 1. The factor loading then becomes the item slope,
a j = rp , j / 1 − rp2, j .
699
11 PARSCALE EXAMPLES This provisional estimate of the slope is then used as the starting value in the iterative EM solutions of the marginal maximum likelihood equations for estimating the parameters of the polytomous item response models. The initial locations shown in the last column of the table are the averages of the category thresholds for each item. Initial item-category threshold parameters
Item-category threshold parameters can be calculated once the polyserial coefficients have been obtained. The expression for the threshold parameter in terms of the cumulative category proportions and the biserial correlation coefficient (Lord & Novick, 1968) is zjk b jk = rB , j with rB , j the biserial correlation for item j and zjk the z score that cuts off p jk proportion of the cases to item j in a unit-normal distribution; that is njk p jk = m ∑ njk v =1
where n jk is the frequency of the categorical response for item j and category k. These provisional thresholds of the categories serve as starting values in MML estimation of the corresponding item parameters. For the rating-scale model, whether or not all items have the same thresholds, the category proportions are computed from frequencies accumulated over all items; i.e., n
pk =
∑n
j =1 n m
jk
∑∑ n j =1 k =1
. jk
In Muraki’s (1990) formulation of the rating-scale model, the category threshold parameter, ck , is expressed as a deviation from the item threshold parameter, b j ; that is y j = α (θ − b j + ck ) + ε j m −1
under the constraint that
∑c
j = k −1
j
= 0.
700
11 PARSCALE EXAMPLES In the context of the rating-scale model, b j is referred to as a “location” parameter. The INITIAL LOCATION column provides the values of the average of the category thresholds for each item. --------------------------------------------------------------------------BLOCK | RESPONSE TOTAL SCORE | PEARSON & | INITIAL INITIAL ITEM | MEAN MEAN | POLYSERIAL | SLOPE LOCATION | S.D.* S.D.* | CORRELATION | --------------------------------------------------------------------------SBLOCK1 | | | 1 0001 | 2.499 49.892 | 0.778 | 1.488 -0.017 | 1.009* 14.754* | 0.830 | 2 0002 | 2.510 49.892 | 0.797 | 1.628 -0.036 | 1.030* 14.754* | 0.852 | 3 0003 | 2.481 49.892 | 0.785 | 1.545 0.013 | 1.031* 14.754* | 0.839 | 4 0004 | 2.515 49.892 | 0.805 | 1.695 -0.053 | 1.037* 14.754* | 0.861 | 5 0005 | 2.511 49.892 | 0.811 | 1.739 -0.038 | 1.032* 14.754* | 0.867 | 6 0006 | 2.137 49.892 | 0.728 | 1.293 0.837 | 1.037* 14.754* | 0.791 | 7 0007 | 2.118 49.892 | 0.735 | 1.336 0.855 | 1.033* 14.754* | 0.801 | 8 0008 | 2.144 49.892 | 0.754 | 1.426 0.758 | 1.029* 14.754* | 0.819 | 9 0009 | 2.136 49.892 | 0.736 | 1.329 0.830 | 1.029* 14.754* | 0.799 | 10 0010 | 2.128 49.892 | 0.730 | 1.293 0.882 | 1.002* 14.754* | 0.791 | 11 0011 | 2.870 49.892 | 0.645 | 0.985 -1.168 | 1.041* 14.754* | 0.702 | 12 0012 | 2.874 49.892 | 0.655 | 1.029 -1.094 | 1.071* 14.754* | 0.717 | 13 0013 | 2.874 49.892 | 0.690 | 1.144 -1.017 | 1.053* 14.754* | 0.753 | 14 0014 | 2.831 49.892 | 0.673 | 1.072 -0.953 | 1.057* 14.754* | 0.731 | 15 0015 | 2.847 49.892 | 0.679 | 1.114 -0.938 | 1.094* 14.754* | 0.744 | 16 0016 | 2.492 49.892 | 0.590 | 0.839 0.010 | 1.161* 14.754* | 0.643 | 17 0017 | 2.541 49.892 | 0.548 | 0.738 -0.173 | 1.125* 14.754* | 0.594 | 18 0018 | 2.463 49.892 | 0.589 | 0.834 0.102 | 1.152* 14.754* | 0.641 | 19 0019 | 2.470 49.892 | 0.573 | 0.798 0.085 | 1.160* 14.754* | 0.624 | 20 0020 | 2.451 49.892 | 0.583 | 0.830 0.048 | 1.184* 14.754* | 0.639 | --------------------------------------------------------------------------CATEGORY | | MEAN | S.D. | PARAMETER 1 | | 36.116 | 10.656 | 0.927 2 | | 46.091 | 11.156 | 0.002 3 | | 54.107 | 11.165 | -0.930 4 | | 63.427 | 10.739 | 0.000 ----------------------------------------------------------------------------
701
11 PARSCALE EXAMPLES At the end of this table, descriptive statistics for the raw total scores of examinees who responded in each of the 4 categories are given. The highest average total score of 63.427 was for respondents who responded in the 4th category. Phase 2 output
An MML approach is used for estimation, and either a normal or empirical latent distribution with mean 0 and standard deviation 1 is assumed. The type of distribution used is controlled by the DIST keyword on the CALIB command. By default, a normal distribution with equally spaced points is used and, for analyses where the LENGTH keyword on the INPUT command is set to a value less than or equal to 50, 10 quadrature points will be used. Because of the potentially wide spacing of category boundary parameters on the latent dimension, it is advisable to use a greater number of quadrature points than in BILOG-MG. In this example, the number of quadrature points was set to 30 (NQPT on the CALIB command). The EM algorithm is used in the solution of the maximum likelihood equations for parameters, starting from the initial values described in the Phase 1 output. At each iteration, the -2 ln L is given, along with information on the parameter for which the largest change between cycles was observed. The number of EM cycles is controlled by the CYCLE keyword on the CALIB command, and the convergence criterion may be set using the CRIT keyword on the same command. By default, 10 EM cycles would be performed when LENGTH ≤ 50 on the INPUT command. In this example, 25 EM cycles with a maximum of 2 inner EM iterations for the item and category parameter estimation were specified. The default convergence criterion is 0.001. For this example, it was set to 0.005. [E-M CYCLES]
GRADED RESPONSE MODEL
CATEGORY AND ITEM PARAMETERS AFTER CYCLE LARGEST CHANGE=
0.000
-2 LOG LIKELIHOOD =
46371.421
CATEGORY AND ITEM PARAMETERS AFTER CYCLE LARGEST CHANGE=
1
0.636 ( -1.168-> -0.532) at Location of Item: 11 0011
-2 LOG LIKELIHOOD =
44229.018
CATEGORY AND ITEM PARAMETERS AFTER CYCLE LARGEST CHANGE=
0
0.033 (
-2 LOG LIKELIHOOD =
0.989->
2
1.022) at Slope
of Item: 13 0013
44224.943
The EM algorithm converged after 3 cycles were completed. After reaching either the maximum number of EM cycles or convergence, the program will perform the Newton-Gauss (Fisher scoring) cycles requested through the NEWTON keyword on the CALIB command. In this example, 702
11 PARSCALE EXAMPLES NEWTON was set to 5. The information matrix for all item parameters is approximated during
each Newton step and then used at convergence to provide large-sample standard errors of estimation for the item parameter estimates. [NEWTON CYCLES]
GRADED RESPONSE MODEL
CATEGORY AND ITEM PARAMETERS AFTER CYCLE LARGEST CHANGE= 0.000 -2 LOG LIKELIHOOD =
44224.833
CATEGORY AND ITEM PARAMETERS AFTER CYCLE LARGEST CHANGE=
0
1
0.004 ( -0.536-> -0.533) at Location of Item: 11 0011
The Newton cycles converged after 2 iterations. As all items were assigned to the same BLOCK, only one table is printed to the output file. At the top of the table, the estimated category parameters are given. For each m category item, there are m-1 category threshold parameters with b j1 ≤ b j 2 ... ≤ b jm −1.
For a polytomous item response model, the discriminating power of a specific categorical response depends on the width of the adjacent category thresholds as well as a slope parameter. Because of this property, the simultaneous estimation of the slope parameter and all m j category parameters is not obtainable. If the model includes the slope parameter for each item j as in this example, the location of the category parameters must be fixed. The CADJUST keyword on the BLOCK command was set to 0, and thus the mean of the category parameters is 0. For each item, the slope and location parameters, along with corresponding standard errors, are given. All guessing parameters are zero for this model. ITEM BLOCK
1
SBLOCK1
CATEGORY PARAMETER S.E.
: :
1.024 0.011
0.005 0.009
-1.030 0.011
+------+-----+---------+---------+---------+---------+---------+---------+ | ITEM |BLOCK| SLOPE | S.E. |LOCATION | S.E. |GUESSING | S.E. | +======+=====+=========+=========+=========+=========+=========+=========+ | 0001 | 1 | 1.486 | 0.063 | 0.006 | 0.042 | 0.000 | 0.000 | | 0002 | 1 | 1.526 | 0.067 | -0.012 | 0.040 | 0.000 | 0.000 | | 0003 | 1 | 1.472 | 0.065 | 0.022 | 0.041 | 0.000 | 0.000 | [Similar output omitted] | 0019 | 1 | 0.699 | 0.030 | 0.048 | 0.060 | 0.000 | 0.000 | | 0020 | 1 | 0.665 | 0.029 | 0.085 | 0.062 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+
703
11 PARSCALE EXAMPLES The average parameter estimates over all 20 items are given next. If the items are regarded as random samples from a real or hypothetical universe, these quantities estimate the means and standard deviations of the parameters. They could serve as item parameter priors in future item calibrations in this universe. SUMMARY STATISTICS OF PARAMETER ESTIMATES +----------+---------+---------+----+ |PARAMETER | MEAN | STN DEV | N | +==========+=========+=========+====+ |SLOPE | 1.111| 0.317| 20| |LOG(SLOPE)| 0.065| 0.296| 20| |THRESHOLD | 0.003| 0.370| 20| |GUESSING | 0.000| 0.000| 0| +----------+---------+---------+----+
The estimated latent distribution is given next. This distribution is the sum of the posterior distributions of θ for all respondents in the sample. It is represented here as point masses, scaled to sum to 1.0, at 30 equally spaced points on the θ dimension. If the population distribution is normal and the test is sufficiently informative over the range of θ , the posterior distributions for all respondents will approach normality and the latent distribution will approach normality. POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3724E+01 -0.3448E+01 -0.3172E+01 -0.2897E+01 0.6912E-04 0.1967E-03 0.5110E-03 0.1201E-03 0.2420E-02
POINT WEIGHT
6 7 8 9 10 -0.2621E+01 -0.2345E+01 -0.2069E+01 -0.1793E+01 -0.1517E+01 0.4662E-02 0.7645E-02 0.1189E-01 0.2005E-01 0.3585E-01
POINT WEIGHT
11 12 13 14 15 -0.1241E+01 -0.9655E+00 -0.6897E+00 -0.4138E+00 -0.1379E+00 0.5568E-01 0.7094E-01 0.8078E-01 0.9708E+00 0.1104E+00
POINT WEIGHT
16 0.1379E+00 0.1086E+00
17 0.4138E+00 0.9806E+00
18 0.6897E+00 0.8301E-01
19 0.9655E+00 0.6999E-01
20 0.1241E+01 0.5416E-01
POINT WEIGHT
21 0.1517E+01 0.3797E-01
22 0.1793E+01 0.2403E-01
23 0.2069E+01 0.1328E-01
24 0.2345E+01 0.6619E-02
25 0.2621E+01 0.2962E-02
POINT WEIGHT
26 0.2897E+01 0.1197E-03
27 0.3172E+01 0.4451E-03
28 0.3448E+01 0.1547E-04
29 0.3724E+01 0.5062E-04
30 0.4000E+01 0.1563E-05
TOTAL WEIGHT: 1.00000 MEAN : 0.00000 S.D. : 0.99970
The goodness-of-fit of the polytomous item response model can be tested item by item. Summation of the item fit can also be used for the goodness-of-fit for the test as a whole. The fit statistics are useful in evaluating the fit of models to the same response data when models are nested 704
11 PARSCALE EXAMPLES in their parameters. Respondents are assigned to H intervals on the θ -continuum. The number of intervals is set using the ITEMFIT keyword on the CALIB command. The expected a posteriori (EAP) score of each respondent is used for assigning respondents to the H intervals. The observed frequency rhjk of the k-th category response to item j in interval h, and N hj , the number of respondents assigned to item j in the h-th interval, are computed. The estimated θ s are rescaled so that the variance of the sample distribution equals that of the latent distribution on which the MML estimation of the parameters is based. Thus an H by m j contingency table is obtained for each item j. In order to avoid expected values less than 5, neighboring intervals and/or categories may be merged. For each interval, the interval mean, θ h , and the value of the fitted response function Pjk (θ h ) , are computed. Finally, a likelihood ratio χ 2 -statistic for each item is computed by H j mj
G = 2∑∑ rhjk ln 2 j
h =1 k =1
rhjk N hj Pjk (θ h )
,
where H j is the number of intervals left after neighboring intervals are merged. The degrees of freedom is
∑
Hj j =1
(m*j − 1) where m*j is the number of categories left after merging.
The likelihood ratio χ 2 -statistic for the test as a whole is simply the summation of the separate χ 2 -statistics. The number of degrees of freedom is also the summation of the degrees of freedom for each item. ITEM FIT STATISTICS ----------------------------------------------| BLOCK | ITEM | CHI-SQUARE | D.F. | PROB. | ----------------------------------------------| SBLOCK1 | 0001 | 25.00714 | 20. | 0.201 | | | 0002 | 23.18082 | 20. | 0.280 | | | 0003 | 25.66873 | 20. | 0.177 | | | 0004 | 31.56813 | 19. | 0.035 | | | 0005 | 19.88483 | 19. | 0.339 | | | 0006 | 13.51922 | 22. | 0.918 |
…
| | 0019 | 12.51549 | 25. | 0.982 | | | 0020 | 25.25502 | 25. | 0.448 | ----------------------------------------------| TOTAL | | 492.43930 | 442. | 0.049 | -----------------------------------------------
705
11 PARSCALE EXAMPLES The null hypothesis tested here is that there are no significant differences between the expected and observed frequencies. A significant χ 2 -statistic indicates that item parameters differ across the raw score groups and that the assumed model is not appropriate for the data. In this case, no item showed poor fit to the assumed model. Phase 3 output
The first information given in the output from the scoring phase is on the scoring function used for scaling. The default function is STANDARD, and thus the standard scoring function (1.0, 2.0) will be used even though a different scoring function may be used for calibration. The scoring function may also be set to CALIBRATION (SCORING keyword on the SCORE command) to use the calibration scoring function specified on the BLOCK command instead. Note that the scoring function only applies to the partial credit model. SCORING FUNCTION FOR SCALING BLOCK:
1 1 2 3 4
SBLOCK1 1.000 2.000 3.000 4.000
Bayes estimates are computed for each examinee with respect to his or her group latent distribution (controlled by the EAP option on the SCORE command used here). A discrete distribution on a finite number of points (see below) is used as prior. The user may select the number of points and the type of prior using the NQPT and DIST keywords on the SCORE command. [EAP SUBJECT ESTIMATION] QUADRATURE POINTS AND PRIOR WEIGHTS:
POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3724E+01 -0.3448E+01 -0.3172E+01 -0.2897E+01 0.3692E-04 0.1071E-03 0.2881E-03 0.7181E-03 0.1659E-02
POINT WEIGHT
6 7 8 9 10 -0.2621E+01 -0.2345E+01 -0.2069E+01 -0.1793E+01 -0.1517E+01 0.3550E-02 0.7042E-02 0.1294E-01 0.2205E-01 0.3481E-01
POINT WEIGHT
11 12 13 14 15 -0.1241E+01 -0.9655E+00 -0.6897E+00 -0.4138E+00 -0.1379E+00 0.5093E-01 0.6905E-01 0.8676E-01 0.1010E+00 0.1090E+00
POINT WEIGHT
16 0.1379E+00 0.1090E+00
17 0.4138E+00 0.1010E+00
18 0.6897E+00 0.8676E-01
19 0.9655E+00 0.6905E-01
20 0.1241E+01 0.5093E-01
706
11 PARSCALE EXAMPLES POINT WEIGHT
21 0.1517E+01 0.3481E-01
22 0.1793E+01 0.2205E-01
23 0.2069E+01 0.1294E-01
24 0.2345E+01 0.7042E-02
25 0.2621E+01 0.3550E-02
POINT WEIGHT
26 0.2897E+01 0.1659E-02
27 0.3172E+01 0.7181E-03
28 0.3448E+01 0.2881E-03
29 0.3724E+01 0.1071E-03
30 0.4000E+01 0.3692E-04
MEANS AND STANDARD DEVIATIONS OF ABILITY DISTRIBUTIONS SCORE MEAN STANDARD TOTAL NAME DEVIATION FREQUENCIES --------------------------------------------EAP 0.000 0.985 1000.00 ---------------------------------------------
In this example, the keywords SMEAN and SSD were set to 0 and 1 respectively on the SCORE command. As a result, the following output reflects the rescaling constants (0.000 and 1.015) used in this particular case. RESCALING DONE WITH RESPECT TO USER SUPPLIED LINEAR TRANSFORMATION SCORE LOCATION SCALING TOTAL NAME CONSTANT CONSTANT FREQUENCIES --------------------------------------------EAP 0.000 1.015 1000.00 ---------------------------------------------
Scores are saved to an external file (keyword SCORE on SAVE command), but the first three scores are printed to the output file for purposes of checking. When EAP is used for scoring, the S.E. column represents the posterior standard deviation. SUBJECT IDENTIFICATION WEIGHT/FREQUENCY SCORE NAME GROUP WEIGHT MEAN CATEGORY ATTEMPTS ABILITY S.E. ---------------------------------------------------------------------------.447 | 1 GROUP 01 1.00 1 EAP 1 | 1.00 3.00 1.00 0.6435 0.2193 -----------------------------------------------------------------------------.934 | 2 GROUP 01 1.00 1 EAP 1 | 1.00 1.95 1.00 -0.7442 0.2164 -------------------------------------------------------------------------.564 | 3 GROUP 01 1.00 1 EAP 1 | 1.00 2.10 1.00 -0.4392 0.2115 ----------------------------------------------------------------------------MEANS AND STANDARD DEVIATIONS OF ABILITY DISTRIBUTIONS SCORE MEAN STANDARD TOTAL NAME DEVIATION FREQUENCIES --------------------------------------------EAP 0.000 1.000 1000.00 ---------------------------------------------
When EAP is selected, an estimate of the population distribution of ability in the form of a discrete distribution of a finite number of points is obtained by accumulating the posterior densities 707
11 PARSCALE EXAMPLES over the subjects at each quadrature point. These sums are then normalized to obtain the estimated probabilities at the points. Improved estimates of the latent distribution may be obtained after one more iteration of the solution. The program also computes the mean and standard deviation for the estimated latent distribution. Sheppard’s correction for coarse grouping is used in the calculation of the standard deviation. The EAP estimate is the mean of the posterior distribution while the standard error is the standard deviation of the posterior distribution. Posterior weights are only given when EAP is used. Note that it is based on all cases, and not just on those cases used in calibration. QUADRATURE POINTS AND POSTERIOR WEIGHTS: SCORE SET # 1 1 2 3 4 5 POINT -0.4000E+01 -0.3724E+01 -0.3448E+01 -0.3172E+01 -0.2897E+01 WEIGHT 0.6822E-04 0.1942E-03 0.5048E-03 0.1187E-03 0.2494E-02
POINT WEIGHT
6 7 8 9 10 -0.2621E+01 -0.2345E+01 -0.2069E+01 -0.1793E+01 -0.1517E+01 0.46622-02 0.7591E-02 0.1180E-01 0.1987E-01 0.3555E-01
POINT WEIGHT
11 12 13 14 15 -0.1241E+01 -0.9655E+00 -0.6897E+00 -0.4138E+00 -0.1379E+00 0.5541E-01 0.7082E-01 0.8069E-01 0.9694E+00 0.1105E+00
POINT WEIGHT
16 0.1379E+00 0.1088E+00
17 0.4138E+00 0.9832E+00
18 0.6897E+00 0.8323E-01
19 0.9655E+00 0.7015E-01
20 0.1241E+01 0.5431E-01
POINT WEIGHT
21 0.1517E+01 0.3809E-01
22 0.1793E+01 0.2411E-01
23 0.2069E+01 0.1333E-01
24 0.2345E+01 0.6645E-02
25 0.2621E+01 0.2974E-02
POINT WEIGHT
26 0.2897E+01 0.1202E-03
27 0.3172E+01 0.4470E-03
28 0.3448E+01 0.1554E-04
29 0.3724E+01 0.5083E-04
30 0.4000E+01 0.1569E-05
TOTAL WEIGHT: 1.00000 MEAN : 0.00012 S.D. : 1.01246
The mean and standard deviation of the latent posterior distribution calculated from posterior weights at quadrature points are also given. In these calculations, the formulas for the variance of grouped data are used, with quadrature points as class marks and posterior weights as class frequencies.
11.2 Examinee maximum likelihood scoring from existing parameters In this example, the item parameter estimates from the Section 11.1, saved in the exampl01.par file, are used in scoring the simulated examinees by the maximum likelihood method (MLE). The item parameter file is used as input (IFNAME keyword on the FILES command) and calibration is suppressed with the NOCALIB option of the CALIB command.
708
11 PARSCALE EXAMPLES Comparison of the results in files example01.ph3 (see Section 11.1, Phase 3 output) and exampl02.ph3 (not shown here) shows that, when the scores are scaled to match the mean and standard deviation of the generating distribution, both the EAP and MLE estimates recover the generating values with good accuracy. EXAMPL02.PSL - ARTIFICIAL EXAMPLE (MONTE CARLO DATA) GRADED MODEL - MLE SCALE SCORES >FILES DFNAME='EXAMPL01.DAT', IFNAME='EXAMPL01.PAR', SAVE; >SAVE SCORE='EXAMPL02.SCO'; >INPUT NIDCHAR=4, NTOTAL=20, LENGTH=20; (4A1,10X,20A1) >TEST1 TNAME=SCALE1, ITEM=(1(1)20), NBLOCK=1; >BLOCK1 BNAME=SBLOCK1, NITEMS=20, NCAT=4; >CALIB GRADED, LOGISTIC, SCALE=1.7, NQPTS=30, CYCLES=(100,1,1,1,1), CRIT=0.005, NOCAL; >SCORE MLE, SMEAN=0.0, SSD=1.0, NAME=MLE, PFQ=5;
11.3 Calibration and scoring with the generalized partial credit rating-scale model: collapsing of categories This example scores and calibrates the data of Section 11.1 assuming the partial credit model with standard scoring function. The command file is shown below. To illustrate the situation where two types of items are involved, the four categories for the second ten items are collapsed into two categories, thus making those items effectively binary. Two blocks are required (each with ten items), and the MODIFIED list in the BLOCK2 command specifies the collapsing. The standard score function assumes 4 is the highest category, so no response modification is required in BLOCK1. In BLOCK2, the scoring function is used to specify scoring function values. CADJUST is not used with the partial credit model, nor is SCALE in the CALIB command. Because the data are now less informative, the number of quadrature points for calibration can be reduced (NQPT=15 instead of the 30 previously used). Despite the different model and the partition of the items into two blocks, the estimated trait scores in exampl03.sco agree well with the estimates from Sections 11.1 and 11.2 after rescaling in the sample. EXAMPL03.PSL - ARTIFICIAL EXAMPLE (MONTE CARLO DATA) GENERALIZED PARTIAL CREDIT MODEL - EAP SCALE SCORES >FILES DFNAME='EXAMPL01.DAT', SAVE; >SAVE SCORE='EXAMPL03.SCO'; >INPUT NIDCHAR=4, NTOTAL=20, NTEST=1, LENGTH=20; (4A1,10X,20A1) >TEST TNAME='SCALE1', ITEM=(1(1)20), NBLOCK=2; >BLOCK1 BNAME='SBLOCK1', NITEMS=10, NCAT=4, SCORING=(1,2,3,4); >BLOCK2 BNAME='SBLOCK2', NITEMS=10, NCAT=4, MODIFIED=(1,1,2,2), SCORE=(1,2); >CALIB PARTIAL, LOGISTIC, NQPTS=15, CYCLE=(100,1,1,1,1), NEWTON=2, CRIT=0.01; >SCORE MLE, SMEAN=0.0, SSD=1.0, NAME='PCR_MLE', PFQ=5;
709
11 PARSCALE EXAMPLES
11.4 Two-group differential item functioning (DIF) analysis with the partial credit model This example illustrates differential item functioning (DIF) analysis of multiple category item responses. The SCORE command is required and thus included in the command file. For the DIF model, however, no scoring is done and there is no Phase 3 output. Raw data are read from the file exampl04.dat using the DFNAME keyword on the FILES command. The data file contains responses to 6 items, as indicated on the INPUT command, where NTOTAL is set to 6. The data file contains the examinee ID and sample group code (1,2), then the responses on the 6 items, and finally the generating trait value for each examinee. The first few lines of the data file are shown below. 0001 0002 0003 0004 0005
1 1 1 1 1
233113 113111 313112 113131 313131
.43930 -.94251 -.57257 -.59414 -.36019
The format statement includes information on three fields in the raw data file. The subject ID (4A1) and group identification field (1A1) are read first, followed by the 6 item responses (6A1). One test, 6 items in length, is considered. The MGROUP keyword on the INPUT command requests a multiple-group analysis for two groups. Note that the MGROUP keyword is used in combination with the MGROUP command, which must follow directly after the BLOCK command(s). On the TEST command, a name for the test is provided using the TNAME keyword. The items on this test are listed using the ITEMS keyword, while the INAMES keyword is used to provide names for the items. Finally, by setting NBLOCK to 6, it is indicated that 6 BLOCK commands will follow the TEST command. In this example, there is one item with three categories originally coded 1, 2, and 3 in each block as indicated by the NITEMS, NCAT and ORIGINAL keywords respectively. Because the rating-scale model is not used here, separate category parameters are estimated for each item, and the REPEAT keyword indicates that the BLOCK command should be repeated six times. The second value (1) assigned to the DIF keyword of the MGROUP command requests a DIF analysis of the item threshold parameters. All other values in this keyword are equal to zero, indicating that only thresholds are allowed to differ between the groups. The GNAME and GCODE keywords are used to assign names and codes to the two groups. By default, the first group will be used as the reference group. To change the reference group, the REFERENCE keyword on the MGROUP command may be used. A partial credit model with logistic response function is requested through the use of the PARTIAL and LOGISTIC options on the CALIB command. The default number of quadrature points is 30. In this case, NQPT is set to 25, because fewer points are needed when the number of items is small. By setting the CYCLES keyword to 100, a maximum of 100 EM cycles will be per710
11 PARSCALE EXAMPLES formed, followed by two Newton cycles (NEWTON=2). The convergence criterion is somewhat relaxed by setting CRIT to 0.01 instead of using the default convergence criterion of 0.001. Finally, the POSTERIOR option is added to the CALIB command. As a default, the posterior distribution is computed after the computation of expected proportions during the E-step as their by-product. Therefore, these expected sample sizes and expected frequencies of categorical responses are computed based on the posterior distribution in the previous EM cycle. Adding the POSTERIOR option forces the program to compute the posterior distribution again after the M-step. Therefore, the expected proportions can be computed during the E-step based on an updated posterior distribution. This was added to be consistent with the BILOG-MG program in the case of two categories. The command file is as follows. EXAMPL04.PSL - DIF ANALYSIS USING PARTIAL CREDIT MODEL ARTIFICAL DATA, TWO SAMPLES (EACH WITH N=500, N=(0,1)), 6 ITEMS >FILES DFNAME='EXAMPL04.DAT'; INPUT NIDCHAR=4, MGROUP=2, NTOTAL=6; (4A1,1X,1A1,1X,6A1) >TEST TNAME='PARV3E', ITEM=(1,2,3,4,5,6), INAME=('I001','I002','I003','I004','I005','I006'), NBLOCK=6; >BLOCK1 REPEAT=6, NIT=1, NCAT=3, ORIGINAL=(1,2,3); >MGROUP DIF=(0,1,0,0), GNAME=('MALE','FEMALE'), GCODE=('1','2'); >CALIB LOGISTIC, PARTIAL, NQPT=25, CYCLES=(100,1,1,1,1,1), NEWTON=20, CRIT=0.01, POSTERIOR; >SCORE ;
Phase 0 output
When the MGROUP keyword and MGROUP command are used or multiple TEST/BLOCK commands are used, additional information is written to the phase 0 output file. NUMBER OF SUBGROUPS: 2 FORMAT OF DATA INPUT IS (4A1,1X,1A1,1X,6A1) >TEST
TNAME=PARV3E, ITEM=(1,2,3,4,5,6), INAME=('I001','I002','I003','I004','I005','I006'), NBLOCK=6 ;
BLOCK CARD: 1 >BLOCK1 REPEAT=6, NIT=1, NCAT=3, ORIGINAL=(1,2,3) ; BLOCK BLOCK BLOCK BLOCK BLOCK
CARD: CARD: CARD: CARD: CARD:
2 3 4 5 6
IS IS IS IS IS
COPIED COPIED COPIED COPIED COPIED
FROM FROM FROM FROM FROM
BLOCK BLOCK BLOCK BLOCK BLOCK
1 1 1 1 1
>MGROUP DIF=(0,1,0,0), GNAME=(MALE,FEMALE), GCODE=('1','2') ;
In the next few lines, the program echoes the information on parameters allowed to be different between groups as specified with the DIF keyword: in this case, only the thresholds are allowed to differ between the two groups. The MALE group will be used as reference group.
711
11 PARSCALE EXAMPLES GROUP PARAMETER GROUP PARAMETER GROUP PARAMETER GROUP PARAMETER REFERENCE GROUP
FOR FOR FOR FOR FOR
SLOPE: THRESHOLD: CATEGORY: GUESSING: DIF MODEL:
NO YES NO NO 1 MALE
SUBGROUP NAME AND CODE ====================== 1 2
MALE FEMALE
1 2
DIF OR COMMON BLOCK =================== 1 BLOCK DIF 2 BLOCK DIF 3 BLOCK DIF 4 BLOCK DIF 5 BLOCK DIF 6 BLOCK DIF
BLOCK BLOCK BLOCK BLOCK BLOCK BLOCK
Phase 1 output
The only difference between the Phase 1 output for a single group analysis and for a multiplegroup analysis is that the summary item statistics are first given by subgroup and then for the total group. The output for the first item is shown below for all three cases. We see that females were more likely to respond in category 3 and less likely to respond in category 1 than the males. In general, 76% of the total responses were in category 3. 1 SUBGROUP: MALE BLOCK NO.: 1 NAME: BLOCK ------------------------------------------------------ITEM | TOTAL NOT OMIT | CATEGORIES | PRESENT | | | 1 2 3 ------------------------------------------------------I001 | | FREQ.| 500 0 0| 152 13 335 PERC.| 0.0 0.0| 30.4 2.6 67.0 ------------------------------------------------------2 SUBGROUP: FEMALE BLOCK NO.: 1 NAME: BLOCK ------------------------------------------------------ITEM | TOTAL NOT OMIT | CATEGORIES | PRESENT | | | 1 2 3 ------------------------------------------------------I001 | | FREQ.| 500 0 0| 69 6 425 PERC.| 0.0 0.0| 13.8 1.2 85.0 -------------------------------------------------------
712
11 PARSCALE EXAMPLES TOTAL BLOCK NO.: 1 NAME: BLOCK ------------------------------------------------------ITEM | TOTAL NOT OMIT | CATEGORIES | PRESENT | | | 1 2 3 ------------------------------------------------------I001 | | FREQ.| 1000 0 0| 221 19 760 PERC.| 0.0 0.0| 22.1 1.9 76.0 -------------------------------------------------------
Item means, initial slope estimates, and Pearson and polyserial item-test correlations are given in the next table. For a detailed discussion of the measures shown here, refer to the discussion of the Phase 1 output of Section 11.1. ---------------------------------------------------------------------------BLOCK | RESPONSE TOTAL SCORE | PEARSON & | INITIAL INITIAL ITEM | MEAN MEAN | POLYSERIAL | SLOPE LOCATION | S.D.* S.D.* | CORRELATION | --------------------------------------------------------------------------BLOCK | | | 1 I001 | 2.539 13.162 | 0.714 | 1.000 0.000 | 0.831* 3.765* | 0.976 | ---------------------------------------------------------------------------CATEGORY | SCORING | MEAN | S.D. | PARAMETER 1 | 1.000 | 8.190 | 2.235 | 0.000 2 | 2.000 | 11.263 | 2.899 | -0.155 3 | 3.000 | 14.655 | 2.735 | 1.596 ----------------------------------------------------------------------------
Phase 2 output
For the DIF model, a separate prior distribution is used for each group member, and the prior distribution is updated after each estimation cycle based on the posterior distribution from the previous cycle. For the DIF model, it is assumed that different groups have different distributions with mean µ g and standard deviation σ g . The distributions are not necessarily normal. These empirical posterior distributions are estimated simultaneously with the estimation of the item parameters. To obtain those parameters, the following constraint is imposed for the DIF model: J
J
j =1
j =1
∑ d R j = ∑ d Fj . This constraint implies the overall difficulty levels of a test or a set of common items given to both the reference group and focal group, indicated by subscripts R and F, respectively, are the same. Therefore, the item difficulty parameters for the focal groups are adjusted. Any overall difference in terms of test difficulty will be assumed to be the difference in ability level for sub-
713
11 PARSCALE EXAMPLES groups. The ability level difference among groups can then be estimated by the posterior distributions. The first difference between the output file discussed here and the Phase 2 output for Section 11.1 concerns the scoring function and step parameters for the multiple blocks. As no scoring function was specified on the CALIB command, the default scoring function 1, 2 will be used. Under the partial credit model, the step parameters, also known as the item step difficulties or category intersections, correspond to the points on the ability scale where two successive item response category characteristic curves (IRCCC) intersect. The increasing difficulty of a step relative to other steps within an item is associated with higher values of the step parameters. In this example, where each item has 3 categories, 2 “steps” are needed to move from the first category to the third category: a respondent needs to move from category 1 to category 2, and a second step is needed to move from category 2 to category 3. From the second step parameters of items 1 and 2 (see below) moving from category 2 to category 3 is harder to do in the case of item 2 for the male respondents. The IRCCC for items 1 and 5 are shown below. Vertical lines were added to indicate the trait level at which the curves for step 0 and step 1 intersect. The most likely response for a male with trait level of -2 would be to complete 0 steps in both cases. For a male with trait level of approximately 1.5, completing the step from category 2 to category 3 would be more likely in the case of item 5. Although there is little difference between the two graphs, it would appear that completing the first step is somewhat easier for item 1 than for item 5, while completing the second step is easier for item 5. This is in agreement with the second step parameters for these items: 1.769 for item 1 and 1.517 for item 5.
714
11 PARSCALE EXAMPLES
MULTIPLE GROUP MODEL [DIF (TREND) MODEL] --------------------------------------[GROUP:
1
MALE
]
ITEM BLOCK 1 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.743 0.168
3.000 1.743 0.163
ITEM BLOCK 2 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.733 0.155
3.000 1.733 0.156
ITEM BLOCK 3 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.733 0.146
3.000 1.733 0.140
ITEM BLOCK 4 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.755 0.149
3.000 1.755 0.147
ITEM BLOCK 5 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.658 0.171
3.000 1.658 0.154
ITEM BLOCK 6 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.974 0.177
3.000 1.974 0.186
715
11 PARSCALE EXAMPLES The step parameter information is followed by the item parameter estimates for the male group. Standard errors are computed from the empirical information matrix in the final Newton cycle. +------+-----+---------+---------+---------+---------+---------+---------+ | ITEM |BLOCK| SLOPE | S.E. |LOCATION | S.E. |GUESSING | S.E. | +======+=====+=========+=========+=========+=========+=========+=========+ | I001 | 1 | 0.846 | 0.054 | -0.590 | 0.070 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I002 | 2 | 0.948 | 0.060 | 0.519 | 0.066 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I003 | 3 | 0.628 | 0.034 | -0.542 | 0.076 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I004 | 4 | 0.615 | 0.034 | 0.544 | 0.077 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I005 | 5 | 0.414 | 0.025 | -0.666 | 0.098 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I006 | 6 | 0.344 | 0.021 | 0.658 | 0.110 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+
Similar information for the female group is given next. Note that the slope for each item is common across the two groups. This implies that the same item discrimination is assumed over the groups. [GROUP:
2
FEMALE
]
ITEM BLOCK 1 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.743 0.168
3.000 1.743 0.163
ITEM BLOCK 2 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.733 0.155
3.000 1.733 0.156
ITEM BLOCK 3 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.773 0.146
3.000 1.773 0.140
ITEM BLOCK 4 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.755 0.149
3.000 1.755 0.147
ITEM BLOCK 5 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.658 0.171
3.000 1.658 0.154
ITEM BLOCK 6 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 -1.974 0.177
3.000 1.974 0.186
716
11 PARSCALE EXAMPLES +------+-----+---------+---------+---------+---------+---------+---------+ | ITEM |BLOCK| SLOPE | S.E. |LOCATION | S.E. |GUESSING | S.E. | +======+=====+=========+=========+=========+=========+=========+=========+ | I001 | 1 | 0.846 | 0.054 | -0.615 | 0.085 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I002 | 2 | 0.948 | 0.060 | 0.644 | 0.057 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I003 | 3 | 0.628 | 0.034 | 0.010 | 0.075 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I004 | 4 | 0.615 | 0.034 | -0.348 | 0.084 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I005 | 5 | 0.414 | 0.025 | -0.645 | 0.118 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | I006 | 6 | 0.344 | 0.021 | 0.877 | 0.098 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+
DIF contrasts are given next. In the table below, the CONTRAST column gives the differences between item locations between the groups and the associated standard error. The STD column contains standardized contrasts, obtained by dividing each contrast by its standard error. The probability that a normal variate exceeds the absolute value of the standardized difference is also given. This is a one-sided test. CONTRAST OF ITEM LOCATIONS: GROUP 2: FEMALE MINUS REFERENCE GROUP +---------------------------------+ |ITEM |BLOCK| CONTRAST | STD | | | | (S.E.) | (PROB. ) | +=====+=====+==========+==========+ |I001 | 1 | -0.025 | -0.230 | | | |( 0.110)|( 0.409)| | | | | | |I002 | 2 | 0.125 | 1.433 | | | |( 0.087)|( 0.076)| | | | | | |I003 | 3 | 0.552 | 5.176 | | | |( 0.107)|( 0.000)| | | | | | |I004 | 4 | -0.892 | -7.798 | | | |( 0.114)|( 0.000)| | | | | | |I005 | 5 | 0.021 | 0.138 | | | |( 0.153)|( 0.445)| | | | | | |I006 | 6 | 0.219 | 1.494 | | | |( 0.147)|( 0.068)| +---------------------------------+
1: MALE
χ 2 -test statistics for the item location contrasts are given in the next section of the output file. In this case, with only one degree of freedom, χ 2 = ( std . difference) 2 . This is a two-sided test. In this table, these χ 2 -test statistics and exceedance probabilities are summarized.
717
11 PARSCALE EXAMPLES CHI-SQUARES OF ITEM LOCATION CONTRASTS: +---------------------------------+ |ITEM BLOCK CHI-SQRS D.F. | | PROB. | +=====+=====+==========+==========+ |I001 | 1 | 0.053 | 1. | | | | | 0.803 | +---------------------------------+ |I002 | 2 | 2.052 | 1. | | | | | 0.148 | +---------------------------------+ |I003 | 3 | 26.789 | 1. | | | | | 0.000 | +---------------------------------+ |I004 | 4 | 60.814 | 1. | | | | | 0.000 | +---------------------------------+ |I005 | 5 | 0.019 | 1. | | | | | 0.861 | +---------------------------------+ |I006 | 6 | 2.231 | 1. | | | | | 0.131 | +---------------------------------+ |TOTAL| | 91.958 | 6. | | | | | 0.000 | +---------------------------------+
When the summary statistics for the 2 groups are compared, we see that only the standard deviation of the threshold differs. Recall that for this example, the DIF keyword on the MGROUP command was used to allow only threshold parameters to differ between the groups. Overall, no large difference between groups over all items is observed. SUMMARY STATISTICS OF PARAMETER ESTIMATES 1 GROUP NAME: MALE +----------+---------+---------+----+ |PARAMETER | MEAN | STN DEV | N | +==========+=========+=========+====+ |SLOPE | 0.633| 0.235| 6| |LOG(SLOPE)| -0.520| 0.394| 6| |THRESHOLD | -0.013| 0.645| 6| |GUESSING | 0.000| 0.000| 0| +----------+---------+---------+----+ 2 GROUP NAME: FEMALE +----------+---------+---------+----+ |PARAMETER | MEAN | STN DEV | N | +==========+=========+=========+====+ |SLOPE | 0.633| 0.235| 6| |LOG(SLOPE)| -0.520| 0.394| 6| |THRESHOLD | -0.013| 0.648| 6| |GUESSING | 0.000| 0.000| 0| +----------+---------+---------+----+
The final output is the estimated latent distributions by group. The origin and unit of the scale are set so that the mean and standard deviation of the reference group are 0 and 1 respectively.
718
11 PARSCALE EXAMPLES A plot of the estimated latent distributions is given below. The solid line represents the distribution for the male group. If there is appreciable DIF, the latent distributions do not represent the same latent variable and no meaningful comparison of the two distributions is possible. If there is no DIF, significant differences between the latent distributions represent real differences between the populations sampled.
QUADRATURE POINTS AND POSTERIOR WEIGHTS: GROUP 1 GROUP NAME: MALE
POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3667E+01 -0.3333E+01 -0.3000E+01 -0.2667E+01 0.5988E-04 0.2137E-03 0.6808E-03 0.1934E-02 0.4887E-02
POINT WEIGHT
6 7 8 9 10 -0.2333E+01 -0.2000E+01 -0.1667E+01 -0.1333E+01 -0.1000E+01 0.1096E-01 0.2172E-01 0.3790E-01 0.5826E-01 0.8000E-01
POINT WEIGHT
11 12 -0.6667E+00 -0.3333E+00 0.1009E+00 0.1178E+00
13 0.3331E-15 0.1249E+00
14 0.3333E+00 0.1190E+00
15 0.6667E+00 0.1034E+00
POINT WEIGHT
16 0.1000E+01 0.8257E-01
17 0.1333E+01 0.5917E-01
18 0.1667E+01 0.3742E-01
19 0.2000E+01 0.2086E-01
20 0.2333E+01 0.1029E-02
POINT WEIGHT
21 0.2667E+01 0.4516E-02
22 0.3000E+01 0.1766E-02
23 0.3333E+01 0.6167E-03
24 0.3667E+01 0.1924E-03
25 0.4000E+01 0.5368E-04
719
11 PARSCALE EXAMPLES TOTAL WEIGHT: 1.00000 MEAN : 0.00000 S.D. : 0.99974 QUADRATURE POINTS AND POSTERIOR WEIGHTS: GROUP
2
GROUP NAME: FEMALE
POINT WEIGHT
1 2 3 4 5 -0.4000E+01 -0.3667E+01 -0.3333E+01 -0.3000E+01 -0.2667E+01 0.1485E-04 0.5381E-04 0.1748E-03 0.5093E-03 0.1331E-02
POINT WEIGHT
6 7 8 9 10 -0.2333E+01 -0.2000E+01 -0.1667E+01 -0.1333E+01 -0.1000E+01 0.3120E-02 0.6569E-02 0.1248E-01 0.2175E-01 0.3608E-01
POINT WEIGHT
11 12 -0.6667E+00 -0.3333E+00 0.5834E-01 0.8712E-01
13 0.3331E-15 0.1130E+00
14 0.3333E+00 0.1320E+00
15 0.6667E+00 0.1437E+00
POINT WEIGHT
16 0.1000E+01 0.1360E+00
17 0.1333E+01 0.1059E+00
18 0.1667E+01 0.6927E-01
19 0.2000E+01 0.3922E-01
20 0.2333E+01 0.1955E-01
POINT WEIGHT
21 0.2667E+01 0.8653E-02
22 0.3000E+01 0.3410E-02
23 0.3333E+01 0.1199E-02
24 0.3667E+01 0.3764E-03
25 0.4000E+01 0.1056E-04
TOTAL WEIGHT: 1.00000 MEAN : 0.00000 S.D. : 0.99974
11.5 A test with 26 multiple-choice items and one 4-category item: threeparameter logistic and generalized partial credit model This example illustrates a test consisting primarily of machine-scorable multiple choice items, but also containing one open-ended item scored in three categories. The latter item appears in the middle. The item responses are from several test forms, and items not represented on a particular form are assigned the not-presented code 9. The not-presented key appears in the exampl05.npc file. The codes 1 and 0 for correct and incorrect response to the multiple-choice items must be recoded 1 and 2, respectively, for the PARSCALE analysis. This is accomplished through use of the ORIGINAL and MODIFIED keywords on the BLOCK commands. The first few lines of the file exampl05.dat are shown below. The data and command files can be found in the examples folder of the PARSCALE installation.
720
11 PARSCALE EXAMPLES 1 2 3 4 5
110000000000199999999999999 110000000011199999999999999 011001000001199999999999999 110000100000199999999999999 101011010011199999999999999
The contents of exampl05.npc, are as shown below. It is indicated in the syntax by the NFNAME keyword on the FILES command. KEY 999999999999999999999999999
The first information read according to the format statement shown below is the case ID, which is read in the format “3A1”. The NIDCHAR keyword is set to 3 to indicate that the case ID is six characters in length. The response to the first item is in column 5, and the format (“27A1”) that follows after skipping of the fourth column using the “X” operator indicates that 27 items are read from each line. The 3-parameter logistic model (3PL) is assumed for the multiple-choice items, and the partial credit model is assumed for the open-ended item. Because the parameters of the 3PL model differ from one item to another, each item must be assigned to a separate block. This is facilitated by the REPEAT keyword of the BLOCK command, which indicates the number of successive items that have the same block specifications. In the present example, the first block specification applies to the first 12 multiple-choice items, the second applies to the open-ended item, and the third applies to the remaining 14 multiple-choice items. Note also the assignment of separate block names using the BNAME keyword. The use of the SPRIOR and GPRIOR options on the CALIB command requests the use of a lognormal prior distribution and a normal prior distribution on the slope and guessing parameters respectively. The Bayes estimates (EAP option on the SCORE command) of the respondents' scale scores are estimated and saved. EXAMPL05.PSL - A TEST WITH 26 MULTIPLE CHOICE ITEMS AND ONE 4-CATEGORY ITEM THREE-PARAMETER LOGISTIC AND GENERALIZED PARTIAL CREDIT MODEL >FILE DFNAME='EXAMPL05.DAT', NFNAME='EXAMPL05.NPC', SAVE ; >SAVE PARM='EXAMPL05.PAR', SCORE='EXAMPL05.SCO' ; >INPUT NIDCHAR=3, NTOTAL=27, NTEST=1, LENGTH=27; (3A1,1X,27A1) >TEST1 TNAME=SOCSCI, ITEM=(1(1)27), NBLOCK=27 ; >BLOCKS BNAME=(MC01,MC02,MC03,MC04,MC05,MC06,MC07,MC08,MC09,MC10,MC11,MC12), NITEMS=1, NCAT=2, ORIGINAL=(0,1), MODIFIED=(1,2), REPEAT=12, GUESSING=(2,ESTIMATE) ; >BLOCK BNAME=OE, NITEMS=1, NCAT=3, SCORING=(1,2,3) ; >BLOCKS BNAME=(MC13,MC14,MC15,MC16,MC17,MC18,MC19,MC20,MC21,MC22,MC23,MC24, MC25,MC26), NITEMS=1, NCAT=2, ORIGINAL=(0,1), MODIFIED=(1,2), REPEAT=14, GUESSING=(2,ESTIMATE) ; >CALIB PARTIAL, LOGISTIC, NQPTS=15, CYCLE=(50,1,1,1,1), NEWTON=2, CRIT=0.01, SPRIOR, GPRIOR ; >SCORE EAP, SMEAN=0.0, SSD=1.0, NAME=SOCSCI ;
721
11 PARSCALE EXAMPLES
11.6 Analysis of three tests containing items with two and three categories: calculation of combined scores A partial credit model based on artificial data is discussed in this example. Six items, with either 2 or 3 categories each, are assigned to three subtests. In all cases, guessing parameters are estimated. The data file used is exampl06.dat in the examples subfolder of the PARSCALE installation. The first few lines of the data file are shown below. 0001 0002 0003 0004 0005 0006 0007 0008 0009 0010
113211 -1.98194 222211 .07151 222211 -.06528 211221 -.72716 222211 -.35792 323212 .73036 212212 -.53729 211211 -1.40260 221212 .09829 122111 -.75451
The case identification is given in the first four columns of each line. Responses to the six items are recorded in columns 6 to 11. At the end of each line, the generating trait value is given. This value is not used in the analysis. The format statement used to read these data is: (4A1,1X,6A1)
The items are analyzed in different ways in three subtests (NTEST=3 on INPUT). The LENGTH keyword on the INPUT command indicates the length of each of the three subtests. The COMBINE keyword on the INPUT command indicates that 3 COMBINE commands follow the SCORE command, while the SAVE option indicates that a SAVE command will follow directly after the FILES command. On the SAVE command, names for external files to which subject scores and combined scores will be saved are provided. The first subtest consists of six items analyzed in six distinct blocks. The REPEAT keyword of the first block indicates that the first three blocks each contain one 3-category item with itemspecific step parameters. The remaining blocks contain multiple-choice items with various guessing parameters. The GPARM keyword is used here to correct the dichotomous item response probabilities in the presence of the GUESSING keyword. These guessing parameters are used for the initial parameter values and have a default value of zero. The value of (2,ESTIMATE) assigned to the GUESSING keywords indicates that the second category is the correct response and that a guessing parameter is to be estimated. In the second subtest, the first 3 items are analyzed separately. In the third subtest, the last 3 items are analyzed separately. The convergence criterion for the iteration procedure is somewhat relaxed for this test calibration (0.005 ==> 0.01) to obtain convergence. Scores for the three subtests are combined in the scoring phase. These scores are saved to the ex722
11 PARSCALE EXAMPLES ternal file exampl06.sco as specified on the SAVE command. They are combined as specified by the COMBINE keyword in the INPUT command and the COMBINE commands following the last SCORE command. The WEIGHT keywords on these commands have as values sets of positive fractions summing to 1. These values are used as the weights for the subscale scores. Subscores are combined linearly. In this example, three different combinations of the scores from the subtests are requested. These scores are saved to the external file exampl06.cmb. The command file exampl06.psl is shown below. EXAMPLE 6: PARTIAL CREDIT MODEL ARTIFICAL DATA, ONE SAMPLE (N=1000, N=(0,1)), 6 ITEMS >FILES DFNAME='EXAMPL06.DAT', SAVE; >SAVE SCORE='EXAMPL06.EAP', COMBINE='EXAMPL06.CMB'; >INPUT NIDCHAR=4, NTOT=6, NTEST=3, LENGTH=(6,3,3), COMBINE=3; (4A1,1X,6A1) >TEST1 TNAME=TEST1, ITEM=(1,2,3,4,5,6), INAME=('P011','P012','P013','D011','D012','D013'), NBLOCK=6; >BLOCK1 REPEAT=3, NIT=1, NCAT=3, ORIGINAL=(1,2,3); >BLOCK4 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.0, GUESS=(2,ESTIMATE); >BLOCK5 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.1, GUESS=(2,ESTIMATE); >BLOCK6 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.3, GUESS=(2,ESTIMATE); >CALIB LOGISTIC, PARTIAL, NQPT=21, CYCLES=(100,1,1,1,1,1), NEWTON=2, CRIT=0.005, SCALE=1.7; >SCORE EAP, NAME=TEST1; >TEST2 TNAME=TEST2, ITEM=(1,2,3), INAME=('P021','P022','P023'), NBLOCK=3; >BLOCK1 REPEAT=3, NIT=1, NCAT=3, ORIGINAL=(1,2,3); >CALIB LOGISTIC, PARTIAL, NQPT=21, CYCLES=(100,1,1,1,1,1), NEWTON=2, CRIT=0.005, SCALE=1.7; >SCORE EAP,NAME=TEST2; >TEST3 TNAME=TEST3, ITEM=(4,5,6), INAME=('D031','D032','D033'), NBLOCK=3; >BLOCK4 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.0, GUESS=(2,ESTIMATE); >BLOCK5 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.1, GUESS=(2,ESTIMATE); >BLOCK6 NIT=1, NCAT=2, ORIGINAL=(1,2), GPARM=0.3, GUESS=(2,ESTIMATE); >CALIB LOGISTIC, PARTIAL, NQPT=21, CYCLES=(100,1,1,1,1,1), NEWTON=2, CRIT=0.01, SCALE=1.7; >SCORE EAP, NAME=TEST3; >COMBINE1 NAME=SUM1, WEIGHT=(0.5,0.25,0.25); >COMBINE2 NAME=SUM2, WEIGHT=(1.0,0.0,0.0); >COMBINE3 NAME=SUM3, WEIGHT=(0.0,0.5,0.5);
11.7 Rater-effect model: multi-record input format with varying numbers of raters per examinee This example illustrates the parameter estimation for multiple raters. The analysis is based on data in the file exampl07.dat in the raters folder of the PARSCALE installation folder. The first few lines of the data are shown below. 00001 00001 00002 00002 00003 00003 00004 00004
12 22 12 22 12 23 12 22
11 21 11 22 12 22 12 22
32 42 32 43 31 43 33 42
32 42 32 42 31 41 32 42
723
11 PARSCALE EXAMPLES 00005 00005
11 22
11 21
31 41
31 42
The data contain the rating on four items administered to each examinee by four raters. The first 5 columns of each line of data contain the examinee ID. After two blank columns, the rater ID is given, directly followed by the rating on the first item. Similar combinations of rater ID and rating for the other three items follow. As can be seen from the data above, the first line of data is associated with examinee 00001 and contains the ratings for raters 1 and 3. The second line of data, associated with the same examinee, contains the ratings for raters 2 and 4. The data are read using the format statement (5A1,4(2X,2A1))
where “5A1” is the format of the examinee ID, and “2X,2A1” the format for reading of one rater ID/rating combination. The latter is repeated four times, using the notation “4( )”. Note that, since the data for each examinee are given on two lines, R-INOPT=2 could have been specified on the INPUT command and the format statement changed to (5A1,4(2X,2A1),/T6,4(2X,2A1)).
The MRATER keyword on the INPUT command requests Rater's-Effect analysis, and indicates the number of raters. The MRATER command provides necessary information about the four raters. The estimated parameters and scores are saved to external output files using the SAVE option on the FILES command and the PARM and SCORE keywords on the SAVE command. The command file for a partial credit model based on these data is shown below. EXAMPL07.PSL - ARTIFICIAL EXAMPLE: MONTE CARLO DATA GENERALIZED PARTIAL CREDIT MODEL: RATERS’ EFFECT MODEL [NESTED DESIGN] >FILES DFNAME='EXAMPL07.DAT', SAVE; >SAVE PARM='EXAMPL07.PAR', SCORE='EXAMPL07.SCO'; >INPUT NIDCHAR=5, NTOT=4, LENGTH=4, NTEST=1, NFMT=1, MRATER=4; (5A1,4(2X,2A1)) >TEST TNAME=RATERN, ITEM=(1,2,3,4), NBLOCK=4; >BLOCK REPEAT=4, NITEMS=1, NCAT=3, ORIGINAL=('1','2','3'), MODIFIED=(1,2,3); >MRATER RNAME=(RaterA,RaterB,RaterC,RaterD), RCODE=('1','2','3','4'); >CAL LOGISTIC, PARTIAL, NQPT=21, CYCLES=(100,1,1,1,1,1), NEWTON=2, CRIT=0.05, DIAG=0, ITEMFIT=10, SCALE=1.7; >SCORE EAP;
Phase 0 output
In addition to the standard Phase 0 output discussed elsewhere, information on the raters’ names, codes, and the weight assigned to each is echoed to the output file. The MRATER command used here only assigns names and codes to the rater. By default, the RATER keyword, not included in the MRATER command shown here, assumes the value (1,1,1,1). The arguments of this keyword are the raters’ weights. For the Raters-effect model, the ability score 724
11 PARSCALE EXAMPLES for each respondent is computed for each subtest (or subscale) and each rater separately. A total score of each respondent for each subtest (or subscale) is computed by summing those scores over items within each subtest and all raters who have rated the respondent. The rater weights of this keyword are used to compute the weighted subtest or subscale score for each respondent. Since the number of raters who rated each respondent’s responses varies, the weights are normalized (divided by their sum) for each respondent. >MRATER RNAME=(RaterA,RaterB,RaterC,RaterD), RCODE=('1','2','3','4'); MULTIPLE GROUP MODEL: RATER’s EFFECT MODEL RATER’s NAME, CODE, AND WEIGHT ============================== 1 2 3 4
RaterA RaterB RaterC RaterD
1 2 3 4
1.00 1.00 1.00 1.00
Also included in the Phase 0 output is a listing of the first two observations, showing the input and recoded responses. The raters responsible for each rating are also listed. This information is provided so that the user can check that the data are read in correctly. If not, the variable format statement (or the data) should be corrected. INPUT AND RECODED RESPONSE OBSERVATION # 1 GROUP: 1 ID: 00001 INPUT RESPONSES: 2 1 RECODED RESPONSES: 2 1 RECODED RATERS : 1 1 OBSERVATION # 2 GROUP: 1 ID: 00001 INPUT RESPONSES: 2 RECODED RESPONSES: 2 RECODED RATERS : 2
1 1 2
OF FIRST AND SECOND OBSERVATIONS
2 2 3
2 2 3
2 2 4
2 2 4
The Phase 0 output also reports that 2000 lines of data were read from the data file, and indicates that these 2000 observations are associated with 1000 examinees. [MAIN TEST: RATERN ] 2000 OBSERVATIONS READ FROM FILE: 2000 OBSERVATIONS WRITTEN TO FILE: MULTIPLE RATERS DATA 1000 CASES READ FROM FILE:
EXAMPL08.DAT exampl08.MFL
EXAMPL08.DAT
725
11 PARSCALE EXAMPLES Phase 1 Output
The Phase 1 output file contains no additional information in this type of analysis. As usual, frequencies and percentages for items nested within blocks are reported here. Information for the first block/item is shown below. SUMMARY ITEM STATISTICS ======================= BLOCK NO.: 1 NAME: BLOCK ------------------------------------------------------ITEM | TOTAL NOT OMIT | CATEGORIES | PRESENT | | | 1 2 3 ------------------------------------------------------0001 | | FREQ.| 2000 0 0| 235 1145 620 PERC.| 0.0 0.0| 11.8 57.2 31.0 | | -------------------------------------------------------
Phase 2 Output
The Phase 2 output file shows the standard output for category parameters and item parameters at convergence. This is followed by rater parameters and their associated standard errors as shown below. ITEM BLOCK 1 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 1.243 0.056
3.000 -1.243 0.041
ITEM BLOCK 2 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 1.183 0.037
3.000 -1.183 0.049
ITEM BLOCK 3 BLOCK SCORING FUNCTION :
1.000
2.000
3.000
STEP PARAMTER
:
0.000
1.298
-1.298
S.E.
:
0.000
0.079
0.064
ITEM BLOCK 4 BLOCK SCORING FUNCTION : STEP PARAMTER : S.E. :
1.000 0.000 0.000
2.000 1.273 0.062
3.000 -1.273 0.077
726
11 PARSCALE EXAMPLES
+------+-----+---------+---------+---------+---------+---------+---------+ | ITEM |BLOCK| SLOPE | S.E. |LOCATION | S.E. |GUESSING | S.E. | +======+=====+=========+=========+=========+=========+=========+=========+ | 0001*| 1 | 0.814 | 0.041 | -0.515 | 0.039 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | 0002*| 2 | 0.935 | 0.047 | 0.410 | 0.037 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | 0003*| 3 | 0.491 | 0.027 | -0.502 | 0.051 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ | 0004*| 4 | 0.505 | 0.028 | 0.508 | 0.050 | 0.000 | 0.000 | +------+-----+---------+---------+---------+---------+---------+---------+ RATER’s EFFECT PARAMETER RATER’s NAME PARAMETER S.E. --------------------------------RaterA -0.008 0.030 RaterB -0.006 0.030 RaterC 0.069 0.044 RaterD -0.055 0.044 NOTE: RATED ITEMS ARE MARKED BY “*'
From the output above, we see a marked difference between the raters, in particular between RaterC and RaterD. The raters differ appreciably in severity.
11.8 Rater-effect model: one-record input format with same number of raters per examinee This example illustrates another option of rater data input (R-INOPT=1). The data in exampl07.dat (see Section 11.7) were reformatted so that rated responses for each respondent are on one same record. This input option needs the NRATER keyword in the INPUT command to indicate the number of times each item was rated. The number of raters is indicated using the MRATER keyword on the same command. EXAMPL08.PSL - ARTIFICIAL EXAMPLE (MONTE CARLO DATA) GENERALIZED PARTIAL CREDIT MODEL: RATERS' EFFECT MODEL [NESTED DESIGN] >FILE DFNAME='EXAMPL08.DAT', SAVE; >SAVE PARAM='EXAMPL08.PAR', SCORE='EXAMPL08.SCO'; >INPUT R-INOPT=2, NIDCHAR=5, NTOT=4, LENGTH=4, NTEST=1, NFMT=1, MRATER=4, NRATER=(2(0)4); (5A1,8(2X,2A1)) >TEST TNAME=RATERN, ITEM=(1,2,3,4), NBLOCK=4; >BLOCK REPEAT=4, NIT=1, NCAT=3, ORIGINAL=('1','2','3'), MOD=(1,2,3); >MRATER RNAME=(RaterA,RaterB,RaterC,RaterD), RCODE=('1','2','3','4'); >CAL LOGISTIC, PARTIAL, NQPT=21, CYCLES=(100,1,1,1,1,1), NEWTON=2, CRIT=0.05, DIAG=0, ITEMFIT=10, SCALE=1.7; >SCORE EAP;
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11 PARSCALE EXAMPLES
11.9 Raters-effect model: one-record input format with varying numbers of records per examinee This example illustrates another form of data input for multiple ratings. It is requested by setting R-INOPT=1 on the INPUT command to indicate one line of data per examinee. The number of items in the test is given in the LENGTH keyword. The data in exampl09.dat (given in the raters folder) are formatted so that a rater ID code precedes each rating of the examinee’s response to an item. The INPUT command must include the NRATER keyword to indicate the number of times each item has been rated. The MRATER keyword is used to give the maximum number of raters for each of the items in the test. If any given item of any particular case record has fewer than the maximum number of raters, the not-presented code must be inserted for the rater code of each missing rater. If an item is multiple-choice or is objectively scored, then the number of raters for the item in the NRATER list must be set to zero. For those items, only the response code appears in the case re-
cord. The total number of responses, NTOTAL, to all items in the data is equal to the sum of the number of multiple-choice items plus the sum of number of raters in NRATER list. The INPUT command must also contain the MRATER keyword, giving the number of different raters in the data. The codes that identify the raters in the data must appear in the MRATER command. Labels for the raters in the output listing may be supplied in the RNAME keyword on the MRATER command. The following is an example of a data record in exampl09.dat. There are 5 open-ended items, but any given examinee is presented only 2 of these items. Rater codes and ratings for the remaining items are assigned the not-presented code 0. There are no multiple-choice items. 14
3 2 10 3
0 0
0 0
5 3 12 2
0 0
0 0
0 0
0 0
Examinee 14 was presented items 1 and 3. The response to item 1 was scored by rater 3, who assigned it category 2, and by rater 10, who assigned it category 3. The response to item 3 was scored by rater 5, who assigned it category 3, and by rater 12, who assigned it category 2. The not-presented key must have the same format as the data records. In this case: NPKY
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
0 0
The raters are nested within items in these data; i.e., any given rater scores one, and only one, response of any given examinee. The complete command file for this analysis is given below.
728
11 PARSCALE EXAMPLES
EXAMPL09.PSL - DATA FROM A STUDY OF MULTIPLE RATINGS OF PERFORMANCE EXERCISES GENERALIZED PARTIAL CREDIT MODEL: RATER EFFECTS MODEL [NESTED DESIGN] >FILE DFNAME='EXAMPL09.DAT',NFNAME='EXAMPL09.DAT',SAVE; >SAVE PARAM='EXAMPL09.PAR',SCORE='EXAMPL09.SCO'; >INPUT R-INOPT=2,NIDCHAR=4,NTOT=5,LENGTH=5,NTEST=1,NFMT=1,MRATER=10, NRATER=(2(0)5); (4A1,5(2X,A2,1X,A1,1X,A2,1X,A1)) >TEST TNAME=RATERN,ITEM=(1,2,3,4,5),NBLOCK=5; >BLOCK REPEAT=4,NIT=1,NCAT=4,ORIGINAL=('1','2','3','4'), MOD=(1,2,3,4); >BLOCK NIT=1,NCAT=3,ORIGINAL=('1','2','3'), MOD=(1,2,3); >MRATER RNAME=(R3,R4,R5,R6,R7,R8,R9,R10,R11,R12), RCODE=(' 3',' 4',' 5',' 6',' 7',' 8',' 9','10','11','12'); >CAL LOGISTIC,PARTIAL,NQPT=21,CYCLES=(50,1,1,1,1,1),NEWTON=2, CRIT=0.05,DIAG=0,ITEMFIT=10,SCALE=1.7; >SCORE EAP;
729
12 MULTILOG EXAMPLES
12 MULTILOG examples 12.1 One-parameter logistic model for a five-item binary-scored test (LSAT6) The so-called LSAT Section 6 data includes the responses of 1000 examinees to five binary items in a short section of the Law School Admission test. The data have been analyzed by Bock & Lieberman (1970), Andersen & Madsen (1977), Bock & Aitkin (1981), Thissen (1982) and others; the 1PL model with a Gaussian population distribution fits quite well. Contents of the data file are shown below. Note that a frequency of 0 was obtained for the 11th and 13th patterns. 1 2 3 4 5 6 7 8 9 10 11 12 13 ... 31 32
00000 00001 00010 00011 00100 00101 00110 00111 01000 01001 01010 01011 01100
3 6 2 11 1 1 3 4 1 8 0 16 0
11110 28 11111 298
The examples in Sections 12.2 and 12.3 fit these data with the 2PL and 3PL models. The PROBLEM command identifies the problem as RANDOM θ (requiring MML estimation) using PATTERN-count data input. The TEST command defines the test as ALL L1, specifying the 1PL model. The data, in the file exampl01.dat, have the response patterns defined by [0-1] strings, with 1 coded for a correct response. The format reads the 5A1 item responses, followed by F4.0 to read the frequency. Results are saved to an output file called exampl01.out. The first few pages of the MULTILOG output give information about the problem; those are omitted from the selected output reproduced here. The results are on the final three pages of the output and are included here. The parameters correspond to those given by Thissen (1982, p. 180). The values in parentheses adjacent to each parameter are approximate standard errors. The final page of the MULTILOG output for PATTERN input has two parts: the left section describes goodness-of-fit, and the right section characterizes the distribution of θ for each response pattern. In the goodness-of-fit section, the observed and expected frequencies are printed, as well as the standardized residuals:
[observed-expected]/ expected.
730
12 MULTILOG EXAMPLES The EAP (Expected A Posteriori) estimate of θ for each pattern is also printed, as well as the posterior standard deviation. At the bottom of the table, the likelihood ratio χ 2 goodness-of-fit statistic value is printed. The command file exampl01.mlg is shown below. EXAMPL01.MLG MML PARAMETER ESTIMATION FOR THE 1PL MODEL, LSAT6 DATA >PROBLEM RANDOM, PATTERN, NITEMS=5, NGROUP=1, NPATTERNS=32, DATA=‘EXAMPL01.DAT’; >TEST ALL, L1; >END; 2 01 11111 N (4X,5A1,F4.0)
Selected output is shown below. Parameter estimates for item 1, with standard errors in parentheses, are given. These may be used to test if a parameter is significantly different from zero (t = estimate/S.E.). ITEM
1: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 6 0.76 (0.05) B( 1) 1 -3.61 (0.29)
The next section of output provides information on the contribution of item 1 to the total information. @THETA: INFORMATION: -3.0 - -1.6 0.135 0.130 -1.4 - 0.0 0.076 0.068 0.2 - 1.6 0.029 0.025 1.8 - 3.0 0.009 0.008
(Theta 0.124 0.061 0.022 0.007
values 0.116 0.054 0.019 0.006
increase in steps of 0.109 0.100 0.092 0.048 0.043 0.037 0.016 0.014 0.012 0.005 0.004 0.004
0.2) 0.084 0.033 0.011
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 OBS. FREQ. 76 924 OBS. PROP. 0.0760 0.9240 EXP. PROP. 0.0760 0.9240
Total information and standard errors, computed as 1/ Information . TOTAL TEST INFORMATION @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 1.548 1.566 1.590 1.578 1.407 1.376 1.186 1.165
1.600 1.519 1.287 1.112
1.604 1.493 1.259 1.098
1.603 1.466 1.233 1.086
1.599 1.437 1.208
@THETA: -3.0 - -1.6 -1.4 - 0.0
POSTERIOR STANDARD DEVIATION: 0.804 0.799 0.795 0.792 0.790 0.793 0.796 0.800 0.805 0.811
0.790 0.818
0.790 0.826
0.791 0.834
1.581 1.561 1.346 1.145
1.593 1.541 1.316 1.128
731
12 MULTILOG EXAMPLES 0.2 1.8 -
1.6 3.0
0.843 0.918
MARGINAL RELIABILITY:
0.852 0.927
OBSERVED(EXPECTED)
3.0( 6.0( 2.0( 11.0( 1.0(
2.4) 5.5) 2.5) 8.2) 0.9)
0.862 0.934
0.872 0.942
0.881 0.948
0.891 0.954
0.901 0.960
0.910
0.2924 STD. RES.
: :
0.41 0.23 -0.30 0.96 0.16
: : : : :
EAP (S.D.)
-1.91 -1.43 -1.43 -0.94 -1.43
NEGATIVE TWICE THE LOGLIKELIHOOD=
( ( ( ( (
0.80) 0.80) 0.80) 0.81) 0.80)
: :
PATTERN
: : : : :
11111 11112 11121 11122 11211
21.8
12.2 Two-parameter model for the five-item test The second example of MULTILOG fits the LSAT6 data with the 2PL model. A 3PL model is fitted to the data in Section 12.1. The test is redefined as L2 (for the 2PL model) on the TEST command. The results follow, in the same format as before. The only differences are that each item has a different estimated slope (A) value, and the value of the likelihood ratio statistic indicates a very slight improvement in fit, from 21.8 for the 1PL model to 21.2 for the 2PL model. The command file exampl02.mlg for this analysis is shown below, followed by the output obtained from this run. EXAMPL02.MLG MML PARAMETER ESTIMATION FOR THE 2PL MODEL, LSAT DATA >PROBLEM RANDOM, PATTERN, NITEMS=5, NGROUP=1, NPATTERNS=32, DATA=‘EXAMPL01.DAT’; >TEST ALL, L2; >END; 2 01 11111 N (4X,5A1,F4.0)
Portions of the output are shown below. ITEM SUMMARY MML PARAMETER ESTIMATION FOR THE 2PL MODEL, LSAT DATA ITEM A B( 1)
1: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 1 0.82 (0.18) 2 -3.36 (0.62)
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12 MULTILOG EXAMPLES ITEM
2: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 3 0.72 (0.11) B( 1) 4 -1.37 (0.21)
MML PARAMETER ESTIMATION FOR THE 2PL MODEL, LSAT DATA OBSERVED(EXPECTED) 3.0( 6.0( 2.0( 11.0( 1.0( 1.0( 3.0( 4.0( 1.0( 8.0( 0.0( 16.0( 0.0( 3.0( 2.0( 15.0( 10.0( 29.0( 14.0( 81.0( 3.0( 28.0( 15.0( 80.0( 16.0( 56.0( 21.0( 173.0( 11.0( 61.0( 28.0( 298.0(
2.3) 5.9) 2.6) 8.9) 0.7) 2.6) 1.2) 6.0) 1.8) 6.4) 2.9) 13.6) 0.9) 4.4) 2.0) 13.9) 9.5) 34.6) 15.6) 76.5) 4.7) 25.0) 11.5) 83.5) 11.2) 56.1) 25.6) 173.5) 8.5) 62.5) 29.1) 296.6)
STD. RES. 0.48 0.06 -0.37 0.69 0.36 -1.00 1.67 -0.80 -0.62 0.63 -1.70 0.66 -0.92 -0.66 0.00 0.29 0.16 -0.95 -0.40 0.51 -0.78 0.60 1.04 -0.38 1.42 -0.01 -0.91 -0.04 0.88 -0.19 -0.20 0.08
: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :
EAP (S.D.) -1.90 -1.47 -1.45 -1.03 -1.33 -0.90 -0.88 -0.44 -1.43 -1.00 -0.98 -0.55 -0.86 -0.42 -0.40 0.05 -1.37 -0.94 -0.92 -0.48 -0.79 -0.35 -0.33 0.12 -0.90 -0.46 -0.44 0.01 -0.31 0.15 0.17 0.65
( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
0.80) 0.80) 0.80) 0.81) 0.80) 0.81) 0.81) 0.82) 0.80) 0.81) 0.81) 0.82) 0.81) 0.82) 0.82) 0.84) 0.80) 0.81) 0.81) 0.82) 0.81) 0.82) 0.82) 0.84) 0.81) 0.82) 0.82) 0.83) 0.82) 0.84) 0.84) 0.86)
: : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : :
PATTERN 11111 11112 11121 11122 11211 11212 11221 11222 12111 12112 12121 12122 12211 12212 12221 12222 21111 21112 21121 21122 21211 21212 21221 21222 22111 22112 22121 22122 22211 22212 22221 22222
NEGATIVE TWICE THE LOGLIKELIHOOD= 21.2 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.3 Three-parameter (and guessing) model for the five-item test The third run of MULTILOG fits the LSAT6 data with the 3PL model. The test is redefined as L3 (for the 3PL) on the TEST command. 1PL and 2PL models for these data are discussed in Sections 12.1 and 12.2. This example also illustrates the use of Bayesian priors for some of the item parameters. Specifically, the PRIORS command indicates that for all five items [ITEMS=(1,2,3,4,5)] the parameter DK=1, which is the logit of the lower asymptote, should be estimated with a Gaussian prior distribution with a mean of –1.4 and a standard deviation of 1.0. The value –1.4 is chosen for the mean because that is the logit of 0.2, and the items of LSAT6 were five-alternative multiple-choice items. The complete command file exampl03.mlg is shown below. 733
12 MULTILOG EXAMPLES EXAMPL03.MLG -MML PARAMETER ESTIMATION 3PL MODEL WITH PRIORS ON THE ASYMPTOTES, LSAT DATA >PROBLEM RANDOM, PATTERN, NITEMS=5, NGROUP=1, NPATTERNS=32, DATA=‘EXAMPL01.DAT’; >TEST ALL,L3; >PRIORS ITEMS=(1,2,3,4,5), DK=1, PARAMS=(-1.4,1.0); >END; 2 01 11111 N (4X,5A1,F4.0)
The presentation of the parameter estimates is different in this third (3PL) output from MULTILOG. MULTILOG interprets the 1PL and 2PL models as binary versions of Samejima’s (1969) graded model, giving the output form in the first two runs. The 3PL is estimated as a binary form of the multiple-choice model; so contrasts between the two slopes (correct and incorrect) and intercepts are estimated, as well as the logit of the lower asymptote. For convenience, the three parameters are transformed into the more commonly used “Traditional 3PL, normal metric” form on the first line for each item. The results indicate that there is very little information about the lower asymptote parameters for these very easy items; all of the estimated values of the lower asymptote are very near their prior expected value of 0.2. The most difficult of the five items (item 3) has an estimated asymptote of 0.18. The likelihood ratio statistic indicates that this model does not fit quite as well as the 2PL model did. That is true, although it seems odd. The Maximum Likelihood estimates (computed with no priors) for the 3PL model for these data are identical to the 2PL estimates: all of the asymptotes are estimated to be zero. The prior holds the estimates of the asymptotes near 0.2, and does not fit quite as well. It does not fit particularly worse, either; there is very little information available for estimating the lower asymptotes for these items. Selected output for this run follows (only item 3 shown here), followed by the total information, observed and expected frequencies, and value of −2 ln L :
ITEM 3: 2 NOMINAL CATEGORIES, 2 HIGH TRADITIONAL 3PL, NORMAL METRIC: A 0.70
B 0.19
C 0.18
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C D CONTRAST P(#) COEFF.[ DEV.] P(#) COEFF.[ DEV.] P(#) COEFF.[ DEV.] 1 7 1.18 (0.25) 8 -0.22 (0.33) 9 -1.54 (0.63) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.003 0.005 0.062 0.082 0.245 0.249 0.126 0.106
(Theta values increase in steps of 0.2) 0.007 0.010 0.015 0.022 0.032 0.045 0.106 0.133 0.161 0.189 0.082 0.233 0.245 0.233 0.216 0.194 0.171 0.148 0.088 0.072 0.059 0.048 0.038
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12 MULTILOG EXAMPLES
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 OBS. FREQ. 447 553 OBS. PROP. 0.4470 0.5530 EXP. PROP. 0.4469 0.5531 TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.275 1.297 -1.4 - 0.0 1.427 1.446 0.2 - 1.6 1.514 1.497 1.8 - 3.0 1.240 1.206 @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
1.355 1.500 1.400 1.124
1.373 1.514 1.360 1.104
1.391 1.522 1.318 1.087
1.409 1.522 1.278
POSTERIOR STANDARD DEVIATION: 0.885 0.878 0.871 0.865 0.859 0.837 0.832 0.826 0.821 0.816 0.813 0.817 0.824 0.834 0.845 0.898 0.911 0.923 0.933 0.943
0.853 0.813 0.858 0.952
0.848 0.811 0.871 0.959
0.843 0.811 0.885
MARGINAL RELIABILITY: OBSERVED(EXPECTED) 3.0( 6.0( 2.0( 11.0( 1.0(
2.0) 5.5) 2.5) 8.9) 0.8)
1.317 1.465 1.471 1.175
1.337 1.484 1.438 1.148
0.3084 STD. : RES. : 0.70 : 0.20 : -0.34 : 0.69 : 0.23 :
EAP (S.D.) -1.77 -1.39 -1.40 -1.00 -1.45
( ( ( ( (
0.78) 0.78) 0.79) 0.79) 0.85)
:
: : : : : :
PATTERN 11111 11112 11121 11122 11211
NEGATIVE TWICE THE LOGLIKELIHOOD= 21.5 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.4 Three-category graded logistic model for a two-item questionnaire Clogg & Goodman (1984) analyzed a set of data for two responses (six weeks apart) to a threealternative graded questionnaire item about “happiness.” Some of their data are analyzed here with ordered latent trait models. The data are in a file called exampl04.dat, there are three response codes: 1 = very happy, 2 = pretty happy, and 3 = not too happy. In this example, we fit these data with Samejima’s (1969) graded model. In the next section, we estimate the parameters of a version of Masters’ (1982) partial credit model for the same data. Another example of a graded model can be found in Section 12.7. The TEST command defines the model as “GRADED,” with 3 categories for each of the two items. The items are labeled “PRE” and “POST” on the LABELS command. The slope parameters are constrained to be equal. The long form of key entry is required for multiple-category items: each response code in the data must be assigned to a category of the model. The graded model assumes that the response corresponding to the highest value of the trait (here, happiness) has the highest value, so response 1 is placed in category 3 for both items, 2 in category 2 and 3 in category 1. 735
12 MULTILOG EXAMPLES The data file exampl04.dat and command file exampl04.mlg are shown below. 11 12 13 21 22 23 31 32 33
46 31 8 20 68 12 1 12 11
EXAMPL04-1.MLG - MML ESTIMATION, SAMEJIMA’S GRADED MODEL FOR THE 2ND-YEAR HAPPINESS DATA >PROBLEM RANDOM, PATTERNS, NITEMS=2, NGROUPS=1, NPATTERNS=9, DATA=‘EXAMPL04.DAT’; >TEST ALL, GRADED, NC=(3,3); >LABELS ITEMS=(1,2), NAMES=(‘PRE’ , ‘POST’); >EQUAL ALL, AJ; >END; 3 123 33 22 11 (2A1,F4.0)
In the MULTILOG output, the estimated parameters are printed in a format similar to those for the 1PL and 2PL models in Sections 12.1 and 12.2, except that there are two thresholds for each of the three-category items. As before, the goodness-of-fit statistics and EAP[ θ ]s are printed on the final page of the MULTILOG listing. The model fits these data satisfactorily: the likelihood ratio χ 2 statistic is 7.4 on 3 d.f., p = 0.07. Selected output is given below. ITEM
1: PRE 3 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 1 1.83 (0.19) B( 1) 2 -1.65 (0.20) B( 2) 3 0.32 (0.12) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.240 0.324 0.846 0.798 0.862 0.852 0.197 0.142
(Theta values increase in steps of 0.2) 0.426 0.542 0.660 0.765 0.837 0.864 0.744 0.707 0.700 0.727 0.798 0.830 0.796 0.702 0.587 0.469 0.361 0.270 0.101 0.072 0.050 0.035 0.025
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 OBS. FREQ. 24 100 85 OBS. PROP. 0.1148 0.4785 0.4067 EXP. PROP. 0.1165 0.4773 0.4062
736
12 MULTILOG EXAMPLES ITEM A B( 1) B( 2)
2: POST 3 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 1 1.83 (0.19) 4 -1.44 (0.18) 5 0.63 (0.14)
@THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.173 0.238 0.860 0.840 0.794 0.844 0.313 0.232
(Theta values increase in steps of 0.2) 0.321 0.423 0.538 0.656 0.761 0.833 0.789 0.727 0.680 0.664 0.840 0.732 0.860 0.829 0.753 0.646 0.528 0.413 0.168 0.120 0.085 0.060 0.042
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 OBS. FREQ. 31 111 67 OBS. PROP. 0.1483 0.5311 0.3206 EXP. PROP. 0.1478 0.5284 0.3238 ITEM 3: GRP1, N[MU: 0.00 SIGMA: 1.00] P(#);(S.E.): 7; (0.00) 8; (0.00) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.413 1.562 -1.4 - 0.0 2.706 2.639 0.2 - 1.6 2.655 2.696 1.8 - 3.0 1.511 1.374
(Theta values increase in steps of 0.2) 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
@THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
2.198 2.381 2.340 1.136
2.421 2.391 2.115 1.095
2.597 2.639 1.888 1.067
2.697 2.563 1.683
POSTERIOR STANDARD DEVIATION: 0.841 0.800 0.756 0.713 0.674 0.608 0.616 0.628 0.641 0.648 0.614 0.609 0.614 0.629 0.654 0.814 0.853 0.888 0.916 0.938
0.643 0.647 0.688 0.956
0.620 0.616 0.728 0.968
0.609 0.625 0.771
MARGINAL RELIABILITY: OBSERVED(EXPECTED) 46.0( 31.0( 8.0( 20.0( 68.0( 12.0( 1.0( 12.0( 11.0(
44.3) 36.8) 3.8) 21.5) 61.3) 16.9) 1.8) 12.3) 10.2)
1.748 2.533 2.656 1.269
1.965 2.434 2.531 1.192
0.5762 STD. : RES. : 0.25 : -0.95 : 2.15 : -0.33 : 0.85 : -1.19 : -0.62 : -0.09 : 0.25 :
EAP (S.D.) 1.09 0.32 -0.27 0.31 -0.34 -0.96 -0.22 -0.93 -1.67
( ( ( ( ( ( ( ( (
0.70) 0.64) 0.72) 0.64) 0.61) 0.64) 0.75) 0.65) 0.66)
:
: : : : : : : : : :
PATTERN 33 32 31 23 22 21 13 12 11
NEGATIVE TWICE THE LOGLIKELIHOOD= 7.4 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
737
12 MULTILOG EXAMPLES
12.5 Three-category partial credit model for the two-item questionnaire In this example, we estimate the parameters of a version of Masters’ (1982) partial credit model for these same “happiness” data considered in Section 12.4 where Samejima’s (1969) graded model was fitted. A description of the data file can also be found in that section. The model for the test items is redefined to be NOMINAL, with 3 categories for each item, and category 3 is “HIGH.” The sequence >TMATRIX ALL, AK, POLYNOMIAL; >EQUAL ALL, AK=1; >FIX ALL, AK=2, VALUE=0.0;
specifies that POLYNOMIAL contrasts are to be used for the ak parameters of the NOMINAL model, with the linear contrasts constrained to be equal for the two items and the quadratic contrasts FIXED at zero. The command >TMATRIX ALL, CK, TRIANGLE;
specifies the “TRIANGLE” contrast matrix for the ck parameters of the NOMINAL model. Thissen & Steinberg (1986) show that this parameterization of the NOMINAL model is equivalent to Master’s (1982) partial credit model; the only difference between the model as fitted here and that fitted by Masters is the inclusion here of the Gaussian population distribution. This model does not fit these data quite as well as Samejima’s (1969) graded model . With this parameterization, the parameter values printed by MULTILOG are the slope contrast, which is the slope of the trace lines relative to the unit standard deviation of the population distribution, and the c-contrasts, which are equivalent to Masters’ δ s : the points at which the successive ordered trace lines cross. A property of the partial credit model is that it is a “Raschtype” model; response patterns with the same total raw score have the same posterior distribution of θ . This means, for instance, that the response patterns that total 5 (32 and 23) have the same EAP[ θ ], 0.34, with the same standard deviation, 0.65. This property of raw-score sufficiency for θ is not obtained with the Samejima graded model, even when the slopes are constrained to be equal, as in the preceding run. It is only obtained with this model when, as here, the slopes are constrained to be equal for all items. The command file exampl05.mlg is given below, followed by selected output for item 1 only. EXAMPL05.MLG - MML ESTIMATION, PARTIAL CREDIT MODEL 2ND YEAR HAPPINESS DATA >PROBLEM RANDOM, PATTERNS, NITEMS=2, NGROUP=1, NPATTERNS=9, DATA=‘EXAMPL04.DAT’; >TEST ALL, NOMINAL, NC=(3,3), HIGH=(3,3); >LABELS ITEMS=(1,2), NAMES=(‘PRE’ , ‘POST’); >TMATRIX ALL, AK, POLYNOMIAL; >EQUAL ALL, AK=1; >FIX ALL, AK=2, VALUE=0.0; >TMATRIX ALL, CK, TRIANGLE; >END; 3
738
12 MULTILOG EXAMPLES 123 33 22 11 (2A1,F4.0) ITEM 1: PRE 3 NOMINAL CATEGORIES, CATEGORY(K): 1 2 3 A(K) -1.64 0.00 1.64 C(K) 0.00 2.66 2.19
3 HIGH
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[POLY.] P(#) COEFF.[ TRI.] 1 1 1.64 (0.19) 2 -2.66 (0.40) 2 6 0.00 (0.00) 3 0.47 (0.21) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.233 0.304 0.815 0.818 0.802 0.761 0.193 0.145
(Theta values increase in steps of 0.2) 0.387 0.480 0.576 0.667 0.741 0.792 0.809 0.799 0.798 0.805 0.818 0.818 0.693 0.607 0.511 0.416 0.329 0.255 0.107 0.079 0.058 0.042 0.030
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 OBS. FREQ. 24 100 85 OBS. PROP. 0.1148 0.4785 0.4067 EXP. PROP. 0.1153 0.4770 0.4077 TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.409 1.536 -1.4 - 0.0 2.586 2.604 0.2 - 1.6 2.583 2.547 1.8 - 3.0 1.493 1.375 @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
2.051 2.544 2.166 1.154
2.236 2.545 1.983 1.113
2.398 2.604 1.802 1.082
2.518 2.582 1.636
POSTERIOR STANDARD DEVIATION: 0.843 0.807 0.770 0.733 0.698 0.622 0.620 0.621 0.625 0.627 0.622 0.627 0.637 0.655 0.680 0.818 0.853 0.883 0.909 0.931
0.669 0.627 0.710 0.948
0.646 0.620 0.745 0.961
0.630 0.622 0.782
MARGINAL RELIABILITY: OBSERVED(EXPECTED) 46.0( 31.0( 8.0( 20.0( 68.0( 12.0( 1.0( 12.0( 11.0(
43.5) 38.0) 3.7) 22.1) 60.4) 17.1) 1.5) 12.3) 10.3)
1.688 2.589 2.463 1.282
1.864 2.563 2.332 1.209
0.5700 STD. : RES. : 0.38 : -1.14 : 2.24 : -0.45 : 0.97 : -1.24 : -0.44 : -0.08 : 0.23 :
EAP (S.D.) 1.08 0.34 -0.33 0.34 -0.33 -0.97 -0.33 -0.97 -1.65
NEGATIVE TWICE THE LOGLIKELIHOOD=
( ( ( ( ( ( ( ( (
0.71) 0.65) 0.63) 0.65) 0.63) 0.63) 0.63) 0.63) 0.66)
:
: : : : : : : : : :
PATTERN 33 32 31 23 22 21 13 12 11
8.4
(CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
739
12 MULTILOG EXAMPLES
12.6 Four-category graded model for a two-item interview scale Klassen & O’Connor (1989) conducted a prospective study of predictors of violence in adult male mental health admissions. One combination of possible predictors of subsequent violence involved data readily available in mental health center records: The number of prior (inpatient) admissions and age at the first such admission. Both a large number of previous admissions and a young age at first admission are considered possible predictors of subsequent violence, presumably because they both reflect more serious psychopathology. In acquiring the interview data, Klassen & O’Connor (1989) divided both age at first admission and number of prior admissions into four ordered categories. The two variables do not really appear to be test items. But they are related to each other, in an obvious sort of way: Those whose first admission was at a relatively young age tend to have had more previous admissions [ χ 2 (9) = 16.4, p = 0.05 for independence]. From the point of view of item response theory, the fact that these two “items” are not independent is explained by their common relationship to an underlying variable: the “long-term nature” or “seriousness” of the mental health problems for which the person is being admitted. From the point of view of the researchers attempting to predict subsequent behavior, estimates of individual values on that underlying continuum may be more useful than either of the two observed variables alone. Thissen (1991) describes fitting these data with Samejima’s (1969) graded model, and the consequences for estimating individual scores. This example illustrates the use of MULTILOG for this purpose. The data are given in the file exampl06.dat. Additional graded models for the happiness data are discussed in Section 12.4 and 12.7. The command file used for this analysis is exampl06.mlg: EXAMPL06.MLG -ADMISSIONS DATA UNCONSTRAINED GRADED MODEL >PROBLEM RANDOM, PATTERNS, NITEMS=2, NGROUPS=1, NPATTERNS=16, DATA=‘EXAMPL06.DAT’; >TEST ALL, GRADED, NC=(4,4); >END; 4 0123 11 22 33 44 (1X,2A1,F5.0)
Selected output is given below. ITEM A B(1) B(2) B(3)
1: 4 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 1 0.87 (0.16) 2 -1.95 (0.37) 3 -0.19 (0.17) 4 2.57 (0.48)
740
12 MULTILOG EXAMPLES MARGINAL RELIABILITY: 0.3359 ADMISSIONS DATA, UNCONSTRAINED GRADED MODEL OBSERVED(EXPECTED) 28.0( 15.0( 8.0( 5.0( 35.0( 23.0( 12.0( 15.0( 43.0( 35.0( 19.0( 29.0( 6.0( 14.0( 6.0( 11.0(
28.0) 15.1) 6.2) 6.7) 35.3) 24.7) 11.5) 13.4) 40.0) 36.9) 20.5) 28.5) 8.9) 10.1) 6.6) 11.4)
STD. RES. 0.00 -0.03 0.70 -0.65 -0.04 -0.34 0.15 0.43 0.47 -0.32 -0.33 0.09 -0.98 1.23 -0.24 -0.13
: : : : : : : : : : : : : : : : : :
EAP (S.D.) -1.01 -0.46 -0.17 0.08 -0.64 -0.17 0.10 0.36 -0.23 0.20 0.49 0.79 0.10 0.54 0.85 1.24
( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
0.86) 0.81) 0.84) 0.90) 0.81) 0.77) 0.79) 0.84) 0.83) 0.78) 0.78) 0.83) 0.89) 0.82) 0.81) 0.86)
: : : : : : : : : : : : : : : : : :
PATTERN 11 12 13 14 21 22 23 24 31 32 33 34 41 42 43 44
NEGATIVE TWICE THE LOGLIKELIHOOD= 4.2 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.7 A graded model analysis of item-wording effect on responses to an opinion survey In this example, we illustrate the use of MULTILOG with data from an experiment conducted during the 1974 General Social Survey. The data involve two questions. The first question (in form A) was, “In general, do you think the courts in this area deal too harshly or not harshly enough with criminals?”; the responses used here (with their codes) are “Courts too harsh” (1), “About right” (2), and “Not harsh enough” (3). The second question produced a classification of the respondents into the three categories “Liberal” (1), “Moderate” (2), and “Conservative” (3). The first question was asked in different wordings on two forms. The first wording is given above; the second wording (used on form B) was “In general, do you think the courts in this area deal too harshly or not harshly enough with criminals, or don’t you have enough information about the courts to say?” The two forms were randomly assigned to the respondents to the survey. The point of the split-ballot experiment was to determine the effect of the explicitly offered “don’t know” alternative in form B. About 7% of the group one (form A) respondents said they “didn’t know,” and about 29% of the group two (form B) respondents said they “didn’t know.” Thus, as expected, explicit provision of “don’t know” as an alternative increased the probability of that response. Here, we consider only the data from the respondents who chose one of the three (coded) substantive alternatives listed above. Setting aside the people (differing numbers in the two groups) who said they “didn’t know,” we consider the hypothesis that the structure of the responses to the two questions is the same for both wordings. To do this, we hypothesize that a single underlying latent variable (in this case, political liberalism-conservativism) accounts for the observed covariances between the responses to the two questions. We fit the data with Samejima’s (1969)
741
12 MULTILOG EXAMPLES graded item response model, and consider the goodness-of-fit, the trace lines, and the consequences of the model for inferences about the political attitudes of the respondents. The data are in the file exampl07.dat. Contents of the command file exampl07.mlg are shown below. The command lines entered here indicate that the problem is one involving RANDOM (MML) item parameter estimation, using response-PATTERN data, for 2 items, and 2 groups. The GRADED model is used, with 3 response categories for each item. EXAMPL07.MLG - ITEM 2: LIB,MOD,CONS; ITEM 1: COURTS HARSH--NOT; 2 FORMS >PROBLEM RANDOM, PATTERN, NITEMS=2, NGROUP=2, NPATTERN=18, DATA=’EXAMPL07.DAT’; >TEST ALL, GRADED, NCATS=(3,3); >END; 3 123 11 22 33 (I1,1X,2A1,F4.0)
The data file is shown below. The first column contains 1 for form A, and 2 for form B. Columns 3 and 4 contain codes (1, 2, and 3) for the responses to the two items. The frequencies for each response pattern for each group are in columns 6-8. This example illustrates MULTILOG’s use of numbers from 1 to the number of groups (in this case, 2) to denote group membership. When there is only one group, no group number is read in the data. 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
11 12 13 21 22 23 31 32 33 11 12 13 21 22 23 31 32 33
16 16 5 24 29 13 122 224 185 21 7 3 16 11 11 112 152 126
Annotated output is given below. On the first page of the output, MULTILOG reports on the state of its internal control codes. This information is used mostly for trouble-shooting.
742
12 MULTILOG EXAMPLES
EXAMPL07.MLG - ITEM 2: LIB,MOD,CONS; ITEM 1: COURTS HARSH--NOT 2 FORMS DATA PARAMETERS: NUMBER OF LINES IN THE DATA FILE: 18 NUMBER OF CATEGORICAL-RESPONSE ITEMS: 2 NUMBER OF CONTINUOUS-RESPONSE ITEMS, AND/OR GROUPS: 2 TOTAL NUMBER OF ‘ITEMS’ (INCLUDING GROUPS): 4 NUMBER OF CHARACTERS IN ID FIELDS: 0 MAXIMUM NUMBER OF RESPONSE-CODES FOR ANY ITEM: 3 THE MISSING VALUE CODE FOR CONTINUOUS DATA: RESPONSE-PATTERN FREQUENCIES WILL BE READ
9.0000
THE DATA WILL BE STORED IN MEMORY ESTIMATION PARAMETERS: THE ITEMS WILL BE CALIBRATED-BY MARGINAL MAXIMUM LIKELIHOOD ESTIMATION MAXIMUM NUMBER OF EM CYCLES PERMITTED: 25 NUMBER OF PARAMETER-SEGMENTS USED IS: 1 NUMBER OF FREE PARAMETERS IS: 7 MAXIMUM NUMBER OF M-STEP ITERATIONS IS 4 TIMES THE NUMBER OF PARAMETERS IN THE SEGMENT THE M-STEP CONVERGENCE CRITERION IS: 0.000100 THE EM-CYCLE CONVERGENCE CRITERION IS: 0.001000 THE RK CONTROL PARAMETER (FOR THE M-STEPS) IS: 0.9000 THE RM CONTROL PARAMETER (FOR THE M-STEPS) IS: 1.0000 THE MAXIMUM ACCELERATION PERMITTED IS: 0.0000 THETA-GROUP LOCATIONS WILL REMAIN UNCHANGED IN-CORE CATEGORICAL DATA STORAGE AVAILABLE FOR N= 10000,
10000 WORDS.
QUADRATURE POINTS FOR MML, AT THETA: -4.500 -3.500 -2.500 -1.500 -0.500 0.500 1.500 2.500 3.500 4.500
The key and format for the data, and the values for the first observation are printed to help determine that the data have been read properly. The values printed next to NORML are the internal representation of group membership: 0 means “in group 1” and 9 means “not in group 2.” The value printed for WT/CR is the frequency (weight). Below, we note that the MML estimation algorithm has essentially converged; since the maximum change between estimation cycles for any parameter is less than 0.004.
743
12 MULTILOG EXAMPLES ITEM 2: LIB, MOD, CONS; ITEM 1:COURTS HARSH-NOT HARSH; TWO FORMS READING DATA... KEYCODE CATEGORY 11 22 33 FORMAT FOR DATA(I1,1X,2A1,F4.0) FIRST OBSERVATION AS READITEMS 11 NORML WT/CR
0.000 16.00
9.000
18 WORDS USED OUT OF 294 WORDS USED OUT OF
10000 AVAILABLE FOR RESPONSE PATTTERNS 40000 AVAILABLE FOR TABLES
FINISHED CYCLE 25 MAXIMUM INTERCYCLE PARAMETER CHANGE=
0.00367 P(
6)
The Maximum Likelihood estimates of the item parameters are printed here: one value for the slope, (A), and two thresholds (B) for each item. ITEM 2: LIB, MOD, CONS; ITEM 1:COURTS HARSH-NOT HARSH; TWO FORMS ITEM A B(1) B(2)
1: 3 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 1 1.08 (0.13) 2 -2.86 (0.34) 3 -1.78 (0.20)
@THETA: -2.0 -1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
I(THETA): 0.34 0.30
0.25
0.19
0.13
0.08
0.05
0.03
0.02
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 OBS. FREQ. 68 104 921 OBS. PROP. 0.06 0.10 0.84 GROUP 1: EXP. PROP. 0.06
0.09
0.85
GROUP 2: EXP. PROP. 0.07
0.10
0.83
744
12 MULTILOG EXAMPLES
ITEM A B(1) B(2)
2: 3 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 4 1.12 (0.10) 5 -0.93 (0.11) 6 0.97 (0.11)
@THETA: -2.0 -1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
I(THETA): 0.22 0.29
0.33
0.35
0.35
0.35
0.34
0.29
0.23
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 OBS. FREQ. 311 439 343 OBS. PROP. 0.28 0.40 0.31 GROUP 1: EXP. PROP. 0.27
0.40
0.33
GROUP 2: EXP. PROP. 0.30
0.40
0.29
Beneath the parameter estimates for each item, MULTILOG prints the information I[ θ ] for that item at nine values of θ from –2 to 2, and the observed and expected frequencies for each response alternative. ITEM 3: GRP1, N[MU: 0.16 SIGMA: 1.00] P(#);(S.E.): 7; (0.05) 1996; (0.00) @THETA: -2.0 -1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
I(THETA): 1.00 1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
ITEM 4: GRP2, N[MU: 0.00 SIGMA: 1.00] P(#);(S.E.): 8; (0.00) 1995; (0.00) @THETA: -2.0 -1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
I(THETA): 1.00 1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
TOTAL TEST INFORMATION FOR GROUP 1: @THETA: -2.0 I(THETA): 1.6 SE(THETA):0.80
-1.5 1.6 0.79
-1.0 1.6 0.79
-0.5 1.5 0.81
0.0 1.5 0.82
0.5 1.4 0.83
1.0 1.4 0.85
1.5 1.3 0.87
2.0 1.2 0.89
MARGINAL RELIABILITY: 0.3126
745
12 MULTILOG EXAMPLES TOTAL TEST INFORMATION FOR GROUP 2: @THETA: -2.0 I(THETA): 1.6 SE(THETA): 0.80
-1.5 1.6 0.79
-1.0 1.6 0.79
-0.5 1.5 0.81
0.0 1.5 0.82
0.5 1.4 0.83
1.0 1.4 0.85
1.5 1.3 0.87
2.0 1.2 0.89
MARGINAL RELIABILITY: 0.3196
In this case, the population distributions of the two groups are assumed to be normal. MULTILOG prints the estimated or fixed means (MU) and the standard deviations. It also prints the total test information I[ θ ] for each group, its inverse square root SE[ θ ], and the marginal reliability. ITEM 2: LIB, MOD, CONS; ITEM 1:COURTS HARSH-NOT HARSH; TWO FORMS GROUP 1 OBSERVED(EXPECTED) 16.0( 16.0( 5.0( 24.0( 29.0( 13.0( 122.0( 224.0( 185.0(
17.3) 13.7) 6.1) 23.8) 22.8) 11.2) 131.6) 217.8) 189.6)
STD. RES. -0.31 0.62 -0.46 0.04 1.30 0.54 -0.84 0.42 -0.34
: : : : : : : : : : :
EAP (S.D.) -1.21 -0.55 -0.04 -0.97 -0.40 0.09 -0.33 0.22 0.84
( ( ( ( ( ( ( ( (
0.84) 0.81) 0.88) 0.79) 0.77) 0.84) 0.84) 0.80) 0.87)
: : : : : : : : : : :
PATTERN 11 12 13 21 22 23 31 32 33
NEGATIVE TWICE THE LOGLIKELIHOOD= 3.5 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) ITEM 2: LIB, MOD, CONS; ITEM 1:COURTS HARSH-NOT HARSH; TWO FORMS GROUP 2 OBSERVED(EXPECTED) 21.0( 7.0( 3.0( 16.0( 11.0( 11.0( 112.0( 152.0( 126.0(
15.6) 11.1) 4.6) 20.6) 18.0) 8.2) 102.9) 155.5) 122.4)
STD. RES. 1.36 -1.23 -0.74 -1.02 -1.65 0.99 0.90 -0.28 0.32
: : : : : : : : : : :
EAP (S.D.) -1.32 -0.66 -0.17 -1.07 -0.50 -0.03 -0.44 0.11 0.72
NEGATIVE TWICE THE LOGLIKELIHOOD=
( ( ( ( ( ( ( ( (
0.84) 0.81) 0.89) 0.78) 0.77) 0.84) 0.84) 0.80) 0.86)
: : : : : : : : : : :
PATTERN 11 12 13 21 22 23 31 32 33
10.2
(CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) TOTAL, NEGATIVE TWICE THE LOGLIKELIHOOD, ALL GROUPS=
13.7
These tables summarize the goodness-of-fit of the model to the data. Observed and expected values are printed for each response pattern in each group, as well as the standardized residual 746
12 MULTILOG EXAMPLES which may be taken to be approximately normally distributed with mean zero and variance one for diagnostic purposes. The χ 2 statistics indicate that the model-fit is satisfactory (on 18 – 2 [group totals] – 7 [parameters fitted] = 9 degrees of freedom). The tables also include EAP[ θ ] for each response pattern, and the corresponding standard deviation. Figure 12.1 shows the trace lines computed from the item parameters in the MULTILOG output. We note that the “Liberal-Conservative” question divides the respondents approximately equally into three “centered” groups, while the question on the courts has trace lines crossing each other far on the left. Only the most liberal respondents consider the courts sufficiently harsh. As expected, the questions are strongly related. For further discussion of part of these data, see Thissen & Steinberg (1988). A scoring run on these data is discussed in the next section.
747
12 MULTILOG EXAMPLES
Figure 12.1: Trace lines for first two items
12.8 Graded-model scores for individual respondents Having concluded that the model fits the data satisfactorily, we set up a “scoring run” in which we compute MAP[ θ ] for each response pattern, as though each line of the input data file represented an individual observation. This sequence of events represents the normal use of MULTILOG: the item analysis and individual scoring are done in two separate runs of the computer program. Frequently, several (sometimes even many) item analysis runs are performed before a satisfactory model is selected. Only after this is accomplished does it make sense to compute estimates of θ for each respondent. To set up a scoring run for the data described in the previous section, the syntax in courtab2.mlg is used. The command lines entered here indicate that the problem is one involving the calculation of SCORE for INDIVIDUAL data, for 2 items, and 2 groups. The GRADED model is used, with 3 response categories for each item. The START command is used to enter the item parameters from the previous run. The parameters are entered in the order that they are printed, following a usersupplied format. Usually, these parameters are read from a file previously saved by MULTILOG. Note that the user should provide information about the key and data format. In the data format, the first 2A1 refers to the NCHARS=2 characters of ID information; in this case, that reads the response pattern as the label. EXAMPL08.MLG - SCORE LIBERAL CONSERVATIVE/COURTS HARSH PARAMETER VALUES IN COMMAND FILE >PROBLEM SCORE, INDIVIDUAL, NITEMS=2, NGROUP=2, NEXAMINEES=18, NCHARS=2, DATA=‘EXAMPL07.DAT’; >TEST ALL, GRADED, NCATS=(3,3); >START ALL;
748
12 MULTILOG EXAMPLES 1.08 -2.86 -1.78 1.12 -0.93 0.97 -1.00 0.16 1.00 -1.00 0.00 1.00 >END; 3 123 11 22 33 (2X,2A1,T1,I1,1X,2A1)
The numbers in the column marked THETAHAT in the output file obtained for this analysis are the values of MAP[ θ ] for each response pattern; the response patterns are used as the ID fields on the right. The estimated standard errors are tabulated, as well as the number of iterations required by the Newton-Raphson algorithm. Comparing these values for each response pattern to the corresponding EAPs, we note very little difference. THETAHAT -1.229 -0.561 -0.028 -1.003 -0.430 0.072 -0.375 0.192 0.826 -1.339 -0.665 -0.153 -1.098 -0.522 -0.041 -0.484 0.092 0.710
S.E. 0.830 0.804 0.883 0.771 0.762 0.841 0.828 0.794 0.853 0.829 0.804 0.886 0.767 0.759 0.840 0.822 0.791 0.852
ITER 6 2 2 4 4 4 4 4 4 6 2 3 4 3 3 4 4 3
ID FIELD 11 12 13 21 22 23 31 32 33 11 12 13 21 22 23 31 32 33
12.9 Five-category ratings of audiogenic seizures in mice in four experimental conditions Bock (1975, pp. 512-547) describes the graded model, a model for ordered categorical data, in detail and includes an application to a set of behavioral data. The data are in a file called exampl09.dat, which contains the following four lines: 1 7 0 6 0 2 3 10
0 0 0 2
2 11 6 10 5 11 0 2
Each of the four lines of data represents one of four groups of mice; each group of mice represents a cell of a 2 x 2 experimental design. The response variable is a classification of the mice in each group according to the ordered severity of audiogenic seizures they exhibit; the columncategories are “crouching,” “wild running,” “clonic seizures,” “tonic seizures,” and “death.”
749
12 MULTILOG EXAMPLES Bock (1975) uses a model for responses in ordered categories, formally identical to Samejima’s graded item response model, to relate the categorical response to effects of the experimental conditions. This example reproduces the estimates for Bock’s “main class and interaction” model. The algorithm used in MULTILOG is very different from that described by Bock, and requires different treatment of the required constraints of location. Bock’s system constrains the group means (called µ there, and θ here) to total zero; he estimates three contrasts among the four group values. That is impossible in MULTILOG, so all four group locations ( θ ) are estimated, and one of the thresholds, called BK=4, is fixed at the value Bock obtains (0.4756). With this constraint, the results obtained with MULTILOG match those printed in the original source. Of course, in real data analysis, one would not have such a value and one of the thresholds would be fixed at some arbitrary value, like zero. If BK=4 had been fixed at zero in the current example, all of the values of θ and the other three thresholds would have been shifted 0.4756 from the values in the text. The command file illustrates user input for FIXED- θ analysis, with data in the form of the table given above. In this case, there is a single item, and the rows of the table are the groups, so NGROUP=4. The TGROUPS command specifies four starting values (1,1,1,–1) for the four values of θ , one for each group. These values must be entered manually. The slope is fixed at a value of 1 and BK=4 is fixed at 0.4756. The contents of the command file exampl09.mlg are given below. To see how to generate this command file using the syntax wizard, please see Section 4.3.3. EXAMPL09.MLG -AUDIOGENEIC SEIZURES IN MICE; BOCK, P. 512FF >PROBLEM FIXED, TABLE, NITEM=1, NGROUP=4, DATA=‘EXAMPL09.DAT’; >TEST ALL, GRADED, NC=5; >TGROUPS NUMBER=4, MIDDLES=(1,1,1,-1); >FIX ITEMS=1, AJ, VALUE=1.0; >FIX ITEMS=1, BK=4, VALUE=0.4756; >END; (5F3.0)
In the MULTILOG output, the values of the thresholds, corresponding to those tabulated on p. 547 by Bock, are printed as the values of B(K) in the item summary; their estimated standard errors differ slightly from those in the original analysis, because MULTILOG uses a somewhat less precise algorithm for computing estimated standard errors. For each θ -group, the estimated value of θ is printed before the word DATA, e.g. 0.32 for group 1. Those four values correspond to the four µs on page 546 in Bock (1975). The remainder of the table gives the observed and expected counts (proportions and probabilities in parentheses) for each cell in the 4 x 5 table, and the values of the likelihood ratio and Pearson goodness-of-fit statistics. A selection of the output follows.
750
12 MULTILOG EXAMPLES ITEM SUMMARY AUDIOGENEIC SEIZURES IN MICE; BOCK, P. 512FF ITEM A B(1) B(2) B(3) B(4)
1: 5 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 7 1.00 (0.00) 1 -3.40 (0.53) 2 -0.51 (0.21) 3 -0.37 (0.22) 8 0.48 (0.00)
ITEM 1 TH-GROUP 0.32 DATA EXPECTED
CATEGORY 1 2 1.0(0.05) 7.0(0.33) 0.5(0.02) 5.9(0.28)
3 0.0(0.00) 0.7(0.03)
4 5 2.0(0.10) 11.0(0.52) 4.3(0.20) 9.7(0.46)
0.41 DATA EXPECTED
0.0(0.00) 0.5(0.02)
6.0(0.27) 5.8(0.26)
0.0(0.00) 0.7(0.03)
6.0(0.27) 10.0(0.45) 4.4(0.20) 10.6(0.48)
1.07 DATA EXPECTED
0.0(0.00) 0.2(0.01)
2.0(0.11) 2.9(0.16)
0.0(0.00) 0.4(0.02)
5.0(0.28) 11.0(0.61) 2.9(0.16) 11.6(0.64)
-1.80 DATA EXPECTED
3.0(0.18) 10.0(0.59) 2.8(0.17) 10.5(0.62)
PEARSON CHI-SQUARE=
15.078
2.0(0.12) 0.4(0.02)
L.R.CHI-SQUARE=
0.0(0.00) 1.7(0.10)
2.0(0.12) 1.6(0.09)
15.885
TOTAL TEST CHI-SQUARES PEARSON CHI-SQUARE= D.F.=
15.078; LIKELIHOOD RATIO CHI-SQUARE=
15.885
WARNING-
9 (IF TEST ALL CATEGORICAL AND THERE ARE NO EMPTY TH-GROUPS) 7 OF THE CELLS INCLUDED IN THESE CHI-SQUARES HAVE EXPECTED VALUES LESS THAN ONE; THE STATISTICS MAY BE MEANINGLESS.
12.10 A nominal model for responses to multiple-choice alternatives The multiple-choice model (Thissen & Steinberg, 1984) includes a separate trace line for each alternative response—the key and all of the distractors—on a multiple-choice item. The model is a development of suggestions by Bock (1972) and Samejima (1979). For this reason, it is referred to as the “BS” model. The procedures involved differ from those used when the responses on multiple-choice items are made binary by scoring correct or incorrect before the item analysis. The data are more complex: for 4 four-alternative multiple-choice items, there are 44 = 256 possible response patterns; if the data are made binary, there are only 24 = 16 response patterns. The model is more complex: the multiple-choice model has eleven free parameters for each fouralternative item, while the 3PL model has only three. The model, its estimation, and its interpretation are described by Thissen & Steinberg (1984) and Thissen, Steinberg & Fitzpatrick (1989). The interested reader is referred to those sources.
751
12 MULTILOG EXAMPLES The first 12 lines of the data file exampl10.dat are shown below. 1 2 1 1 1 2 1 2 2 1 2 5
1111 1113 1121 1133 1134 1143 1144 1222 1232 1233 1242 1243
This example shows the MULTILOG output for an item parameter estimation problem. The data are the responses of 976 examinees to 4 four-alternative multiple-choice vocabulary items. The model is fitted with constraints described by Thissen & Steinberg (1984) as the “ABCD(C), ABCD(D)” model. In addition to the constraints on ak giving the “ABCD(C), ABCD(D)” model, two of the relatively ill-determined c-contrasts are fixed at zero, increasing the precision of estimation of the entire model, without damaging the fit. On the PROBLEM command, the four-choice items are defined as having five response categories [NC=(5(0)4)], because the Multiple-choice (“BS”) model appends an additional latent response category to each item. This category is denoted DK (for “Don’t Know”) by Thissen & Steinberg (1984), and must be category 1 in MULTILOG; i.e. the “real” responses are keyed into categories 2, 3, 4, and 5. The correct answers for these four items are [D,C,C,D], so HIGH = (5,4,4,5). The EQUAL commands impose the constraint that the proportions of the “DK” curve distributed into each of the observed response categories are the same within the pairs of items with the same keyed correct response. The two c-contrasts are fixed at zero, because we have found since the publication of the original paper that the model is better-conditioned with the addition of such constraints, and there is no apparent damage to the fit. As a matter of fact, this run produces a better fit than that reported for the slightly less constrained ABCD(C), ABCD(D) model in the original paper, because this version of MULTILOG appears to converge somewhat more completely than the version of MULTILOG (4.0) that provided the findings reported in the paper. The two parameters fixed at zero had estimated standard errors several times larger than their absolute values when they were estimated. Because of the substantial error covariances among the parameters of this model, their estimation induced large standard error estimates in several of the other parameters. Fixing the two ck s produces much more stable results. The parameter estimates are printed in the following selection of the MULTILOG output, both in contrast form and as the ak s , ck s and d k s . The goodness-of fit statistics and EAP[ θ ]s for each observed pattern are printed on the final pages of the output. The more heavily constrained model also runs faster in MULTILOG. In general, poorly identified models require much more computing time than more highly constrained models.
752
12 MULTILOG EXAMPLES The parameterization
The relationship among the unconstrained (or contrast) parameters estimated by MULTILOG and the constrained parameters of the model is fairly complex; here we provide illustrations, based on item 1 of the example. The model is
P( x = k ) =
exp[akθ + ck ] + d k [a1θ + c1 ]
∑
m +1
i =1
exp[aiθ + ci ]
in which k = 2, 3, 4, 5 represent responses A, B, C, and D, respectively, for this multiple-choice item. Response category 1 is used (internally) by MULTILOG to represent the latent “Don’t know” category. The slope parameters ak , in the vector a , are computed as a ' = α 'Tα , where α contains the unconstrained parameters estimated by MULTILOG. For item 1, this is (with the vectors transposed to fit on the page):
−2.98 ' −2.03 −0.20 0.80 −0.20 −0.20 −5.01 6.89 −0.20 −0.20 0.80 −0.20 3.91 = 2.33 −0.20 −0.20 −0.20 0.80 −0.66 7.73 −0.20 −0.20 −0.20 −0.20 4.75 '
−0.20 −0.20 −0.20 0.80
The estimates of the parameters ak , in the vector a , are printed in the row marked A(K) in the MULTILOG output, and the estimates of the (unconstrained) parameters in the vector α are printed in the column marked CONTRAST COEFFICIENTS FOR A. Using the (default) deviation contrasts in T , there is a fairly straightforward scalar interpretation of the parameters:
a1 = –2.98 = –0.20 [–2.03 + 6.89 + 2.33 + 7.73 ] and
a2 = –2.98 + (–2.03) = –5.01, a3 = –2.98 + 6.89 = 3.91, a4 = –2.98 + 2.33 = –0.66,
753
12 MULTILOG EXAMPLES and
a5 = –2.98 + 7.73 = 4.75, where α ' = [–2.03 6.89 2.33 7.73 ] contains the parameters estimated by MULTILOG. This has a direct bearing on the imposition of equality constraints using the MULTILOG commandlanguage. If, for instance, one wanted to constrain a3 and a5 to be equal, one would enter the command >EQUAL ITEMS=1, WITH=1, AK=(2,4);
because this would set the second and fourth contrasts among the as equal (they are currently estimated as 6.89 and 7.73); the consequence of this would be that a3 and a5 would be equal. Any constraints involving a1 are different: To constrain a1 and a2 to be equal, for instance, one would enter the command >FIX ITEMS=1, AK=1, VA=0.0;
which would have the effect of fixing the first contrast among the as (currently estimated to be – 2.03) at a value of zero. If that is true, a1 = a2 . The computation of the cs is parallel in all respects to that for the as. Note that, in the example as printed, the command >FIX ITEMS=1, CK=3, VALUE=0.0;
has the effect of imposing the constraint that c1 = c4 = −0.74.
The use of different T -matrices (Polynomial or Triangle) changes the relationship between the unconstrained parameters estimated by MULTILOG and the as. However, MULTILOG commands to FIX or EQUAL parameters always refer to the unconstrained contrast parameters, and algebraic manipulation similar to that described here is necessary to obtain any desired constraints on the as or cs themselves. The relationship between the ds and the unconstrained parameters estimated by MULTILOG is somewhat more complex, because the parameters represented by d k are proportions (representing the proportion of those who “don’t know” who respond in each category on a multiplechoice item; see Thissen & Steinberg, 1984). Therefore, the constraint that ∑ d k = 1 is required. This is enforced by estimating d k such that dk =
exp[d k* ] ∑ exp[d k* ]
754
12 MULTILOG EXAMPLES and
d*' = δ'Td . The elements of the vector δ are the parameters estimated by MULTILOG, and printed in the column marked CONTRAST COEFFICIENTS FOR D; in the case of item 1 of this example, δ ' = [0.76 –0.13 1.28].
These values are used (internally) by MULTILOG to compute the values of d*k . In this case, they are:
−0.47 ' 0.76 −0.25 0.75 −0.25 −0.25 0.28 = −0.13 −0.25 −0.25 0.75 −0.25 −0.61 1.28 −0.25 −0.25 −0.25 0.75 0.80 '
Then
∑ exp[d
* k
] = exp[−0.47] + exp[0.28] + exp[−0.61] + exp[0.80] = 0.625 + 1.323 + 0.543 + 2.226 = 4.717.
So
d1 =
d3 =
0.625 1.323 = 0.13 d 2 = = 0.28 4.717 4.717 , ,
0.543 2.226 = 0.12 and d 4 = = 0.47. 4.717 4.717 ,
The four proportions [0.13, 0.28, 0.12, 0.47] are printed as D(K) in the MULTILOG output, in columns 2, 3, 4 and 5 because those columns represent the parameters for the observed item responses. The example illustrates the imposition of equality constraints on the ds between items. To impose equality constraints on the ds within an item, the procedure is parallel to that described previously for imposing within-item equality constraints on as and cs. For instance, to impose the constraint that d 2 and d3 should be equal, one would enter the command >EQUAL ITEMS=1, WITH=1, DK=(1,2);
755
12 MULTILOG EXAMPLES because this would set the first and second contrasts among the ds equal (they are currently estimated as 0.76 and –0.13). The consequence of this would be that d 2 and d3 would be equal. Any constraints involving d1 are different: To constrain d1 and d3 to be equal, for instance, one would enter the command >FIX ITEMS=1, DK=2, VALUE=0.0;
which would have the effect of fixing the second contrast among the ds (currently estimated to be –0.13) at a value of zero. If that is true, then d1 = d3 . The command file exampl10.mlg is shown below. Another example of the fitting of a multiplechoice model is given in Section 12.11. EXAMPL10.MLG ABCD(C) ABCD(D) WITH TWO C(K)S FIXED AT ZERO >PROBLEM RANDOM, PATTERNS, NITEMS=4, NGROUP=1, NPATTERNS=156, DATA=‘EXAMPL10.DAT’; >TEST ALL, BS, NC=(5(0)4), HIGH=(5,4,4,5); >EQUAL ITEMS=(1,4), DK=(1,2,3); >EQUAL ITEMS=(2,3), DK=(1,2,3); >FIX ITEMS=1, CK=3, VALUE=0.0; >FIX ITEMS=2, CK=2, VALUE=0.0; >SAVE; >ESTIMATE NC=25; >TGROUPS NUMBERS=10, QP=(-4.5(1.00)4.5); >END; 4 1234 2222 3333 4444 5555 (10X,4A1,T3,F4.0)
Selected output follows. ABCD(C) ABCD(D) WITH TWO C(K)S FIXED AT ZERO ITEM 1: 5 NOMINAL CATEGORIES, 5 HIGH CATEGORY(K): 1 2 3 4 5 A(K) -2.98 -5.01 3.91 -0.66 4.75 C(K) -0.74 -5.88 3.80 -0.74 3.56 D(K) 0.13 0.28 0.12 0.47 CONTRAST-COEFFICIENTS (STANDARD FOR: A C CONTRAST P(#) COEFF.[ DEV.] P(#) COEFF.[ 1 1 -2.03 (1.00) 5 -5.15 2 2 6.89 (3.45) 6 4.53 3 3 2.33 (0.99) 37 0.00 4 4 7.73 (3.46) 7 4.29
ERRORS) D DEV.] P(#) COEFF.[ (2.02) 8 0.76 (1.68) 9 -0.13 (0.00) 10 1.28 (1.68)
DEV.] (0.29) (0.45) (0.26)
756
12 MULTILOG EXAMPLES TOTAL TEST INFORMATION @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 2.520 2.883 2.088 2.257 6.688 8.513 1.400 1.300
3.163 5.243 2.957 1.144
2.879 3.507 2.194 1.116
2.525 2.811 1.787 1.095
2.224 3.882 1.550
@THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
POSTERIOR STANDARD DEVIATION: 0.630 0.589 0.562 0.553 0.562 0.692 0.666 0.575 0.457 0.437 0.387 0.343 0.382 0.474 0.582 0.845 0.877 0.901 0.920 0.935
0.589 0.534 0.675 0.947
0.629 0.596 0.748 0.956
0.671 0.508 0.803
MARGINAL RELIABILITY:
3.164 3.024 6.862 1.231
3.272 4.791 4.443 1.181
0.6903
ABCD(C) ABCD(D) WITH TWO C(K)S FIXED AT ZERO OBSERVED(EXPECTED) STD. : EAP (S.D.) RES. : 1.0( 0.1) 2.33 : -2.23 ( 0.63) 2.0( 0.6) 1.78 : -2.20 ( 0.63) 1.0( 0.2) 2.16 : -1.53 ( 0.58) 1.0( 0.8) 0.26 : -0.96 ( 0.56) 1.0( 0.4) 0.97 : -1.01 ( 0.58) 2.0( 1.5) 0.38 : -1.27 ( 0.57) 1.0( 0.8) 0.20 : -1.32 ( 0.58) 2.0( 0.5) 2.08 : -1.63 ( 0.49) 2.0( 0.4) 2.64 : -1.25 ( 0.54) 1.0( 0.7) 0.35 : -1.33 ( 0.51) 2.0( 1.0) 1.05 : -1.47 ( 0.47) 5.0( 1.9) 2.22 : -1.51 ( 0.45)
: : : : : : : : : : : : : :
PATTERN 2222 2224 2232 2244 2245 2254 2255 2333 2343 2344 2353 2354
Many similar lines omitted here NEGATIVE TWICE THE LOGLIKELIHOOD= 243.2 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.11 A constrained nonlinear model for multiple-choice alternatives Thissen, Steinberg & Fitzpatrick (1989) described the use of the multiple-choice model with four items from a nation-wide tryout of achievement test items conducted in 1987 by CTB/McGrawHill. The data comprised the responses of 959 examinees that responded to four items on a single page of one of the tryout forms. The items are included in the report by Thissen, Steinberg & Fitzpatrick (1989). The data for the analysis were the observed counts of examinees giving each of the 44 = 256 possible response patterns to the four items. Fitting the 256-cell contingency table with the multiple-choice model with no constraints, the likelihood ratio G 2 with 211 d.f. was 226.0, which indicates a satisfactory fit. However, examination of the item parameters, the trace lines, and the items themselves lead us to impose a number of constraints on the model. Using MULTILOG subscripts, where category 1 =“don’t know,” and the observed responses are in categories 2-5:
757
12 MULTILOG EXAMPLES
For items 2, 3, and 4, we constrained d k = 0.25 for all four alternatives with >FIX ITEMS=(2,3,4), DK=(1,2,3) VALUE=0.0. For item 1, we constrained d1 = d3 = d 4 with >FIX ITEMS=1, DK=(2,3), VALUE=0.0. For items 1 and 2 we constrained a2 = a1 with >FIX ITEMS=1, AK=1, VALUE=0.0 and >FIX ITEMS=2, AK=1, VALUE=0.0. For item 2, we constrained a3 = a5 with >EQUAL ITEMS=2, WITH=2, AK=(2,4); for item 3 we constrain a3 = a4 with >EQUAL ITEMS=3, WITH=3, AK=(2,3); and for item 4, we
constrained a2 = a3 = a5 with >EQUAL ITEMS=4, WITH=4, AK=(1,2,4). For item 3, we constrained a2 = a5 with >EQUAL ITEMS=3, WITH=3, AK=(1,4).
These constraints reduce the number of parameters (contrasts) estimated from 44 to 26. The goodness-of-fit statistic under all of the constraints is χ 2 (229) = 236.9 , which is very close to expectation. The overall test of significance of the 18 contrasts among the parameters eliminated in these constraints is χ 2 (18) = 236.9 − 226.0 = 10.9. Thus no significant differences among the trace lines have been eliminated in the imposition of these constraints. However, the remaining parameters are much more precisely estimated and the corresponding trace lines are smoother than those involving many parameters that are not wellspecified by the data. On the following pages we illustrate the use of MULTILOG to compute the estimates. Note that we increased the number of quadrature points (with the TGROUPS command) from the default 10 to 13. This increases the usefulness of the approximate standard errors. We also impose a gentle Bayesian prior on d-contrast 1 for item 1 (the only estimated d-contrast); as with the 3PL model, weak priors on the d-contrasts are usually helpful. Syntax for this run, as shown below, is given in exampl11.mlg while the data file is exampl11.dat. EXAMPL11.MLG "CALORIC CONSUMPTION ITEMS", TSF, JEM 89 >PROBLEM RANDOM, PATTERNS, NITEMS=4, NGROUP=1, NPATTERNS=148, DATA=‘EXAMPL11.DAT’; >TEST ALL, BS, NC=(5(0)4), HIGH=(3,4,5,4); >SAVE; >TGROUPS NUMBERS=13, QP=(-4.5(0.75)4.5); >FIX ITEMS=1, AK=1, VA=0.0; >FIX ITEMS=2, AK=1, VA=0.0; >EQUAL ITEMS=2, WITH=2, AK=(2,4); >EQUAL ITEMS=3, WITH=3, AK=(1,4); >EQUAL ITEMS=3, WITH=3, AK=(2,3); >EQUAL ITEMS=4, WITH=4, AK=(1,2,4); >FIX ITEMS=1, DK=(2,3), VALUE=0.0; >FIX ITEMS=(2,3,4), DK=(1,2,3), VALUE=0.0; >PRIOR ITEMS=1, DK=1, PA=(0.0,1.0); >ESTIMATE NC=100; >END; 4
758
12 MULTILOG EXAMPLES 1234 2222 3333 4444 5555 (4A1,F4.0)
Selected output for this run follows. ITEM
1:
5 NOMINAL CATEGORIES,
CATEGORY(K): 1 A(K) -1.94 C(K) 0.05 D(K)
2 -1.94 -0.29 0.12
3 1.31 1.17 0.65
3 HIGH
4 0.68 1.74 0.12
5 1.88 -2.68 0.12
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C D CONTRAST P(#) COEFF.[ DEV.] P(#) COEFF.[ DEV.] P(#) COEFF.[ 1 27 0.00 (0.00) 4 -0.34 (0.26) 8 1.71 2 1 3.26 (0.44) 5 1.12 (0.41) 28 0.00 3 2 2.63 (0.40) 6 1.69 (0.32) 29 0.00 4 3 3.83 (0.84) 7 -2.73 (1.19) MARGINAL RELIABILITY:
DEV.] (0.35) (0.00) (0.00)
0.6005
‘CALORIC CONSUMPTION ITEMS’, TSF, JEM 89 OBSERVED(EXPECTED) 2.0( 8.0( 1.0( 6.0( 7.0( 28.0(
3.0) 6.7) 2.0) 6.1) 6.9) 22.9)
STD. RES. -0.56 0.52 -0.69 -0.05 0.06 1.06
: : : : : : : :
EAP (S.D.) -1.53 -1.16 -1.64 -1.12 -1.09 -0.79
( ( ( ( ( (
0.60) 0.70) 0.58) 0.61) 0.60) 0.53)
: : : : : : : :
PATTERN 2223 2224 2225 2232 2233 2234
NEGATIVE TWICE THE LOGLIKELIHOOD= 236.4 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.12 A nominal model for testlets In their description of the use of latent class models for the validation of the structure of knowledge domains, Bergan & Stone (1985) report a number of analyses of the data in this example. The data were collected as the responses to four items measuring the numerical knowledge of a sample of preschool children in the Head Start program. The first two items required the children to identify numerals (3 and 4), and the second two items required the children to match the correct numeral (again, 3 or 4) represented by a number of blocks. In an analysis reported in Thissen & Steinberg (1988), the items were redefined as two pseudoitems, each of which has four response categories. The first of these pseudo-items is denoted “Identify,” which has four categories of response: correctly identifying neither numeral, only 3, only 4, or both correct. The second pseudo-item is called “Match,” with the same four response
759
12 MULTILOG EXAMPLES categories. The pseudo-items are logically equivalent to testlets described by Wainer & Kiely (1987): They are clusters of items between which conditional independence may reasonably be expected. The trace line model used here is Bock’s (1972) nominal model. Equality constraints are imposed among the parameters: for “Identify,” a2 = a1 ; for “Match,” a3 = a2 and c3 = c2 . Given the use of “Triangle” T -matrices, these constraints are imposed by fixing a- and c-contrasts at zero, because those contrasts represent the differences between successive as and cs. This example also illustrates entry of starting values; MULTILOG’s default starting values do not perform well in this example. The fit of the model is quite good: χ 2 = 8.4, p = 0.2. Syntax for this model, as shown below, is contained in the file exampl12.mlg and is based on data in exampl12.dat. Additional examples of nominal models are given in the next two sections. EXAMPL12.MLG >PROBLEM >TEST >TMATRIX >TMATRIX >FIX >FIX >FIX >START >END; 4 N34B 11 22 33 44 (2A1,F4.0)
BERGAN & STONE DATA ON PRESCHOOLERS AND ‘3 AND 4’ RANDOM, PATTERNS, NITEMS=2, NGGROUPS=1, NPATTERNS=16, DATA=‘EXAMPL12.DAT’; ALL, NOMINAL, NC=(4,4), HIGH=(4,4); ALL, AK, TRIANGLE; ALL, CK, TRIANGLE; ITEMS=1, AK=1, VALUE=0.0; ITEMS=2, AK=2, VALUE=0.0; ITEMS=2, CK=2, VALUE=0.0; ITEMS=(1,2), PARAMS=‘EXAMPL12.PRM’;
Selected output is given below. ITEM 1: 4 NOMINAL CATEGORIES, 4 HIGH CATEGORY(K): 1 2 3 4 A(K) 0.00 0.00 1.19 2.95 C(K) 0.00 -0.86 -0.67 0.69 CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[ TRI.] P(#) COEFF.[ TRI.] 1 10 0.00 (0.00) 3 0.86 (0.14) 2 1 -1.19 (0.33) 4 -0.19 (0.27) 3 2 -1.76 (0.35) 5 -1.36 (0.21) TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.048 1.058 -1.4 - 0.0 1.448 1.650
1.071 1.940
1.090 2.335
1.118 2.819
1.159 3.315
1.220 3.681
1.311 3.775
760
12 MULTILOG EXAMPLES 0.2 1.8 -
1.6 3.0
3.566 1.206
MARGINAL RELIABILITY:
3.152 1.141
OBSERVED(EXPECTED) 71.0( 34.0( 30.0( 38.0( 30.0( 13.0( 15.0( 15.0( 13.0( 4.0( 15.0( 19.0( 43.0( 30.0( 25.0( 197.0(
70.2) 32.8) 32.8) 37.1) 29.6) 13.9) 13.9) 15.6) 15.1) 8.3) 8.3) 19.4) 42.1) 28.0) 28.0) 196.9)
2.678 1.097
2.245 1.066
1.894 1.045
1.629 1.031
1.437 1.021
1.301
0.5409 STD. : RES. : 0.09 : 0.20 : -0.50 : 0.15 : 0.07 : -0.23 : 0.31 : -0.16 : -0.53 : -1.48 : 2.34 : -0.09 : 0.15 : 0.37 : -0.57 : 0.01 :
EAP (S.D.) -0.99 -0.84 -0.84 -0.18 -0.99 -0.84 -0.84 -0.18 -0.49 -0.37 -0.37 0.22 0.11 0.22 0.22 0.91
( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (
0.70) 0.67) 0.67) 0.58) 0.70) 0.67) 0.67) 0.58) 0.61) 0.59) 0.59) 0.58) 0.58) 0.58) 0.58) 0.69)
:
: : : : : : : : : : : : : : : : :
PATTERN 11 12 13 14 21 22 23 24 31 32 33 34 41 42 43 44
NEGATIVE TWICE THE LOGLIKELIHOOD= 8.4 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.13 A constrained nominal model for questionnaire items This example illustrates the computations involved in the analysis of the “life satisfaction” data described by Thissen & Steinberg (1988). The data consist of the counts of respondents in a 33 cross-classification based on the responses of 1472 respondents to the 1975 General Social Survey (Davis, 1975), to three questions concerning satisfaction with family (F), hobbies (H), and residence (R). In the original data, there were seven responses available. In previous analyses, Clogg (1979) re-classified the data into three categories, and Masters (1985) used the trichotomized data. Better data analysis would probably be obtained with the original seven-category data, or at least a more sensible reduction; Muraki (1984), for instance, used a different fourcategory system for the same seven responses. However, the analysis illustrated here corresponds to that described by Thissen & Steinberg (1988) and uses the trichotomized data. In this illustration, we again use Bock’s (1972) nominal model. This model for the trace lines is extremely flexible; however, it is frequently too flexible and some additional constraints on the item parameters are required to give a satisfactory solution. When fitted without constraints, for item F, the difference between a1 and a2 is nearly zero. For items H and R, the difference between a1 and a2 is small and similar; and the difference between a1 and a3 is about the same for all three items. In this example, we impose equality constraints to make these small differences exactly zero. Using the (default) deviation contrasts, this is done with >FIX ITEMS=1, AK=1, VALUE=0.0 [to set a1 = a2 for item 1], >EQUAL ITEMS=(2,3), AK=1 [to set ( a1 − a2 ) equal for items 2 and 3] and >EQUAL ITEMS=(1,2,3), AK=2 [to set (a1 − a3 ) equal for all three items]. Imposing these equality constraints gives a version of the nominal model that (barely) fits:
761
12 MULTILOG EXAMPLES
χ 2 (18) = 28.3,
p = 0.06.
Additional examples of nominal models are given in Sections 12.10 and 12.12. The contents of the command file exampl13.mlg are shown below. EXAMPL13.MLG: SATISFACTION DATA FOR THE PARAMETERS IN TABLE 7, T&S 88 >PROBLEM RANDOM, PATTERNS, NITEMS=3, NGROUP=1, NPATTERNS=27, DATA=‘EXAMPL13.DAT’; >TEST ALL, NOMINAL, NC=(3,3,3), HIGH=(3,3,3); >FIX ITEMS=1, AK=1, VALUE=0.0; >EQUAL ITEMS=(2,3), AK=1; >EQUAL ITEMS=(1,2,3), AK=2; >SAVE; >END; 3 123 111 222 333 (1X,3A1,F4.0)
Selected output is given below. ITEM 1: 3 NOMINAL CATEGORIES, CATEGORY(K): 1 2 3 A(K) -0.52 -0.52 1.04 C(K) -1.37 -0.33 1.69
3 HIGH
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[ DEV.] P(#) COEFF.[ DEV.] 1 9 0.00 (0.00) 2 1.04 (0.12) 2 1 1.55 (0.08) 3 3.06 (0.14) MARGINAL RELIABILITY: OBSERVED(EXPECTED) 15.0( 11.0( 7.0( 3.0( 12.0(
6.5) 11.1) 4.6) 9.2) 16.9)
0.4670 STD. : RES. : 3.31 : -0.03 : 1.12 : -2.04 : -1.20 :
EAP (S.D.) -1.53 -1.33 -0.78 -1.33 -1.13
NEGATIVE TWICE THE LOGLIKELIHOOD=
( ( ( ( (
0.72) 0.70) 0.68) 0.70) 0.69)
:
: : : : : :
PATTERN 111 112 113 121 122
28.3
(CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.14 A constrained generalized partial credit model In this example, we consider the responses of 3866 examinees to a 4-passage, 22-item test of reading comprehension. For a complete description of the data and the analysis, see Thissen, Steinberg & Mooney (1989). The reading passages were of varying lengths, and they were fol762
12 MULTILOG EXAMPLES lowed by varying numbers of questions about their content, from three to eight questions. Instead of considering the test to be comprised of 22 binary items, we considered it to be made up of four testlets (Wainer & Kiely, 1987). Each testlet has q questions (q = 7, 4, 3, 8), and the four testlet responses for each examinee are the number of questions correct for each of the four passages. Thus the seven questions following the first passage constitute a single testlet, with responses x=0, 1, 2, …, 7. The model we used for the number-correct for each passage was the nominal model (Bock, 1972). We reparameterized the model using centered polynomials of the associated scores to represent the category-to-category change in the ak s and ck s (with TMATRIX … POLYNOMIAL). Thissen & Steinberg (1986) showed that the polynomial-contrast version of the nominal model is equivalent to Masters’ (1982) “partial credit” model for ordered item responses when the contrasts among the as are restricted to be linear, and constant for all items. We did not expect that such a simple model will fit the data; for instance, we did not expect a priori that the testlets would be equally related to proficiency, so we permitted the linear contrast among the as to vary over items. Guessing may cause a score of one on a multi-question passage to reflect little more proficiency than a score of zero, but higher scores should be more ordered. The linear-plus-quadratic polynomial for the a-contrasts was intended to produce as that may be similar for scores of zero and one, and increasing for higher scores. The polynomial parameterization for the cs is intended to capture the smoothness in the distribution of response proportions for adjacent scores. To improve the stability of estimation of the item parameters, we located the lowest-degree polynomials, which provided a satisfactory fit to the data. We used the likelihood ratio statistics to evaluate the models. For the unconstrained nominal model twice the negative log likelihood was 1048.3 (this is not distributed as χ 2 with any clear degrees of freedom; only 652 of the 1440 cells of the 4-way contingency table are non-zero). Upon reducing the rank of the polynomials for the as to one (linear in number-correct) for testlets 1, 3, and 4, and to two (quadratic in number-correct) for testlet 2, we obtained a value of 1082.2; the likelihood ratio test for the significance of this reduction is χ 2 (17) = 33.9, p = 0.01. While this value is significant, it is not highly significant given the sample size (3866). No individual term among those eliminated was extremely significant. The significance arose from moderately large χ 2s for two or three relatively high-order polynomial terms (e.g., χ 2s of about 5 for fourth- and seventh-degree terms). Upon finding that any further reduction in the rank of the a-parameterization induces a highly significant change in the goodness of fit, we settled on linear as for testlets 1, 3, and 4 and quadratic as for testlet 2. Using the reduced-rank as, we then reduced the rank of the polynomials for the cs to {3, 3, 2, 4} for the four testlets; χ 2 (10) = 12.0, p = 0.3 for the ten high-order polynomial terms eliminated. Any further reduction caused a highly significant change in the goodness-of-fit. On the following pages, we fit the model to the 8 x 5 x 4 x 9 cross-classification of observed response-pattern frequencies, with constraints imposed to give the final model. The data are given in exampl14.dat and the syntax for this run (exampl14.mlg) is given below.
763
12 MULTILOG EXAMPLES EXAMPL14.MLG READING COMPREHENSION AS 4 TESTLETS, FINAL MODEL, TSM 89 JEM >PROBLEM RANDOM, PATTERNS, NITEMS=4, NGROUPS=1, NPATTERNS=652, DATA=‘EXAMPL14.DAT’; >TEST ALL, NOMINAL, NC=(8,5,4,9), HIGH=(8,5,4,9); >SAVE; >TMATRIX ALL, AK, POLYNOMIAL; >TMATRIX ALL, CK, POLYNOMIAL; >FIX ITEMS=1, AK=(2(1)7), VALUE=0.0; >FIX ITEMS=2, AK=(3,4), VALUE=0.0; >FIX ITEMS=3, AK=(2,3), VALUE=0.0; >FIX ITEMS=4, AK=(2(1)8), VALUE=0.0; >FIX ITEMS=1, CK=(4,5,6,7), VALUE=0.0; >FIX ITEMS=2, CK=4, VALUE=0.0; >FIX ITEMS=3, CK=3, VALUE=0.0; >FIX ITEMS=4, CK=(5,6,7,8), VALUE=0.0; >END; 9 123456789 1111 2222 3333 4444 5505 6006 7007 8008 0009 (4A1,F5.0)
Selected output is given below. READING COMPREHENSION AS 4 TESTLETS, FINAL MODEL, TSM 89 JEM ITEM 1: 8 NOMINAL CATEGORIES, 8 HIGH CATEGORY(K): 1 2 3 4 5 A(K) -1.94 -1.39 -0.83 -0.28 0.28 C(K) -4.00 -1.68 0.14 1.39 2.00
6 0.83 1.90
7 1.39 1.01
8 1.94 -0.75
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[POLY.] P(#) COEFF.[POLY.] 1 1 0.56 (0.02) 2 0.50 (0.03) 2 18 0.00 (0.00) 3 -0.68 (0.02) 3 19 0.00 (0.00) 4 -0.05 (0.02) 4 20 0.00 (0.00) 24 0.00 (0.00) 5 21 0.00 (0.00) 25 0.00 (0.00) 6 22 0.00 (0.00) 26 0.00 (0.00) 7 23 0.00 (0.00) 27 0.00 (0.00) ITEM 2: 5 NOMINAL CATEGORIES, 5 HIGH CATEGORY(K): 1 2 3 4 5 A(K) -0.88 -0.64 -0.20 0.44 1.28 C(K) -1.71 -1.21 0.05 1.25 1.62
764
12 MULTILOG EXAMPLES
CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[POLY.] P(#) COEFF.[POLY.] 1 5 0.54 (0.03) 7 0.91 (0.04) 2 6 0.12 (0.02) 8 -0.03 (0.03) 3 35 0.00 (0.00) 9 -0.16 (0.02) 4 36 0.00 (0.00) 37 0.00 (0.00) MARGINAL RELIABILITY: OBSERVED(EXPECTED) 1.0( 1.0( 1.0( 1.0( 1.0(
0.2) 0.2) 0.4) 0.1) 0.3)
0.6570 STD. : RES. : 1.77 : 2.11 : 1.02 : 3.95 : 1.29 :
EAP (S.D.) -2.89 -2.57 -2.05 -1.31 -2.26
( ( ( ( (
0.64) 0.63) 0.60) 0.57) 0.61)
:
: : : : : :
PATTERN 1111 1121 1123 1135 1213
NEGATIVE TWICE THE LOGLIKELIHOOD= 1094.2 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.15 A mixed nominal and graded model for self-report inventory items In research concerned with eating disorders among college women, Irving (1987) used a questionnaire called the BULIT, a 36-item index created to identify individuals with, or at risk for developing bulimia (Smith & Thelen, 1984). All of the items on the scale have five response alternatives; most are “Likert-type” items. The questionnaire was developed to be scored by adding the numbers (from 1 to 5) associated with each response; high scores imply high risk. But the BULIT also includes items for which the responses are not so obviously ordered. In this example, we illustrate the use of MULTILOG to fit different models to different items of the same scale, as described by Thissen (1991). We use Bock’s (1972) nominal model for item 1 of exampl15.dat, while we use Samejima’s (1969) graded model for items 2 and 3. For the 5 x 5 x 5 table arising from the cross-classification based on the three items described by Thissen (1991), the graded model for items 2 and 3 and the nominal model for item 1 give χ 2 (108) = 99.9, p = 0.6. Syntax for this run, from the file exampl15.mlg, is shown below. EXAMPL15.MLG HYBRID GRADED-NOMINAL SET OF ITEMS FROM THE BULIT >PROBLEM RANDOM, PATTERNS, NITEMS=3, NGROUP=1, NPATTERNS=69, DATA=‘EXAMPL15.DAT’; >TEST ITEMS=1, NOMINAL, NC=5, HIGH=5; >TEST ITEMS=(2,3), GRADED, NC=(5,5); >FIX ITEMS=1, AK=(1,3), VALUE=0.0; >END; 5 12345 111 222
765
12 MULTILOG EXAMPLES 333 444 555 (1X,3A1,F5.0)
Selected output is shown below. ITEM 1: 5 NOMINAL CATEGORIES, 5 HIGH CATEGORY(K): 1 2 3 4 5 A(K) -0.39 -0.39 0.24 -0.39 0.94 C(K) 2.02 -1.49 -0.26 0.57 -0.83 CONTRAST-COEFFICIENTS (STANDARD ERRORS) FOR: A C CONTRAST P(#) COEFF.[ DEV.] P(#) COEFF.[ DEV.] 1 17 0.00 (0.00) 3 -3.51 (0.34) 2 1 0.63 (0.26) 4 -2.28 (0.26) 3 18 0.00 (0.00) 5 -1.45 (0.13) 4 2 1.33 (0.26) 6 -2.85 (0.31) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.006 0.007 0.024 0.029 0.108 0.130 0.339 0.360
(Theta values increase in steps of 0.2) 0.008 0.010 0.012 0.014 0.017 0.020 0.035 0.042 0.051 0.061 0.029 0.090 0.155 0.183 0.214 0.247 0.280 0.312 0.373 0.375 0.368 0.351 0.326
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 3 4 5 OBS. FREQ. 302 9 34 71 32 OBS. PROP. 0.6741 0.0201 0.0759 0.1585 0.0714 EXP. PROP. 0.6740 0.0201 0.0759 0.1584 0.0716 ITEM A B( B( B( B(
1) 2) 3) 4)
2: 5 GRADED CATEGORIES P(#) ESTIMATE (S.E.) 7 2.47 (0.24) 8 0.22 (0.07) 9 0.98 (0.08) 10 1.67 (0.12) 11 2.49 (0.22)
TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.049 1.073 -1.4 - 0.0 1.940 2.210 0.2 - 1.6 4.226 4.348 1.8 - 3.0 3.803 3.619 MARGINAL RELIABILITY: OBSERVED(EXPECTED) 72.0( 47.0( 36.0( 15.0( 10.0(
76.9) 47.8) 33.5) 14.3) 12.5)
1.109 2.489 4.407 3.466
1.162 2.769 4.432 3.315
1.240 3.058 4.409 3.103
1.350 3.373 4.321 2.802
1.502 2.210 4.177 2.450
1.699 4.008 3.998
0.6768 STD. : RES. : -0.56 : -0.11 : 0.43 : 0.20 : -0.70 :
EAP (S.D.) -1.11 -0.50 -0.19 0.05 0.26
( ( ( ( (
0.68) 0.54) 0.53) 0.54) 0.60)
:
: : : : : :
PATTERN 111 112 113 114 115
766
12 MULTILOG EXAMPLES NEGATIVE TWICE THE LOGLIKELIHOOD= 99.9 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.16 A mixed three-parameter logistic and partial credit model for a 26-item test In this example, we illustrate the use of MULTILOG for item analysis for a test comprising 26 conventional multiple-choice items (scored dichotomously: correct or incorrect), and a 27th item with three response categories. We use the 3PL model for items 1-26, and Bock’s (1972) nominal model (with constraints making it equivalent to Masters’ partial credit model) for item 27. Note that, as in the previous section, the specification of two distinct item response models is done with two TEST commands. In this example, we use Bayesian prior distributions for all three parameters of the 3PL model: we assume that the slopes (as) are distributed normally with an average value of 1.7 (equal to a slope of 1.0 in the usual “normal metric” of the 3PL) and a standard deviation of 1. We assume that the bs are distributed normally with mean zero and standard deviation 3 (this serves only to limit the bs for very easy or very difficult items); and we assume that the logit of the lower asymptote is normally distributed with an average of –1.4 and a standard deviation of 1. The TMATRIX commands establish the partial credit parameterization for item 27. Using MULTILOG, there is no problem combining item response models to analyze and score items with different kinds of responses on the same test. The data file exampl16.dat is used in this example and the command file (exampl16.mlg) is shown below. EXAMPL16.MLG MIXTURE OF 26 3PL ITEMS AND ONE PARTIAL CREDIT ITEM >PROBLEM RANDOM, INDIVIDUAL, NITEMS=27, NGROUP=1, NEXAMINEES=668, DATA=‘EXAMPL16.DAT’; >TEST ITEMS=(1(1)26), L3; >TEST ITEMS=27, NOMINAL, NC=3, HIGH=3; >PRIORS ITEMS=(1(1)26), AJ, PARAMS=(1.7,1.0); >PRIORS ITEMS=(1(1)26), BJ, PARAMS=(0.0,2.0); >PRIORS ITEMS=(1(1)26), CJ, PARAMS=(-1.4,0.5); >TMATRIX ITEMS=27, AK, POLYNOMIAL; >TMATRIX ITEMS=27, CK, TRIANGLE; >FIX ITEMS=27, AK=2, VALUE=0.0; >SAVE ; >END; 5 01239 111111111111111111111111110 222222222222222222222222221 000000000000000000000000002 000000000000000000000000003 000000000000000000000000000 (12A1,2X,15A1)
Selected output follows.
767
12 MULTILOG EXAMPLES ITEM 1: 2 NOMINAL CATEGORIES, 2 HIGH TRADITIONAL 3PL, NORMAL METRIC: A 0.78
B -1.28
C 0.21
ITEM 26: 2 NOMINAL CATEGORIES, 2 HIGH TRADITIONAL 3PL, NORMAL METRIC: A 0.72
B 1.21
C 0.21
ITEM 27: 3 NOMINAL CATEGORIES, CATEGORY(K): 1 2 3 A(K) -2.09 0.00 2.09 C(K) 0.00 0.59 -0.93
3 HIGH
TOTAL TEST INFORMATION @THETA: INFORMATION: -3.0 - -1.6 1.127 1.184 1.267 1.383 1.547 -1.4 - 0.0 3.071 3.774 4.634 5.656 6.843 0.2 - 1.6 12.245 13.022 13.097 12.418 11.228 1.8 - 3.0 6.452 5.612 4.862 4.177 3.561 MARGINAL RELIABILITY:
1.776 8.179 9.867 3.028
2.091 2.515 3.774 11.023 8.568 7.426 2.590
0.8453
NEGATIVE TWICE THE LOGLIKELIHOOD= 2629.5 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS)
12.17 Equivalent groups equating of two forms of a four-item personality inventory In an attempt to link the study of social norms and the study of personality, Stouffer & Toby (1951) devised three forms of a questionnaire designed to measure a personality disposition toward “particularistic” (as opposed to “universalistic”) solutions to social-role conflicts. Form A of their questionnaire consisted of four vignettes designed to invoke social role conflict and the items elicited particularistic or universalistic responses. The four items are reproduced by Thissen & Steinberg (1988), along with a discussion of the data analysis in this example. In Form B, the stories were worded so that a friend of the respondent faced the role conflict and items measured expectations for particularistic or universalistic actions on the part of friends. Here, we consider the fit of the 2PL model to these data. In the data, the item responses for Form A are in columns 3-6 (as items 1-4), and the item responses for Form B are in columns 7-10 (as items 5-8). The trace lines have been fitted with the constraint that the slopes are the same for a given item on the two forms [using >EQUAL AJ, ITEMS=(5,6,7,8), WITH=(1,2,3,4)], but the thresholds may vary between forms. The respondents were randomly assigned to the different forms; therefore we constrained the population means of the two groups to be equal. Because the mean for group 2 is fixed at zero as an identifiability constraint, this is done by fixing the mean for group 1 at zero as well. The model fits the data adequately; the goodness-of-fit likelihood ratio statistic is 21.9 on 18 d.f., p = 0.2. In the output, note that when there are two (or more) groups, MULTILOG prints the observed frequencies and proportions in each response category for the entire sample, but the expected
768
12 MULTILOG EXAMPLES proportions are computed separately for each group. The command file exampl17.mlg, using the data file exampl17.dat, is given below. EXAMPL17.MLG - STOUFER-TOBY, FORMS A&B, MODEL FOR PARAMETERS IN TABLE 2, T&S 88 >PROBLEM RANDOM, PATTERNS, NITEMS=8, NGROUP=2, NPATTERNS=32, DATA=‘EXAMPL17.DAT’; >TEST ALL, L2; >EQUAL AJ, ITEMS=(5,6,7,8), WITH=(1,2,3,4); >FIX MU, GROUPS=1, VALUE=0.0; >END; 2 +++++++++ N (I1,1X,8A1,F3.0)
Selected output is given below. ITEM SUMMARY ITEM
1: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 1 1.26 (0.16) B( 1) 2 1.37 (0.19) TOTAL TEST INFORMATION FOR GROUP 1: @THETA: INFORMATION: -3.0 - -1.6 1.238 1.309 -1.4 - 0.0 2.745 3.148 0.2 - 1.6 5.087 4.667 1.8 - 3.0 2.196 2.020
MARGINAL RELIABILITY:
1.402 3.602 4.168 1.865
1.522 4.095 3.684 1.728
1.675 4.590 3.262 1.608
1.868 5.019 2.915 1.503
2.108 3.148 2.632 1.414
2.399 5.316 2.397
1.522 4.095 3.684 1.728
1.675 4.590 3.262 1.608
1.868 5.019 2.915 1.503
2.108 3.148 2.632 1.414
2.399 5.316 2.397
0.7221
TOTAL TEST INFORMATION FOR GROUP 2: @THETA: INFORMATION: -3.0 - -1.6 1.238 1.309 -1.4 - 0.0 2.745 3.148 0.2 - 1.6 5.087 4.667 1.8 - 3.0 2.196 2.020 MARGINAL RELIABILITY:
1.402 3.602 4.168 1.865
0.7221
Note: In this situation, MULTILOG “thinks” there are eight items when, in fact, each respondent answered only four. MULTILOG computes TOTAL TEST INFORMATION and MARGINAL RELIABILITY assuming that each respondent answered all (eight) items; as a result, these values are not correct for the four-item tests that were actually administered. MULTILOG cannot know the difference between real “missing data” and this kind of artificial “missing data.” In situations
769
12 MULTILOG EXAMPLES like this, the TOTAL TEST INFORMATION and MARGINAL RELIABILITY values printed cannot be used. GROUP 1 OBSERVED(EXPECTED) 20.0( 9.0( 6.0( 2.0( 2.0(
21.9) 8.9) 4.0) 1.7) 1.3)
STD. RES. -0.42 0.04 0.99 0.24 0.66
: : : : : : :
STD. RES. -0.96 1.34 -0.03 0.77 0.32
: : : : : : :
EAP (S.D.) 1.27 0.83 0.31 0.57 0.21
( ( ( ( (
0.71) 0.66) 0.61) 0.63) 0.61)
: : : : : : :
PATTERN
: : : : : : :
PATTERN
22220000 21220000 22120000 22210000 21210000
... NEGATIVE TWICE THE LOGLIKELIHOOD= 11.6 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) GROUP 2 OBSERVED(EXPECTED)
…
20.0( 23.0( 4.0( 3.0( 3.0(
24.8) 17.4) 4.1) 1.9) 2.5)
EAP (S.D.) 1.23 0.79 0.27 0.53 0.18
( ( ( ( (
0.71) 0.66) 0.61) 0.63) 0.60)
00002222 00002122 00002212 00002221 00002121
NEGATIVE TWICE THE LOGLIKELIHOOD= 10.3 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) TOTAL, NEGATIVE TWICE THE LOGLIKELIHOOD, ALL GROUPS=
21.9
12.18 Differential item functioning (DIF) analysis of eight items from the 100Item Spelling Test Thissen, Steinberg & Wainer (1993) illustrated the application of a number of likelihood-based procedures for the detection of Differential Item Functioning (DIF) using a set of data derived from a conventional orally-administered spelling test, with data obtained from 659 undergraduates at the University of Kansas. A description of these data are given in Section 2.4.1. The reference group included the male students (N = 285), and the focal group was made up of the female students (N = 374). The original test had 100 words, but only four (infidelity, panoramic, succumb, and girder) are used here. The words infidelity, panoramic, and succumb were selected to comprise an “anchor” (a set of items believed to involve no DIF) with information over a range of the θ -continuum. The word girder is the “studied” item. It was selected because it shows substantial differential difficulty for the two groups in these data. Thissen, Steinberg & Wainer (1993) included (in an appendix) a description of the procedures followed to compute the estimates using MULTILOG version 5. In this section, the same analysis is reproduced using version 7. The item responses for the males are read as items 1-4, and those for the females as items 5-8.
770
12 MULTILOG EXAMPLES Syntax from the file exampl18.mlg is given below. This analysis is based on the data in exampl18.dat. EXAMPL18.MLG -‘GIRDER’ DIF 3-ITEM ANCHOR (ITEMS 5,4,25), 1PL >PROBLEM RANDOM, PATTERNS, NITEMS=8, NGROUP=2, NPATTERNS=32, DATA=‘EXAMPL18.DAT’; >TEST ALL, L1; >EQUAL BJ, ITEMS=(5,6,7), WITH=(1,2,3); >END; 2 01 11111111 N (I1,8A1,F3.0)
Selected output follows. ITEM
1: 2 GRADED CATEGORIES P(#) ESTIMATE (S.E.) A 6 1.25 (0.08) B( 1) 1 -1.34 (0.13) @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 0.155 0.187 0.390 0.387 0.174 0.143 0.030 0.023
(Theta values increase in steps of 0.2) 0.222 0.259 0.296 0.331 0.360 0.380 0.373 0.349 0.318 0.282 0.387 0.208 0.117 0.094 0.076 0.060 0.048 0.038 0.018 0.014 0.011 0.009 0.007
OBSERVED AND EXPECTED COUNTS/PROPORTIONS IN CATEGORY(K): 1 2 OBS. FREQ. 70 215 OBS. PROP. 0.2456 0.7544 GROUP 1: EXP. PROP.
0.2093 0.7907
GROUP 2: EXP. PROP.
0.2135 0.7865
ITEM 9: GRP1, N[MU: 0.03 SIGMA: 1.00] P(#);(S.E.): 7; (0.07) 9; (0.00) TOTAL TEST INFORMATION FOR GROUP 1: @THETA: -3.0 - -1.6 -1.4 - 0.0 0.2 - 1.6 1.8 - 3.0
INFORMATION: 1.556 1.683 2.985 3.158 3.354 3.239 2.024 1.860
MARGINAL RELIABILITY:
1.831 3.301 3.099 1.714
1.999 3.409 2.939 1.585
2.184 3.477 2.763 1.475
2.383 3.504 2.577 1.383
2.589 3.158 2.388 1.306
2.792 3.439 2.202
0.6651
771
12 MULTILOG EXAMPLES TOTAL TEST INFORMATION FOR GROUP 2: @THETA: INFORMATION: -3.0 - -1.6 1.556 1.683 -1.4 - 0.0 2.985 3.158 0.2 - 1.6 3.354 3.239 1.8 - 3.0 2.024 1.860 MARGINAL RELIABILITY: GROUP 1 OBSERVED(EXPECTED)
29.0( 7.0( 50.0( 30.0( 15.0(
...
32.3) 7.3) 50.4) 24.3) 17.9)
1.831 3.301 3.099 1.714
1.999 3.409 2.939 1.585
2.184 3.477 2.763 1.475
2.383 3.504 2.577 1.383
2.589 3.158 2.388 1.306
2.792 3.439 2.202
0.6658
STD. RES.
: :
-0.57 -0.12 -0.05 1.16 -0.69
: : : : :
EAP (S.D.)
-1.30 -0.68 -0.68 -0.09 -0.68
( ( ( ( (
0.72) 0.69) 0.69) 0.68) 0.69)
: :
PATTERN
: : : : :
00001111 00001112 00002111 00002112 00001211
NEGATIVE TWICE THE LOGLIKELIHOOD= 14.7 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) ‘GIRDER’ DIF; 3-ITEM ANCHOR (ITEMS 5,4,25), 1PL GROUP 2 OBSERVED(EXPECTED)
22.0( 10.0( 30.0( 27.0( 13.0(
...
20.7) 10.4) 28.0) 29.6) 10.0)
STD. RES.
: :
0.30 -0.14 0.37 -0.48 0.96
: : : : :
EAP (S.D.)
-1.40 -0.80 -0.80 -0.22 -0.80
( ( ( ( (
0.71) 0.68) 0.68) 0.68) 0.68)
: :
PATTERN
: : : : :
11110000 11120000 21110000 21120000 12110000
NEGATIVE TWICE THE LOGLIKELIHOOD= 10.5 (CHI-SQUARE FOR SEVERAL TIMES MORE EXAMINEES THAN CELLS) TOTAL, NEGATIVE TWICE THE LOGLIKELIHOOD, ALL GROUPS=
25.2
12.19 Individual scores for a skeletal maturity scale based on graded ratings of ossification sites in the knee Roche, Wainer, & Thissen (1975) calibrated 34 “indicators” (items) of skeletal maturity using Samejima’s (1969) graded model ; a description of the model and methods used is in Chapter V of that volume. The parameters of the data for the males are used here to “score” (estimate θ = skeletal age) using the following data in the file exampl19.dat: 40 33 33 29 8
1 1 1 1 1
0.5 1.0 2.0 3.0 5.0
2112111111112111111111111111111111 3113211111112122111111111111111111 4333211111113122111111111111111111 4543211111113122111111011111111111 5553211211114222112111111111323111
772
12 MULTILOG EXAMPLES 10 23 26 35 10 23 24 46
1 1 1 1 1 1 1 1
6.0 7.0 8.0 9.0 12.0 14.0 16.0 18.0
5553211211115322121111121111323011 5553211311115322111001111111323011 5553212221115322221001221111423211 5553211321115422222111211111423111 5553212321115522222111222111523021 5553210320115522022100220011523121 5553222323105522222222222221523222 5553222323025522222202222202523224
The parameters for the 34 indicators are in a file called exampl19.prm. This file was produced by MULTILOG in a (previous) calibration run. Note that the parameters in the Roche et al. (1975) table (in which the thresholds are called τ and the slopes are called d) are in years, instead of the usual standard units, so the results appear in years. The MULTILOG command file includes instructions to SCORE INDIVIDUAL data on the PROBLEM command, as well as to use no population distribution, because RWT skeletal ages are not normally computed using a population distribution. We also use CRITERION, which instructs MULTILOG to read the chronological age of each individual to use as a starting value for the iterative modal estimation procedure. The first ten characters (NCHARS=10) on each record are read as an identification field; using T-format, the age in that field is also read later as the CRITERION. The “test” has varying numbers of response categories for the 34 indicators, which are entered in the NC list on the TEST command. The command file exampl19.mlg is shown below. To see how to generate this command file using the syntax wizard, see Section 4.3.1. EXAMPL19.MLG ESTIMATION OF SKELETAL MATURITY BY THE RWT (1975) METHOD >PROBLEM SCORE, INDIVIDUAL, CRITERION, NEXAMINEES=13, NITEMS=34, NCHARS=10, DATA=‘EXAMPL19.DAT’; >TEST ALL, GRADED, NC=(5,5,5,3,2,2,2,3,2,3,3,3,5,5,2(0)12,3,3,5,2,3,2,2,4); >START ALL, FORMAT, PARAM=‘EXAMPL19.PRM’; >SAVE; >END; 6 123450 1111111111111111111111111111111111 2222222222222222222222222222222222 3333333333333333333333333333333333 4444444444444444444444444444444444 5555555555555555555555555555555555 0000000000000000000000000000000000 (10A1,1X,34A1,T7,F4.0)
The RWT estimates of skeletal age are modal estimates of θ , labeled THETAHAT on the last page of the MULTILOG output. Their estimated standard errors, the number of iterations required to compute each, and the contents of the ID field are also printed there. When using MULTILOG, modal estimates of θ are always computed in this way, in a subsequent run after the item parameters have been estimated. Frequently, several item analysis runs are required with a set of item-response data before a satisfactory set of item parameters is obtained; only then is it useful to score the individuals. Selected output for this run follows.
773
12 MULTILOG EXAMPLES ESTIMATION OF SKELETAL MATURITY BY THE RWT (1975) METHOD SCORING DATA... THETAHAT -2.306 -1.730 -1.352 -1.243 -0.507 -0.276 -0.340 0.376 0.155 0.814 1.012 1.963 2.383
S.E. 0.432 0.386 0.361 0.367 0.328 0.331 0.346 0.337 0.330 0.358 0.397 0.422 0.465
ITER 5 3 4 4 4 4 4 3 4 3 3 4 6
ID 40 33 33 29 8 10 23 26 35 10 23 24 46
FIELD 1 0.5 1 1.0 1 2.0 1 3.0 1 5.0 1 6.0 1 7.0 1 8.0 1 9.0 1 12.0 1 14.0 1 16.0 1 18.0
774
13 TESTFACT EXAMPLES
13 TESTFACT examples 13.1 Classical item analysis and scoring on a geography test with an external criterion The geography test discussed in this example consists of 20 items. The total score on the test is used as the criterion score. The items are testing the following topics:
Structure and landforms Erosion, transport, and deposition Climate and vegetation Mineral resources Agriculture and industry Population and transport Miscellaneous
This example illustrates the running of stacked problems. The two problems use the same data, but with different variable format statements. The same data are found in two identical data files, exampl01.da1 and exampl01.da2. The reason for the duplication is that, in the case of stacked problems, the same data file cannot be opened more than once during the analysis. The first ten lines of the data files exampl01.da1 and exampl01.da2 are shown below. 1201903390B32325251253531212145 62531 2201903400B12223111431231122312 02535 3201903410B12123432542455323111 92231 4201903420B15323121415431524135 91827 5201903430B43123221153531522151 81220 6201903440B45124321343431512313101121 7201903450B14523224514521123411 81826 8201903460B45125422444211421213 51217 9201903470B34423221541453322131122638 10201903480B44423525451431313114121628
The persons sitting for the test are classified by sex, with “G” denoting a girl, and “B” a boy. Columns 1 to 3 inclusive contain the case identification, while the gender classification is given in column 13. These fields are denoted by “3A1” and “A1” in the variable format statement. Note that the “X” operator is used to skip from column 3 to column 13. The width of the case identification field is also indicated by the NIDCHAR keyword on the INPUT command. The variable format statement for the first problem is (3A1,9X,A1,20A1,F2.0)
The item responses are given in columns 14 to 33 and are represented by “20A1” in the format statement. Finally, the criterion score is given in columns 34 and 35. Note that this score is read as a real number with format “F2.0”.
775
13 TESTFACT EXAMPLES The format statement for the second analysis is (3A1,9X,1X,20A1)
and is the same as for the first analysis, with the exceptions of the omission of the criterion score specification and the omission of the gender classification. In the first problem, an external criterion score is used. The PROBLEM command specifies that 20 items, with 6 responses each, are to be analyzed in the first problem (NITEM=20 and RESPONSE=6). To obtain estimated item statistics for the two gender groups, the responses are divided into two classes (CLASS=2) and the definition of the two classes is given in the CLASS command. The INPUT command indicates that the data are in the external data file exampl01.da1 (FILE keyword) and that it consists of scores (SCORES option). The external criterion score used is a score input with item responses (CRITMARK option on the CRITERION command) named “TWENTY” (NAME keyword). By specifying the ALPHA option on the RELIABILITY command, the calculation of coefficient alpha is requested. Alternatively, the Kuder-Richardson formula 20, which is the default reliability measure, may be requested using the KR20 option. The PLOT command requests line plots of the point biserial coefficient (PBISERIAL option) as discrimination index and with discriminating power with respect to the external criterion (CRITERION option). The measure of item difficulty is plotted in terms of the item facility (percent correct; default FACILITY option). Note that the use of the CONTINUE command in the case of stacked problems is optional. In the second part of exampl01.tsf the geography test is split into 2 subtests. This is indicated by the use of the SUBTEST keyword on the PROBLEM command and the SUBTEST command in which the BOUNDARY keyword is used to indicate that the 12th item is the last item in the first subtest, and the 20th item is the last item in subtest 2. The subtests are named using the NAME keyword on this command. The subtests are composed of items testing the following abilities:
Factual recall, comprehension and application Analysis, evaluation and synthesis.
The reordering of the items is indicated by the SELECT keyword on the PROBLEM command. The reordering is specified on the SELECT command, which lists the items in the order in which they are to be used. The fractile option is used to investigate behavior of items across the ability spectrum. The FRACTILES command is used to group scores into fractiles by score boundaries (SCORES option). The boundaries, consisting of the cumulative upper scores on the test bands, are defined using the BOUNDARY keyword on the FRACTILES command. The FRACTILES keyword on the PROBLEM command indicates that 3 fractiles will be used for score divisions. The INPUT command indicates that, as in the first analysis, scores are used as input. In addition, the LIST option requests 776
13 TESTFACT EXAMPLES the listing, for all subjects, of the identification, main and subtest scores in the output file. Each TESTFACT run produces output under headings labelled Phase 0 to Phase 7. The Phase 1 to Phase 4 output contains data description, plots, basic statistics, and item statistics. These are discussed in detail in Section 13.4.1. In the present example, the Phase 1 to Phase 4 output is suppressed using by setting the SKIP keyword on the PROBLEM command to 1. Phase 5 output provides information about tetrachoric correlations, while Phase 6 and 7 output are only produced if a FACTOR or BIFACTOR command is present in the command file. >TITLE EXAMPL01.TSF- GEOGRAPHY TEST WITH EXTERNAL CRITERION SCORE ITEM AND TEST STATISTICS >PROBLEM NITEM=20,RESPONSE=6,CLASS=2; >NAMES MISCELL1,MISCELL2,EROSION1,EROSION2,EROSION3, STRUCTU1,MINERAL1,MINERAL2,MINERAL3,AGRICUL1, MISCELL3,STRUCTU2,EROSION4,CLIMATE1,CLIMATE2, MINERAL4,AGRICUL2,AGR,POPULAT1,STRUCTU3; >RESPONSE ‘0’,’1’,’2’,’3’,’4’,’5’; >KEY 14423321441435112111; >CLASS IDEN=(G,B),NAME=(GIRLS,BOYS); >CRITERION CRITMARK, NAME=’TWENTY’; >RELIABIITY ALPHA; >PLOT PBISERIAL,CRITERION,FACILITY; >INPUT NIDCHAR=3,SCORES,FILE=‘EXAMPL01.DA1’; (3A1,9X,A1,20A1,F2.0) >TITLE GEOGRAPHY TEST SPLIT INTO 2 SUBTESTS AND USE OF FRACTILES ITEMS REORDERED >PROBLEM NITEM=20,RESPONSE=6,SELECT=20,SUBTEST=2,FRACTILES=3; >NAMES MISCELL1,MISCELL2,EROSION1,EROSION2,EROSION3, STRUCTU1,MINERAL1,MINERAL2,MINERAL3,AGRICUL1, MISCELL3,STRUCTU2,EROSION4,CLIMATE1,CLIMATE2, MINERAL4,AGRICUL2,AGR,POPULAT1,STRUCTU3; >RESPONSE ‘0’,’1’,’2’,’3’,’4’,’5’; >KEY 14423321441435112111; >SELECT 3,4,7(1)12,16(1)19,1,2,5,6,13,14,15,20; >SUBTEST BOUNDARY=(12,20),NAME=(RECALL,ANALYSIS); >FRACTILE SCORE,BOUNDARY=(7,13,20); >INPUT NIDCHAR=3,SCORES,LIST,FILE=‘EXAMPL01.DA2’; (3A1,9X,1X,20A1) >STOP
Portions of the Phase 5 output are shown below. The first part of the output contains, for each selected item, the number of cases, % correct, % omitted, % not reached and % not-presented. The summary shows that 2.5% of the respondents omitted item number 6. MAIN TEST MISSING RESPONSE INFORMATION ------------------------------------------------------------------------------ITEM
NUMBER PERCENT PERCENT PERCENT PERCENT OF CASES CORRECT OMITTED NOT REACHED NOT PRESENTED ------------------------------------------------------------------------------1. EROSION1 40 32.5 0.0 0.0 0.0 2. EROSION2 40 97.5 0.0 0.0 0.0 3. MINERAL1 40 57.5 0.0 0.0 0.0 4. MINERAL2 40 77.5 0.0 0.0 0.0 5. MINERAL3 40 62.5 0.0 0.0 0.0
777
13 TESTFACT EXAMPLES 6. AGRICUL1 39 50.0 2.5 0.0 0.0 7. MISCELL3 40 52.5 0.0 0.0 0.0 8. STRUCTU2 40 65.0 0.0 0.0 0.0 9. MINERAL4 40 22.5 0.0 0.0 0.0 10. AGRICUL2 40 47.5 0.0 0.0 0.0 11. AGRICUL3 40 60.0 0.0 0.0 0.0 12. POPULAT1 40 65.0 0.0 0.0 0.0 13. MISCELL1 40 70.0 0.0 0.0 0.0 14. MISCELL2 40 55.0 0.0 0.0 0.0 15. EROSION3 40 85.0 0.0 0.0 0.0 16. STRUCTU1 40 7.5 0.0 0.0 0.0 17. EROSION4 40 67.5 0.0 0.0 0.0 18. CLIMATE1 40 32.5 0.0 0.0 0.0 19. CLIMATE2 40 30.0 0.0 0.0 0.0 20. STRUCTU3 40 42.5 0.0 0.0 0.0 ------------------------------------------------------------------------------
Use is made of n × (n − 1) / 2 2 × 2 frequency tables to calculate the tetrachoric correlations. Since n (the number of items) equals 20, the number of possible tables is 20 × 19 / 2 = 190 . In this case, there are only 162 valid pairs of tables. Examples of non-valid pairs are given in Section 13.4.1. The output below shows a listing of the first four non-valid pairs, followed by the average tetrachoric correlation. -->ITEM PAIR( 2, 1): CELL FREQUENCIES ARE TOO SMALL FOR A MEANINGFUL RESULT; RTET=-1.00 SUBSTITUTED. -->ITEM PAIR( 3, 2): CELL FREQUENCIES ARE TOO SMALL FOR A MEANINGFUL RESULT; RTET= 1.00 SUBSTITUTED. -->ITEM PAIR( 4, 2): CELL FREQUENCIES ARE TOO SMALL FOR A MEANINGFUL RESULT; RTET= 1.00 SUBSTITUTED. -->ITEM PAIR( 5, 2): CELL FREQUENCIES ARE TOO SMALL FOR A MEANINGFUL RESULT; RTET= 1.00 SUBSTITUTED. AVERAGE TETRACHORIC CORRELATION = 0.0862 STANDARD DEVIATION = 0.2817 NUMBER OF VALID ITEM PAIRS = 162
13.2 Two-factor non-adaptive full information factor analysis on a five-item test (LSAT7) In this example, a non-adaptive full information item factor analysis is performed on 5 items, with 3 responses each, from the LSAT data (Bock & Lieberman (1970), LSAT data Section 7). The number of items and responses are indicated by the NITEMS and RESPONSE keywords on the PROBLEM command. The data are in the file exampl02.dat, and have the following layout:
Columns 1 to 2: Pattern number (ID) Columns 3 to 7: Item responses Columns 11 to 13: Weight (number of occurrences of pattern)
778
13 TESTFACT EXAMPLES The variable format statement (2A1,5A1,3X,I3)
lists these three fields in the same order, and the “X” operator is used to skip from column 7 to column 11. The INPUT command indicates that item scores are used as input (SCORES option) and that each data record starts with an identification field 2 characters in length (NIDCHAR=2). The WEIGHT keyword is set to PATTERN to indicate that each data record consists of an answer pattern with a frequency. Note that the frequency is read as an integer (I3) in the variable format statement. The three responses are listed on the RESPONSE command, while the KEY command indicates that a “1” is the correct response to all 5 items. By default, the RECODE option will be used on the TETRACHORIC command, and thus all omits will be recoded as wrong responses. The TETRACHORIC command specifies details concerning the tetrachoric correlation matrix. Coefficients will be printed to 3 decimal places (NDEC=3) and the matrix of tetrachoric correlations will appear in the printed output (LIST option). This matrix may also be saved to an external file if the CORRELAT option is included on the (optional) SAVE command. The FACTOR and FULL commands are used to specify parameters for the full information item factor analysis. Two factors and 3 latent roots are to be extracted, as indicated by the NFAC and NROOT keywords respectively. A PROMAX rotation is requested. Note that this keyword may not be abbreviated in the FACTOR command. The residual correlation matrix will be computed as the initial correlation matrix minus the final correction matrix (RESIDUAL option). An f-factor positive definite estimate of the latent response process correlation matrix will be computed (SMOOTH option). This option affects only the output of the final smoothed correlation matrix. A maximum of 20 EM cycles will be performed (CYCLES keyword on the FULL command). The NOADAPT option on the TECHNICAL command specifies that non-adaptive quadrature should be used to obtain the full information solution. Note that, if NFAC > 5, the presence of this option will be ignored and adaptive fractional quadrature will be performed. The smoothed correlation matrix, rotated factor loadings and item parameters are saved to external files (exampl02.smo, exampl02.rot and exampl02.par respectively) using the SMOOTH, ROTATE and PARM options on the SAVE command. >TITLE EXAMPL02.TSF- LSAT DATA NON-ADAPTIVE FULL INFORMATION ITEM FACTOR ANALYSIS COUNTED RESPONSE PATTERNS >PROBLEM NITEM=5,RESPONSE=3; >NAMES ITEM1,ITEM2,ITEM3,ITEM4,ITEM5; >RESPONSE ‘8’, ‘0’, ‘1’; >KEY 11111; >TETRACHORIC NDEC=3,LIST; >FACTOR NFAC=2,NROOT=3,ROTATE=PROMAX,RESIDUAL,SMOOTH; >FULL CYCLES=20; >TECHNICAL NOADAPT;
779
13 TESTFACT EXAMPLES >SAVE SMOOTH,ROTATE,PARM; >INPUT NIDCHAR=2,SCORES,WEIGHT=PATTERN, FILE=’EXAMPL02.DAT’; (2A1,5A1,3X,I3) >STOP;
13.3 One-factor non-adaptive full information item factor analysis of the fiveitem test In this example, the LSAT data of Section 13.2 are analyzed assuming a one-factor model. The purpose of the analysis is to compare the goodness-of-fit with that of the two-factor model, and to use the change in χ 2 between the models as a test of statistical significance of the second factor. The computation of classical item statistics is skipped (SKIP=1), and the factor loadings are not rotated or saved. >TITLE EXAMPL03.TSF- LSAT DATA NON-ADAPTIVE FULL INFORMATION ITEM FACTOR ANALYSIS TEST OF FIT >PROBLEM NITEM=5,RESPONSE=3,SKIP=1; >NAMES ITEM1,ITEM2,ITEM3,ITEM4,ITEM5; >RESPONSE ‘8’,‘0’,‘1’; >KEY 11111; >TETRACHORIC NDEC=3,LIST; >FACTOR NFAC=1,NROOT=3; >FULL CYCLES=16; >TECHNICAL NOADAPT; >INPUT NIDCHAR=2,SCORES,WEIGHT=PATTERN,FILE=’EXAMPL02.DAT’; (2A1,5A1,3X,I3) >STOP;
13.4 A three-factor adaptive item factor analysis with Bayes (EAP) estimation of factor scores: 32 items from an activity survey This example analyzes 32 items selected from the 48-item version of the Jenkins Activity Survey for Health Prediction, Form B (Jenkins, Rosenman, & Zyzanski, 1972). The data are responses of 598 men from central Finland drawn from a larger survey sample. Most of the items are rated on three-point scales representing little or no, occasional, or frequent occurrence of the activity or behavior in question. For purposes of the present analysis, the scales have been dichotomized near the median. Wording in the positive or negative direction varies from item to item as follows (item numbers are those of the original pool of items from which those of the present form were selected): -Q156,-Q157,+Q158,-Q165,-Q166,-Q167,+Q247,+Q248,-Q249,-Q250,+Q251, +Q252,+Q253,+Q254,+Q255,+Q256,+Q257,-Q258,-259,+Q260,+Q261,+Q262, +Q263,+Q264,+Q265,-Q266,+Q267,+Q268,+Q269,+Q270,+Q271,+Q272,-Q273, -Q274,-Q275,+Q276,+Q277,+Q278,-Q279,-Q280,+Q307,+Q308,+Q309,+Q310, +Q311,-Q312,-Q313,-Q314.
The first 7 lines of the data file exampl04.dat are shown below.
780
13 TESTFACT EXAMPLES 201000220122112221022212202112211101122112222000 001221211011100111111111111110111102211111211020 0010.02100222122021221222112112212.0011111222001 002020220212012120011112112221221022211111222202 201000221000211221221112012211122112211111222000 001001221022011120022222212222211101121112222101 102100111022112120021212212221121212111022200021
The first 10 columns of each record are used as case identification and are read first. Starting again in the first column by using the “T” operator, the responses to the 48 items are read as single fields (48A1). (10A1,T1,48A1)
The SELECT keyword on the PROBLEM command indicates that 32 items are selected from the original 48 items. The SELECT command provides the selected items in the order in which they will be used. The RESPONSE command lists the 5 responses indicated on the PROBLEM command (RESPONSE keyword) and the KEY command provides the correct responses for each of the 48 items. The NOTPRESENTED option on the PROBLEM command is required if one of the response codes identifies not-presented items. The “.” code on the RESPONSE command identifies these responses. The TETRACHORIC command requests the printing of the coefficients to 3 decimal places (NDEC=3) in the printed output file (LIST option). The tetrachoric correlation matrix, item parameters, rotated factor loadings, and the factor scores will be saved in the files exampl04.cor, exampl04.par, example04.rot, and exampl04.fsc, respectively, as specified on the SAVE command. The FACTOR and FULL commands are used to specify parameters for the full information item factor analysis. Three factors and ten latent roots are to be extracted, as indicated by the NFAC and NROOT keywords respectively. A VARIMAX rotation is requested. Note that this keyword may not be abbreviated in the FACTOR command. A maximum of 80 EM cycles will be performed (CYCLES keyword on the FULL command). The convergence criterion for the EM cycles is given by the PRECISION keyword on the TECHNICAL command. Cases will be scored by EAP (Expected A Posteriori, or Bayes) estimation with adaptive quadrature (METHOD=2 on the SCORE command). Posterior standard deviations will also be computed. Results will be saved in the exampl04.fsc file (FSCORES option on the SAVE command). The factor scores for the first 20 cases will be listed in the output file (LIST=20). See Section 13.5 for MAP (Maximum A Posteriori, or Bayes Modal) estimation for the same cases. >TITLE EXAMPL04.TSF-ITEMS FROM THE JENKINS ACTIVITY SURVEY ADAPTIVE ITEM FACTOR ANALYSIS AND FACTOR SCORE ESTIMATION >PROBLEM NITEMS=48,SELECT=32,RESPONSES=5,NOTPRESENTED; >NAMES Q156,Q157,Q158,Q165,Q166,Q167,Q247,Q248,Q249,Q250,Q251,Q252, Q253,Q254,Q255,Q256,Q257,Q258,Q259,Q260,Q261,Q262,Q263,Q264, Q265,Q266,Q267,Q268,Q269,Q270,Q271,Q272,Q273,Q274,Q275,Q276, Q277,Q278,Q279,Q280,Q307,Q308,Q309,Q310,Q311,Q312,Q313,Q314; >RESPONSE ‘8’, ‘0’, ‘1’, ‘2’, ‘.’; >KEY 002000220022222220022222202222220002220022222000;
781
13 TESTFACT EXAMPLES >SELECT 3,5,6,7,9,11(1)14,17(1)23,25(1)30,32,33,35,36,39(1)42,47,48; >TETRACHORIC LIST, NDEC=3; >FACTOR NFAC=3,NROOT=10,ROTATE=VARIMAX; >FULL CYCLES=80; >TECHNICAL PRECISION=0.005; >SCORE METHOD=2,LIST=20; >SAVE CORR,PARM,FSCORE,ROTATE; >INPUT NIDCHAR=10,SCORES,FILE=‘EXAMPL04.DAT’; (10A1,T1,48A1) >STOP
13.4.1 Discussion of output
The first part of the output lists the name of the command file (exampl04.tsf) and the name of the output file (exampl04.out). Each TESTFACT run produces output under one or more of the following headings, depending on the type of analysis.
Phase 0: input commands Phase 1: data description, histogram and basic statistics Phase 2: item statistics Phase 3: item difficulty x discrimination plot Phase 4: class item statistics Phase 5: tetrachoric correlations / response by fractile tables Phase 6: factor analysis / bifactor analysis Phase 7: general bifactor score: EAP estimate/factor score EAP estimates/factor score MAP estimates.
The analysis specified in exampl04.tsf produces Phase 0, Phase 1, Phase 2, Phase 5, and Phase 7 output. Phase 0: Input commands
Regardless of the type of analysis, a Phase 0 output is produced, being an echo of the input commands in the *.tsf file. PHASE 0: INPUT COMMANDS ITEMS FROM THE JENKINS ACTIVITY SURVEY ADAPTIVE ITEM FACTOR ANALYSIS AND FACTOR SCORE ESTIMATION ----------------------------------------------------------->PROBLEM NITEM=48,SELECT=32,RESPONSES=5,NOTPRESENTED; This example analyzes 32 items selected from the 48-item version of the Jenkins Activity Survey for Health Prediction, Form B (Jenkins, Rosenman, and Zyzanski, 1972). The data are responses of 598 men from central Finland drawn from a larger survey sample. Most of the items are rated on three-point scales representing little or no, occasional, or frequent occurence of the activity or behavior in question. For purposes of the present analysis, the scales have been dichotomized near the median. Wording in the positive or negative direction varies from item to item as follows (item numbers are those of the original pool of items from which those of the present form were selected): -Q156,-Q157,+Q158,-Q165,-Q166,-Q167,+Q247,+Q248,-Q249,-Q250, +Q251,+Q252,+Q253,+Q254,+Q255,+Q256,+Q257,-Q258,-Q259,+Q260,+Q261,
782
13 TESTFACT EXAMPLES +Q262,+Q263,+Q264,+Q265,-Q266,+Q267,+Q268,+Q269,+Q270,+Q271,+Q272, -Q273,-Q274,-Q275,+Q276,+Q277,+Q278,-Q279,-Q280,+Q307,+Q308,+Q309, +Q310,+Q311,-Q312,-Q313,-Q314. The tetrachoric correlation matrix, item parameters, rotated factor loadings, and the factor scores will be saved in the files EXAMPL03.COR, EXAMPL03.PAR, EXAMPL03.ROT, and EXAMPL03.FSC, respectively.Cases will be scored by EAP (Expected A Posteriori, or Bayes) estimation with adaptive quadrature (Method 2). Posterior standard deviations will also be computed. Results will be saved in the EXAMPL03.FSC file. See Exampl3a.tsf for MAP (Maximum A Posteriori, or Bayes Modal) estimation for the same cases. >NAMES Q156,Q157,Q158,Q165,Q166,Q167,Q247,Q248,Q249,Q250,Q251,Q252, Q253,Q254,Q255,Q256,Q257,Q258,Q259,Q260,Q261,Q262,Q263,Q264, Q265,Q266,Q267,Q268,Q269,Q270,Q271,Q272,Q273,Q274,Q275,Q276, Q277,Q278,Q279,Q280,Q307,Q308,Q309,Q310,Q311,Q312,Q313,Q314; >RESPONSE ‘8’, ‘0’, ‘1’, ‘2’, ‘.’; >KEY 002000220022222220022222202222220002220022222000; >SELECT 3,5,6,7,9,11(1)14,17(1)23,25(1)30,32,33,35,36,39(1)42,47,48; >TETRACHORIC LIST, NDEC=3; >FACTOR NFAC=3,NROOT=10,ROTATE=VARIMAX; >FULL CYCLES=80; >TECHNICAL PRECISION=0.005; >SCORE METHOD=2,LIST=20; >SAVE CORR,PARM,FSCORE,ROTATE; >INPUT NIDCHAR=10,SCORES,FILE=‘EXAMPL04.DAT’; DATA FILENAME IS EXAMPL04.DAT DATA FORMAT= (10A1,T1,48A1)
Phase 1: Data description
Values of the response categories (8, 0, 1, 2, .), the answer key, contents of the first observation, the sum of weights and number of records are given. This information enables you to verify that the data values were read correctly from the data file exampl04.dat. The response categories indicate a code of “8” for omitted responses (first value) and a code of “.” for not-presented items (last value). Thirty-two items were selected from the 48-item test. Based on the answer key values, a total score for each of the 598 respondents is computed. Each item has a set of responses: right, wrong, omit, or not-presented. For item j, j = 1, 2, …, 32, the response of person i, i = 1, 2, …, 598 can be written as xij = 1
if the response is correct, and
xij = 0
if the response is incorrect.
783
13 TESTFACT EXAMPLES At your option, omitted items can be considered either wrong or not presented. The total test score X i for person i is 32
X i = ∑ xij . j =1
Respondent 1, for example, has a total score of 19 correct out of a possible 32 as shown below. Answer key: 20020222220022222022222002002200
Respondent 1: 10020221121022212021121101211200 EXAMPL04.TSF- ITEMS FROM THE JENKINS ACTIVITY SURVEY ADAPTIVE ITEM FACTOR ANALYSIS AND FACTOR SCORE ESTIMATION -----------------------------------------------------------RESPONSE CATEGORIES: 8 0 1 2 . ANSWER KEY: 20020222220022222022222002002200 CONTENTS OF FIRST OBSERVATION: ID=2010002201 WEIGHT= 1 ITEM RESPONSES= 201000220122112221022212202112211101122112222000 ITEM RESPONSES AFTER SELECTION = 10020221121022212021121101211200 SUM OF WEIGHTS = NUMBER OF RECORDS=
598 598
Using this information, a frequency table of the score distribution is calculated and presented graphically. PHASE 1: HISTOGRAM AND BASIC STATISTICS NUMBER OF OBSERVATIONS AT EACH SCORE SCORE COUNT FREQ | SCORE 0 0 0.0 | 11 1 0 0.0 | 12 2 0 0.0 | 13 3 0 0.0 | 14 4 1 0.2 | 15 5 2 0.3 | 16 6 1 0.2 | 17 7 5 0.8 | 18 8 7 1.2 | 19 9 18 3.0 | 20 10 20 3.3 | 21
COUNT 35 40 38 52 54 54 56 57 36 43 32
FREQ 5.9 6.7 6.4 8.7 9.0 9.0 9.4 9.5 6.0 7.2 5.4
| | | | | | | | | | | |
SCORE 22 23 24 25 26 27 28 29 30 31 32
COUNT 21 10 8 6 1 1 0 0 0 0 0
FREQ 3.5 1.7 1.3 1.0 0.2 0.2 0.0 0.0 0.0 0.0 0.0
784
13 TESTFACT EXAMPLES MAIN TEST HISTOGRAM FREQUENCY : | | | ** | **** | ***** 8.0+ ***** | ***** | ***** | ***** * | * ***** * | ********* | ********** | *********** | *********** | *********** 4.0+ *********** | *********** | ************* | ************** | ************** | ************** | *************** | **************** | ******************* | ******************* 0.0+-----+----+----+----+----+----+----+----+----+----+----+----+----+-0. 5. 10. 15. 20. 25. 30. SCORES
The last portion of the Phase 1 output gives the mean (15.9) and standard deviation (4.0) of the Total Scores. TEST MAIN
RECORD 598
NUMBER 598
MEAN 15.9
S.D. 4.0
PROPORTION 0.497
S.D. 0.500
The proportion of correct responses, p, is 598 32
p = ∑∑ xij /(32 × 598) = 0.497 i =1 j =1
with a standard deviation
p (1 − p ) = 0.5. Phase 2: Item statistics
For each item, eight statistics are produced. The Number, Mean and S.D. for item 2, for example, are 590, 15.92, and 4.03 respectively. These values are obtained by “deleting” each row of the data if a not-presented code is encountered for item 2. Since 8 rows contain not-presented codes, the mean and standard deviation of the Total Scores is calculated for the remaining 590 cases.
785
13 TESTFACT EXAMPLES Note, for example, that item 1 was presented to all 598 persons, while item 4 was presented to 592 persons. PHASE 2: ITEM STATISTICS MAIN TEST ITEM STATISTICS ITEM 1 Q158 2 Q166 3 Q167 4 Q247 5 Q249 6 Q251 7 Q252 8 Q253 9 Q254 10 Q257 ... 31 Q313 32 Q314
NUMBER 598 590 596 592 594 598 598 598 597 597
MEAN 15.91 15.92 15.90 15.93 15.92 15.91 15.91 15.91 15.91 15.92
S.D. 4.01 4.03 4.01 4.01 4.01 4.01 4.01 4.01 4.02 4.01
RMEAN FACILITY 14.46 0.206 17.13 0.653 16.35 0.790 16.71 0.694 15.89 0.466 17.16 0.532 17.39 0.490 18.16 0.410 18.99 0.203 17.99 0.449
DIFF 16.29 11.43 9.77 10.97 13.34 12.68 13.10 13.91 16.33 13.51
BIS -0.262 0.532 0.305 0.384 -0.008 0.417 0.451 0.591 0.551 0.585
P.BIS -0.185 0.413 0.215 0.292 -0.006 0.332 0.360 0.467 0.387 0.466
597 594
15.91 15.93
4.02 4.02
16.31 16.86
8.98 12.13
0.349 0.351
0.231 0.278
0.843 0.586
The mean score for those subjects who get a specific item correct is denoted by RMEAN. For example, since 385 respondents selected the correct response for item 2, RMEAN for item 2 is calculated as the mean of the corresponding 385 Total Scores and equals 17.13. The item facility (FACILITY) is the proportion correct response for a specific item. For example, 385 of the 590 respondents presented with item 2 selected the correct response, and hence p2 = 385 / 590 = 0.653. The delta statistic ( ∆ or DIFF) is calculated as ∆ = −4Φ −1 ( p ) + 13, where p is the item facility and Φ −1 denotes the inverse normal transformation. This statistic has an effective range of 1 to 25, with a mean and standard deviation of 13 and 4 respectively.
The last 2 statistics are the biserial (BIS) and point biserial (P.BIS) correlations. The formula for the sample point biserial correlation is P.BIS =
RMEAN − MEAN facility S .D. 1 − facility .
For item 8, for example,
786
13 TESTFACT EXAMPLES P.BIS =
18.16 − 15.91 0.41 = 0.467. 4.01 0.59
The point biserial correlation is the correlation between the item score and the total score, or subtest score. Theoretically −1 ≤ P.BIS ≤ 1 but in practice −0.20 ≤ P.BIS ≤ 0.75. Therefore, 0.467 indicates a relatively strong association between item 8 and the Total Score. The formula for calculating the sample biserial correlation coefficient, BIS, is
BIS =
RMEAN − MEAN facility . × S .D. h( facility )
Consider, for example, the item 3 facility, which equals 0.790. From the inverse normal tables, this corresponds to a z p -value of 0.8062.
1 1 exp(− z 2p ) 2 2π = 0.399 × 0.723 = 0.29.
h( facility ) =
For item 3, 16.35 − 15.90 0.79 × 4.01 0.29 = 0.305.
BIS =
Phase 5: Tetrachoric correlations
The first part of the output contains, for each selected item, the Number of Cases, Percent Correct, Percent Omitted, Percent Not Reached and Percent Not Presented. PHASE 5:
TETRACHORIC CORRELATIONS
MAIN TEST MISSING RESPONSE INFORMATION ---------------------------------------------------------------------------ITEM NUMBER PERCENT PERCENT PERCENT PERCENT OF CASES CORRECT OMITTED NOT REACHED NOT PRESENTED ---------------------------------------------------------------------------1. Q158 598 20.6 0.0 0.0 0.0 2. Q166 590 64.4 0.0 0.0 1.3 3. Q167 596 78.8 0.0 0.0 0.3 4. Q247 592 68.7 0.0 0.0 1.0 5. Q249 594 46.3 0.0 0.0 0.7 … 31. Q313 597 84.1 0.0 0.0 0.2 32. Q314 594 58.2 0.0 0.0 0.7 ----------------------------------------------------------------------------
787
13 TESTFACT EXAMPLES This summary indicates that there were no omitted codes in the data and that all 598 respondents could complete the test. The percent Not Presented varies from 0.0 to a maximum of 1.3 for item 2. For item 2, this percentage is calculated as 598 − 590 ×100 = 1.3%. 598 Note that the Percent Correct is calculated here as the number of respondents who selected the correct answer, divided by the total number of cases. For item 2
PERCENT CORRECT =
385 ×100 = 64.38%. 598
This value differs from the facility estimate (385/590) given under Phase 2 of the output. Display 1: Tetrachoric correlation matrix
The tetrachoric correlation coefficient is widely used as a measure of association between two dichotomous items. Tetrachoric correlations are obtained by hypothesizing, for each item, the existence of a continuous “latent” variable underlying the “right-wrong” dichotomy imposed in scoring. It is additionally hypothesized that, for each pair of items, the corresponding two continuous “latent” variables have a bivariate normal distribution. AVERAGE TETRACHORIC CORRELATION = 0.0654 STANDARD DEVIATION = 0.2384 NUMBER OF VALID ITEM PAIRS = 496 DISPLAY
1 2 3 4 5 6
1.
Q158 Q166 Q167 Q247 Q249 Q251
TETRACHORIC CORRELATION MATRIX 1 Q158 1.000 -0.383 -0.145 -0.535 0.106 -0.065 ...
2 Q166
3 Q167
4 Q247
5 Q249
6 Q251
1.000 0.124 0.368 -0.019 0.017
1.000 0.054 0.016 0.019
1.000 -0.161 0.016
1.000 -0.126
1.000
In TESTFACT, use is made of n(n − 1) / 2 , (n = number of items) 2 × 2 frequency tables to calculate the tetrachoric coefficients. From the computer output, the number of valid item pairs is 496. Since the number of items equals 32, 32(32 – 1)/2 = 496, this data set contains no non-valid pairs. Non-valid pairs have zero off-diagonal or marginal frequencies. Examples of non-valid pairs are
788
13 TESTFACT EXAMPLES R W
O
R W O
R W R O O W
and R W R
O
W
O
The average tetrachoric correlation equals 0.0654. Since the output has both negative and positive correlation coefficients, the average value does not shed much light on the actual strength of association between item pairs. Note that tetrachoric correlation matrices are not necessarily positive definite. Phase 6: Factor analysis Display 2: The positive latent roots of the correlation matrix
By definition, a symmetric matrix is positive definite if all its characteristic roots are positive. From the output below, it is seen that only the first 31 of the 32 roots are positive, and therefore the 32 × 32 matrix of tetrachoric correlations is not positive definite. This problem can be corrected by replacing the negative roots of the matrix by zero or a small non-zero quantity. DISPLAY
2.
THE POSITIVE LATENT ROOTS OF THE CORRELATION MATRIX
1
1 7.491350
2 3.442602
3 2.592276
4 1.745235
5 1.576302
6 1.442306
1
7 1.248438
8 1.118638
9 1.015248
10 0.971235
11 0.908476
12 0.835705
1
13 0.768426
14 0.719607
15 0.657375
16 0.638227
17 0.631485
18 0.555802
789
13 TESTFACT EXAMPLES 1
19 0.514488
20 0.461871
21 0.398661
22 0.375292
23 0.349726
24 0.312994
1
25 0.292964
26 0.243591
27 0.218973
28 0.183170
29 0.167582
30 0.117183
1
31 0.055375
Display 3: Number of items and sum of latent roots and their ratio
This section of the output shows the sum of positive roots and the ratio with which each root has to be multiplied to obtain a sum of “corrected roots” which equals the number of items. To illustrate, consider a 5 × 5 correlation matrix with latent roots 3, 1, 0.8, 0.3, and –0.1. The sum of the roots equals 5. In general, for any correlation matrix based on n items, the sum of roots equals n. Suppose the value of –0.1 is replaced by 0.0001, then the new sum of roots equals 5.1001. However, by multiplying each root by the ratio 5/5.1001 = 0.9804, a “corrected” set of roots is obtained in the sense that their sum equals 5. From the Display 3 part of the output, the ratio required to obtain a corrected set of latent roots equals 0.9984211. The corrected set is given under the Display 4 heading. DISPLAY
3.
NUMBER OF ITEMS AND SUM OF LATENT ROOTS AND THEIR RATIO 32 32.0506033 0.9984211
Display 4: Corrected latent roots DISPLAY
1
4.
THE CORRECTED LATENT ROOTS OF THE CORRELATION MATRIX
1 2 3 4 5 6 7.479522 3.437167 2.588184 1.742479 1.573814 1.440029 ...
Display 5: Initial smoothed inter-item correlation matrix
A tetrachoric correlation matrix is not necessarily positive definite and in TESTFACT it is replaced by a so-called smoothed inter-item correlation matrix. For the reader familiar with matrix algebra, a short description of the smoothing procedure follows. Any symmetric matrix can be decomposed as R = VDV '
where D is a diagonal matrix with diagonal elements the characteristic roots of ng = 32. As mentioned previously, if all roots are positive, that is, all the diagonal elements of D are positive, R is a positive definite matrix. When this is not the case, a “smoothed” correlation matrix, R* may 790
13 TESTFACT EXAMPLES be obtained by replacing the elements of D with the corrected roots and negative roots with either 0 or some small positive quantity, so that R * = VD*V ' ,
where the columns of V are eigenvectors and the elements of D* the corrected latent roots. The elements of the smoothed correlation matrix for the first 6 of the 32 items are given below. DISPLAY
1 2 3 4 5 6
5.
Q158 Q166 Q167 Q247 Q249 Q251
INITIAL SMOOTHED INTER-ITEM CORRELATION MATRIX 1 Q158 1.000 -0.383 -0.145 -0.534 0.106 -0.066
2 Q166 1.000 0.124 0.368 -0.019 0.017
3 Q167 1.000 0.054 0.016 0.019
4 Q247
5 Q249
1.000 -0.161 0.016
1.000 -0.126
6 Q251
1.000
Display 6: Iterated communality estimates
A communality is defined as the squared multiple correlation between an observed variable and the set of factors. The output below shows the estimated communalities for iterations 1, 2, 3, and 4. Note the small changes in the estimated values going from iteration 3 to iteration 4. At iteration 1, the squared multiple correlation of an item with all other items is calculated for each of the 32 items. The MINRES method (see Display 7) is subsequently used to obtain postsolution improvements to these initial multiple regression communality estimates. DISPLAY
1 2 3 4 5 6 31 32
6.
Q158 Q166 Q167 Q247 Q249 Q251 … Q313 Q314
ITERATED COMMUNALITY ESTIMATES 1 0.413 0.370 0.156 0.516 0.142 0.351
2 0.373 0.325 0.116 0.471 0.088 0.269
3 0.371 0.323 0.115 0.466 0.087 0.257
4 0.371 0.322 0.115 0.465 0.087 0.255
0.477 0.458
0.422 0.396
0.415 0.387
0.414 0.386
Display 7: The NROOT largest latent roots of the correlation matrix
TESTFACT uses the minimum squared residuals (MINRES) method to extract factors from the smoothed correlation matrix R* . Let eij denote the difference between a smoothed correlation coefficient rij* and the corresponding estimated correlation coefficient pij . These estimated coefficients are functions of the factor loadings and unique variances. The MINRES method mimimizes the residual sum of squares, 791
13 TESTFACT EXAMPLES
∑e
2 ij
, using ordinary least squares. A more technical description, which may be skipped, fol-
lows. The MINRES method minimizes the sum of squares of the residuals in a matrix ∆ , where
∆ p× p = R* − ( ΛΛ ' + Du ) where Λ is a p × k common factor matrix and the diagonal elements uii of Du , the unique variances, i = 1, 2, …, p. If ρi2 denotes the communality for item i, then uii equals 1 − ρi2 . The sum of squares of the residuals is expressed as a statistical function (see, e.g. Tucker & MacCallum, 1997), which is minimized by the determination of the matrix of factor loadings Λ and uniqueness Du . In this part of the output, the NROOT largest roots of the matrix R * − Du
are reported. Note that, since uii equals 1 − ρi2 , characteristic roots are actually obtained from the smoothed correlation matrix with the unit diagonal elements replaced by the communalities. In general, the matrix R* − Du will be non-positive definite and hence a subset of the roots will be negative. If one replaces NROOT=10 in the FACTOR command with, for example, NROOT=20, the output shows that roots with numbers 16, 17, 18 and higher are all negative. An empirical rule for the selection of the number of factors, k, is to set k equal to the number of latent roots larger than 1. For the present example it appears as if 3 or 4 factors are appropriate. Usually, the number of factors is selected on the basis of some theoretical framework concerning the items included in the analysis. DISPLAY
7.
THE NROOT LARGEST LATENT ROOTS OF THE CORRELATION MATRIX
1
1 6.886994
2 2.861018
3 1.961481
4 1.149766
1
7 0.582337
8 0.423875
9 0.326571
10 0.270941
5 0.934423
6 0.738751
Display 8: MINRES principal factor loadings
The estimated factor loadings at convergence of the MINRES method are given below. These values are used to obtain starting values for the marginal maximum likelihood procedure specified in the FULL (full information) command. Note that each communality is equal to the sum of squares of the corresponding factor loadings. 792
13 TESTFACT EXAMPLES For example, for item 12, the 3 factor loadings are 0.406, 0.275, and 0.555. Hence, p122 = 0.4062 + 0.2752 + 0.5552 = 0.549
(see Display 6 at iteration 4 as given in the complete output file). DISPLAY
1 2 3 4 5 6 31 32
8.
MINRES PRINCIPAL FACTOR LOADINGS
Q158 Q166 Q167 Q247 Q249 Q251 ... Q313 Q314
1 2 3 -0.579 0.189 0.022 0.519 -0.230 -0.001 0.246 0.215 -0.091 0.535 -0.420 -0.049 -0.152 -0.022 -0.251 0.250 0.245 0.364 0.431 -0.478 -0.018 0.338 -0.511 0.105
Display 9: Initial intercept and slope estimates
The intercept and slope estimates are functions of the item facility and factor loadings. If the ROTATE keyword is omitted in the FACTOR command, the factor loadings are the MINRES factor loadings (see Display 8). Otherwise the initial rotated factor loadings are used (not shown in the output). Suppose the factor loadings for item 1 and a 3-factor solution are denoted by f11 , f12 , and f13 , respectively. Let c1 = 1 − f112 − f122 − f132 , and denote the slopes corresponding to item 1 by s11 , s12 , and s13 respectively. Then
s11 =
Intercepts are computed as
f f11 f , s12 = 12 , s13 = 13 . c1 c1 c1
zi , where ci 3
ci = 1 − ∑ f ij2 j =1
and zi is the z-value corresponding to an area under the N(0,1) curve equal to the item i facility.
793
13 TESTFACT EXAMPLES For item 1, for example, facility equals 0.206 and the corresponding z-value is –0.8202. For item 1, c1 = 0.791 and therefore the item 1 intercept estimate is INTERCEPT =
−0.8202 = −1.036. 0.791
Conversely, factor loadings are related to the slopes. Let fij and sij respectively denote the j-th factor loading and slope of item i, j = 1, 2, …, nfac. Then fij =
sij ki
,
where 3
ki = 1 + ∑ sij2 . j =1
The initial intercept and slope values are used as initial estimates for the full information maximum likelihood procedure specified by the FULL command. DISPLAY 1 2 3 4 5 6
Q158 Q166 Q167 Q247 Q249 Q251 ... 31 Q313 32 Q314
9.
INITIAL INTERCEPT AND SLOPE ESTIMATES INTERCEPT SLOPES 1 2 3 -1.036 0.387 0.636 0.191 0.476 -0.285 -0.609 -0.156 0.858 -0.341 0.023 -0.115 0.695 -0.245 -0.900 -0.033 -0.088 -0.030 0.092 0.293 0.092 -0.097 0.025 -0.576 1.313 0.277
-0.083 0.107
-0.837 -0.784
0.020 -0.045
Display 10: The EM estimation of parameters
This part of the output shows that parameter estimates will be based on the EM (Expectation Maximization) method and that the number of quadrature points equals 4. Quadrature is a numeric integration method that is often used in practice to calculate the value of an integral, when no closed-form solution exists. For the interested reader, a brief description of the quadrature method to calculate the loglikelihood function is presented next.
794
13 TESTFACT EXAMPLES For a one-factor analysis, for example, the log-likelihood function can be expressed as N
∞
=1
−∞
log ∫ gα (θ , x)dx ∑ α where N denotes the number of cases, and θ a set of unknown parameters. The integrals, or so-called marginal probabilities, are approximated by ∞
NQUAD
−∞
k =1
∫ gα (θ , x)dx = ∑
wk gα (θ , xk )
where wk denote the weights and xk the quadrature points. Display 11: quadrature points and weights
The numeric values of the 4 quadrature points and weights are listed. Note that the weights are always positive and that the quadrature points are symmetric. DISPLAY
11.
1 2 3 4
4
QUADRATURE POINTS AND WEIGHTS:
-2.334414 -0.741964 0.741964 2.334414
0.045876 0.454124 0.454124 0.045876
The next part of the output shows the progress of the iterative procedure. At each cycle, -2 x LOG-LIKELIHOOD is reported as well as the maximum change in the intercept and slope values. For example, the maximum change in slope 1 estimates is equal to 0.098630. In other words, starting from the initial slope values of 0.387 (item 1), 0.467 (item 2), …, 0.277 (item 32), the differences between these values and the revised cycle 1 slope 1 estimates are at the most 0.098630 units. Small maximum changes in intercept and slope estimates are therefore an indication of convergence. Note that, starting from cycle 6, the difference between –2 log L of the previous cycle and the present cycle is reported. At cycle 19, for example, this value, reported as CHANGE, is 0.0726. SUM OF MARGINAL PROBABILITIES = CYCLE
1
0.17040D-02
- 2 X MARGINAL LOG LIKELIHOOD =
MAXIMUM CHANGE OF ESTIMATES INTERCEPT = 0.038118
SLOPE =
0.2084060567D+05
0.098630 0.056828 0.037478
795
13 TESTFACT EXAMPLES Number of patterns with zero probability = ... SUM OF MARGINAL PROBABILITIES = CYCLE 32
0
0.17930D-02
- 2 X MARGINAL LOG LIKELIHOOD = CHANGE =
MAXIMUM CHANGE OF ESTIMATES INTERCEPT = 0.002038
0.2080175353D+05 -0.3000105835D-02
SLOPE =
Number of patterns with zero probability =
0.005042 0.001369 0.003811 0
Display 12: χ 2 and degrees of freedom
The χ 2 -statistic reported below is calculated as NR
Wj W × p j T
χ = ∑ W j log 2
j =1
,
where N R denotes the number of unique observed response patterns, W j the sum of weights and p j the marginal probability (marginal likelihood function) for pattern j. The degrees of freedom, ndf, equal ndf = ( N R − 1) − [(nfac + 1)n − nfac(nfac − 1) / 2]. For this example, N R = 598 , nfac = 3, and n (number of items) equals 32. Hence ndf = 597 − [128 − 3] = 472. This χ 2 statistic can be used to test hypotheses of the form:
H 0 : A k- factor model provides an adequate description of the data.
H1 : A (k + 1)- factor model provides an adequate description of the data.
The resultant test statistic is the difference between the χ 2 under H 0 and the χ 2 under H1 with degrees of freedom equal to the difference in degrees of freedom for H 0 and H1 .
796
13 TESTFACT EXAMPLES If we replace the NFAC=3 keyword in the FACTOR command with NFAC=2, then
χ 02 = 13498.63 ndf = 502. From the output below, χ12 = 13155.03 with 472 degrees of freedom. The χ 2 for a 2-factor versus a 3-factor model is 13498.63 – 13155.03 = 343.60 with 502 – 472 = 30 degrees of freedom. Since this value is highly significant, we reject the 2-factor model in favor of the 3-factor model. DISPLAY
12.
CHI-SQUARE =
13155.03
DF =
472.00
P = 0.000
Display 13: Untransformed item parameters
The output below shows the estimated intercept and slope estimates after convergence is attained, or alternatively, after the maximum number of cycles specified is used. The number of EM cycles can be specified by one of the following commands: >FULL CYCLES = ncycles; >TECHNICAL ITER(a,b,c); DISPLAY 1 2 3 4 5 6
13.
Q158 Q166 Q167 Q247 Q249 Q251 ... 31 Q313 32 Q314
UNTRANSFORMED ITEM PARAMETERS INTERCEPT SLOPE ESTIMATES 1 2 3 -1.048 0.280 0.620 0.264 0.482 -0.244 -0.562 -0.181 0.868 -0.294 0.038 -0.135 0.693 -0.141 -0.853 -0.125 -0.086 -0.028 0.083 0.277 0.093 -0.066 0.015 -0.591 1.361 0.278
0.063 0.115
-0.863 -0.757
-0.133 -0.069
Display 14: Standardized difficulty, communality, and principal factors
Each communality is equal to the sum of squared factor loadings for the corresponding item. For example, for item 1 the factor loadings are –0.533, –0.194, and 0.069. The communality is equal to c j ∼ N (m, s ). The standardized difficulty for item i is calculated as – intercept / ki , where (see comments for Display 9)
ki = 1/ si21 + si22 + si23 and sij denotes the j-th slope for item j. For item 1, for example,
si21 + si22 + si23 = 0.2792 + 0.6212 + 0.2642 = 0.533.
797
13 TESTFACT EXAMPLES Hence, the standardized difficulty for item 1 = –( –1.048)/1.238 = 0.846. An item with a standardized difficulty of 0 can be regarded as an item with “average” difficulty. Standardized difficulty scores above 0 are associated with the more difficult items and a value of 1.0, for example, indicates that examinees can be expected to find this item more difficult to answer than an item with standardized difficulty of less than 1. On the other hand, items with standardized difficulty of less than 0 (for example item 31) can be expected to be much easier to answer correctly. As mentioned earlier (see Display 9), the relationship between slopes and unrotated factor loadings is given by
fij =
sij ki
,
where i is the item number, j the slope number and ki as defined above. The principal factor loadings given below are obtained as follows. Let F be a n × nfac matrix of factor loadings with typical elements fij = sij / kij and define S as the n × n symmetric matrix
FF ' with column rank equal to the number of factors, nfac. This implies that S has a maximum of nfac non-zero characteristic roots c1 , c2 ,..., cnfac . If we denote the corresponding eigenvectors by e1 , e 2 ,..., e nfac , then the principal factor loadings shown in the output below are computed as
f1* = e1 c1 , f 2* = e 2 c2 and f3* = e3 c3 where the elements of f *j denote the factor loadings for the j-th factor, j = 1, 2, 3. DISPLAY
1 2 3 4 5 6
14.
Q158 Q166 Q167 Q247 Q249 Q251 ... 31 Q313 32 Q314
STANDARDIZED DIFFICULTY, COMMUNALITY, AND PRINCIPAL FACTORS DIFF.
COMM.
FACTORS
0.846 -0.406 -0.825 -0.522 0.083 -0.080
0.348 0.290 0.096 0.433 0.078 0.261
1 -0.553 0.496 0.215 0.512 -0.146 0.246
2 -0.194 0.208 -0.215 0.410 0.032 -0.242
3 0.069 -0.030 0.062 -0.050 0.235 -0.377
-1.024 -0.221
0.434 0.372
0.419 0.341
0.487 0.488
-0.145 -0.131
798
13 TESTFACT EXAMPLES
Display 15: Percent of variance explained The percentage variance explained by factor j is calculated as
cj n
×100%, j = 1, 2, … , nfac
where c j is the j-th characteristic root of FF ' (see Display 14) and n the number of items. From the values reported in the output, it is seen that 20.31% of the total variance is explained by the first factor, 8.64% by the second and 5.68% by the third factor. Since 20.31 =
c1 ×100, 32
it follows that c1 = 6.50. DISPLAY
1
15.
PERCENT OF VARIANCE
1 20.31014
2 8.64630
3 5.68340
Display 16: Standardized difficulty, communality and VARIMAX factors The output below contains the VARIMAX rotated factors. Let Λ be a n × k matrix of factor loadings. This matrix represents the relationships between the original n items and k linear combinations of these items. To illustrate, suppose the number of items (p) is 4 and the number of factors (k) equals 2:
F1 = λ11 Item1 + λ21 Item2 + λ31 Item3 + λ41 Item4 F2 = λ12 Item1 + λ22 Item2 + λ32 Item3 + λ42 Item4 where F1 and F2 are uncorrelated and the variances of F1 and F2 are the so-called eigenvalues. The factor loadings {λij } are only unique up to a rotation in k-dimensional space. A suitable rotation of these factor loadings can result in a simplified structure between the factors and items if the new set of factor loadings {λij*} are either relatively large or small. Rotations may be found by minimizing the criterion (see, e.g. Lawley and Maxwell (1971))
γ
2
n V = ∑∑ (λ ) − ∑ ∑ (λij* ) 2 , n j =1 i =1 j =1 i =1 k
n
* 4 ij
k
799
13 TESTFACT EXAMPLES where the constant γ gives a family of rotations with γ = 1 giving VARIMAX rotations and γ = 0 QUARTIMAX rotations. Note that the standardized difficulty and communality estimates are the same as those given in Display 14. To determine which items are associated with a specific factor, one may select, for each item, the column with the highest loading (ignoring the sign of the loading). The following items appear to be indicators of Factor 2, for example: items 1, 2, 4, 8, 20, 24, 25, 26, 31 and 32. DISPLAY 16. STANDARDIZED DIFFICULTY, COMMUNALITY, AND VARIMAX FACTORS DIFF. COMM. FACTORS 1 2 3 1 Q158 0.846 0.348 0.261 0.499 0.175 2 Q166 -0.406 0.290 -0.234 -0.470 -0.117 3 Q167 -0.825 0.096 -0.287 0.043 -0.110 4 Q247 -0.522 0.433 -0.138 -0.641 -0.058 5 Q249 0.083 0.078 -0.005 0.091 0.263 6 Q251 -0.080 0.261 -0.092 -0.006 -0.503 ... 31 Q313 -1.024 0.434 0.014 -0.654 -0.075 32 Q314 -0.221 0.372 0.063 -0.605 -0.035
Phase 7: Factor scores using EAP estimates
The factor scores are Bayes estimates computed under the assumption that the corresponding ability factors are normally distributed in the population from which the sample of examinees was drawn. Let θ ik denote the j-th ability score, k = 1, 2, …, nfac for examinee i, i = 1, 2, …, N, then the factor scores are E (θ ik | xi1 , xi 2 ,..., xin ) where xij is the item j score for examinee i (see the discussion of the output in Section 13.7 for more details). Display 17: Quadrature points and weights
To obtain these conditional expectations, a 5-point quadrature formula is employed. The points and weights are shown below. DISPLAY 1 2 3 4 5
17.
5
FACTOR SCORE QUADRATURE POINTS AND WEIGHTS:
-2.856970 -1.355626 0.000000 1.355626 2.856970
0.011257 0.222076 0.533333 0.222076 0.011257
800
13 TESTFACT EXAMPLES Display 18: Factor scores and standard error estimates
The command file contains the command >SCORE METHOD=2, LIST=20;
This command requests that the factor ability scores for the first 20 cases should be listed as part of the output. The full set of factor scores is written to the file exampl03.fsc. For each case, the case ID, number of items presented, percent correct and percent omitted are reported. Below these values, the ability scores for each factor, with estimated standard errors marked with an asterisk, are given. Case 3, for example, was presented with 30 items of which 13 were answered correctly. Hence the percentage correct for this case is 13 ×100 = 43.3. 30 Case 10 answered 84.4% percent correctly and had factor scores of 0.898, 1.234 and 1.710 respectively. Since the means of the 598 factor scores (see the last part of the output) are approximately 0 with standard deviations of 0.86, 0.86 and 0.82 respectively, it can be concluded that examinee 10 attained factor scores that are at least one standard deviation above average. Factor scores are not unique in the sense that multiplication of any column of factor scores by –1 does not affect the validity of the estimates. It may therefore happen that negative scores are associated with above average percent responses and vice versa for below average responses. TESTFACT attempts to reverse the signs in such a way that scores above zero are usually assigned with above average achievement. DISPLAY
18.
FACTOR SCORES AND STANDARD ERRORS (S.E.)
CASE
NUMBER PERCENT PERCENT CASE ID PRESENTED CORRECT OMITTED SCORES: 1 2 3 S.E.* ============================================================== 1 32 59.4 0.0 2010002201 0.264 1.018 0.120 0.560* 0.543* 0.576* 2 32 12.5 0.0 0012212110 -1.329 -0.100 -1.495 0.483* 0.469* 0.645* 3 30 43.3 0.0 0010.02100 -0.572 0.346 0.035 0.420* 0.511* 0.527* 4 32 43.8 0.0 0020202202 -0.612 -1.378 0.584 0.530* 0.521* 0.587* 5 32 37.5 0.0 2010002210 -0.901 -0.061 -0.123 0.446* 0.482* 0.541* … 7 32 59.4 0.0 1021001110 0.548 -1.653 1.132
801
13 TESTFACT EXAMPLES 8
32
9
32
...
0.456* 34.4 -0.156 0.436* 28.1 -0.204 0.433* 0.699*
0.532* 0.611* 0.0 0010012210 -0.332 -0.817 0.484* 0.574* 0.0 2010011100 -0.590 -0.597 0.478* 0.556* 0.654* 0.709*
13.5 Adaptive item factor analysis and Bayes modal (MAP) factor score estimation for the activity survey This example analyzes 32 items selected from the 48-item version of the Jenkins Activity Survey for Health Prediction, Form B (Jenkins, Rosenman, & Zyzanski, 1972). The data are responses of 598 men from central Finland drawn from a larger survey sample. Most of the items are rated on three-point scales representing little or no, occasional, or frequent occurrence of the activity or behavior in question. For purposes of the present analysis, the scales have been dichotomized near the median. For a complete discussion of the contents of the data file and variable format statement used to read these data, see Section 13.4. In Section 13.4, EAP factor score estimation was performed. This example, illustrating MAP factor score estimation, imports the exampl04.par file from the previous example (FILE keyword on the SCORE command) to score the respondents to the survey using the VARIMAX rotated factor pattern. The PROBLEM, RESPONSE, KEY, SELECT and INPUT commands are the same as used in Section 13.4, with the exception of the addition of the SKIP keyword on the PROBLEM command. Classical item analysis is skipped, and the removal of the TETRACHORIC, FACTOR and FULL commands confirms that item factor analysis is also skipped, as indicated by SKIP=2. The SAVE command is still present, but only used to save factor scores to the file exampl04.fsc (FSCORES option on the SAVE command). The SCORE command now indicates the use of MAP estimation (METHOD=3). The FILE keyword indicates the parameter file to be used while the NFAC keyword specifies the number of factors used when estimating the factor scores (recall that in the previous example 3 factors were extracted). Factor scores for the first 20 cases are to be written to the output file (LIST=20) and the convergence for the MAP iterations is set by the SPRECISION keyword. Cases will be scored by the MAP (Maximum A Posteriori, or Bayes Modal) method. Standard error estimates will be computed from the posterior information at the estimated values. >TITLE EXAMPL05.TSF-ITEMS FROM THE JENKINS ACTIVITY SURVEY SCORING THE RESPONDENTS (MAP METHOD) >PROBLEM NITEMS=48,SELECT=32,RESPONSES=5,NOTPRESENTED,SKIP=2; >NAMES Q156,Q157,Q158,Q165,Q166,Q167,Q247,Q248,Q249,Q250,Q251,Q252, Q253,Q254,Q255,Q256,Q257,Q258,Q259,Q260,Q261,Q262,Q263,Q264, Q265,Q266,Q267,Q268,Q269,Q270,Q271,Q272,Q273,Q274,Q275,Q276, Q277,Q278,Q279,Q280,Q307,Q308,Q309,Q310,Q311,Q312,Q313,Q314; >RESPONSE ‘8’, ‘0’, ‘1’, ‘2’, ‘.’; >KEY 002000220022222220022222202222220002220022222000; >SELECT 3,5,6,7,9,11(1)14,17(1)23,25(1)30,32,33,35,36,39(1)42,47,48; >SAVE FSCORES;
802
13 TESTFACT EXAMPLES >SCORE
METHOD=3,LIST=20,NFAC=3,SPRECISION=0.0001, FILE=‘EXAMPL04.PAR’; >INPUT NIDCHAR=10,SCORES,FILE=‘EXAMPL04.DAT’; (10A1,T1,48A1) >STOP
13.6 Six-factor analysis of the activity survey by Monte Carlo full information analysis This example illustrates a six-dimensional analysis by the Monte Carlo version of adaptive EM estimation. The same 32 items selected from the 48-item version of the Jenkins Activity Survey for Health Prediction, Form B (Jenkins, Rosenman, and Zyzanski, 1972) as in the previous 2 examples are used. For a complete discussion of the contents of the data file and variable format statement used to read these data, see Section 13.4. The TETRACHORIC command requests the printing of the coefficients to 3 decimal places (NDEC=3) in the printed output file (LIST option). The FACTOR and FULL commands are used to specify parameters for the full information item factor analysis. Six factors and six latent roots are to be extracted, as indicated by the NFAC and NROOT keywords respectively. A PROMAX rotation is requested. Note that this keyword may not be abbreviated in the FACTOR command. A maximum of 24 EM cycles will be performed (CYCLES keyword on the FULL command). In place of the default method of integration by fractional quadrature of the posterior distributions, the program performs Monte Carlo integration in the corresponding number of dimensions. Random points are drawn at each E-step from the provisional posterior distribution for each case, which is assumed multivariate normal in the number of factors. After the specified iteration limit is reached, the points for each case at the iteration limit are saved and used in all subsequent EM cycles. The number of points sampled for the Monte Carlo EM solution is set to a value of 74596 using the MCEMSEED keyword on the TECHNICAL command. Monte Carlo integration is also used in computing EAP factor scores (METHOD=2 on the SCORE command). Factor scores for the first 20 cases are to be written to the output file (LIST=20). >TITLE EXAMPL06.TSF- ITEMS FROM THE JENKINS ACTIVITY SURVEY SIX-FACTOR ANALYSIS BY MONTE CARLO EM FULL INFORMATION ANALYSIS >PROBLEM NITEMS=48,SELECT=32,RESPONSES=5,NOTPRESENTED; >NAMES Q156,Q157,Q158,Q165,Q166,Q167,Q247,Q248,Q249,Q250,Q251,Q252, Q253,Q254,Q255,Q256,Q257,Q258,Q259,Q260,Q261,Q262,Q263,Q264, Q265,Q266,Q267,Q268,Q269,Q270,Q271,Q272,Q273,Q274,Q275,Q276, Q277,Q278,Q279,Q280,Q307,Q308,Q309,Q310,Q311,Q312,Q313,Q314; >RESPONSE ‘8’,‘0’, ‘1’, ‘2’, ‘.’; >KEY 002000220022222220022222202222220002220022222000; >SELECT 3,5,6,7,9,11(1)14,17(1)23,25(1)30,32,33,35,36,39(1)42,47,48; >TETRACHORIC LIST, NDEC=3; >FACTOR NFAC=6,NROOT=6,ROTATE=PROMAX; >FULL CYCLES=24; >SCORE METHOD=2,LIST=20; >TECHNICAL MCEMSEED=4593; >INPUT NIDCHAR=10,SCORES,FILE=‘EXAMPL04.DAT’; (10A1,T1,48A1) >STOP
803
13 TESTFACT EXAMPLES
13.7 Item bifactor analysis of a 12th-grade science assessment test Data for this example are based on 32 items from a science assessment test in the subjects of biology, chemistry, and physics administered to twelfth-grade students near the end of the school year. The items were classified by subject matter for purposes of the bifactor analysis. The first five cases from the data file exampl07.dat are shown below. The FILE keyword on the INPUT command denotes this file as the data source and the SCORES option indicates that it contains item scores. Case001 Case002 Case003 Case004 Case005
14523121312421534414334135131545 34283328312821524114338184145848 14543223322131554134331134134441 24423324322421524134315254134242 24523221122421544514333115131241
The case identification is given in the first 7 columns, and is listed first in the variable format statement. The length of this field is also indicated by the NIDCHAR keyword on the INPUT command. After using the “T” operator to tab to column 11, the 32 item responses are read as single characters (32A1). (7A1,T11,32A1)
32 items from the science test are used as indicated by the NITEMS keyword on the PROBLEM command, and the RESPONSE keyword denotes the number of possible responses. The six responses are listed in the RESPONSE command. Naming of the items is done using the NAMES command, while the KEY command lists the correct response to each item. The BIFACTOR command is used to request full information estimation of loadings on a general factor in the presence of item-group factors. Three item-group factors are present (NIGROUP=3), with allocation of the items to these groups as specified with the IGROUPS keyword. The CPARMS keyword lists the probabilities of chance success on each item. By setting the LIST keyword to 3, the bifactor loadings will be printed in both item and in item-group order in the output file. A total of 30 EM cycles (CYCLES=30) will be performed in the bifactor solution. The SCORE command is used to obtain, for each distinct pattern, the EAP score of the general factor of the bifactor model and to obtain the standard error estimate of the general factor score allowing for conditional dependence introduced by the group factors. Factor scores for the first 10 cases will be printed to the output file (LIST=10) and the guessing model will be used in the computation of the factor scores (CHANCE option). >TITLE EXAMPL07.TSF- ITEM BIFACTOR ANALYSIS OF A TWELFTH-GRADE SCIENCE ASSESSMENT TEST THE GENERAL FACTOR WILL BE SCORED >PROBLEM NITEMS=32,RESPONSE=6; >NAMES CHEM01,PHYS02,CHEM03,PHYS04,PHYS05,CHEM06,BIOL07,CHEM08, BIOL09,BIOL10,BIOL11,PHYS12,BIOL13,PHYS14,BIOL15,CHEM16, BIOL17,BIOL18,PHYS19,PHYS20,BIOL21,BIOL22,PHYS23,BIOL24, PHYS25,PHYS26,BIOL27,PHYS29,CHEM29,PHYS30,BIOL31,CHEM32;
804
13 TESTFACT EXAMPLES >RESPONSE >KEY >BIFACTOR
8,1,2,3,4,5; 14523121312421534414334135131545; NIGROUP=3,LIST=3,CYCLES=30,QUAD=9, IGROUPS=(2,3,2,3,3,2,1,2,1,1,1,3,1,3,1,2,1,1,3,3,1,1, 3,1,3,3,1,3,2,3,1,2), CPARMS=(0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1, 0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1,0.1); >SCORE LIST=10,CHANCE; >SAVE PARM,FSCORES; >INPUT NIDCHAR=4,SCORES,FILE=‘EXAMPL07.DAT’; (7A1,T11,32A1) >STOP;
13.7.1 Discussion of bifactor analysis output Exampl04.tsf illustrates the extension of a one-factor model to a so-called bifactor model by the inclusion of group factors. The bifactor model is applicable when an achievement test contains more than one subject matter content area. The data set exampl04.dat consists of the results of a 32-item science assessment test in the subjects biology, chemistry, and physics. Items are classified according to subject matter where 1 = biology, 2 = chemistry and 3 = physics (see the IGROUPS keyword in the BIFACTOR command, discussed in Section 5.3.3). Note that TESTFACT does not estimate guessing parameters, but does allow the user to specify the values (see CPARMS keyword) in which case a 3-parameter model that provides for the effect of guessing is fitted to the data.
The analysis specified in exampl04.tsf produces Phase 0, Phase 1, Phase 2, Phase 6 and Phase 7 output. The interpretation of Phases 0, 1, and 2 is omitted here since a detailed discussion of these parts of the output elsewhere. Phase 6: Bifactor analysis
Display 1 lists the chance and initial intercept and slope estimates. Note that the initial intercept estimates are set equal to zero, the initial slope estimates are set to 1.414 for the general factor and 1.00 for the group factors. These initial values are routinely used in TESTFACT for bifactor models. DISPLAY 1 2 3 4 5 6 31 32
1. CHEM01 PHYS02 CHEM03 PHYS04 PHYS05 CHEM06 ... BIOL31 CHEM32
CHANCE AND INITIAL INTERCEPT AND SLOPE ESTIMATES CHANCE INTERCEPT SLOPES 1 2 0.100 0.000 1.414 1.000 0.100 0.000 1.414 1.000 0.100 0.000 1.414 1.000 0.100 0.000 1.414 1.000 0.100 0.000 1.414 1.000 0.100 0.000 1.414 1.000 0.100 0.100
0.000 0.000
1.414 1.414
1.000 1.000
One may optionally include the TETRACHORIC command (see exampl03.tsf) when fitting a bifactor model. This command is required if a printout of residuals is requested. If a TETRACHORIC
805
13 TESTFACT EXAMPLES command is used, tetrachoric correlations are computed pairwise for the 32 × (32 − 1) / 2 = 496 pairs of items. There are a total of 20 item pairs that cannot be used since their corresponding 2 × 2 frequency tables contain zero or near-zero off-diagonal or marginal frequencies. In these cases, a tetrachoric correlation of 1 is substituted in the matrix of tetrachoric correlations. The inclusion or exclusion of the TETRACHORIC command has no effect on the estimation procedure, since the starting values for the marginal maximum likelihood procedure are fixed, and do not depend on the matrix of tetrachoric coefficients. Display 2-3: EM estimation and quadrature points and weights
The bifactor procedure uses the 9 quadrature points and weights listed below. MML estimation for the bifactor model requires quadrature in only two dimensions. For a more detailed discussion, see the Phase 7 part of the output. DISPLAY
2.
THE EM ESTIMATION OF PARAMETERS
9 QUADRATURE POINTS DISPLAY
3.
1 2 3 4 5 6 7 8 9
9 QUADRATURE POINTS AND WEIGHTS -4.000000 -3.000000 -2.000000 -1.000000 0.000000 1.000000 2.000000 3.000000 4.000000
0.000134 0.004432 0.053991 0.241971 0.398942 0.241971 0.053991 0.004432 0.000134
The number of cycles for the EM algorithm is set equal to 30 (CYCLES=30 on the BIFACTOR command). At each cycle, the value of –2 log L as well as the maximum change in the intercept and slope parameters are given. At cycle 30 the maximum change in intercept is 0.0050. The general factor slope estimates for the 32 items changed at most by 0.0047 while the corresponding value for the group factor equals 0.0095. These values indicate that, although convergence was not-reached within the specified 30 cycles, the solution after 30 cycles is probably acceptable for all practical purposes. CYCLE 30
-2 X MARGINAL LOG-LIKELIHOOD = 0.1882667932D+05 CHANGE = 0.4390039691D-01
MAXIMUM CHANGE OF ESTIMATES INTERCEPT = 0.004952
SLOPE =
0.004758 0.009507
Display 4: χ 2 and degrees of freedom DISPLAY
4.
CHI-SQUARE = 11150.36
DF = 503.00
P = 0.000
806
13 TESTFACT EXAMPLES The χ 2 -value is 1150.36 with 503 degrees of freedom. The number of degrees of freedom is calculated as df = N − 1 − 2n − ng where N is the number of distinct patterns, n is the number of items, and ng is the number of items assigned to group factors. For this example, N = 600, n = 32 and, since all the items are assigned to group factors, ng = 32. The χ 2 -statistic is only correct when all possible 2n patterns are observed. For the present sample, since N 2n , the χ 2 -statistic is too inaccurate to be used as a goodness-of-fit test statistic. The difference in the χ 2 -statistics for alternative models, however, yields a valid test statistic for judging whether the inclusion of additional parameters results in a significant improvement of model fit. Example It is hypothesized that the 12 physics items are indicators of a general factor only, while the biology and chemistry items are indicators of a general and two uncorrelated group factors. We wish to test
H 0 : The 32 items are indicators of a general factor, but the 13 biology and 7 chemistry items are also indicators of two uncorrelated group factors. H1 : The 32 items are indicators of a general as well as three uncorrelated group factors.
To obtain the χ 2 -statistic and degrees of freedom under H 0 , the BIFACTOR command is modified as follows: >BIFACTOR NIGROUP=2, LIST=3, CYCLES=30, IGROUPS=(2,0,2,0,0,2,1,2,1,1,1,0,1,0,1,2,1,1,0,0,1,1,0,1,0,0,1,0,2,0,1,2), CPARMS=(0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1);
Note that the NIGROUPS keyword is set equal to 2 and that each value of 3, corresponding to the position of the physics items in the data set, is substituted by a value of 0 in the IGROUPS keyword. A “0” symbol indicates that the corresponding items are not assigned to any group factors. A graphical presentation of the H 0 model is shown below.
807
13 TESTFACT EXAMPLES
If we run exampl04.tsf with the changes to the BIFACTOR command as discussed above, the χ 2 statistic value and degrees of freedom shown below are obtained. DISPLAY 4.
CHI-SQUARE = 11179.71
DF=515.00 P=0.000
To test H 0 against H1 , one computes the difference in the corresponding χ 2 s and degrees of freedom. Hence χ 2 = 11179.71 – 11150.36 = 29.35 with 515 – 503 = 12 degrees of freedom. Since P( χ 2 (12) ≥ 29.35) = 0.0034 , H 0 is rejected and it is concluded that items from all 3 subjects should be used for the group factors.
Display 5: Untransformed item parameters The estimates for the intercept and slope parameters are listed below.
808
13 TESTFACT EXAMPLES
DISPLAY
1 2 3 4 5 6
5.
CHEM01 PHYS02 CHEM03 PHYS04 PHYS05 CHEM06 ... 31 BIOL31 32 CHEM32
UNTRANSFORMED ITEM PARAMETERS CHANCE INTERCEPT SLOPES
0.100 0.100 0.100 0.100 0.100 0.100
1 -1.054 0.126 -1.360 -0.578 0.263 -2.729
2 0.709 1.019 1.265 0.469 0.635 1.706
0.417 0.548 -0.182 0.377 0.337 0.308
0.100 0.100
1.608 -1.522
1.447 0.190
-0.005 0.066
An alternative way to present these estimated parameters is shown below for the first 10 items.
1 2 3 4 5 6 7 8 9 10
Item Chance Intercept General Group1 Group2 Group3 -------------------------------------------------------------------CHEM01 0.100 -1.054 0.709 0.000 0.417 0.000 PHYS02 0.100 0.126 1.019 0.000 0.000 0.548 CHEM03 0.100 -1.360 1.265 0.000 -0.182 0.000 PHYS04 0.100 -0.578 0.469 0.000 0.000 0.377 PHYS05 0.100 0.263 0.635 0.000 0.000 0.337 CHEM06 0.100 -2.729 1.706 0.000 0.308 0.000 BIOL07 0.100 0.839 0.586 0.636 0.000 0.000 CHEM08 0.100 -2.220 1.144 0.000 0.929 0.000 BIOL09 0.100 1.287 0.212 0.476 0.000 0.000 BIOL10 0.100 -0.464 0.762 0.444 0.000 0.000
Display 6: Percent of variance DISPLAY 6. PERCENT OF VARIANCE ---------------------------GENERAL 0 31.7580 ITEM GROUP 1 3.8018 ITEM GROUP 2 2.7716 ITEM GROUP 3 2.9551 UNIQUENESS 58.7134 ----------------------------
The percentage variance explained by each of the four factors is calculated as follows. Let sij denote the j-th slope parameter for item i, i = 1, 2, …, 32. If we define ki = 1 + ∑ sij2 , j
then slopes are transformed to factor loadings (see Display 9 in the discussion of the Section 13.4 output) using the relationship
fij =
sij ki . 809
13 TESTFACT EXAMPLES
Example For item 7,
k7 = 1 + 0.5862 + 0.6362 = 1.322. The item 7 loadings are therefore 0.586/1.322 = 0.443 and 0.636/1.322 = 0.481 respectively. Let F be a 32 × 4 matrix of factor loadings with elements (see Display 7)
0.5475 0.0000 0.6663 0.0000 F= 0.8227 −0.0029 0.1859 0.0000
0.3222 0.0000 0.0000 0.3585 . 0.0000 0.0000 0.0649 0.0000
The percentage variance explained by factor j is calculated as
cj n
×100%,
j = 1, 2,3, 4,
where n = 32 and c j the j-th characteristic root of FF ' (See also the discussion of the output, Display 15 in Section 13.4). The uniqueness component is calculated as
n − ∑cj j
n
×100%.
Display 7: Standardized difficulties, communalities and bifactor loadings The bifactor loadings are derived from the slope estimates using the formula fij = sij / ki (see Display 6 above). The standardized item i difficulty equals −intercept / ki .
Example For item 7, k7 = 1.322 so that the standardized difficulty is −0.839 /1.322 = −0.635. Communalities are equal to the sum of the squares of the factor loadings. For example, the item 1 communality is equal to 0.54752 + 0.32222 = 0.4036. 810
13 TESTFACT EXAMPLES DISPLAY 7. BIFACTOR RESULTS IN ITEM SEQUENTIAL ITEM ORDER ITEM GROUP DIFFICULTY COMMUNALITY GENERAL SPECIFIC ---------------------------------------------------------------1 CHEM01 2 0.8138 0.4036 0.5475 0.3222 2 PHYS02 3 -0.0821 0.5725 0.6663 0.3585 3 CHEM03 2 0.8380 0.6204 0.7797 -0.1120 4 PHYS04 3 0.4952 0.2659 0.4022 0.3226 ... 31 BIOL31 1 -0.9140 0.6768 0.8227 -0.0029 32 CHEM32 2 1.4923 0.0388 0.1859 0.0649 ----------------------------------------------------------------
Display 8: Bifactor results in item group order The printout below shows the same information as for Display 7, except that the items are re-ordered by group number. All 32 items have positive loadings on the general factor, while the group factor loadings for BIOL31, CHEM03 and PHYS30 are negative, but relatively small. DISPLAY 8. BIFACTOR RESULTS IN ITEM GROUPORDER ITEM GROUP DIFFICULTY COMMUNALITY GENERAL SPECIFIC ---------------------------------------------------------------7 BIOL07 1 -0.6347 0.4277 0.4430 0.4812 9 BIOL09 1 -1.1417 0.2136 0.1882 0.4221 10 BIOL10 1 0.3483 0.4372 0.5713 0.3330 11 BIOL11 1 -2.0915 0.3887 0.5390 0.3133 13 BIOL13 1 -0.3328 0.3918 0.5249 0.3411 15 BIOL15 1 -0.8412 0.4683 0.5493 0.4081 17 BIOL17 1 -1.7506 0.3391 0.5395 0.2192 18 BIOL18 1 0.5366 0.7089 0.8358 0.1022 21 BIOL21 1 -1.3192 0.2177 0.2152 0.4140 22 BIOL22 1 -1.4669 0.3685 0.5956 0.1175 24 BIOL24 1 -0.5344 0.3935 0.5922 0.2069 27 BIOL27 1 -1.0345 0.5244 0.7042 0.1686 31 BIOL31 1 -0.9140 0.6768 0.8227 -0.0029 1 3 6 8 16 29 32
CHEM01 CHEM03 CHEM06 CHEM08 CHEM16 CHEM29 CHEM32
2 2 2 2 2 2 2
0.8138 0.8380 1.3636 1.2465 0.3761 0.6132 1.4923
0.4036 0.6204 0.7504 0.6847 0.3262 0.6825 0.0388
0.5475 0.7797 0.8524 0.6422 0.4820 0.5533 0.1859
0.3222 -0.1120 0.1540 0.5219 0.3065 0.6135 0.0649
2 PHYS02 3 -0.0821 0.5725 0.6663 0.3585 4 PHYS04 3 0.4952 0.2659 0.4022 0.3226 5 PHYS05 3 -0.2137 0.3408 0.5156 0.2739 12 PHYS12 3 0.3828 0.0437 0.0999 0.1836 14 PHYS14 3 -0.5195 0.4537 0.4988 0.4527 19 PHYS19 3 -0.0128 0.2476 0.4967 0.0289 20 PHYS20 3 -1.0889 0.4226 0.6200 0.1954 23 PHYS23 3 0.7102 0.3164 0.4571 0.3279 25 PHYS25 3 0.5005 0.4205 0.4814 0.4345 26 PHYS26 3 0.2243 0.6128 0.7500 0.2242 28 PHYS29 3 0.0337 0.3544 0.5948 0.0255 30 PHYS30 3 0.3034 0.0979 0.2912 -0.1146 ----------------------------------------------------------------
811
13 TESTFACT EXAMPLES
Phase 7: General bifactor score: EAP estimate The factor scores are so-called expected a-posteriori estimates of the general ability factor under the assumption of normality (see Phase 7, exampl03.out). Let θ i denote the general ability for examinee i. The EAP score is the conditional expectation
E (θ i | xi1 , xi 2 ,..., xin ), where xij is the item j score for examinee i. It can be shown that this conditional expectation follows as the solution of a two-dimensional integral that is approximated by a Gauss-quadrature formula. A brief description is provided below for the interested reader. From well-known results for conditional distributions it follows that
E (θ i | xi1 , xi 2 ,..., xin ) = ∫ θ f (θ i | xi1 , xi 2 ,..., xin )∂θ , where
f (θ i | xi1 , xi 2 ,..., xin ) = =
f ( xi1 , xi 2 ,..., xin ,θ i ) f ( xi1 , xi 2 ,..., xin )
f ( xi1 , xi 2 ,..., xin | θ i ) g (θ i ) . f ( xi1 , xi 2 ,..., xin )
The marginal probability function f ( xi1 , xi 2 ,..., xin ) is obtained in the EM step using a twodimensional quadrature formula. Suppose yi1 , yi 2 ,..., yin denotes the set of ordered item scores for the three groups. Under the assumption of uncorrelated group factors, it follows from Section 13.7 that f ( yi1 , yi 2 ,..., yi 32 | θ i ,θ i1 ,θ i 2 ,θ i 3 ) = f ( yi1 , yi 2 ,..., yi13 | θ i ,θ i1 ) × f ( yi14 , yi15 ,..., yi 20 | θ i ,θ i 2 ) × f ( yi 21 , yi 22 ,..., yi 32 | θ i ,θ i 3 ), where θ i denotes the general ability for examinee i, θ i1 is the group 1 (biology), θ i 2 the group 2 (chemistry) and θ i 3 the group 3 (physics) ability respectively. Note that (see Display 9) yi1 = xi 7 , yi 2 = xi 9 , …, yi 32 = xi 30 . Under the independence assumption, it follows that f ( yi1 , yi 2 ,..., yi 32 ) = f ( yi1 , yi 2 ,..., yi13 ) × f ( yi14 , yi15 ,..., yi 20 ) × f ( yi 21 , yi 22 ,..., yi 32 )
.
Each term in this product can be expressed as a two-dimensional integral.
812
13 TESTFACT EXAMPLES The first term, for example, can be evaluated from f ( yi1 , yi 2 ,..., yi13 ) = ∫ ∫ f ( yi1 , yi 2 ,..., yi13 | θ ,θ1 ) g (θ ,θ1 )∂θ ∂θ1. θ θ1
This integral can be approximated by
∑∑ w w fθ θ ( x , x ), k
l
* ,
1
k
l
where wk and wl are the weights and xk and xl the points shown as Display 9 of the output.
Display 10: General factor scores and standard errors The ability scores for each case and corresponding standard error estimates are tabulated. Examinee 5, for example, selected the correct answers to 22 of the 32 items. Therefore, the percentage correct is 22 / 32 × 100 = 68.8%. The estimated ability score for this candidate is 0.8 with a standard error of 0.411. Candidate 7 also had correct answers to 22 of the 32 items, but the ability estimate is 0.691. It is evident that the ability estimated depends on the number of correct items as well as which subset of items was answered correctly. DISPLAY
10.
GENERAL FACTOR SCORE AND STANDARD ERROR (S.E.)
CASE HEADER: CASE NUMBER PERCENT PERCENT CASE ID PRESENTED CORRECT OMITTED SCORE AND S.E. ============================================================================ 1 32 100.0 0.0 Case001 2.507 0.591 2 32 53.1 0.0 Case002 -0.066 0.323 3 32 56.2 0.0 Case003 0.032 0.380 4 32 50.0 0.0 Case004 -0.559 0.505 5 32 68.8 0.0 Case005 0.800 0.411 6 32 62.5 0.0 Case006 0.342 0.491 7 32 68.8 0.0 Case007 0.691 0.469 8 32 65.6 0.0 Case008 0.286 0.463 9 32 28.1 0.0 Case009 -1.434 0.518 10 32 46.9 0.0 Case010 -0.965 0.342
813
13 TESTFACT EXAMPLES
Summary statistics for score estimates The number of cases scored is equal to 600, with a mean of –0.0258 and standard deviation of 0.9011. Note that the ability scores are estimated under the assumption that the general factor ability has a normal distribution with mean 0 and standard deviation 1. For large data sets, one ideally wants the estimated ability scores to have mean 0 and standard deviation 1. The root-mean-square posterior standard deviations are calculated as follows. RMS =
SE12 + SE22 + ... + SEN2 N
where SE12 = 0.591 , SE22 = 0.323 etc. The RMS value of 0.4394 is relatively large, and indicates that, in general, 95% confidence intervals for the estimated scores will be wide. For example, a 95% confidence interval for examinee 5 is 0.8 ± 1.96(0.411) = (−0.006;1.6806). The empirical reliability is a measure of how close the observed scores are to the true, but unobserved, scores. A reliability of 1, for example, implies that one can safely substitute the observed test scores for the unknown true scores. SUMMARY STATISTICS FOR SCORE ESTIMATES ====================================== CASES SCORED 600 MEAN: -0.0258 S.D.: 0.9011 VARIANCE: 0.8119 ROOT-MEAN-SQUARE POSTERIOR STANDARD DEVIATIONS RMS: 0.4344 VARIANCE: 0.1887 EMPIRICAL RELIABILITY:
0.8114
13.8 Conventional three-factor analysis of the 12th-grade science assessment test Data for this example are based on 32 items from a science assessment test in the subjects of biology, chemistry, and physics administered to twelfth-grade students near the end of the school year. For a description of the data file and variable format statement, see the example discussed in Section 13.7.
814
13 TESTFACT EXAMPLES
Although the items are classified by subject matter for purposes of a bifactor analysis (see Section 13.4), an item factor analysis is specified. The FULL command specifies that a maximum of 24 EM cycles (CYCLES=24) is to be performed in the full information item factor analysis in which 3 factors and 3 latent roots are to be extracted (NFAC=3, NROOT=3 on FACTOR command). Non-adaptive quadrature is requested through the use of the NOADAPT option on the TECHNICAL command. The SAVE command is used to write the unrotated factor loadings to exampl8.unr (UNROTATE option). See Section 13.14 for more details on the use of this file. >TITLE EXAMPL08.TSF-THREE FACTOR ANALYSIS OF A TWELFTH-GRADE SCIENCE ASSESSMENT TEST UNROTATED FACTOR LOADINGS ARE SAVED FOR USE IN EXAMPL14. >PROBLEM NITEM=32,RESPONSE=6; >NAMES CHEM01,PHYS02,CHEM03,PHYS04,PHYS05,CHEM06,BIOL07,CHEM08,BIOL09, BIOL10,BIOL11,PHYS12,BIOL13,PHYS14,BIOL15,CHEM16,BIOL17,BIOL18, PHYS19,PHYS20,BIOL21,BIOL22,PHYS23,BIOL24,PHYS25,PHYS26,BIOL27, PHYS29,CHEM29,PHYS30,BIOL31,CHEM32; >RESPONSE 8,1,2,3,4,5; >KEY 14523121312421534414334135131545; >TETRACHORIC NDEC=3,LIST; >FACTOR NFAC=3,NROOT=3; >FULL CYCLES=24; >TECHNICAL NOADAPT; >SAVE UNROTATE; >INPUT NIDCHAR=7,SCORES,FILE=‘EXAMPL07.DAT’; (7A1,T11,32A1) >STOP
13.9 Computing examinee general factor scores from parameters of a previous bifactor analysis This example illustrates bifactor scoring from saved parameters. Data for this example are based on 32 items from a science assessment test in the subjects of biology, chemistry, and physics administered to twelfth-grade students near the end of the school year. For a description of the data file and variable format statement, see the example discussed in Section 13.7. The assignment of items to group factors is not included in the parameter file exampl07.par read using the FILE keyword on the SCORE command. The assignment of items must be supplied in the bifactor command. The BIFACTOR command is used to request full information estimation of loadings on a general factor in the presence of item-group factors. Three item-group factors are present (NIGROUP=3), with allocation of the items to these groups as specified with the IGROUPS keyword. By setting the LIST keyword to 3, the bifactor loadings will be printed in both item and in item-group order in the output file. A total of 30 EM cycles (CYCLES=30) will be performed in the bifactor solution. The chance parameters are supplied in the file and do not need to be reentered in the command.
815
13 TESTFACT EXAMPLES For the purpose of scoring from supplied parameters, the number of factors (NFAC) is set to 1 in the SCORE command. Factor scores for the first 10 students will be printed to the output file (LIST=10). The factor scores are also saved to the file exampl09.fsc (FSCORES on the SAVE command). The guessing model will be used in the computation of the factor scores (CHANCE option). >TITLE EXAMPL09.TSF- ITEM BIFACTOR ANALYSIS OF A TWELFTH-GRADE SCIENCE ASSESSMENT TEST: THE GENERAL FACTOR WILL BE SCORED >PROBLEM NITEM=32,RESPONSE=6; >NAMES CHEM01,PHYS02,CHEM03,PHYS04,PHYS05,CHEM06,BIOL07,CHEM08, BIOL09,BIOL10,BIOL11,PHYS12,BIOL13,PHYS14,BIOL15,CHEM16, BIOL17,BIOL18,PHYS19,PHYS20,BIOL21,BIOL22,PHYS23,BIOL24, PHYS25,PHYS26,BIOL27,PHYS29,CHEM29,PHYS30,BIOL31,CHEM32; >RESPONSE 8,1,2,3,4,5; >KEY 14523121312421534414334135131545; >BIFACTOR NIGROUP=3,LIST=3,CYCLES=30, IGROUPS=(2,3,2,3,3,2,1,2,1,1,1,3,1,3,1,2,1,1,3,3,1,1, 3,1,3,3,1,3,2,3,1,2); >SCORE NFAC=1,LIST=10,CHANCE,FILE=‘EXAMPL07.PAR’; >SAVE FSCORES; >INPUT NIDCHAR=7,SCORES,FILE=‘EXAMPL07.DAT’; (7A1,T11,32A1) >STOP;
A graphical presentation of the general factor scores using a bifactor analysis with 3 groups is shown below.
816
13 TESTFACT EXAMPLES
13.10 One-factor analysis of the 12th-grade science assessment test The saved parameters from Section 13.7 are used in scoring the general factor by adaptive quadrature. Data for this example are based on 32 items from a science assessment test in the subjects of biology, chemistry, and physics administered to twelfth-grade students near the end of the school year. For a description of the data file and variable format statement, see Section 13.7. The PROBLEM, KEY, RESPONSE, SAVE, and INPUT commands are also the same as those used in Section 13.7. Conditional dependence due to the group factors is not accounted for. A one-factor analysis is requested by replacing the BIFACTOR command used in Section 13.7 with the FACTOR command shown here. EAP factor scores are requested (METHOD=2 on the SCORE command). The first ten cases are also printed to the output file (LIST=10) and factor scores for all cases are saved to the file exampl10.fsc (FSCORES on SAVE command). As before, the guessing model will be used in the computation of the factor scores (CHANCE option). >TITLE EXAMPL10.TSF-ONE-FACTOR ANALYSIS OF A TWELFTH-GRADE SCIENCE ASSESSMENT TEST ADAPTIVE SCORING OF GENERAL FACTOR FROM SUPPLIED BIFACTOR PARAMETERS >PROBLEM NITEM=32,RESPONSE=6; >NAMES CHEM01,PHYS02,CHEM03,PHYS04,PHYS05,CHEM06,BIOL07,CHEM08, BIOL09,BIOL10,BIOL11,PHYS12,BIOL13,PHYS14,BIOL15,CHEM16, BIOL17,BIOL18,PHYS19,PHYS20,BIOL21,BIOL22,PHYS23,BIOL24, PHYS25,PHYS26,BIOL27,PHYS29,CHEM29,PHYS30,BIOL31,CHEM32; >RESPONSE 8,1,2,3,4,5; >KEY 14523121312421534414334135131545; >FACTOR NFAC=1; >SCORE METHOD=2,NFAC=1,LIST=10,CHANCE,FILE=‘EXAMPL07.PAR’; >SAVE FSCORES; >INPUT NIDCHAR=7,SCORES,FILE=‘EXAMPL07.DAT’; (7A1,T11,32A1) >STOP;
A histogram of the factor scores obtained from the one-factor model is shown below. The distribution of scores follow a bell-shaped curve with mean –0.028 and standard deviation of 0.933. In contrast to this, the bifactor solution (see previous section) yields scores for the general factor which exhibit much less variation (standard deviation = 0.273) about the mean.
817
13 TESTFACT EXAMPLES
13.11 Item factor analysis of a user-supplied correlation matrix This example illustrates a MINRES factor analysis of a correlation matrix imported from the file exampl04.cor saved in Section 13.4. The import file is named using the FILE keyword on the INPUT command. The CORRELAT option on this command indicates that the input is a correlation matrix for MINRES factor analysis (full information factor analysis requires item response data and cannot be carried out directly on the correlation matrix). In this instance, the matrix contains item tetrachoric correlations, but a correlation matrix from any source could be analyzed. For convenience in handling large correlation matrices, the tetrachoric correlation matrix is saved and imported in format-free space delimited form. Note that names are supplied in the NAMES command for the variables represented in the correlation matrix. The 48 items, from which 32 are selected using the SELECT keyword on the PROBLEM command and the SELECT command specifying the items and the order of selection, are from the Jenkins Activity Survey. SKIP=2 on the PROBLEM command bypasses the calculations and printing of classical item statistics. The FACTOR command specifies the extraction of 3 factors and 6 roots (NFAC=3; NROOT=6). A PROMAX rotation is requested and the rotated factor loadings will be saved in the file exampl03.rot (ROTATE option on the SAVE command). Note that the PROMAX option may not be abbreviated on the FACTOR command.
818
13 TESTFACT EXAMPLES >TITLE EXAMPL11.TSF- ITEMS FROM THE JENKINS ACTIVITY SURVEY ITEM FACTOR ANALYSIS OF A USER-SUPPLIED CORRELATION MATRIX >PROBLEM NITEM=48,SELECT=32,SKIP=2; >NAMES Q156,Q157,Q158,Q165,Q166,Q167,Q247,Q248,Q249,Q250,Q251,Q252, Q253,Q254,Q255,Q256,Q257,Q258,Q259,Q260,Q261,Q262,Q263,Q264, Q265,Q266,Q267,Q268,Q269,Q270,Q271,Q272,Q273,Q274,Q275,Q276, Q277,Q278,Q279,Q280,Q307,Q308,Q309,Q310,Q311,Q312,Q313,Q314; >SELECT 3,5,6,7,9,11(1)14,17(1)23,25(1)30,32,33,35,36,39(1)42,47,48; >FACTOR NFAC=3,NROOT=6,ROTATE=PROMAX; >SAVE ROTATE; >INPUT CORRELAT,FILE=‘EXAMPL04.COR’; >STOP
13.12 Simulating examinee responses to a three-factor test with user-supplied parameters This example illustrates the simulation of a sample of 1500 responses to 32 items. Sampling is from a multivariate latent distribution of factor scores with user-specified vector mean and fixed correlation matrix. A three-factor model is assumed. The user must supply standard item difficulties and NFAC factor loadings (or intercepts or factor slopes) for each item. Note that the PROBLEM command only indicates the number of items, and that the syntax contains no INPUT command, but only the SIMULATE command. The NFAC keyword on this command indicates the use of a three-factor model, and NCASES denotes the required sample size. The presence of the SLOPES option indicates that the item parameters provided are the intercept and the NFAC slopes. These parameter values are read in from the file exampl12.prm using the FILE keyword. The MEAN keyword indicates the population means of the factor scores from which the responses are generated. These means will be added to the random standard normal deviates representing the ability of each case on the corresponding factors. If the MEAN keyword is omitted, zero means are assumed and written to the *.sim file. The simulated responses are written to a file with file extension *.sim, in this case exampl12.sim. The first line of each new record contains the case number, group number, form number and factor means. The next line is the set of responses, where 0 indicates an incorrect answer and 1 a correct answer. The GROUP keyword is set to its default value of 1. Similarly, test form identification may be requested using the FORM keyword. By default, all records will be assumed to belong to the same test form. >TITLE EXAMPL12.TSF- SIMULATE RESPONSES TO 32 ITEMS THREE FACTOR MODEL; FACTOR SLOPES; SAMPLE SIZE= 1500 >PROBLEM NITEM=32; >SIMULATE NFAC=3,NCASES=1500,FORM=2,GROUP=1,SLOPES, MEAN=(0,0.0,0.0),FILE=‘EXAMPL12.PRM’; >STOP
The first few records of the exampl12.prm file are: 819
13 TESTFACT EXAMPLES (6X,4F8.3) 1 1.041 2 0.480 3 0.868
-0.675 0.585 0.240
0.246 -0.261 0.230
-0.049 0.024 -0.063
13.13 Simulating examinee responses in the presence of guessing and nonzero factor means This example illustrates the simulation of a sample of 1500 responses to 32 items. Sampling is from a multivariate latent distribution of factor scores with user-specified vector mean and fixed correlation matrix. A three-factor model is assumed and simulation is with guessing and nonzero factor means. The user must supply standard item difficulties and NFAC factor loadings (or intercepts or factor slopes) for each item. Note that the PROBLEM command only indicates the number of items, and that the syntax contains no INPUT command, but only the SIMULATE command. The NFAC keyword on this command indicates the use of a three-factor model, and NCASES denotes the required sample size. The presence of the CHANCE and LOADINGS options indicates that each item has a guessing, standardized difficulty and three factor loading parameters. The parameters are read in from the file exampl3.prm using the FILE keyword. The MEAN keyword indicates the population means of the factor scores from which the responses are generated. These means will be added to the random standard normal deviates representing the ability of each case on the corresponding factors. If the MEAN keyword is omitted, zero means are assumed and written to the *.sim file. The simulated responses are written to a file with file extension *.sim, in this case exampl13.sim. The first line of each new record contains the case number, group number, form number and factor means. The next line is the set of responses, where 0 indicates an incorrect answer and 1 a correct answer. The GROUP keyword is set to its default value of 1. Similarly, test form identification may be requested using the FORMS keyword. By default, all records will be assumed to belong to the same test form. The SCORESEED keyword specifies the random number generator seed for the simulation of mean abilities, the GUESSSEED keyword the seed for the simulation of chance parameters with population values specified in the exampl13.prm file, and the ERRORSEED is the seed associated with the simulation of the binary responses based on the difficulty and slope parameters. >TITLE EXAMPL13.TSF-SIMULATE RESPONSES TO 32 ITEMS WITH GUESSING AND NON-ZERO FACTOR MEANS; THRE-FACTOR MODEL; FACTOR LOADINGS; N=1500 >PROBLEM NITEM=32; >SIMULATE NFAC=3, NCASES=1500, FORM=2, GROUP=3, LOADINGS, ERRORSEED=1231, SCORESEED=71893, GUESSSEED=3451, FILE=‘EX7SIM.PAR’, CHANCE, MEAN=(0.5,-0.5,1.0); >STOP
820
13 TESTFACT EXAMPLES The first few records of the exampl13.prm file are: (6X,5F8.3) 1 0.200 0.844 2 0.200 -0.405 3 0.200 -0.824
0.552 -0.197 0.046 0.497 0.215 -0.019 0.222 -0.216 0.065
Discussion of simulation output This example illustrates how to simulate data under the assumption that there are 32 binary items that measure three ability factors. The model considered allows for guessing and for non-zero factor means. It is assumed that for each item, the population values for the guessing, standardized difficulty and factor loadings are known. These values are stored in the file ex7sim.par.
Phase 0: Input commands The COMMENT command is used to show the format statement and the parameter values for the first 5 of the 32 items. (6X,5F8.3) 1 0.200 2 0.200 3 0.200 4 0.200 5 0.200
0.844 -0.405 -0.824 -0.520 0.083
-0.552 0.497 0.222 0.508 -0.145
-0.197 0.215 -0.216 0.415 0.026
0.046 -0.019 0.065 -0.030 0.244
The LOADINGS option on the SIMULATE command specifies that the population parameters are standardized difficulties and factor loadings. Note that FORM=2, GROUP=3, ERRORSEED=1231, GUESSSEED=3451, and SCORESEED=71893 are optional keywords. The values of the chance, difficulty, and factor loadings for each item (the contents of exampl13.prm) are given below. NUMBER OF ITEMS = 32 NUMBER OF CASES = 1500 NUMBER OF FACTORS = 3 CHANCE MODEL VALUES OF CHANCE, DIFFICULTY, AND FACTOR LOADINGS ITEM CHANCE DIFFICULTY FACTOR LOADINGS ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM
1 2 3 4 5 6 7 8 9 10 11 12
0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200
0.844 -0.405 -0.824 -0.520 0.083 -0.080 0.026 0.220 0.842 0.125 0.133 -0.080
-0.552 0.497 0.222 0.508 -0.145 0.245 0.323 0.524 0.544 0.492 0.342 0.398
-0.197 0.215 -0.216 0.415 0.026 -0.232 -0.158 0.226 -0.002 -0.273 -0.197 -0.280
0.046 -0.019 0.065 -0.030 0.244 -0.384 -0.412 -0.191 -0.312 -0.261 -0.234 -0.525
821
13 TESTFACT EXAMPLES ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM ITEM
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32
0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200 0.200
0.032 -0.494 -0.259 0.980 -0.740 0.456 -0.609 -1.096 -0.410 -0.443 -0.072 0.845 -0.143 0.136 0.809 0.996 0.328 0.565 -1.022 -0.219
0.604 0.741 0.745 -0.543 0.506 -0.288 0.323 -0.238 0.391 0.539 0.025 0.521 0.478 -0.487 -0.368 -0.321 0.572 0.345 0.413 0.333
-0.016 0.070 0.012 0.186 -0.201 0.120 -0.663 -0.502 -0.562 -0.259 -0.031 0.233 0.286 -0.463 0.202 0.268 -0.127 -0.142 0.491 0.497
0.198 0.483 0.423 -0.109 -0.002 -0.296 -0.127 -0.085 0.074 0.159 -0.292 -0.244 -0.046 0.109 -0.268 -0.229 0.064 0.121 -0.112 -0.121
The simulated data are written to the file exampl13.sim. The first line of the *.sim file gives the case, form and group number, as well as the simulated abilities for the three factors (1.056, 0.605 and 0.873 for case 1). Note that, if the keywords FORM=f and GROUP=g are omitted from the SIMULATE command, default values of one are written to the *.sim file. The values of the simulated abilities will change if a different value for the keyword SCORESEED is used. The default value is 345261. By changing both or either of the ERRORSEED and GUESSSEED values, one will obtain a new set of simulated responses. The GUESSSEED default value is 543612 while the ERRORSEED default value is 453612. Note that the GUESSSEED parameter only has an effect if a chance model is simulated. It determines the sequence of the simulated values from a normal population with a mean equal to the chance parameter (in the case of exampl13.tsf this value is 0.2 for each item). 1 2 3 1.056 0.605 11110011000111100011010010000111 2 2 3 0.233 0.795 11111011111011101011101110010011 3 2 3 0.744 0.181 01110010011101101011111110101011 4 2 3 1.070 -0.490 01111111010101101011111111111111 5 2 3 0.934 0.457 01010001010011110110111110000111 6 2 3 0.440 -0.561 11111111010011001011111010001100 7 2 3 0.286 -2.143 00100110010111101011111100001111 8 2 3 -0.786 0.282 11101010010001111111101010000010 9 2 3 -0.740 -0.866 10101110110100101111010101001010 10 2 3 0.495 -0.182 11110011110111101111111011100011
0.873 0.078 0.215 0.611 0.824 0.881 1.583 1.838 0.929 -0.244
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13 TESTFACT EXAMPLES
Means and standard deviations of ability variables The values below are based on the simulated ability for each factor. For example, the mean of factor 1 is computed as (1.056 + 0.233 + 0.844 + 1.070 + ...) /1500 = 0.513. Note that the means are close to the assumed population values of 0.5, -0.5 and 1.0 respectively. The correlations between the simulated ability variables are close to zero showing that the simulated factor abilities are, for practical purposes, uncorrelated. MEANS AND STANDARD DEVIATIONS OF ABILITY VARIABLES FACTOR MEAN S.D. 1 0.513 0.984 2 -0.521 0.972 3 1.025 0.958 CORRELATION COEFFICIENT MATRIX OF CASE ABILITIES VARIABLES FACTOR 1
2
1.000 -0.005 0.000
3 1.000 0.039
1.000
13.14 Three-factor analysis with PROMAX rotation: 32 items from the science assessment test In this example, a PROMAX rotation is performed, using a three-factor model and 32 items from a science assessment test in the subjects of biology, chemistry, and physics administered to twelfth-grade students near the end of the school year. For a description of the data file and variable format statement, see Section 13.7. As input, a factor pattern from a 3-factor analysis of the data discussed in Section 13.8 is used (FILE keyword on the INPUT command). The FACTORS option on the same command indicates that the input is in the form of factor loadings. This option is used for rotation only. The first few records of the exampl08.unr file are: (15X,5F10.6,2(/15X,5F10.6)) 1 2 3 4 5
CHEM01 PHYS02 CHEM03 PHYS04 PHYS05
0.368242 0.475550 0.439431 0.285406 0.425255
-0.149557 -0.028675 -0.008526 0.150567 -0.017829 -0.100431 -0.197091 0.186737 0.003244 0.155182
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13 TESTFACT EXAMPLES The SKIP keyword on the PROBLEM command is set to 2, and TESTFACT will thus proceed directly to rotation after input of the factor pattern. The rotation is specified by the ROTATE keyword on the FACTOR command, while the NFAC keyword confirms this to be a three-factor model. >TITLE EXAMPL14.TSF- PROMAX ROTATION FOR 32 ITEMS 3-FACTOR MODEL >PROBLEM NITEM=32,SKIP=2; >NAMES CHEM01,PHYS02,CHEM03,PHYS04,PHYS05,CHEM06,BIOL07,CHEM08, BIOL09,BIOL10,BIOL11,PHYS12,BIOL13,PHYS14,BIOL15,CHEM16, BIOL17,BIOL18,PHYS19,PHYS20,BIOL21,BIOL22,PHYS23,BIOL24, PHYS25,PHYS26,BIOL27,PHYS29,CHEM29,PHYS30,BIOL31,CHEM32; >FACTOR NFAC=3,ROTATE=PROMAX; >INPUT FACTOR,FILE=‘EXAMPL08.UNR’; >STOP
Each row of factor loadings can be viewed as a point in multidimensional space, so that each factor corresponds to a coordinate axis. A factor rotation is equivalent to rotating those axes, resulting in a new set of factor loadings. There are various rotation methods, some (e.g. VARIMAX) leave the axes orthogonal, while others are so-called oblique methods that change the angles between axes. The oblique method used in TESTFACT is called PROMAX. This method often creates a simpler structure in the sense that loadings on each factor are either large or small. Note that with oblique rotation, factors are no longer uncorrelated. Sections of the output are shown below. DISPLAY
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
2.
CHEM01 PHYS02 CHEM03 PHYS04 PHYS05 CHEM06 BIOL07 CHEM08 BIOL09 BIOL10 BIOL11 PHYS12 BIOL13 PHYS14 BIOL15 CHEM16 BIOL17 BIOL18 PHYS19 PHYS20 BIOL21 BIOL22 PHYS23 BIOL24 PHYS25 PHYS26 BIOL27
PROMAX ROTATED FACTOR LOADINGS 1 -0.021 0.235 0.105 -0.006 0.229 0.014 0.469 -0.019 0.419 0.275 0.301 -0.117 0.361 0.127 0.332 -0.051 0.232 0.113 -0.022 0.208 0.485 -0.022 -0.177 0.239 -0.115 0.110 0.297
2 0.271 0.381 0.119 0.491 0.352 0.332 -0.106 0.138 -0.027 0.142 0.041 0.275 0.090 0.577 -0.146 0.298 0.018 0.086 0.123 0.309 0.016 -0.180 0.378 -0.023 0.397 0.409 0.092
3 0.170 -0.035 0.300 -0.162 -0.030 0.286 -0.023 0.056 -0.056 0.001 0.016 0.002 -0.013 -0.050 0.034 0.042 0.034 0.068 0.073 0.000 -0.086 0.121 0.049 0.043 0.045 0.009 0.017
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13 TESTFACT EXAMPLES 28 29 30 31 32
PHYS29 CHEM29 PHYS30 BIOL31 CHEM32
0.168 0.284 -0.144 0.267 0.105 -0.018 0.185 0.277 0.035 0.018
0.007 0.075 0.027 0.017 0.003
From the output of PROMAX factor loadings we conclude that there are only two factors, these being Biology (factor 1) and Chemistry-physics (factor 2). Except for item 3 (CHEM03), all items have larger loadings on either of the first two factors than on the third factor. DISPLAY 1 2 3
3. PROMAX FACTOR CORRELATIONS 1 2 3 1.000 0.409 1.000 0.453 0.694 1.000
The correlation between factors 2 and 3 equals 0.694. This relatively high correlation may explain why two factors appear to be sufficient.
13.15 Principal factor solution of a factor analysis on simulated data: no guessing 32 items from the simulated data set in the file exampl15.dat are used as indicated by the NITEMSS keyword on the PROBLEM command and the FILE keyword on the INPUT command. The input is in the form of input subject records containing scores. The RESPONSE keyword denotes the number of possible responses. The three responses are listed in the RESPONSE command. Naming of the items is done using the NAMES command, while the KEY command lists the correct response to each item. The TETRACHORIC command requests the recoding of omits to wrong responses (RECODE option), prior to the computation of the tetrachoric correlation coefficients. The FACTOR and FULL commands are used to specify parameters for the factor analysis. A two-factor model will be fitted to the data (NFAC=2) and the first 6 characteristic roots of the smoothed correlation matrix (NROOT=6) will be written to the output file. A maximum of 10 EM cycles will be performed (CYCLES keyword on the FULL command). The OMIT keyword on this command indicates recoding of omits to wrong responses. The QUAD keyword sets the number of quadrature points for the EM estimation of the parameters to 9, instead of the default of 15 for the 2-factor case when the NOADAPT option is selected. Non-adaptive quadrature will be performed (NOADAPT option on the TECHNICAL command). Trial intercept and slope estimates after 10 cycles will be saved in exampl15.tri as indicated by the TRIAL option on the SAVE command. >TITLE EXAMPL15.TSF- 2-FACTOR MODEL. SIMULATED DATA: PRINCIPAL FACTOR SOLUTION, NO GUESSING. NON-ADAPTIVE QUADRATURE. SAVE TRAIL VALUES FOR CONTINUED EM CYCLES. >PROBLEM NITEM=32,RESPONSE=3; >NAMES I1,I2,I3,I4,I5,I6,I7,I8,I9,I10, I11,I12,I13,I14,I15,I16,I17,I18,I19,I20, I21,I22,I23,I24,I25,I26,I27,I28,I29,I30,
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13 TESTFACT EXAMPLES I31,I32; >RESPONSE ‘8’, ‘0’, ‘1’; >KEY 11111111111111111111111111111111; >TETRACHORIC RECODE; >FACTOR NFAC=2,NROOT=6; >FULL CYCLES=10,OMIT=RECODE,QUAD=9; >TECHNICAL NOADAPT; >SAVE TRIAL; >INPUT NIDCHAR=3,SCORES,FILE=‘EXAMPL15.DAT’; (3A1,T31,32A1) >STOP
13.16 Non-adaptive factor analysis of simulated data: principal factor solution, no guessing 32 items from the simulated data set in the file example15.dat are again used as input (see Section 13.15) as indicated by the NITEMS keyword on the PROBLEM command and the FILE keyword on the INPUT command. The input is in the form of input subject records containing scores (SCORES option). Trial intercept and slope estimates will be read from the previously saved file exampl15.tri. Item numbers are required. The first few lines of the trial values file are: (15X,6F9.5,2(/24X,5F9.5)) 1 I1 0.02547 0.68219 -0.55756 2 I2 0.01425 0.64937 -0.88348 3 I3 -0.00925 0.80883 -0.84193
The RESPONSE keyword denotes the number of possible responses. The three responses are listed in the RESPONSE command. Naming of the items is done using the NAMES command, while the KEY command lists the correct response to each item. The inclusion of the SKIP=1 keyword on the PROBLEM command indicates that the classical and item analysis phase should be skipped. The program will proceed to the calculation of tetrachoric correlations immediately after data entry. The FACTOR and FULL commands are used to specify parameters for the factor analysis. Two factors and six latent roots are to be printed, as indicated by the NFAC and NROOT keywords respectively. The OMIT keyword on the FULL command indicates recoding of omits to wrong responses. The QUAD keyword sets the number of quadrature points for the EM estimation of the parameters to 9, instead of the default of 15 for the 2-factor case when the NOADAPT option is selected. Nonadaptive quadrature will be performed (NOADAPT option on the TECHNICAL command). The parameters assigned to the ITER keyword requests a maximum of 15 EM cycles, with a maximum of 5 iterations and a convergence criterion of 0.001 for the M-step. Trial values will be saved again in case further EM cycles are necessary. The trial values and the intercepts, factor slopes, and guessing parameters (in a form suitable for computing factor scores at a later time) are saved in exampl16.tri and exampl16.par as indicated by the TRIAL and PARM options on the SAVE command.
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13 TESTFACT EXAMPLES >TITLE EXAMPL16.TSF- 2-FACTOR MODEL SIMULATION: PRINCIPAL FACTOR SOLUTION, NO GUESSING. NON-ADAPTIVE QUADRATURE. CONTINUE WITH AN ADDITIONAL 15 CYCLES. >PROBLEM NITEM=32,RESPONSE=3,SKIP=1; >NAMES
I1,I2,I3,I4,I5,I6,I7,I8,I9,I10, I11,I12,I13,I14,I15,I16,I17,I18,I19,I20, I21,I22,I23,I24,I25,I26,I27,I28,I29,I30, I31,I32; >RESPONSE ‘8’, ‘0’, ‘1’; >KEY 11111111111111111111111111111111; >FACTOR NFAC=2,NROOT=6; >FULL OMIT=RECODE,QUAD=9; >TECHNICAL NOADAPT,ITER=(15,5,0.001); >SAVE PARM, TRIAL; >INPUT TRIAL=‘EXAMPL15.TRI’,NIDCHAR=3,SCORES,FILE=‘EXAMPL15.DAT’; (3A1,T31,32A1) >STOP
13.17 Adaptive item factor analysis of 25 spelling items from the 100-Item Spelling Test Data from a 100 word spelling test are used in this example. A complete description of these data are given in Section 2.4.1. The data file example17.dat contains individual responses to all 100 items, of which 25 are used here. Data are read using the FILE keyword on the INPUT command. The SCORES option indicates that the data file contains item scores, and that the case identification is 11 characters in width (NIDCHAR). The first 11 columns of every line of data contain the case identification, which is represented by “11A1” in the variable format statement given below. Responses to the first 25 items start in column 13, and the “X” operator is used to skip over the 11th column after the case identification has been read. The next set of 25 responses are contained in columns 39 to 63 inclusive and are read in the same format as the previous set (25A1). The third set of responses follows after 1 blank column, which is skipped using the “X” operator. The final set of 25 items is again separated from the previous set by a single blank column (1X,25A1). (11A1,1X,25A1,1X,25A1,1X,25A1,1X,25A1)
The number of items read by the variable format statement corresponds to the number of items indicated on the PROBLEM command (NITEMS keyword). The 12 possible responses to each item are listed on the RESPONSE command, and the RESPONSE key on the PROBLEM command indicates the total number. The answer key, given in the KEY command, indicates that a “1” is the correct response to all 100 items. SELECT=25 on the PROBLEM command indicates that only 25 items will be used in the analysis. These items are listed in the order to be used on the SELECT command.
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13 TESTFACT EXAMPLES The TETRACHORIC command specifies how the count matrix to be used in the calculation of the tetrachoric correlations is to be formed. By using the (default) RECODE option, all omitted responses will be recoded as wrong responses. The matrix of tetrachoric correlations, with elements printed up to 3 decimal places (NDEC keyword), will be printed in the output (LIST) and saved to the file exampl17.cor through the use of the CORRELAT option on the SAVE command. Factor scores and their posterior standard deviations are saved to exampl17.fsc with the FSCORES option on the SAVE command. The FACTOR command requests and controls the parameters for the item factor analysis. Two factors (NFAC=2) are to be extracted, along with 6 latent roots (NROOT=6). The ROTATE keyword is used to request a PROMAX rotation. Note that this keyword may not be abbreviated in the FACTOR command. By default, NFAC leading factors will be rotated and the constant for the PROMAX rotation is equal to 3. The FULL command is used to request full information item factor analysis, starting from the principal factor solution. The OMIT keyword is set to RECODE, and omitted responses are thus recoded as wrong responses (similar to the request on the TETRACHORIC command). Note that RECODE may not be abbreviated in the FULL command. The SCORE command specifies that the factor scores for 100 cases are to be listed in the output. >TITLE EXAMPL17.TSF- ITEM FACTOR ANALYSIS OF 25 SPELLING ITEMS SELECTED FROM THE 100 WORD SPELLING TEST. USING TETRACHORIC OPTION >PROBLEM NITEM=100,RESPONSE=12,SELECT=25; >NAMES S01,S02,S03,S04,S05,S06,S07,S08,S09,S10,S11,S12,S13,S14,S15,S16, S17,S18,S19,S20,S21,S22,S23,S24,S25,S26,S27,S28,S29,S30,S31,S32, S33,S34,S35,S36,S37,S38,S39,S40,S41,S42,S43,S44,S45,S46,S47,S48, S49,S50,S51,S52,S53,S54,S55,S56,S57,S58,S59,S60,S61,S62,S64,S64, S65,S66,S67,S68,S69,S70,S71,S72,S73,S74,S75,S76,S77,S78,S79,S80, S81,S82,S83,S84,S85,S86,S87,S88,S89,S90,S91,S92,S93,S94,S95,S96, S97,S98,S99,S100; >RESPONSE ‘ ’,‘0’, ‘1’, ‘2’, ‘3’, ‘4’, ‘5’, ‘6’, ‘7’, ‘8’, ‘9’, ‘A’; >KEY 00000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000; >SELECT 1,4,6,8,9,10,15,20,23(1)29,33,34,36,39,48,49,54,59,64,72; >TETRACHORIC RECODE,NDEC=3,LIST; >FACTOR NFAC=2,NROOT=6,NIT=(5,0.02),ROTATE=PROMAX; >FULL ITER=(8,3,0.01),OMIT=RECODE; >SCORE LIST=100; >SAVE CORRELAT,FSCORES; >INPUT NIDCHAR=11,SCORES,FILE=‘EXAMPL17.DAT’; (11A1,1X,25A1,1X,25A11X,25A1,1X,25A1) >STOP
13.18 Classical item factor analysis of spelling data from a tetrachoric correlation matrix The analysis in this example is based on the spelling data used in Section 13.17. For a discussion of the data, variable format statement, and INPUT command, see the previous section. A classical analysis is carried out on all 100 items in the data. Thus the SELECT keyword previously used is omitted from the PROBLEM command, which only indicates the total number of 828
13 TESTFACT EXAMPLES items (NITEM) and the total number of possible responses (RESPONSE). All 12 responses are listed on the RESPONSE command, and the KEY command contains the answer key for all the items. The TETRACHORIC command specifies how the count matrix, to be used in the calculation of the tetrachoric correlations, is to be formed. By using the (default) RECODE option, all omitted responses will be recoded as wrong responses. The FACTOR command requests and controls the parameters for the item factor analysis. Two factors (NFAC=2) are to be printed, along with 6 latent roots (NROOT=6). The ROTATE keyword is used to request a PROMAX rotation. Note that this keyword may not be abbreviated in the FACTOR command. By default, NFAC leading factors will be rotated and the constant for the PROMAX rotation is equal to 3. The NIT keyword specifies the number of iterations for the MINRES factor solution and the convergence criterion. A value of 0.01, for example, implies that if the largest change in factor loadings is less than 0.01, the iteration procedure will terminate. The default values are 3 and 0.0001 respectively. Matrix plots of the biserial coefficient (BISERIAL option) and item facility (percent correct; FACILITY option) against discriminating power are requested using the PLOT command. By default, the internal test score is used as discriminating power. To use an external criterion score, the CRITERION option should be included on the PLOT command. >TITLE EXAMPL18.TSF- CLASSICAL ANALYSIS OF SPELLING DATA: 100 ITEMS USING TETRACHORIC OPTION AND PLOT >PROBLEM NITEM=100,RESPONSE=12; >RESPONSE ‘ ’,‘0’,‘1’,‘2’,‘3’,‘4’,‘5’,‘6’,‘7’,‘8’,‘9’,‘A’; >KEY 00000000000000000000000000000000000000000000000000 00000000000000000000000000000000000000000000000000; >PLOT BISERIAL,FACILITY; >TETRACHORIC RECODE; >FACTOR NFAC=2,NROOT=6,NIT=(5,0.01),ROTATE=PROMAX; >INPUT NIDCHAR=11,SCORES,FILE=‘EXAMPL17.DAT’; (11A1,4(1X,25A1)) >CONTINUE >STOP
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859
INDEX
1PL model, 51, 101, 123, 137-139, 355, 367, 379380, 398-400, 510, 524, 539-540, 543, 567-568, 605, 616-617, 645, 730, 732, 843 2PL model, 36, 51, 92-93, 101, 123, 137-139, 169, 355, 367, 379, 398-400, 524, 539-540, 565, 567568, 606, 608, 732-734, 736, 768, 842 3PL model, 268, 355, 367, 379, 398-400, 523, 538, 540, 569
A Ability score file saving, 242-243 ACCEL keyword on CALIB command, 78, 89, 115-117, 123, 650 on TECHNICAL command, 491 ACCEL option on CALIB command, 264, 274-275 Acceleration for full information factor analysis, 491 using or suppressing of routine, 275 value of constant, 77, 117, 382 ACCMAX keyword on ESTIMATE command, 382 Adaptive quadrature, 410, 491, 493, 495, 530, 589, 781, 815, 817, 825-826, 842
for score estimation, 410, 530 invoking 3-point, 491 setting values of extreme points, 496 setting values of extreme weights, 497 Adaptive testing, 632 Advanced tab on Item Analysis dialog box, 51, 88-89 Aggregate-level fit statistic, 210 IRT models, 610 Aitchison, J., 844 Aitkin, M., 576, 585, 588, 599, 601, 617, 730, 842, 843 AJ keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393 AK keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393 AK option on TMATRIX command, 404 ALL keyword on EQUAL command, 381 on FIX command, 386 on GROUPS command, 381 on ITEMS command, 381 ALL option on LABELS command, 387
on PRIORS command, 394 on START command, 396 on TEST command, 355, 367, 398, 730 on TMATRIX command, 403 ALPHA keyword on PRIORS command, 47, 90, 115, 187, 189190 ALPHA option on RELIABILITY command, 776 Analysis display of details, 84 indication of successful termination of, 82 selecting steps of, 457 specifying in MULTILOG, 388389 Andersen, E.B., 730, 840 Andrich, D., 550, 556-557, 846 Answer key using, 26, 59, 92, 149, 167-171, 178-179, 236, 238, 241-242, 254, 355-356, 448449, 480, 634, 652, 658, 666, 670, 783, 827, 829 Answer Key tab on Item Keys dialog box, 59, 87 Assessment testing, 618619 Assign Calibration Prior Latent Distribution dialog box, 75
860
INDEX Assign Calibration Prior Latent Distribution option on Technical menu, 70, 75, 90 Assign Fixed Items dialog box, 68, 109, 225, 227 Assign Fixed Items option on Technical menu, 68, 88, 109, 225, 227 Assign Item Parameter Prior Constraints option on Technical menu, 8990 Assign Item Parameter Starting Values dialog box Import/Enter Values tab, 87-88 Assign Item Parameter Starting Values option on Technical menu, 65, 87-88 Assign Scoring Prior Latent Distribution dialog box, 70, 75 Normal tab, 75, 90 User-Supplied tab, 76, 90-91 Assign Scoring Prior Latent Distribution option on Technical menu, 70, 75, 90-91 Axis labels editing in plot, 517 Axis Labels dialog box, 517
B Baker, F.B., 599 Bar Graph Parameters dialog box, 517-518
Bars, 520 editing characteristics of, 518 Bartholomew, D.J., 843 Bayes estimation, 31, 3435, 214, 317, 410, 418, 476, 529, 530, 537, 544, 576, 588, 590, 601, 605, 607, 609-610, 616, 624625, 655, 664, 679, 692693, 706, 721, 780-781, 800, 802, 837-838, 841, 843-844, 847 Bayes Modal or Maximum A Posteriori (MAP), 25, 31, 35-37, 75, 211, 214, 349-350, 357, 389, 392, 395, 474, 476, 529, 544, 590, 605, 607-610, 625, 655, 664, 685, 748-749, 781-782, 802, 837-838, 841, 843 Bayes or Expected A Posteriori (EAP), 25, 29, 31-37, 75, 196, 205,208, 211-212, 214, 216-217, 221, 244, 264, 316-317, 319, 418, 474, 476, 478, 498, 543, 561-562, 588, 594, 604, 607-608, 610, 615, 625, 649, 654-656, 659, 664, 666, 669, 671, 685-686, 693, 695, 705709, 721, 731, 736, 738, 747, 752, 780-782, 800, 802- 804, 812, 817, 837 Bergan, J.R., 759 Beta function, 612 parameter for distribution, 452 prior on uniquenesses, 452 supplying priors for distribution, 278
BETA keyword on PRIORS command, 47, 90, 115, 187, 189, 190 Bifactor analysis, 410, 418, 782, 804, 815 assigning items to item groups, 419 number of cycles, 419 number of decimals in solution, 420 printing of loadings, 420 printing of residual matrix, 421 printing of smoothed matrix, 421 BIFACTOR command, 410, 418, 468, 472-473, 495, 804 CPARMS keyword, 418, 804-805 CYCLES keyword, 419, 804, 806, 815 IGROUPS keyword, 419, 804-805, 815 LIST keyword, 420, 804, 815 LORD option, 413-414 MISS option, 413-414 NDEC keyword, 420 NIGROUPS keyword, 419, 421, 804, 815 NOLIST option, 421 OMIT keyword, 421, 423, 477 QUAD keyword, 422 RECODE option, 413414 RESIDUAL option, 422 SMOOTH option, 422 TIME option, 423 Bifactor loadings, 810 Bifactor solution, 420 number of EM cycles, 419
861
INDEX reproducing expected correlation matrix, 422 BILOG-MG allocating items, 40 calibration phase, 27 example of 2PL model, 92 example of DIF analysis, 100 format statement, 57, 59, 64, 88, 114, 145, 149, 155, 160, 163, 167, 170-171, 173175, 178, 235 input files, 57, 149, 156, 173, 241 input of response record, 110 input phase, 26 new features, 24 opening or creating syntax file, 38 options on main menu bar, 38 order of commands, 114 output files, 149, 199207, 241-242 overview of syntax, 113 providing item and group labels, 41 providing job description, 41 scoring phase, 31 specifying input files, 147, 163 specifying model, 41 specifying number of parameters, 152 specifying Rasch model, 133 specifying type of analysis, 40 user interface, 37 using calibration file as input, 147
using item parameter file as input, 150, 200, 204 using master file as input, 151 using provisional values file as input, 156 using raw data file as input, 148 using the interface, 91 using weighted response records, 154 Binet, A., 616, 619, 621622, 628, 830-832 Birnbaum model, 539-540, 606, 842 Birnbaum, A., 523, 538539, 594, 837, 840-841 Biserial coefficient as discrimination index, 450 Biserial correlation, 462-5, 467, 471, 562-563, 579, 667, 700, 786-787 BISERIAL option on PLOT command, 450 Bivariate Plot option Graphics procedure, 505, 513 Bivariate plots, 505, 513 displaying, 513 editing and saving, 513 BIWEIGHT option on SCORE command, 53, 91, 115, 208, 209 Biweighting, 31, 52, 209 BJ keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393 BK keyword on EQUAL command, 379 on FIX command, 385
on PRIORS command, 393 Bliss, C.I., 833 BLOCK command, 263, 265, 295, 322, 327, 692 BNAME keyword, 263, 265, 266, 721 CADJUST keyword, 263, 265-6, 270, 692, 703 CATEGORY keyword, 263, 265-266, 269 CNAME keyword, 263, 265, 267, 269 CSLOPE option, 263, 265, 267 GPARM keyword, 263, 265, 268-269, 722 GUESSING keyword, 263, 265, 268, 722 MODIFIED keyword, 263, 265, 267, 269, 272, 696, 709, 720 NCAT keyword, 263, 265, 269-271, 339, 692, 710 NITEMS keyword, 263, 265, 270, 339, 342, 692, 710 NOCADJUST option, 263, 265, 270 ORIGINAL keyword, 263, 265, 270, 333, 335-336, 696, 710, 720 RATER keyword, 263, 265, 271, 281, 320 REPEAT keyword, 263, 265-266, 271, 710, 721-722 SCORING keyword, 263, 265, 272, 709 SKIP keyword, 263, 265, 272
862
INDEX Blocks assigning names, 265266 number of categories in, 269 number of items in, 270 of common items for DIF analysis, 300 repeating, 271 requesting common slope for all items, 267, 275 required number of, 265 skipping estimation of, 272 BNAME keyword on BLOCK command, 263, 265-266, 721 Bock main class and interaction model, 750 Bock, R.D., 25, 34-35, 209, 257, 345, 399, 404, 528-529, 531, 535-537, 546, 558, 560-561, 568, 570, 576, 584-585, 588591, 597, 599, 601, 607, 610, 614, 616-618, 628, 679, 688, 730, 749-751, 760-761, 763, 765, 767, 778, 830, 835, 838-839, 841-847 Boundaries before partitioning into subtests, 489 number of, 458 BOUNDARY keyword on FRACTILES command, 435, 776 on SUBTEST command, 489, 776 Bowers, J., 580 Bradley, R.A., 835 Browne, M.W., 844
BS option on TEST command, 355, 367, 379, 398400, 403, 752 Build Syntax option on Run menu, 82, 97, 105 Burt, C., 830, 832
C CADJUST keyword on BLOCK command, 263, 265-266, 270, 692, 703 CALIB command, 51, 78, 88, 115-116, 264, 274, 289, 295 ACCEL keyword, 78, 89, 115-117, 123, 650 ACCEL option, 264, 274-275 CHI keyword, 25, 51, 88, 115-116, 118, 634 COMMON option, 89, 115-116, 118 CRIT keyword, 51, 88, 115-117, 119, 264, 274-275, 639, 666, 692, 711 CSLOPE option, 264, 274-275 CYCLES keyword, 51, 88, 115-117, 120, 126-127, 136, 226, 264, 274, 276, 666, 692, 702, 711 DIAGNOSIS keyword, 89, 115-116, 121, 264, 274, 276, 320 DIST keyword, 264, 274, 277, 702 EMPIRICAL option, 51, 89, 115-116, 122, 129, 675
ESTORDER option, 264, 274, 277 FIXED option, 25, 78, 79, 89, 115-116, 122, 652 FLOAT option, 51, 89, 115-116, 122, 264, 274, 277, 652, 675 FREE keyword, 264, 274, 278 GPRIOR option, 51, 89, 115-116, 123, 138, 140, 264, 274, 278, 721 GRADED option, 264, 274, 279, 692 GROUP-PLOTS option, 25, 79, 89, 115-116, 124 IDIST keyword, 88, 115-116, 122, 125, 189, 193-195, 675 ITEMFIT keyword, 264, 274, 279, 692 LOGISTIC keyword, 710 LOGISTIC option, 264, 274, 279, 692 NEWTON keyword, 51, 88, 115-116, 120-121, 126, 136, 226, 264, 274, 280, 692, 703, 711 NFULL keyword, 78, 89, 115-116, 127 NOACCEL option, 264, 274-275 NOADJUST option, 25, 37, 79, 89, 115-116, 127-128, 226-227 NOCALIB option, 264, 274, 280, 288, 324, 708 NOFLOAT option, 89, 115-116, 122-123, 187-188, 675
863
INDEX NOGPRIOR option, 89, 115-116, 123, 138, 140 NORMAL option, 89, 115-116, 128-129, 264, 274, 279, 659 NOSPRIOR option, 89, 115-116, 124, 137, 140 NOTPRIOR option, 89, 115-116, 124, 138139 NQPT keyword, 51, 88, 115-117, 121-122, 129, 194-195, 264, 274, 279-280, 308309, 342, 639, 666, 692, 702, 709-710 NRATER option, 264, 274, 281 NSD keyword, 89, 115116, 130 PARTIAL keyword, 710 PARTIAL option, 264, 274, 279 PLOT keyword, 88, 115-116, 124-125, 130-131, 634 POSTERIOR option, 264, 274, 281, 711 PRINT keyword, 88, 115-116, 131, 157 PRIORREAD option, 264, 274, 281, 305 QPREAD option, 264, 274, 282, 308 QRANGE keyword, 264, 274, 282 RASCH option, 26, 79, 89, 115-116, 133 READPRIOR option, 70, 89, 115-117, 133, 187, 189-192, 666, 675
REFERENCE keyword, 42, 89, 115-116, 128, 134-135, 220, 638, 659, 675 RIDGE keyword, 89, 115-117, 135, 264, 274, 283, 666 SCALE keyword, 264, 274, 283, 692 SELECT keyword, 47, 89, 115-116, 136, 686 SKIPC option, 264, 274, 283 SPRIOR option, 51, 89, 115-116, 124, 137, 140, 264, 274, 721 THRESHOLD option, 264, 274, 284 TPRIOR option, 51, 89, 115-117, 124, 138139, 264, 274, 284, 666 CALIB keyword on SAVE command, 86, 114, 148, 199, 204, 263, 288, 312, 337 Calibration, 34, 40, 47, 70, 75, 82, 116, 276, 288, 349-350, 364, 371, 389, 599, 611, 694, 742, 752 controlling iterative procedure, 46, 51, 274, 382 information on prior distributions, 64 sample, 609, 616 selecting blocks for, 272 selecting subtests for, 46, 136 skipping of, 280, 283 suppressing adjustment for category parameters, 318 suppressing correction for information function, 281
user-supplied parameter values, 272 Calibration file as input in BILOG-MG, 147 naming of, 289 saving, 199, 312 Calibration Only option on Run menu, 82 Calibration Options dialog box, 78, 117, 122, 125, 128, 133 Calibration Options option on Technical menu, 78, 89, 117, 122, 125, 128, 133 Calibration Prior Latent Distribution dialog box, 70, 194, 195 Calibration Prior Latent Distribution option on Technical menu, 70, 194, 195 Carroll, J.B., 585 CASE option on INPUT command, 413-414 Case score information saving to file, 469 Case weights, 27, 298 Categories assigning names to, 267 average of threshold parameters, 700-701 collapsing, 269-270 number of, 399 number of highest, 355, 399 number of in graded model, 375 number of per block, 269 threshold parameters, 546, 564, 700, 703
864
INDEX Category coefficient generalized partial credit model, 557 CATEGORY keyword on BLOCK command, 263, 265-266, 269 Category parameters, 341 providing initial values, 266 setting mean of, 266, 270 skipping estimation of, 280, 283 suppressing calibration adjustment, 318 CCRIT keyword on ESTIMATE command, 383 CCRIT option on SAVE command, 461 CFNAME keyword on FILES command, 263, 288-289 on GLOBAL command, 85, 114, 147 CHANCE option on SAVE command, 816-817 on SCORE command, 474, 804 on SIMULATE command, 483-484, 820 Changing attributes of graphs, 517 size of window display, 83 CHI keyword on CALIB command, 25, 51, 88, 115-116, 118, 634 Chi-square, 634 ability intervals for, 25, 51, 118
change in likelihood ratio, 30 likelihood ratio, 437, 731-732, 736, 747, 750, 763, 768, 780, 796 test statistic, 796, 807 CJ keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393 CK keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393 CK option on TMATRIX command, 404 Class codes assigning in TESTFACT, 424 CLASS command, 424, 776 IDENTITY keyword, 424 NAMES keyword, 425 Class item statistics saving to file, 461 CLASS keyword on PROBLEM command, 446, 454, 776 Classes identifying, 424 number of, 454 saving item statistics for file, 465 saving separate estimates, 462
Classical item statistics, 27, 37, 82, 106, 120, 126, 154, 203, 242, 244, 410, 530, 541, 584, 635, 642, 651, 659, 667, 672, 676, 780, 818 saving to file, 203, 242, 244, 467 Classical reliability, 32-33 Classical Statistics Only option on Run menu, 82 Clinical testing, 618-619 Clogg, C.C., 735, 761 CMAIN option on SAVE command, 462 CNAME keyword on BLOCK command, 263, 265, 267, 269 Code class identification, 424 correct-response in TESTFACT, 448 identifying raters, 304 identifying subgroups, 301 not-presented items, 43 omitted items, 43 responses, 43 Coefficient alpha, 459 of kurtosis, 26, 79, 214215 of skewness, 26, 79, 214-215 Collapsing of interval for test statistic, 544, 562, 605, 705 COMBINE command, 264, 285, 292, 722 NAME keyword, 264, 285 WEIGHTS keyword, 264, 285, 286, 723
865
INDEX COMBINE keyword on INPUT command, 263, 285, 292, 338, 722 on SAVE command, 263, 285, 312, 313, 337 Combined subscale scores, 285-286 Combined score file saving, 313, 337 Commands BIFACTOR, 410, 418, 468, 472-473, 495, 804 BLOCK, 263, 265, 295, 322, 327, 692 CALIB, 51, 78, 88, 115116, 264, 274, 289, 295 CLASS, 424, 776 COMBINE, 264, 285, 292, 722 COMMENT, 42, 85, 114, 141, 263, 287, 426 CONTINUE, 427, 482, 775 CRITERION, 428, 470 DRIFT, 88, 114, 163, 165 END, 378, 405-406, 409 EQUAL, 379, 752, 754, 758, 761, 768 ESTIMATE, 382 EXTERNAL, 428, 430, 455 FACTOR, 410, 418, 431, 468, 478 FILES, 263, 280, 287288, 312 FIX, 738, 754, 758, 761 FORM, 88, 108-112, 114, 159, 163, 173174, 183, 215-216,
219-220, 236, 238, 240, 652 FRACTILES, 435, 455 FULL, 410, 437, 478 GLOBAL, 85, 114, 141, 199, 242 GROUP, 88, 108-112, 114, 163, 165, 173174, 183, 236 INPUT, 55, 62, 86, 114, 241-242, 263, 280, 289, 291-292, 320, 329, 331, 441, 482, 504 ITEMS, 87, 108, 111112, 114, 145-146, 159-161, 173, 182, 224-225, 228-233 KEY, 448, 779, 781, 804 LABELS, 387 LENGTH, 86, 109, 114, 153-154, 159, 185, 229, 231 MGROUP, 263, 294, 295, 300, 710, 711 MRATER, 263, 294, 303, 728 NAMES, 449 NAMES, 429-430, 449, 455-456, 482, 490, 804 PLOT, 450 PRIORS, 69, 90, 115, 117, 134, 187, 193, 264, 281, 305, 393, 452, 675 PROBLEM, 388, 435, 454, 482 QUAD, 75, 90, 115, 117, 122, 126, 187189, 193, 675 QUADP, 264, 282, 308 QUADS, 75, 91, 115, 196, 211-212, 264, 310, 321
RELIABILITY, 459 RESPONSE, 456-457, 460, 781, 804 SAVE, 80, 86, 114, 158, 199, 242-244, 247248, 250, 253, 255256, 263, 289, 291, 312, 337, 395, 409, 420-421, 442, 461, 482, 499-500, 652, 722 SCORE, 75, 90, 115, 206, 208, 264, 285, 289, 292, 316, 410, 474 SELECT, 418-419, 457, 480, 489-490, 504, 781, 818 SIMULATE, 410, 461, 474, 482, 819 START, 395-396, 748 STOP, 482, 488 SUBTEST, 428, 458, 489 TECHNICAL, 410, 491 TEST, 66, 87, 108-109, 114, 121, 127, 153154, 183, 185-186, 224, 263, 274, 297, 316, 325, 327, 398 TETRACHORIC, 499, 806 TGROUPS, 401, 403, 750, 758 TITLE, 42, 85, 114, 234, 263, 330, 482, 502 TMATRIX, 754, 767 COMMENT command, 42, 85, 114, 141, 263, 287, 426 COMMON keyword on MGROUP command, 263, 300
866
INDEX COMMON option on CALIB command, 89, 115-116, 118 Communality estimates, 791-792, 810 Communality improvements convergence criterion for, 494 COMPLETE option on TETRACHORIC command, 500-501 Components specifying characteristics, 454 Constraints equal slopes, 738 equality, 761 for group parameters, 560 imposing in MULTILOG, 379, 735, 754, 758, 760, 768 of location parameters, 750 pairwise, 380 using in MULTILOG, 757 Construct definition, 629 Contingency table per item, 562 CONTINUE command, 427, 482, 775 Contrasts deviation, 404 in MULTILOG, 379, 385, 393, 570 standardized, 717 Convergence criterion, 51, 117, 120, 382-383, 493, 495, 638, 692, 702, 711, 781, 826, 829 for communality improvements, 494 for M-step, 383
specifying in BILOGMG, 119 Copying of graphs, 515 CORRELAT option on INPUT command, 445, 818 on SAVE command, 463 Count matrix for calculating tetrachoric correlations, 499 Covariance file saving, 200, 242, 248 COVARIANCE keyword on SAVE command, 86, 114, 199-201, 248 CPARMS keyword on BIFACTOR command, 418, 804805 on FULL command, 437, 474 CRIT keyword on CALIB command, 51, 88, 115-117, 119, 264, 274-275, 639, 666, 692, 711 Criterion convergence of EM cycles, 383 reading from file, 359 referencing, 632 CRITERION command, 428, 470 CRITMARK option, 428, 776 EXTERNAL option, 428, 429 NAME keyword, 429 SUBTEST option, 428 WEIGHTS keyword, 428-429
CRITERION option on PLOT command, 451, 776 on PROBLEM command, 358, 388, 773 on SAVE command, 464 Criterion score defining in TESTFACT, 428 external, 451, 776, 829 mean, 462, 464 naming, 429 saving item statistics based on, 464 CRITMARK option on CRITERION command, 428, 776 Cronbach, L.J., 837 CROSS option on TETRACHORIC command, 499 CSLOPE option on BLOCK command, 263, 265, 267 on CALIB command, 264, 274, 275 CSUB option on SAVE command, 465 CYCLES keyword on BIFACTOR command, 419, 804, 806, 815 on CALIB command, 51, 88, 115-117, 120, 126-127, 136, 226, 264, 274, 276, 666, 692, 702, 711 on FULL command, 438, 779, 781, 794, 803, 815, 825
867
INDEX
D Data aggregate-level, 210 as input in BILOG-MG, 148, 163 counts of response patterns, 352, 366, 407 DIF model, 334 entering interactively, 58 fixed-effects table of counts, 352-353, 407 fixed-format, 56 for item or factor analysis, 441 format of, 56 group-level, 54, 62-63, 208, 211, 213, 221, 293, 320, 333, 335, 344, 537, 631, 666669, 685, 841 in compressed format, 110, 112 in expanded format, 110, 112, 173 individual level, 333 matrix sampling, 116, 187, 238, 537, 610, 630, 666 multiple-group Rater'sEffect, 333, 335 number of records, 391 rewinding, 445 sampling from file, 178, 298 single-group model, 334 single-subject, 54, 55, 62, 64, 180 specifying in MULTILOG, 388-90 specifying order of items, 144 specifying type of, 180, 445
using subset of records, 179, 298 using weighted, 154 Data file providing information on, 54, 289 Data File tab on Examinee Data dialog box, 55 Data File/Enter Data tab on Examinee Data dialog box, 85-86, 88 on Group-Level Data dialog box, 85-86, 88 DATA keyword on PROBLEM command, 352, 359, 389 Data menu Examinee Data dialog box, 55, 95, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Examinee Data option, 85-94, 103 Group-Level Data dialog box, 62, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Group-Level Data option, 85- 88 Item Keys dialog box, 59, 163, 169, 172, 177, 242 Item Keys option, 87 Data Options dialog box, 77, 117, 127, 163, 167 Data Options option on Technical menu, 77, 87, 89, 117, 127, 163, 167 Davis, J.A., 761 Decimals number for tetrachorics, 500
Degrees of freedom, 30, 535, 543, 544, 562, 587588, 603, 605, 634, 651, 670, 705, 747, 763, 796797, 806-808 Delta statistic, 577-578, 786 Dempster, A.P., 585, 842 DEVIATION option on TMATRIX command, 404 DFNAME keyword on FILES command, 263, 288-289, 301, 304, 333, 692, 710 on GLOBAL command, 57, 59, 64, 85, 114, 147-149, 151, 172, 177, 240-241 DIAGNOSE keyword on INPUT command, 87, 114, 163, 164 DIAGNOSIS keyword on CALIB command, 89, 115-116, 121, 264, 274, 276, 320 Dialog boxes Assign Calibration Prior Latent Distribution, 75 Assign Fixed Items, 68, 109, 225, 227 Assign Scoring Prior Latent Distribution, 70, 75 Axis Labels, 517 Bar Graph Parameters, 517-518 Calibration Options, 78, 117, 122, 125, 128, 133 Calibration Prior Latent Distribution, 70, 194195 Data Options, 77, 117, 127, 163, 167 868
INDEX Examinee Data, 55, 95, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Fixed Theta, 350, 358, 371, 377, 388-389 General, 41, 59, 93, 108-111, 113, 117, 135, 141, 145, 147, 151-153, 156, 160163, 165, 170, 174, 176, 183, 225, 229, 233-234 Graph Parameters, 516 Group-Level Data, 62, 147, 149, 163, 167, 171, 175, 178-179, 180-181, 240, 242 Input Data, 350-353, 358, 365, 371, 377, 388, 390 Input Parameters, 352355, 359, 362, 366368, 372-374, 377, 388-392, 398-399 Item Analysis, 46, 94, 102, 108-113, 117118, 120-124, 127, 130, 137-138, 140, 145-146, 160-161, 185-186, 225, 231 Item Keys, 59, 163, 169, 172, 177, 242 Item Parameter Prior Constraints, 69, 117, 134, 189 Item Parameter Starting Values, 65, 109, 225, 226, 228, 230, 232 Item Prior Constraints, 189-192 Legend Parameters, 517, 520 Line Parameters, 516517, 519, 521-522
New Analysis, 349-350, 357, 364, 370-371, 377, 388-390 Plot Parameters, 521522 Project Settings, 362, 369 Response Codes (Binary Data), 355-356, 368, 384, 405-406 Response Codes (NonBinary Data), 355, 361 Save Output to File, 78, 80 Score Options, 79, 209210, 215 Scoring Prior Latent Distribution, 197-198, 209, 212, 217-218, 219 Settings, 83, 85 Test Model, 352-354, 360, 367, 373, 377, 398-400 Test Scoring, 147, 150, 157, 209, 211-214, 216, 221-222 Text Parameters, 517518, 521-522 DIF, 24, 29, 42-43, 86-87, 100-101, 106-108, 114, 131, 133-135, 155, 163165, 175, 187, 191-192, 199, 201, 225, 233, 242, 245, 257, 263, 274, 278, 286, 294, 300-302, 333337, 340, 343-345, 528529, 531-533, 535-536, 546, 560-561, 626-627, 638, 645-646, 651, 710713, 717-719, 770, 844845, 858 and common blocks of items, 300 and Rasch model, 133
saving parameters to file, 201 DIF keyword on MGROUP command, 263, 300-301, 710, 711 on SAVE command, 86, 114, 164-165, 199, 201, 245, 638 DIF option on INPUT command, 43, 87, 114, 135, 163164, 192, 638 DIF parameter file saving, 242, 245 Difficulty of item, 539 Difficulty index, 451, 462465, 467, 471, 577 of item, 562, 642 Digvi, D.R., 583 Discriminating power, 575, 622, 632 defining, 451 of item in 2PL model, 531, 535, 538-539, 576, 578, 623, 692, 840 plot against item difficulty, 450 Discrimination parameter, 594 Dispersion starting values for, 225 DISPERSN keyword on TEST command, 47, 68, 88, 114, 224-225, 232 Displaying bivariate plots, 513 histogram of ability scores, 512 item characteristic curve, 506, 508, 510 item information curve, 507-508
869
INDEX test information, 509 DIST keyword on CALIB command, 264, 274, 277, 702 on SCORE command, 264, 282, 316, 695, 706 DK keyword on EQUAL command, 379 on FIX command, 385 on PRIORS command, 393, 733 DK option on TMATRIX command, 404 DOMAIN keyword on SCORE command, 26, 79, 91, 115, 208210 Domain referencing, 632, 846 Domain scores, 26, 79, 209-210, 621, 631-632, 688-691, 846 calculating, 65 Dorans, N.J., 564 Drasgow, F., 564 DRIFT, 24, 29, 37, 42-43, 86-88, 114, 123, 133135, 142, 163, 165, 187, 191-192, 199, 202, 242, 247, 254, 278, 528, 531532, 536, 652, 844, 845 and Rasch model, 133 requesting analysis, 165 saving parameters to file, 202 specifying polynomial, 142 specifying time points, 143 DRIFT command, 88, 114, 163, 165 MAXPOWER keyword, 88, 114, 142, 192
MIDPOINT keyword, 88, 114, 142, 143 DRIFT keyword on SAVE command, 86, 114, 165, 199, 202, 247 DRIFT option on INPUT command, 43, 87, 114, 135, 142, 163, 165, 192, 202 DRIFT parameter file saving, 242, 247 du Toit, S.H.C, 844
E EAP controlling precision for factor scores, 478 estimating scores on general factor, 418 EAP option on SCORE command, 264, 316-317, 693, 707, 721 Edit menu, 40, 515 Editing axis labels of graphs, 517 bar parameters, 518 bivariate plots, 513 histogram of ability scores, 512 item characteristic curve, 506, 508, 512 item information curve, 507-508 legends of graphs, 520 lines in graph, 521 plot parameters, 522 test information curve, 509 text in graph, 522
Editor tab on Settings dialog box, 83 EM algorithm, 28, 644 reversing estimation order, 277 EMPIRICAL option on CALIB command, 51, 89, 115-116, 122, 129, 675 END command, 378, 405406, 409 Engelhard, G., 588, 591 Enter Data tab on Examinee Data dialog box, 58 Enter Values tab on Item Parameter Starting Values dialog box, 66 EQUAL command, 379, 752, 754, 758, 761, 768 AJ keyword, 379 AK keyword, 379 ALL keyword, 381 BJ keyword, 379 BK keyword, 379 CJ keyword, 379 CK keyword, 379 DK keyword, 379 GROUPS keyword, 381 ITEMS keyword, 381 MU keyword, 379 SD keyword, 379 Equality constraints of item parameters, 379, 575 Equating equivalent groups, 532533, 627, 631, 652, 685, 768, 845 linear, 627, 838, 845 non-equivalent groups, 122, 627-628, 632 of forms, 626 870
INDEX vertical, 24, 116, 120, 129, 138, 528, 532, 534, 621, 627-628, 658, 661, 845 Equipercentile method, 533, 627 Equivalent groups equating, 532-533, 627, 631, 652, 768 ERRORSEED keyword on SIMULATE command, 483, 820, 822 E-step methods of integration, 410, 530 saving results of final, 465 ESTIMATE command, 382 ACCMAX keyword, 382 CCRIT keyword, 383 ICRIT keyword, 383 ITERATIONS keyword, 383 NCYCLES keyword, 383 VAIM keyword, 356, 384 Estimated error variance, 32-35, 249, 271, 583, 620, 623, 625, 655-656, 684-685, 699, 838, 843 Estimates a-posteriori, 812 provisional, 496 Estimating common value for lower asymptote, 118 means of prior distributions, 123 score distribution as discrete distribution, 121 Estimation
Bayes, 31, 34-35, 214, 317, 410, 418, 476, 529-530, 537, 544, 576, 588, 590, 601, 605, 607, 609-610, 616, 624-625, 655, 664, 679, 692-693, 706, 721, 780-781, 800-802, 837-838, 841, 843-844, 847 Bayes modal, 605, 607, 841, 843 marginal maximum likelihood, 28, 116, 121, 123, 345, 349350, 364, 389, 401403, 407, 529, 544, 562, 576, 584-587, 589-590, 599-602, 604-605, 607, 611, 644, 675, 699-700, 702, 705, 730, 742743, 806, 842-843 maximum likelihood, 25, 28, 30-31, 33-36, 75, 122, 128, 208, 213-214, 222, 277, 317-318, 320, 323, 345, 410, 452, 495, 529-530, 532, 537, 543-544, 564, 568, 576, 584-585, 586587, 591, 594-595, 597- 600, 606-611, 615, 652, 655, 685, 693, 700, 702, 708, 734, 744, 792, 794, 806, 833-834, 836838, 840-844, 847 maximum marginal a posteriori, 25, 29, 31, 35-37, 75, 211, 214, 349-350, 357, 389, 392, 395, 474, 476, 529, 544, 590, 607610, 625, 655, 664,
685, 748-749, 781782, 802, 837-838 Minimized squared residuals (MINRES), 791, 793 Newton-Gauss, 28, 31, 120, 601, 644, 692, 702, 833-834 Newton-Raphson, 31, 615, 749, 833-834 reversing order of EM, 277 Warm's weighted maximum likelihood, 264, 316-317, 615 ESTORDER option on CALIB command, 264, 274, 277 Examinee Data dialog box, 55, 95, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Data File tab, 55 Data File/Enter Data tab, 85-86, 88 Enter Data tab, 58 General tab, 55, 86-87, 103 Examinee Data option on Data menu, 55, 8588, 94-95, 103, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Example, 359, 362 2PL model with BILOG-MG, 92 3PL model, 364, 733 DIF analysis with BILOG-MG, 100 fixed-theta model, 370 reading of an external criterion, 357 Expected correlations in bifactor solution, 422 Expected frequencies, 438
871
INDEX marginal, 30 saving to file, 202, 242, 250 EXPECTED keyword on SAVE command, 86, 114, 199, 202, 250 EXPECTED option on SAVE command, 465 EXTERNAL command, 428, 430, 455 EXTERNAL keyword on INPUT command, 55, 63, 87, 114, 163, 166 on PROBLEM command, 430, 446, 455 EXTERNAL option on CRITERION command, 428-429 External variables in computation of item parameters, 166 naming, 430 number of, 455 Extreme points setting values for adaptive quadrature, 496 Extreme weights setting values for adaptive quadrature, 497
F Facilities plot, 582 Facility, 27, 244, 345, 410, 529, 542, 575, 578, 581583, 787-788, 793-794 FACILITY option on PLOT command, 451, 776
Factor analysis changing values of default constants, 491 classical, 410, 530 controlling, 431 exploratory, 584 full information, 410, 432, 437, 442, 446, 530, 629, 778, 818 latent response process correlation matrix, 434 MINRES, 431 not-reached items, 586 number of factors, 431, 486 of inter-item tetrachoric correlations, 410, 529, 575 principal, 434, 442, 530, 583 requesting rotation, 433 statistical test of number of factors, 587 FACTOR command, 410, 418, 431, 468, 478 NDEC keyword, 431 NFAC keyword, 431433, 446, 466, 468, 469, 473, 484, 486, 779, 781, 797, 803, 815, 818, 824-825 NIT keyword, 432 NROOT keyword, 432, 779, 781, 792, 803, 815, 818, 825 PROMAX option, 413 RESIDUAL option, 433, 779 ROTATE keyword, 433, 468, 472, 779, 781, 793, 803, 818, 824 ROTATE option, 414 SMOOTH option, 434, 779 VARIMAX option, 413
Factor correlations, 576 Factor loadings, 410-411, 442-443, 446-477, 482, 484, 485, 487, 530, 575576, 580, 584-589, 629, 645, 699, 779-781, 792794, 797-798, 809-811, 815, 818-821, 823, 829, 842-843 MINRES principal, 792 parameter file as input, 476 saving to file, 468, 472 unrotated, 420, 798 Factor scores, 410-411, 421, 439, 468, 474-475, 477-478, 482, 498, 530, 576, 584, 588-590, 629, 780-781, 800-804, 812813, 815-817, 819-820, 826, 828 controlling EAP/MAP precision, 478 number of leading cases in output, 475 number of with usersupplied file, 477 providing population means for, 486 saving to external file, 466 using guessing model, 474 Factors number of, 410 number of item-group, 421 FACTORS option on INPUT command, 445, 823 Fechner, G.T., 832 FILE keyword on INPUT command, 443, 446, 776, 804, 818, 823, 825
872
INDEX on SCORE command, 26, 79, 91, 115, 208210, 468, 475, 477478, 690, 802, 815 on SIMULATE command, 483, 819820 File menu, 38, 54, 514-515 Print current page option, 514 Print selected graph option, 514 Printer Setup option, 514 Printing Options option, 514 Save as Metafile option, 514 Save option, 97 Show Selectors option, 514 Filename for input in TESTFACT, 443 Files opening multiple syntax, 84-85 FILES command, 263, 280, 287-288, 312 CFNAME keyword, 263, 288-289 DFNAME keyword, 263, 288-289, 301, 304, 333, 692, 710 IFNAME keyword, 263, 274, 288-289, 312, 708 MFNAME keyword, 263, 288, 290 NFNAME keyword, 263, 288, 290, 337 OFNAME keyword, 263, 288, 290, 337 SAVE keyword, 722, 724
SAVE option, 263, 288, 291, 312 Fill Page option Graphs menu, 516 Finney, D.J., 834 Fisher information, 31, 590, 606, 609, 655, 834, 837 Fisher scoring, 28, 31, 36, 120, 126-127, 280, 601, 606, 608, 644, 692, 702, 833-834, 841-842 requesting use of full information matrix, 127 Fisher, R.A., 28, 31, 36, 126-127, 267, 280, 590, 592, 598-599, 601, 606, 608-610, 628, 655, 702, 833-834, 837, 841-842, 844, 845 Fit group-level statistics, 208, 211, 213, 221, 317, 666 likelihood ratio test, 30, 530, 535-536, 587, 705, 763 probability for group, 244 root-mean-square of posterior deviates, 604 test for small number of items, 30, 436, 605, 710, 841 test of improved, 30 FIT keyword on SAVE command, 263, 312-313, 337, 339, 344 FIT option on SCORE command, 53, 91, 115, 208, 210, 264, 316-317, 344, 666
Fit statistics group-level, 317 Fit statistics file saving, 313, 338 Fitzpatrick, A.R., 751, 757 FIX command, 738, 754, 758, 761 AJ keyword, 385 AK keyword, 385 ALL keyword, 386 BJ keyword, 385 BK keyword, 385 CJ keyword, 385 CK keyword, 385 DK keyword, 385 GROUPS keyword, 386 ITEMS keyword, 386 MU keyword, 385- 386 SD keyword, 385-386 VALUE keyword, 386 FIX keyword on TEST command, 25, 36, 47, 68, 88, 114, 224, 226 Fixed number of groups, 401 parameters, 226, 386, 575 FIXED option on CALIB command, 25, 78, 79, 89, 115116, 122, 652 on PROBLEM command, 349-350, 370, 389, 392 Fixed Theta dialog box, 350, 358, 371, 377, 388389 Fixed-theta parameter estimation, 349, 350, 371, 401 Fixed-theta parameter estimation., 750 FLOAT option on CALIB command, 51, 89, 115-116, 122,
873
INDEX 264, 274, 277, 652, 675 Follman, D., 840, 843 Font changing, 83 Form of T-matrix in MULTILOG, 403404 FORM command, 88, 108112, 114, 159, 163, 173174, 183, 215-216, 219220, 236, 238, 240, 652 INAMES keyword, 50, 88, 114, 144, 145 INUMBERS keyword, 50, 88, 114, 144, 146, 686 LENGTH keyword, 50, 88, 114, 144, 146 Form Items tab on Item Analysis dialog box, 49, 88 Format ability score file, 242243 classical item statistics file, 242, 244 combined score file, 313, 337 covariance file, 242, 248 DIF parameter file, 242, 245 DRIFT parameter file, 242, 247 expected frequencies file, 242, 250 fit statistics file, 313, 338 for multiple raters, 297 item information file, 314, 342 item parameter file, 242, 253, 315, 336, 340
marginal posterior probability file, 243, 255 not-presented key, 337 of binary item data, 356 of multiple response item data, 356 omit key, 336 output files, 312-314, 337 subject scores file, 315, 337 FORMAT option on INPUT command, 444 on SAVE command, 395 on START command, 396-397 Format statement, 56-59, 64, 88, 96, 104, 106, 114, 144-145, 149, 154155, 159-160, 163, 166167, 170-175, 178, 210, 235-236, 238, 262-263, 296-297, 299, 331-336, 350, 352, 359-360, 366, 368, 372, 396, 398, 405, 408, 415, 442, 444, 447, 461, 475, 484, 502-504, 634, 638, 641, 652, 666, 670, 690, 692, 710, 721722, 724-725, 775-776, 779, 802-804, 814-815, 817, 821, 823, 827-828 number of records, 170, 295 PARSCALE, 263, 331, 334, 336 Forms allocating items to, 40 as reference for scoring, 215, 219 assigning items, 46 equating, 626 for scoring, 219
length of, 49 number of, 42, 173 position of ID, 58 vertical equating, 24 FORMS keyword on SIMULATE command, 484, 819, 820 Fractile tables, 580 Fractiles, 435-436, 455, 576, 580-582, 776 grouping scores into, 435, 776 number of, 435, 455, 582 specifying, 436 FRACTILES command, 435, 455 BOUNDARY keyword, 435, 776 PERCENTIL option, 435, 436 SCORES option, 435, 436, 776 FRACTILES keyword on FRACTILES command, 776 on PROBLEM command, 455 FRACTION option on TECHNICAL command, 491 Fractional factorial design, 495, 590 quadrature, 494, 589, 590, 779, 803 FREE keyword on CALIB command, 264, 274, 278 French, J.L., 833 FREQ keyword on FULL command, 438 FREQ option on TECHNICAL command, 492 874
INDEX Frequencies expected, 30 expected response pattern, 492 joint per pair, 499 FSCORES option on SAVE command, 466, 476, 781, 802, 816-817 FULL command, 410, 437, 478 CPARMS keyword, 437, 474 CYCLES keyword, 438, 779, 781, 794, 803, 815, 825 FREQ keyword, 438 LORD option, 413-414, 437-438 MISS option, 413-414, 437-438 OMIT keyword, 438, 440, 825 QUAD keyword, 439, 825 RECODE option, 413414, 437-438 TIME option, 440 Full information factor analysis, 410, 432, 437, 442, 446, 530, 629, 778, 818 acceleration, 491 and omits, 421, 438 input of trial values, 446 number of quad points per dimension, 497 saving result of E-step, 465 use of non-adaptive quadrature, 494 Full information matrix use in Fisher scoring, 127 Full information procedures, 584
G Gauss-Hermite quadrature, 492, 589, 693, 841 GCODE keyword on MGROUP command, 263, 300-302, 334336 General dialog box, 41, 59, 93, 108-111, 113, 117, 135, 141, 145, 147, 151153, 156, 160-163, 165, 170, 174, 176, 183, 225, 229, 233, 234 Job Description tab, 42, 49, 85-89, 93, 100 Labels tab, 44, 87-88 Model tab, 42, 85, 87, 93, 101 Response tab, 43, 59, 86 General option on Setup menu, 40-41, 59, 85-89, 93, 100, 108-111, 113, 117, 135, 141, 145, 147, 151-153, 156, 160163, 165, 170, 174, 176, 183, 225, 229, 233-234 General tab on Examinee Data dialog box, 55, 86-87, 103 on Group-Level Data dialog box, 62, 86-87 on Settings dialog box, 83 on Test Scoring dialog box, 52, 85-86, 90-91 Gibbons, R.D., 529, 585586, 591, 843 Glas, C.A.W, 844 Glass, G.V., 580 GLOBAL command, 85, 114, 141, 199, 242
CFNAME keyword, 85, 114, 147 DFNAME keyword, 57, 59, 64, 85, 114, 147149, 151, 172, 177, 240-241 IFNAME keyword, 53, 85, 114, 147, 150, 180-181, 201, 204205, 226-227, 686 LOGISTIC option, 43, 85, 114, 147, 150, 638 MFNAME keyword, 85, 114, 147, 151 NPARM keyword, 43, 85, 114, 119, 133, 147, 152, 170, 177, 189-190, 228, 638 NTEST keyword, 42, 85, 114, 137, 147, 152-154, 159, 175, 178, 185-186, 189, 194-195, 197-198, 217-219, 224-225, 229, 230-231, 233 NVTEST keyword, 43, 85, 114, 147, 153, 159, 175, 185-186, 224-225, 229-233 NWGHT keyword, 85, 114, 147, 154, 240241 OMITS option, 44, 86, 114, 147, 155 PRNAME keyword, 25, 53, 86, 114, 147, 156157, 226-227, 241 SAVE option, 80, 86, 114, 147, 157, 164165, 199-207, 638, 652 GMU keyword on PRIORS command, 264, 305
875
INDEX GNAME keyword on GROUP command, 46, 88, 114, 159-160 on MGROUP command, 263, 300, 302 Goldstein, H., 536 Goodman, L.A., 735 Goodness-of-fit test statistics, 37, 279, 705, 717, 730, 732, 736, 746, 750, 752, 758, 763, 768, 780, 796, 807-808 Goodness-of-fit test, 561 collapsing of intervals, 544, 562, 605, 705 GPARM keyword on BLOCK command, 263, 265, 268-269, 722 GPRIOR option on CALIB command, 51, 89, 115-116, 123, 138, 140, 264, 274, 278, 721 GR option on TEST command, 355, 361, 367, 374, 379, 398-400 Grade equivalents, 628, 832 Graded category response function, 547 GRADED option on CALIB command, 264, 274, 279, 692 on TEST command, 735, 742, 748 Graded response model, 279, 340, 355, 367, 379, 398-400, 523, 529, 546549, 553, 556, 612, 614, 616, 692, 735, 738, 740, 742, 748, 765, 772
listing threshold parameters, 379, 385, 393 logistic form, 547 metric, 279 number of categories, 375 slopes, 379, 385, 393 Graded responses, 345, 529, 596 Graphics procedure, 505 Bivariate Plot option, 505, 513 Histogram option, 505, 512, 518 ICC and Info option, 505, 508, 526 ICC option, 505-506 Information option, 505, 507, 526 Main menu, 505 Matrix Plot option, 505, 510 Total Info option, 505, 509, 527 Graphs bivariate, 505, 513 changing attributes of, 517 copying of, 515 editing axis labels, 517 editing bar characteristics, 518 editing bar parameters, 518 editing legends, 520 editing lines, 521 editing plot parameters, 522 editing text, 522 item characteristic curve, 505, 507, 509, 523 item difficulty against discriminating power, 450
item information curve, 505, 508-509, 512, 524 matrix, 829 measurement error, 527 modifying, 514 printing of, 514 resizing, 516 saving and printing of, 514 selecting of, 515 total information curve, 505 Graphs menu, 516 Fill Page option, 516 Parameters option, 516 Graphs Parameters dialog box, 516 Green, B.F., 836 Green, S.B., 582 Group identification code, 301 Group box Test Model, 367 GROUP command, 88, 108-109, 111, 114, 163, 165, 173-174, 183, 236 GNAME keyword, 46, 88, 114, 159-160 INAMES keyword, 50, 88, 114, 159-160 INUMBERS keyword, 50, 88, 114, 159, 161 LENGTH keyword, 50, 88, 114, 159, 161-162 Group Items tab on Item Analysis dialog box, 49, 88, 102 GROUP keyword on SIMULATE command, 484, 819820 Group-level data, 335, 667 as input, 293 printing fit statistics, 317 876
INDEX scoring of, 316 Group-Level Data dialog box, 62, 147, 149, 163, 167, 171, 175, 178-181, 240, 242 Data File/Enter Data tab, 85-86, 88 General tab, 62, 86-87 Group-Level Data option on Data menu, 62, 8588, 147, 149, 163, 167, 171, 175, 178181, 240, 242 GROUPLEVEL option on INPUT command, 263, 292-293, 336 GROUP-PLOTS option on CALIB command, 25, 79, 89, 115-116, 124 Groups adjustment of item difficulty, 561, 713 allocating items to, 40 assigning codes, 424 assigning items, 46 assigning items for general factor, 419 assigning names, 44, 302 assumed prior distributions, 37 different quad points and weights, 76 estimates of means, 37 estimates of standard errors, 37 fit probability, 244 identification in simulation of records, 484 identifying respondents, 111 identifying sets of items, 111 length of, 49
listing of item names, 160-161 listing of item numbers, 160-161 mean of population distribution, 379, 385, 393 naming of, 160 number of, 42, 62, 174, 294, 352, 360, 367, 373, 391 number of fixed, 401 parameter constraints, 560 position of ID, 58, 62 posterior distributions for, 37 providing labels for, 41 providing quadrature points and weights by, 70 separate item plots, 78 setting reference, 302 setting reference for scoring, 220 specifying means of normal prior, 217 specifying number of items, 162 specifying quadrature points, 129 specifying standard deviation of normal prior, 218 standard deviation of population distribution, 379, 385, 393 GROUPS command, 112 GROUPS keyword on FIX command, 386 GROUPS option on PRIORS command, 394
GSIGMA keyword on PRIORS command, 264, 305-306 GUESS keyword on TEST command, 68, 87, 114, 224, 226-227 GUESSING keyword on BLOCK command, 263, 265, 268, 722 Guessing model, 523, 538, 540, 569 and computing factor scores, 474 and simulating data, 483 Guessing parameter, 28, 29, 31, 118, 123, 137, 169, 189-190, 249, 254, 268, 273, 301, 305-306, 379, 385, 393, 474-475, 483, 540-541, 585, 601, 609, 612, 733-734, 767, 815, 820, 822, 843 in TESTFACT, 437, 582, 805 requesting beta prior, 278 requesting in PARSCALE, 268 selecting prior, 123 starting values for, 227, 268 GUESSSEED keyword on SIMULATE command, 485, 822 Gulliksen, H., 592
H Haberman, S.J., 588, 835 Hale, R.L., 833 Hales, L.W., 578 Half-item rule for extreme cases, 31 Hambleton, R.K., 524-525, 846 877
INDEX Harman, H.H., 583 Harvey, W.R., 583 Harwell, M.R., 599 Hedeker, D.R., 586 Help menu, 85 Hendrickson, E.A., 583 Henryssen, S., 577 Heywood case, 123, 137, 576, 587, 589, 601, 843 HIGH keyword on TEST command, 367, 399, 738, 752 Histogram estimated abilities, 505 of ability scores, 512 scores in TESTFACT, 577 Histogram option Graphics procedure, 505, 512, 518 Hively, W., 846 Holland, P.W., 844 Holzinger, K.J., 586 Horst, P., 592
I ICC and Info option Graphics procedure, 505, 508, 526 ICC option Graphics procedure, 505-506 ICRIT keyword on ESTIMATE command, 383 IDENTITY keyword on CLASS command, 424 IDIST keyword on CALIB command, 88, 115-116, 122, 125, 189, 193-195, 675
on SCORE command, 75, 78, 90, 115, 196198, 208, 211, 213, 666, 675 IFNAME keyword on FILES command, 263, 274, 288-289, 312, 708 on GLOBAL command, 53, 85, 114, 147, 150, 180-181, 201, 204205, 226-227, 686 on INPUT command, 650 IGROUPS keyword on BIFACTOR command, 419, 804805, 815 Import/Enter Values tab Assign Item Parameter Starting Values dialog box, 65, 87-88 INAMES keyword on FORM command, 50, 88, 114, 144-145 on GROUP command, 50, 88, 114, 159-160 on ITEMS command, 46, 87, 114, 159, 182, 183, 638, 686 on TEST command, 87, 114, 224, 228, 263, 325-326, 710 Indeterminacy, 531, 549, 703 INDIVIDUAL option on PROBLEM command, 352, 359, 390-391, 748, 773 INFO keyword on SCORE command, 36, 90, 115, 208, 212, 218, 222-223, 226227, 256, 634, 675 Information, 25, 28, 31, 33-36, 208, 212, 218,
222, 226, 243, 248, 250, 256, 282, 342, 510, 524, 525, 527, 592, 599, 601, 606, 683, 685, 703, 745, 842-843, 847 curves, 678 expected, 652 maximum value of, 524 Information axis scaling of, 507 Information curves, 25, 36 Information function, 271, 524-526, 599, 609, 613614, 623, 625 correcting, 319 INFORMATION keyword on SAVE command, 263, 312, 314, 337, 342 Information option Graphics procedure, 505, 507, 526 Information statistics requesting, 212 Initial slope parameter, 564 Initialize option on Run menu, 82 INOPT keyword on INPUT command, 263, 292-293, 335336 Input counts of response patterns, 352, 366, 407 data for item or factor analysis, 441 file in TESTFACT, 443 fixed-effects table of counts, 352-353, 407 trial values for full information factor analysis, 446 INPUT command, 55, 62, 86, 114, 241-242, 263, 280, 289, 291-292, 320, 878
INDEX 329, 331, 441, 482, 504 CASE option, 413-414 COMBINE keyword, 263, 285, 292, 338, 722 CORRELAT option, 445, 818 DIAGNOSE keyword, 87, 114, 163-164 DIF option, 43, 87, 114, 135, 163-164, 192, 638 DRIFT option, 43, 87, 114, 135, 142, 163, 165, 192, 202 EXTERNAL keyword, 55, 63, 87, 114, 163, 166 FACTORS option, 445, 823 FILE keyword, 443, 446, 776, 804, 818, 823, 825 FORMAT option, 444 GROUPLEVEL option, 263, 292-293, 336 IFNAME keyword, 650 INOPT keyword, 263, 292, 293, 335, 336 ISEED keyword, 78, 87, 114, 163, 167 KFNAME keyword, 59, 87, 114, 149, 163, 167, 169, 172, 177, 236, 241, 670 LENGTH keyword, 263, 270, 276, 292, 294, 327, 692, 702, 722, 728 LIST option, 444, 777 MGROUP keyword, 263, 292, 294, 300304, 334-336, 339342, 710 MRATER keyword, 263, 292, 294, 296,
300-304, 334, 336, 342, 724, 727-728 NALT keyword, 44, 86, 114, 134, 155, 163, 169 NFMT keyword, 57, 59, 64, 86, 114, 163, 170, 263, 292, 295, 445 NFNAME keyword, 6061, 87, 114, 163, 169, 171, 177, 241-242 NFORM keyword, 42, 87, 114, 144-146, 159, 163, 168-169, 171-173, 176-177, 235, 240-241 NFORMS keyword, 652 NGROUP keyword, 42, 87, 114, 117, 121, 122-123, 128-130, 135, 142-143, 159, 163, 165, 173-174, 187, 189, 192, 194198, 218-219, 241, 638 NIDCHAR keyword, 55, 63, 87, 114, 163, 174, 241, 263, 292, 295, 333-336, 343344, 443, 446, 469, 692, 721, 775, 779, 804 NRATER keyword, 263, 292, 296, 727728 NTEST keyword, 263264, 292, 296, 325, 329, 338, 692, 722 NTOTAL keyword, 42, 86, 114, 146, 161, 162-163, 175, 183184, 229-230, 263, 292, 297, 333-334, 638, 710, 728 NWEIGHT keyword, 638
OFNAME keyword, 61, 62, 87, 114, 155, 163, 169, 172, 176-177, 241-242 PATTERN option, 413, 730 PERSONAL option, 55, 87, 114, 159, 163, 177, 240 REWIND option, 445 R-INOPT keyword, 263, 292, 296-297, 724, 727-728 SAMPLE keyword, 55, 63, 77-78, 86, 114, 148, 163, 167, 178180, 263, 288, 292, 298, 671, 697 SCORES option, 430, 444-445, 776, 779, 781, 804 TAKE keyword, 55, 63, 87, 114, 163, 167, 179, 263, 292, 298, 697 TRIAL keyword, 446, 472 TYPE keyword, 55, 63, 86, 114, 147, 148, 150-152, 154-155, 163, 180, 238, 240241, 638, 666 UNFORMAT option, 444 WEIGHT keyword, 441, 444, 446-447, 470, 779 WEIGHT option, 263, 292, 298, 333-336 Input data type of, 390 Input Data dialog box, 350-353, 358, 365, 371, 377, 388, 390 Input files answer key, 167
879
INDEX BILOG-MG, 57, 149, 156, 173, 241 calibration file in BILOG-MG, 147 in PARSCALE, 288-290 item parameter file in BILOG-MG, 150, 200, 204 item parameters for scoring, 475 item provisional values file in BILOG-MG, 156 item standard difficulties, 476 master file in BILOGMG, 151 not-presented key, 171 omit key, 176 PARSCALE, 289, 333 raw data in BILOG-MG, 148 specifying in BILOGMG, 147, 163 Input Parameters dialog box, 352-355, 359, 362, 366-368, 372-374, 377, 388-392, 398-399 Instruments multiple test forms, 112 single test form, 111 INTER keyword on PRIOR command, 452 on SLOPE command, 452 INTERCEPT keyword on TEST command, 263, 325-326, 328 Intercepts, 539 normal prior distribution, 452 starting values for, 229, 326
INTERCPT keyword on TEST command, 47, 68, 87, 114, 224, 226, 229, 232 Internal consistency, 459, 582 measure of, 459 Intervals assigning respondents to, 543, 604 assigning scores to, 562 for displaying response proportions, 29 tolerance, 605, 649 Intra-class correlation coefficient, 582 INUMBERS keyword on FORM command, 50, 88, 114, 144, 146, 686 on GROUP command, 50, 88, 114, 159, 161 on ITEMS command, 87, 114, 159, 182183, 638 on TEST command, 47, 48, 87, 114, 224, 230, 233 IQUAD keyword on TECHNICAL command, 492 Irving, L.M., 765 ISEED keyword on INPUT command, 78, 87, 114, 163, 167 ISTAT keyword on SAVE command, 86, 114, 199, 203, 244 Item Analysis dialog box, 46, 94, 102, 108-111, 113, 117-118, 120-124, 127, 130, 137-138, 140, 145-146, 160-161, 185186, 225, 231 Advanced tab, 51, 88-89 Form Items tab, 49, 88
Group Items tab, 49, 88, 102 Subtest Items tab, 48, 87 Subtests tab, 46, 86, 89 Item Analysis option on Setup menu, 40, 46, 86-89, 94, 102, 108111, 113, 117-118, 120-124, 127, 130, 137-138, 140, 145146, 160-161, 185186, 225, 231 Item characteristic curves, 505-509, 515, 523, 525526, 594, 644, 653 displaying, 506, 508 displaying simultaneously, 510 editing and saving, 506, 508, 512 Item difficulty, 482, 819820 as input, 476 plot, 582 plot against discriminating power, 450 Item dimensionality, 576 Item facility, 451, 462-465, 467, 471, 577, 579, 587, 624, 776, 786, 793, 829 Item factor analysis, 410, 432, 465, 530, 575-576, 584, 586, 589-601, 620, 629, 778-781, 802-803, 815, 818, 827-829, 840, 842 Item fit statistics, 29 Item information, 32, 40, 200, 224, 248, 253, 256, 314, 337, 342, 505, 507508, 515, 524-526, 543, 599, 608-609, 613-615, 623, 625, 847 suppressing correction for, 281 880
INDEX Item information curves, 505, 507-509, 512, 515, 524-525 displaying, 507-508 editing and saving, 507508 Item information file saving, 314, 342 Item information function, 524-526, 599, 609, 613615, 623, 625 Item Keys dialog box, 59, 163, 169, 172, 177, 242 Answer Key tab, 59, 87 Not Presented Key tab, 87 Omit Key tab, 61, 87 Item Keys option on Data menu, 59, 87, 163, 169, 172, 177, 242 Item location parameter, 284, 535, 538, 547, 560, 613 estimating as threshold, 284 Item option curves, 523 Item parameter file as input in BILOG-MG, 150, 200, 204 naming of, 204, 289 saving, 242, 253, 314315, 336, 340 Item Parameter Prior Constraints dialog box, 69, 117, 134, 189-192 Item Parameter Prior Constraints option on Technical menu, 69, 117, 134, 189-192 Item Parameter Starting Values dialog box, 65, 109, 225-226, 228, 230, 232 Enter Values tab, 66
Import/Enter Values tab, 65 Item Parameter Starting Values option on Technical menu, 65, 109, 225-226, 228, 230, 232 Item parameters as input for simulation model, 483 fixing to, 386 form provided in, 485 imposing priors, 393 prior densities for, 612 saving to file, 395, 461 starting values, 396, 397 untransformed, 808 Item plots by group, 78 Item statistics, 461, 465 output in TESTFACT, 785 saving, 464 Item-category threshold parameter, 546, 553, 700 ITEMFIT keyword on CALIB command, 264, 274, 279, 692 Items adjustment of difficulty parameters, 561, 713 assigning for general factor, 419 assigning starting values, 64 assigning to item groups, 419 assigning to subtests, forms, groups, 46 assigning to tests, 224 assigning variant, 48 common response codes, 460 computation using external variable, 166
constraining in MULTILOG, 379 contingency tables, 562 controlling estimation of, 51 difficulty index, 562, 642 discriminating power, 531, 535, 538-539, 575, 578, 622-623, 632, 692, 840 entering starting values interactively, 65-66 estimated parameters, 25, 37, 205, 616, 693 fixing at starting values, 68, 226 fixing parameters, 51, 385 free to be estimated, 68 guessing parameter, 540 importing labels from file, 44 importing parameters for scoring, 52 importing starting values, 65 intercept parameter, 539 joint frequencies per pair, 499 naming and numbering in BILOG-MG, 108 naming in MULTILOG, 387 naming of, 449 not-reached in factor analysis, 586 number in block, 270 number in test, 185, 294 number of, 42, 270, 391 number of times rated, 296 number of variant, 186 printing provisional parameter estimates, 131
881
INDEX prior constraints, 51 probability of chance success, 418 requesting single common slope parameter, 267, 275 response curves, 28, 30 saving covariances to file, 200 saving DIF parameters, 201 saving DRIFT parameters, 202 saving subtest parameter estimates, 471 selecting or re-ordering, 480 skipping estimation of parameters, 280 slope parameter, 539 specifying as free or fixed, 226 specifying names, 183 specifying number on form, 146 specifying numbers, 183 specifying order in data, 144 specifying prior distributions, 29 threshold, 539, 624 total number of, 175, 455 ITEMS command, 87, 108, 110-112, 114, 145-146, 159-161, 173, 182, 224225, 228-231, 233 INAMES keyword, 46, 87, 114, 159, 182183, 638, 686 INUMBERS keyword, 87, 114, 159, 182183, 638 ITEMS keyword on FIX command, 386
on LABELS command, 387 on PRIORS command, 394, 733 on START command, 396 on TEST command, 263, 325-326, 332, 355, 367, 398, 710 on TMATRIX command, 403 Item-test correlation, 542, 578, 642, 672, 698, 713 Item-trait correlation, 542 ITER keyword on TECHNICAL command, 492, 496 ITERATION keyword on SCORE command, 264, 316, 318 Iterations number of, 51, 120, 126, 276, 280, 383, 419, 438, 493, 496, 702 number of for MINRES, 432, 829 number of prior to fixing of conditional, 493 stopping at criterion, 318 ITERATIONS keyword on ESTIMATE command, 383 ITLIMIT keyword on TECHNICAL command, 493
J
Johnson, E.G., 845 Johnson, N.L., 843 Jones, L.V., 835, 840 Jurgensen, C., 846
K Kaiser, H.F., 583 Kelley, T.L., 837 Kendall, M., 598 KEY command, 448, 779, 781, 804 Key file answer, 26, 59, 92, 149, 167-171, 178-179, 236, 238, 241-242, 254, 355-356, 449, 480, 634, 652, 658, 666, 670, 783, 827, 829 not-presented, 54, 60, 171-172, 176-177, 238, 241-242, 333, 658, 728 omit, 61, 155, 168, 171172, 176-177, 241242, 634, 697 KFNAME keyword on INPUT command, 59, 87, 114, 149, 163, 167, 169, 172, 177, 236, 241, 670 Kiefer, J., 840, 843 Kiely, G.L., 760, 763 Klassen, D., 740 Kolakowski, D., 617 Kuder-Richardson formula 20, 459, 776
Jenkins, C.D., 780, 802, 818 Job Description tab on General dialog box, 42, 49, 85-89, 93, 100 882
INDEX
L L1 option on TEST command, 355, 367, 379, 398400, 730 L2 option on TEST command, 355, 367, 379, 398400, 732 L3 option on TEST command, 355, 367, 379, 398400, 733 Labels importing from external file, 44 providing for items, 41, 44 LABELS command, 387 ALL option, 387 ITEMS keyword, 387 NAMES keyword, 387, 735 Labels tab on General dialog box, 44, 87-88 Laird, N.M., 585, 842 Latent distributions assigning by test, 70, 75 estimating, 32, 537 preventing adjustment of, 65 rescaling, 37 Latent roots, 779, 781, 789-791, 803, 815, 826, 828-829 number of, 432, 792 Latent variable space number of points sampled, 495 Lawley, D.N., 592, 594, 835 Lazarsfeld, P.F., 594, 836, 841
Legend Parameters dialog box, 517, 520 Legends editing of, 520 Length of ID field, 352, 360, 367, 443 LENGTH command, 86, 109, 114, 153-154, 159, 185, 229, 231 NITEMS keyword, 47, 48, 86, 114, 127, 185, 638, 652, 671 NVARIANT keyword, 43, 47, 86, 114, 185186, 671 LENGTH keyword on FORM command, 50, 88, 114, 144, 146 on GROUP command, 50, 88, 114, 159, 161162 on INPUT command, 263, 270, 276, 292, 294, 327, 692, 702, 722, 728 Lieberman, M., 617, 730, 778, 841-842 Likelihood ratio test, 30, 530, 535-536, 587, 705, 763 Likert scale analysis, 257, 327, 345, 529, 545, 547, 556, 563, 765, 851 Likert, R., 327, 345, 529, 545 Lin, H.Y., 602 Linacre, J.M., 560 Line Parameters dialog box, 516-517, 519, 521522 Linear equating, 627, 838, 845 Linking of forms, 534
Linn, R.L., 846 LIST keyword on BIFACTOR command, 420, 804, 815 on SCORE command, 475, 801-804, 816817 LIST option on INPUT command, 444, 777 on TETRACHORIC command, 499, 779, 781, 803 Little, R.J.A., 845 LOADINGS keyword on SCORE command, 476 LOADINGS option on SIMULATE command, 414, 485, 820-821 Location adjustment for category parameters, 266, 270 constant, 316, 549, 552 parameter of 1PL model, 539 parameter of 2PL model, 539 specifying in rescaling, 213 LOCATION keyword on SCORE command, 53, 54, 90, 115, 208, 213, 221-222, 652, 666 Location parameter of rating-scale model, 701 Log likelihood, 543 Logistic model 1PL, 539-540, 543, 567, 605, 843 2PL, 539-540, 567, 606, 842
883
INDEX 3PL, 523, 538, 540, 569 and relationship to normal ogive, 541 LOGISTIC option on CALIB command, 264, 274, 279, 692, 710 on GLOBAL command, 43, 85, 114, 147, 150, 638 Logit, 31, 150, 393, 539, 541, 570, 602, 734, 834, 842 Longford, N.T., 842 LORD option on BIFACTOR command, 413-414 on FULL command, 413-414, 437-438 Lord, F.M., 421, 438, 533, 534, 537, 541, 563, 569, 580, 592, 594, 610, 700, 835-838, 840-842, 845, 847 Louis, T.A., 830, 842 Lower asymptote 3PL model, 393 Luce, R.D., 835
M Madsen, M., 730, 843 MAIN option on SAVE command, 467 Mantel, N., 626, 835 Marginal reliability, 617, 746 Marginal maximum likelihood (MML), 28, 116, 121, 123, 345, 349350, 364, 389, 401-403, 407, 529, 544, 562, 576, 584-587, 589-590, 599-
602, 604-605, 607, 611, 644, 675, 699-700, 702, 705, 730, 742-743, 806, 842-843 Marginal probability, 32 of pattern, 30, 32, 244, 600-603, 796, 812, 836 saving to file, 206, 243, 255 Marshall, J.C., 578 Master file as input in BILOG-MG, 151 naming of, 204, 290 saving, 314 MASTER keyword on SAVE command, 78, 86, 114, 152, 199, 204, 263, 288, 312, 314, 337 Masters, G.N., 403, 404, 523, 529, 545, 550, 552, 735, 738, 761, 763, 767, 842, 846 Matrix plot, 829 Matrix Plot option Graphics procedure, 505, 510 Matrix sampling data, 116, 187, 238, 537, 610, 630, 666 Maximum effectiveness point, 250 Maximum information, 32, 249, 256, 524, 632 Maximum Likelihood (ML), 25, 28, 30-36, 75, 122, 128, 208, 213-214, 222, 277, 317-318, 320, 323, 345, 410, 452, 495, 529-530, 532, 537, 543544, 564, 568, 576, 584587, 591, 594-595, 597600, 606-611, 615, 652, 655, 685, 693, 700, 702,
708, 734, 744, 792, 794, 806, 833-834, 836-838, 840-844, 847 and Warm's weighted, 264, 316-317, 615 Maximum marginal a posteriori (MAP), 25, 29, 31, 35-37, 75, 211, 214, 349-350, 357, 389, 392, 395, 474, 476, 529, 544, 590, 607-610, 625, 655, 664, 685, 748-749, 781, 782, 802, 837-838 controlling precision for factor scores, 478 MAXPOWER keyword on DRIFT command, 88, 114, 142, 192 MCEMSEED keyword on TECHNICAL command, 493, 803 Mean criterion score, 462, 464 of normal distribution for intercepts, 452 MEAN keyword on SIMULATE command, 486, 819820 Means estimates for groups, 37 of population distribution, 379, 385, 393 of population of factor scores, 486 of prior distributions, 51, 123, 277, 394, 652 Mean-square of measurement errors, 589 Measurement standard error, 525-526, 589, 606, 608-609, 622 884
INDEX Measurement error graph, 527 mean-square, 589 root-mean-square, 589 Meng, X.L., 590 Menu Edit, 515 File, 514-515 Graphs, 516 Options, 515 METHOD keyword on SCORE command, 90, 115, 208, 213214, 216-217, 476, 652, 666, 685, 781, 801- 804, 817 Method of estimation for scoring, 214, 317 in TESTFACT, 410, 530 MFNAME keyword on FILES command, 263, 288, 290 on GLOBAL command, 85, 114, 147, 151 MGROUP command, 263, 294-295, 300, 710, 711 COMMON keyword, 263, 300 DIF keyword, 263, 300, 301, 710-711 GCODE keyword, 263, 300-302, 334-336 GNAME keyword, 263, 300, 302 REFERENCE keyword, 263, 300, 302, 710 MGROUP keyword on INPUT command, 263, 292, 294, 300302, 304, 334-336, 339-342, 710 MIDDLE keyword on TGROUPS command, 401
MIDPOINT keyword on DRIFT command, 88, 114, 142-143 Minimized squared residuals (MINRES) method, 791, 793 MINRES factor analysis, 431-432, 829 Mislevy, R.J., 28, 34-35, 209, 528, 531, 561, 576, 585, 602, 607, 610, 679, 838-839, 843-845, 847 MISS option on BIFACTOR command, 413-414 on FULL command, 413-414, 437-438 MISSING option on SCORE command, 477 Missing value code in MULTILOG, 356, 384 ML scores moving to adjacent category, 320 MLE option on SCORE command, 264, 316-317 MML, 277 number of quadrature points, 129 Model defining in MULTILOG, 398 Model tab on General dialog box, 42, 85, 87, 93, 101 MODIFIED keyword on BLOCK command, 263, 265, 267, 269, 272, 696, 709, 720 Modifying graphs, 514
MOMENTS option on SCORE command, 26, 79, 91, 115, 208, 214-215 Monte Carlo integration, 410, 494, 530, 590-591, 803 generating random multivariate normal variables, 493 MRATER command, 263, 294, 303, 728 RATER keyword, 263, 303 RCODE keyword, 263, 303-304 RNAME keyword, 263, 303-304, 728 MRATER keyword on INPUT command, 263, 292, 294, 296, 300-304, 334, 336, 342, 724, 727-728 M-step convergence criterion in MULTILOG, 383 number of iterations, 383 MU keyword on EQUAL command, 379 on FIX command, 385386 on PRIORS command, 393 MULTILOG class of problem, 389 contrasts, 570 convergence for M-step, 383 example of syntax generation, 357, 364, 370, 733 Fixed-theta analysis, 349-350, 371, 401 imposing priors, 393
885
INDEX listing contrasts, 379, 385, 393 missing value code, 356 naming items, 387 number of groups, 352, 391 number of items, 352, 391 number of patterns, 352 order of commands, 375 specifying data file, 389 T-matrix, 403 type of data, 388, 390 user interface, 345 Multiple-category models, 399, 612 Multiple-choice model, 355, 367, 379, 398-400, 403, 751, 757 Multiple-group analysis, 34, 36, 42, 70, 104, 111112, 124, 300, 531, 625626, 652, 710, 712, 844 DRIFT analysis, 165 identifying groups, 111 identifying sets of items, 111 resolving indeterminacy, 134 setting reference, 302 suppressing rescaling in, 25, 128 Multiple-group model response data, 334 Multiple-response model, 510, 523, 567-568 Muraki, E., 16, 257, 528529, 536, 544, 546, 560, 576, 585, 588, 591, 611, 679, 700, 761, 843-846
N NALT keyword on INPUT command, 44, 86, 114, 134, 155, 163, 169 NAME keyword on COMBINE command, 264, 285 on CRITERION command, 429 on SCORE command, 264, 316, 318 Names assigning to categories, 267 assigning to groups, 302 assigning to raters, 304 NAMES command, 429430, 449, 455-456, 482, 490, 804 NAMES keyword on CLASS command, 425 on LABELS command, 387, 735 on SUBTEST command, 490 Natural metric logistic response function, 150, 279, 306 Naylor, J.C., 589 NBLOCK keyword on SCORE command, 264 on TEST command, 263-265, 325, 327, 339-340, 342, 692 NC keyword on PROBLEM command, 752 on TEST command, 367, 375, 399, 738, 773
NCASES keyword on SIMULATE command, 486, 819820 NCAT keyword on BLOCK command, 263, 265, 269-271, 339, 692, 710 NCHAR keyword on PROBLEM command, 352 NCHARS keyword on PROBLEM command, 353, 359360, 367, 390, 409, 748, 773 NCYCLES keyword on ESTIMATE command, 383 NDEC keyword on BIFACTOR command, 420 on FACTOR command, 431 on TETRACHORIC command, 500, 779, 781, 803 Nested models, 543 New Analysis dialog box, 349-350, 357, 364, 370371, 377, 388-390 NEWTON keyword on CALIB command, 51, 88, 115-116, 120121, 126, 136, 226, 264, 274, 280, 692, 703, 711 Newton-Gauss, 31, 280, 833 Newton-Raphson estimation, 31, 615, 749, 833-834 NEXAMINEES keyword on PROBLEM command, 353, 390391 886
INDEX Neyman, J.A., 840 NFAC keyword on FACTOR command, 431-433, 446, 466, 468, 469, 473, 484, 486, 779, 781, 797, 803, 815, 818, 824825 on SCORE command, 477, 802, 816 on SIMULATE command, 486, 819, 820 NFMT keyword on INPUT command, 57, 59, 64, 86, 114, 163, 170, 263, 292, 295, 445 NFNAME keyword on FILES command, 263, 288, 290, 337 on INPUT command, 60-61, 87, 114, 163, 169, 171, 177, 241, 242 NFORM keyword on INPUT command, 42, 87, 114, 144-146, 159, 163, 168-169, 171-173, 176-177, 235, 240-241 on SCORE command, 91, 115, 208, 215, 219-220 NFORMS keyword on INPUT command, 652 NFULL keyword on CALIB command, 78, 89, 115-116, 127 NGROUP keyword on INPUT command, 42, 87, 114, 117, 121123, 128-130, 135, 142-143, 159, 163, 165, 173-174, 187,
189, 192, 194-198, 218-219, 241, 638 on PROBLEM command, 360, 367, 373, 391, 409 NGROUPS keyword on PROBLEM command, 352, 353 NIDCHAR keyword on INPUT command, 55, 63, 87, 114, 163, 174, 241, 263, 292, 295, 333-336, 343344, 443, 446, 469, 692, 721, 775, 779, 804 NIGROUPS keyword on BIFACTOR command, 419, 421, 804, 815 NIT keyword on FACTOR command, 432 NITEMS keyword on BLOCK command, 263, 265, 270, 339, 342, 692, 710 on LENGTH command, 47-48, 86, 114, 127, 185, 638, 652, 671 on PROBLEM command, 352-353, 360, 367, 373, 391400, 409, 432, 436, 446, 455, 457, 464, 469, 470, 473, 484, 776, 778, 804, 825 NITER keyword on TECHNICAL command, 494 NO option on TEST command, 355, 367, 379, 398400, 403
NOACCEL option on CALIB command, 264, 274-275 NOADAPT option, 815 on TECHNICAL command, 432, 439440, 466, 471, 494, 779, 825 NOADJUST option on CALIB command, 25, 37, 79, 89, 115116, 127-128, 226227 on SCORE command, 264, 316, 318 NOCADJUST option on BLOCK command, 263, 265, 270 NOCALIB option on CALIB command, 264, 274, 280, 288, 324, 708 NOCRITERION option on PLOT command, 451 NOFLOAT option on CALIB command, 89, 115-116, 122-123, 187-188, 675 NOGPRIOR option on CALIB command, 89, 115-116, 123, 138, 140 NOLIST option on BIFACTOR command, 421 Nominal model, 345, 355, 367, 379, 398-400, 403, 506, 508, 511, 523-524, 529, 545, 558, 568, 570, 625, 738, 751, 759-763, 765, 767, 841 scoring function, 558 NOMINAL option on TEST command, 738 Non-adaptive quadrature, 410, 493-495, 530, 779
887
INDEX for score estimation, 410, 530 in full information solution, 494 Non-equivalent groups equating, 24, 122, 533, 627-628, 632 NOPOP option on PROBLEM command, 392 NOPRINT option on SCORE command, 53, 91, 115, 208, 216, 634 Normal generating ability distribution, 487 prior on intercepts, 452 Normal metric, 28, 386, 541-542, 557, 570, 608, 692, 734, 767 scaling factor, 28, 549, 613 using, 279 Normal ogive model, 150, 151, 279, 538, 541, 546547, 563, 582, 621, 832, 834, 837, 840-842 and relationship to logistic, 541 NORMAL option on CALIB command, 89, 115-116, 128-129, 264, 274, 279, 659 Normal prior specifying, 394 Normal tab on Assign Scoring Prior Latent Distribution dialog box, 75, 90 NOSCORE option on SCORE command, 264, 316, 319 NOSORT option on TECHNICAL command, 495
NOSPRIOR option on CALIB command, 89, 115-116, 124, 137, 140 Not Presented Key tab on Item Keys dialog box, 87 NOTPRES option on PROBLEM command, 456, 460 Not-presented codes for, 43 Not-presented key format of file, 337 using, 54, 59-60, 171, 172, 176-177, 238, 241-242, 290, 333, 658, 728 NOT-PRESENTED option on PROBLEM command, 781 NOTPRIOR option on CALIB command, 89, 115-116, 124, 138-139 Not-reached items in factor analysis, 586 Novick, M.R., 533, 541, 563, 580, 700, 837-838, 841 NPARM keyword on GLOBAL command, 43, 85, 114, 119, 133, 147, 152, 170, 177, 189-190, 228, 638 NPATTERNS keyword on PROBLEM command, 352-353, 360, 367, 390-391 NQPT keyword on CALIB command, 51, 88, 115-117, 121122, 129, 194-195, 264, 274, 279-280, 308-309, 342, 639,
666, 692, 702, 709710 on SCORE command, 77, 90, 115, 197-198, 208, 216, 264, 310311, 316, 319, 706 NRATER keyword on INPUT command, 263, 292, 296, 727728 NRATER option on CALIB command, 264, 274, 281 on SCORE command, 264, 316, 319 NROOT keyword on FACTOR command, 432, 779, 781, 792, 803, 815, 818, 825 NSAMPLE keyword on TECHNICAL command, 495 NSD keyword on CALIB command, 89, 115-116, 130 NTEST keyword on GLOBAL command, 42, 85, 114, 137, 147, 152-154, 159, 175, 178, 185-186, 189, 194-195, 197-198, 217-219, 224-225, 229-231, 233 on INPUT command, 263-264, 292, 296, 325, 329, 338, 692, 722 NTOTAL keyword on INPUT command, 42, 86, 114, 146, 161163, 175, 183-184, 229-230, 263, 292, 297, 333-334, 638, 710, 728 Number of boundaries, 458 888
INDEX of cases generated, 486 of categories for graded model, 375 of classes, 454 of COMBINE commands, 292 of cycles of MML estimation, 383 of cycles prior to fixing, 493 of decimals for residuals, 431 of decimals for tetrachorics, 500 of EM cycles, 493, 496 of examinees, 391 of examinees in MULTILOG, 352 of external variates, 455 of factors, 431, 477, 486 of factors to be extracted, 431 of format records, 170 of forms, 42 of fractiles, 435, 455, 582 of generated response records, 486 of groups, 42, 62, 174, 294, 352, 360, 367, 373, 391 of groups in MULTILOG, 352, 353 of item-group factors, 421 of items, 42, 183, 270, 352, 391 of items in form, 146, 162 of items in MULTILOG, 352, 353 of items in test, 185, 294
of iterations, 51, 120, 276, 383, 438, 493, 496, 702 of iterations for MINRES, 432, 829 of iterations in the Mstep, 383, 493 of iterative communality improvements, 494 of latent roots, 432, 792 of parameter values, 487 of parameters in BILOG-MG model, 152 of patterns, 352, 360, 366, 367, 391 of points sampled, 495 of quadrature points, 76, 117, 129, 208, 216217, 221, 280, 319, 401, 422, 439, 478, 497-498, 600, 611, 666, 669, 693, 694, 702, 709, 710, 758, 794, 825, 826 of quadrature points for EAP estimation, 498 of records in data file, 54-55, 58, 62, 391 of response alternatives, 43, 169 of response categories, 399 of response categories in MULTILOG, 355 of response codes, 405, 456 of response patterns, 390-391 of response patterns in MULTILOG, 352 of selected items, 457 of subtests, 42, 152-153, 296 of test forms, 173
of tests, 24, 325, 352, 354, 360, 367, 373, 528 of the highest category, 355, 399 of times item is rated, 296 of unique items, 175 of variable format records, 445 NUMBER keyword on TGROUPS command, 401-402 NVARIANT keyword on LENGTH command, 43, 47, 86, 114, 185186, 671 NVTEST keyword on GLOBAL command, 43, 85, 114, 147, 153, 159, 175, 185-186, 224-225, 229-231, 233 NWEIGHT keyword on INPUT command, 638 NWGHT keyword on GLOBAL command, 85, 114, 147, 154, 240-241
O O’Connor, W.A., 740 Oblique rotation, 588 Observed frequencies of patterns, 438 OFNAME keyword on FILES command, 263, 288, 290, 337 on INPUT command, 61-62, 87, 114, 155, 163, 169, 172, 176177, 241-242 889
INDEX Olsson, U., 564 Omit key format of file, 336 using, 59, 61, 155, 168, 171-172, 176-177, 241-242, 290, 634, 697 Omit Key tab on Item Keys dialog box, 61, 87 OMIT keyword on BIFACTOR command, 421, 423, 477 on FULL command, 438, 440, 825 Omits codes for, 43 scoring fractionally correct, 43, 155, 169 specifying treatment of, 500 treatment of in full information factor analysis, 421, 438 OMITS option on GLOBAL command, 44, 86, 114, 147, 155 Options menu, 82-83, 515 Settings dialog box, 83, 85 Order of commands BILOG-MG, 114 MULTILOG, 375 PARSCALE, 262 TESTFACT, 413-414 ORIGINAL keyword on BLOCK command, 263, 265, 270, 333, 335-336, 696, 710, 720 Orthogonal rotation, 482, 576, 588, 590, 629 Output diagnostic, 164, 276
number of leading cases with factor scores, 475 printing tetrachoric correlation matrix to file, 499 Output files, 37, 149, 199207, 242, 337 BILOG-MG, 149, 199207, 241-242 format of, 312-314, 337 PARSCALE, 312-313, 337 requesting, 199 saving case weights, 206 saving item parameter file, 204 saving marginal probabilities, 206 saving master file, 204 saving score file, 206 saving to, 461 viewing of, 82 Output menu, 82, 98 Owen, R.J., 847
P Pairwise constraints, 380 PAIRWISE option on TETRACHORIC command, 500, 501 PARAM keyword on SCORE command, 477 on START command, 409 Parameters option on Graphs menu, 516 PARAMS keyword on PRIORS command, 394 PARM keyword on SAVE command, 86, 114, 147-148, 150,
158, 180-181, 199, 204, 226-227, 253, 263, 274, 312, 314, 336-337, 342, 638, 693, 724, 779 on SIMULATE command, 487 on START command, 396-397 PARM option on SAVE command, 468 PARSCALE DIF analysis, 301 format statement, 263, 331, 334, 336 input files, 288-289, 333 order of commands, 262 output files, 312-313, 337 overview of syntax, 261 requesting guessing parameter, 268 ridge constant, 282 setting workspace, 259 user interface, 258 Partial credit model, 279, 340, 529, 550, 552-559, 565, 612, 614, 706, 709710, 714, 720, 722, 724, 735, 738, 763, 767, 842 category coefficient, 557 generalized, 550, 557, 558, 564, 762 intersection of trace lines, 552-553, 555, 558 Likert version, 556 operating characteristic, 551 partially unordered, 559 scoring function for generalized, 557, 558 specifying scoring function for, 272
890
INDEX PARTIAL keyword on CALIB command, 710 PARTIAL option on CALIB command, 264, 274, 279 PATTERN option on INPUT command, 413, 730 on PROBLEM command, 352, 390391, 742 Patterns number of, 352, 360, 366, 367, 391 printing frequencies of, 492 PBISERIAL option on PLOT command, 450, 776 PDISTRIB keyword on SAVE command, 25, 86, 114, 199, 205 Pearson product-moment correlation, 629 PERCENTIL option on FRACTILES command, 435, 436 PERSONAL option on INPUT command, 55, 87, 114, 159, 163, 177, 240 Personalizing by subtest, 177 Pfiffenberger, W., 536, 844 PFQ keyword on SCORE command, 264, 316, 320, 693 PLOT command, 450 BISERIAL option, 450 CRITERION option, 451, 776 FACILITY option, 451, 776
NOCRITERION option, 451 PBISERIAL option, 450, 776 PLOT keyword on CALIB command, 88, 115-116, 124-125, 130-131, 634 Plot Parameters dialog box, 521-522 Plots editing legends, 520 facilities, 582 item and test information, 208 item by group, 78 item difficulties, 582 of proportion correct responses, 124 specifying significance level, 130 PMN keyword on SCORE command, 75, 90, 115, 208, 214, 217, 219 Point biserial coefficient as dicrimination index, 450 Point biserial correlation, 462-467, 471, 786 Point polyserial correlation, 563, 642, 698-699 Points for quadrature, 70, 128, 194-195, 250-251, 253, 282, 308, 310, 321, 342, 402, 492, 561, 589, 600, 639, 644, 668, 692, 695, 795, 800, 806 POINTS keyword on QUAD command, 51, 75, 77, 90, 115, 193-194
on QUADP command, 264, 308 on QUADS command, 91, 115, 196-197, 264, 310 POLYNOMIAL option on TMATRIX command, 404, 738, 763 Polyserial correlation coefficient, 563-564, 642-643, 699 POP option on SCORE command, 91, 115, 208, 213, 218, 223, 634, 653, 675 Population distribution excluding from scoring, 392 Population percentiles, 625 Position of case ID, 58 POST keyword on SAVE command, 86, 114, 199, 206, 255 Posterior distribution, 28, 31, 34, 35, 37, 212, 278, 281, 560-561, 589-590, 601, 607, 656, 704, 708, 711, 713-714, 738, 803, 837 distribution after Mstep, 281 distributions for groups, 37 information, 36, 608, 609-610, 655, 802 probability, 255-256, 589, 603, 655 saving points and weights of distribution, 25, 205 saving residuals to file, 202
891
INDEX standard deviation, 35, 36, 75, 90, 115, 208, 214, 217-219, 466, 561, 588, 607-608, 610, 655-656, 693, 707, 731, 814, 828 standardized residuals, 202, 250, 252, 603604 POSTERIOR option on CALIB command, 264, 274, 281, 711 PRECISION keyword on TECHNICAL command, 495, 781 Print current page option on File menu, 514 PRINT keyword on CALIB command, 88, 115-116, 131, 157 PRINT option on SCORE command, 264, 316, 320 Print selected graph option on File menu, 514 Printer Setup option on File menu, 514 Printing, 514 bifactor loadings, 420 of graphs, 514 pattern frequencies, 492 provisional item parameter estimates, 131, 496 subject scores to output file, 320 Printing Options option on File menu, 514 PRIOR command, 675 PRIORREAD option on CALIB command, 264, 274, 281, 305 Priors beta on uniquenesses, 452 by subtest, 69
constraints on items, 51 densities for item parameters, 612 distribution information for calibration, 64 distribution information for scoring, 64 distributions assumed for groups, 37 estimating means, 277 estimating means of distribution, 51, 128, 652 estimating means of distributions, 123 for guessing parameter, 123 for item parameters, 393 for slope parameter, 123, 137 for threshold parameter, 138 keeping distributions fixed, 122 mean and s.d. for normal, 394 normal for intercepts, 452 normal for threshold parameter, 284 parameters for item slopes, 190, 306 parameters for item thresholds, 191, 307 providing arbitrary discrete, 75 requesting reading of, 281 requesting use of beta for guessing, 278 slopes in natural metric, 306 specifying constraints, 64 specifying distribution for items, 29
specifying distributions for items, 187, 305 specifying means of normal, 217 specifying range of, 130 specifying standard deviation of normal, 218 specifying type, 125, 211, 277 standard deviations for item slopes, 191, 307 standard deviations for item thresholds, 192 type of distribution for scoring, 75, 316 user-supplied, 125, 188, 193, 675 PRIORS command, 69, 90, 115, 117, 134, 187, 193, 264, 281, 305, 393, 452 AJ keyword, 393 AK keyword, 393 ALL option, 394 ALPHA keyword, 47, 90, 115, 187, 189-190 BETA keyword, 47, 90, 115, 187, 189-190 BJ keyword, 393 BK keyword, 393 CJ keyword, 393 CK keyword, 393 DK keyword, 393, 733 GMU keyword, 264, 305 GROUPS option, 394 GSIGMA keyword, 264, 305-306 INTER keyword, 452 ITEMS keyword, 394, 733 MU keyword, 393 PARAMS keyword, 394 SD keyword, 393 SLOPE keyword, 452 SMU keyword, 26, 47, 892
INDEX 70, 90, 115, 187, 190, 264, 305-306 SOPTION option, 264, 305, 306 SSIGMA keyword, 26, 47, 70, 90, 115, 187, 191, 264, 305, 307 TMU keyword, 47, 70, 90, 115, 187, 191, 264, 305, 307 TSIGMA keyword, 47, 70, 90, 115, 187, 192, 264, 305, 307 PRNAME keyword on GLOBAL command, 25, 53, 86, 114, 147, 156-157, 226-227, 241 Probability marginal of pattern, 30, 32, 244, 600, 603, 796, 812, 836 observed response, 634 of chance success, 418 posterior, 255-256, 589, 603, 655 PROBLEM command, 388, 435, 454, 482 CLASS keyword, 446, 454, 776 CRITERION option, 358, 388, 773 DATA keyword, 352, 359, 389 EXTERNAL keyword, 430, 446, 455 FIXED option, 349, 350, 370, 389, 392 FRACTILES keyword, 455, 776 INDIVIDUAL option, 352, 359, 390-391, 748, 773 NC keyword, 752
NCHARS keyword, 359-360, 367, 390, 409, 748, 773 NEXAMINEES keyword, 390-391 NGROUP keyword, 360, 367, 373, 391, 409 NITEMS keyword, 360, 367, 373, 391, 400, 409, 432, 436, 446, 455, 457, 464, 469470, 473, 484, 776, 778, 804, 825 NOPOP option, 392 NOTPRES option, 456, 460 NOT-PRESENTED option, 781 NPATTERNS keyword, 360, 367, 390-391 PATTERN option, 352, 390-391, 742 RANDOM option, 349350, 389, 392, 730, 742 RESPONSE keyword, 456, 776, 778, 781, 804, 825 SCORE option, 748, 773 SCORES option, 349350, 357, 389, 392 SELECT keyword, 457, 480-481, 776, 781, 818 SKIP keyword, 457, 780, 802, 818, 824 SUBTEST keyword, 458, 776 TABLE option, 352, 390-391 Program evaluation, 618-619 information, 85
Project Settings dialog box, 362, 369 PROMAX option on FACTOR command, 413 PROMAX rotation, 413, 431, 433, 441-442, 468, 576, 583, 588, 629, 779, 803, 818, 823, 828-829 Provisional estimates controlling printing of, 496 Provisional values file as input in BILOG-MG, 156 PRV keyword on TECHNICAL command, 496 PSD keyword on SCORE command, 75, 90, 115, 208, 214, 217, 218-219
Q QP keyword on TGROUPS command, 402 QPREAD option on CALIB command, 264, 274, 282, 308 on SCORE command, 264, 310, 316, 321 QRANGE keyword on CALIB command, 264, 274, 282 on SCORE command, 264, 316, 321 QSCALE keyword on TECHNICAL command, 496 QUAD command, 75, 90, 115, 117, 122, 126, 187189, 193, 675
893
INDEX POINTS keyword, 51, 75, 77, 90, 115, 193194 WEIGHTS keyword, 51, 75, 77, 90, 115, 193, 195 QUAD keyword on BIFACTOR command, 422 on FULL command, 439, 825 on SCORE command, 478 on TECHNICAL command, 496-497 QUADP command, 264, 282, 308 POINTS keyword, 264, 308 WEIGHTS keyword, 264, 308-309 Quadrature adaptive, 410, 491, 493, 495, 530, 589, 781, 815, 817, 825-826, 842 fractional, 494, 589-590, 779, 803 Gauss-Hermite, 492, 589, 693, 841 non-adaptive, 410, 493495, 530, 779 points, 70, 128, 194, 195, 250-251, 253, 282, 308, 310, 321, 342, 402, 492, 561, 589, 600, 639, 644, 668, 692, 695, 795, 806 points and weights, 70, 308, 310, 800 type of, 492 weights, 251, 590 Quadrature points, 76, 117, 129, 208, 216-217, 221, 280, 282, 319, 321, 401,
422, 439, 478, 497-498, 600, 611, 666, 669, 693694, 702, 709, 710, 758, 794, 825, 826 user-supplied, 193-194, 196-197 Quadrature weights user-supplied, 193, 195197 QUADS command, 75, 91, 115, 196, 211-212, 264, 310, 321 POINTS keyword, 91, 115, 196-197, 264, 310 WEIGHTS keyword, 91, 115, 196-197, 264, 310-311 Qualification testing, 618619 QWEIGHT keyword on TECHNICAL command, 497
R Random number seed, 77, 167, 483, 485, 487, 820 RANDOM option on PROBLEM command, 349-350, 389, 392, 730, 742 Range of prior distribution, 130 of quadrature points, 321 Rasch model, 26, 65, 78, 539-540, 543, 568, 605, 843 and DIF/DRIFT, 133 specifying in BILOGMG, 133
RASCH option on CALIB command, 26, 79, 89, 115-116, 133 Rasch, G., 26, 65, 78, 133, 523, 538-540, 550, 568, 594, 616, 738, 840 RATER keyword on BLOCK command, 263, 265, 271, 281, 320 on MRATER command, 263, 303 Raters identification code, 304 naming of, 304 Rater's Effect model adjusting for differences in severity, 529 data, 333, 335 weights, 561 Rating-scale model, 257, 524, 528-529, 547, 549550, 565-566, 692 RCODE keyword on MRATER command, 263, 303-304 READF option on SCORE command, 91, 115, 208, 215, 216, 219-220 READPRIOR option on CALIB command, 70, 89, 115-117, 133, 187-192, 666, 675 RECODE option on BIFACTOR command, 413-414 on FULL command, 413-414, 437-438 on TETRACHORIC command, 500-501, 825 Reference group multiple-group analysis, 302 894
INDEX REFERENCE keyword on CALIB command, 42, 89, 115-116, 128, 134-135, 220, 638, 659, 675 on MGROUP command, 263, 300, 302, 710 on SCORE command, 91, 115, 208, 215216, 219-220 Referencing criterion, 632 domain, 632, 846 Relative difficulty, 557 Reliability, 25, 35, 36, 459, 575, 607-608, 623, 625, 637, 684-685, 840 classical, 32-33 empirical, 25, 34-36, 589, 656, 814 index, 542 marginal, 617, 746 theoretical, 25, 33-34, 36 Reliability coefficient, 675 RELIABILITY command, 459 ALPHA option, 776 REPEAT keyword on BLOCK command, 263, 265-266, 271, 710, 721-722 on SCORE command, 264 on TEST command, 264 RESCALE option on SCORE command, 264, 316, 321 Rescaling, 323, 652 constants, 321 specifying, 208, 634 specifying location constant, 213 specifying scale constant, 221
suppressing of in multiple-group analysis, 25, 128 type of, 221 Rescaling tab on Test Scoring dialog box, 53, 90 Residual matrix computing, 433 number of decimals, 420 printing for bifactor solution, 421 RESIDUAL option on BIFACTOR command, 422 on FACTOR command, 433, 779 Residuals number of decimal places, 431 standardized posterior, 202, 250, 252, 603604 Response all correct or incorrect, 31 codes common to all items, 460 graded, 345, 529, 596 graded category function, 547 individual respondents, 333-334 marginal probabilities of patterns, 32 metric of function, 42, 279, 692 number of alternatives, 169 number of codes, 405, 456 number of patterns, 390 plots of proportion correct, 124 specifying number of alternatives, 43
Response Codes (Binary Data) dialog box, 355356, 368, 384, 405-406 Response Codes (NonBinary Data) dialog box, 355, 361 RESPONSE command, 456-457, 460, 781, 804 RESPONSE keyword on PROBLEM command, 456, 776, 778, 781, 804, 825 Response pattern goodness-of-fit statistic, 210 listing observed and expected frequencies, 438 suppressing sorting of, 495 Response records number to be generated, 486 Response tab on General dialog box, 43, 59, 86 REWIND option on INPUT command, 445 Rewinding of data file, 445 Richardson, M.W., 459, 542, 583, 623, 776 Ridge constant specifying in BILOGMG, 135 specifying in PARSCALE, 282 RIDGE keyword on CALIB command, 89, 115-117, 135, 264, 274, 283, 666 R-INOPT keyword on INPUT command, 263, 292, 296-297, 724, 727-728
895
INDEX RNAME keyword on MRATER command, 263, 303-304, 728 Roche, A.F., 616, 772-773 Root-mean-square of measurement errors, 589 of posterior deviates, 604 Rosenman, R.H., 780, 802803 ROTATE keyword on FACTOR command, 414, 433, 468, 472, 779, 781, 793, 803, 818, 824 ROTATE option on SAVE command, 468, 779, 818 Rotated factor loadings saving to file, 468 Rotation oblique, 576, 588 of factors, 433 orthogonal, 482, 576, 588, 590, 629 PROMAX, 413, 431, 433, 441-442, 468, 576, 583, 588, 629, 779, 803, 818, 823, 828-829 VARIMAX, 413, 431, 433, 441-442, 468, 576, 583, 588, 629, 781, 799, 802 RSCTYPE keyword on SCORE command, 53-54, 90, 115, 208, 213, 221-222, 652, 659, 675 Rubin, D.B., 585, 844 Run menu, 81 Build Syntax option, 82, 97, 105 Calibration Only option, 82
Classical Statistics Only option, 82 Initialize option, 82 Scoring Only option, 82 Stats, Calibration and Scoring option, 82, 98, 106
S Samejima, F., 271, 345, 399, 524, 529, 545-547, 567-569, 596, 613-614, 734-735, 738, 740-741, 750-751, 765, 772, 835, 841, 846 Sample calibration, 609, 616 matrix, 238, 537, 630, 666 of records, 27, 167, 178, 298 SAMPLE keyword on INPUT command, 55, 63, 77-78, 86, 114, 148, 163, 167, 178-180, 263, 288, 292, 298, 671, 697 SAMPLE option on SCORE command, 264, 316, 322 Save as Metafile option on File menu, 514 SAVE command, 80, 86, 114, 158, 199, 242-244, 247-248, 250, 253, 255256, 263, 289, 291, 312, 337, 395, 409, 420-421, 442, 461, 482, 499-500, 652, 722 CALIB keyword, 86, 114, 148, 199, 204, 263, 288, 312, 337 CCRIT option, 461 CHANCE option, 816817
CMAIN option, 462 COMBINE keyword, 263, 285, 312-313, 337 CORRELAT option, 463 COVARIANCE keyword, 86, 114, 199-201, 248 CRITERION option, 464 CSUB option, 465 DIF keyword, 86, 114, 164-165, 199, 201, 245, 638 DRIFT keyword, 86, 114, 165, 199, 202, 247 EXPECTED keyword, 86, 114, 199, 202, 250 EXPECTED option, 465 FIT keyword, 263, 312, 313, 337, 339, 344 FORMAT option, 395 FSCORES option, 466, 476, 781, 802, 816, 817 INFORMATION keyword, 263, 312, 314, 337, 342 ISTAT keyword, 86, 114, 199, 203, 244 MAIN option, 467 MASTER keyword, 78, 86, 114, 152, 199, 204, 263, 288, 312, 314, 337 PARM keyword, 86, 114, 147-148, 150, 158, 180-181, 199, 204, 226-227, 253, 263, 274, 312, 314, 336-337, 342, 638, 693, 724, 779 PARM option, 468 896
INDEX PDISTRIB keyword, 25, 86, 114, 199, 205 POST keyword, 86, 114, 199, 206, 255 ROTATE option, 468, 779, 818 SCORE keyword, 52, 86, 114, 158, 199, 206, 207, 216, 243, 263, 312, 315-316, 337, 693, 707, 724 SCORES option, 469 SMOOTH option, 470, 779 SORTED option, 470 SUBTESTS option, 471 TRIAL option, 447, 468, 472, 475, 825 TSTAT keyword, 86, 114, 199, 207, 256 UNROTATE option, 472, 815 SAVE keyword on FILES command, 722, 724 Save menu, 80, 147, 158, 199-207, 243-244, 247248, 250, 253, 255-256 Save option on File menu, 97 on main menu, 86 SAVE option on FILES command, 263, 288, 291, 312 on GLOBAL command, 80, 86, 114, 147, 157, 164-165, 199-207, 638, 652 Save Output to File dialog box, 78, 80 Save Output to File option on Technical menu, 80 Saving ability score file, 242243 bivariate plots, 513
calibration file, 199, 312 case score information, 469 case weights, 206 class item statistics, 461 classical item statistics to file, 203, 242, 244, 467 combined score file, 313, 337 command factor approximation, 470 covariance file, 200, 242, 248 DIF parameter file, 201, 242, 245 DRIFT parameter file, 202, 242, 247 estimates for classes, 462 expected frequencies to file, 202, 242, 250 factor scores, 466 factor scores and posterior s.d., 466 fit statistics file, 313, 338 histogram of ability scores, 512 in TESTFACT, 461 item characteristic curve, 506, 508, 512 item information curve, 507-508 item information file, 314, 342 item parameter file, 242, 253, 314-315, 336, 340, 395, 461, 468 item statistics for classes, 465 item statistics from criterion score, 464 item subtest parameter estimates, 471
marginal probabilities, 206, 243, 255 master data file, 314 output files, 80 points and weights, 25, 205 results of E-step of fifa, 465 results of final E-step, 465 rotated factor loadings, 468 scores to external file, 52, 395, 461 separate estimates for each class, 462 sorted file, 470 standardized posterior residuals, 202 subject scores file, 315, 337 test information curve, 509 test information statistics to file, 207 tetrachoric correlation matrix, 463 trial values, 472 unrotated factor loadings to file, 472 Scale specifying in rescaling, 221 Scale constant, 283 SCALE keyword on CALIB command, 264, 274, 283, 692 on SCORE command, 53-54, 90, 115, 208, 213, 221, 652, 666 Scaling factor, 28, 549, 613 Schilling, S., 529, 584, 589-590, 842 Schultz, M.E., 845
897
INDEX SCORE command, 75, 90, 115, 206, 208, 264, 285, 289, 292, 316, 410, 474 BIWEIGHT option, 53, 91, 115, 208-209 CHANCE option, 474, 804 DIST keyword, 264, 282, 316, 695, 706 DOMAIN keyword, 26, 79, 91, 115, 208-210 EAP option, 264, 316317, 693, 707, 721 FILE keyword, 26, 79, 91, 115, 208-210, 468, 475, 477-478, 690, 802, 815 FIT option, 53, 91, 115, 208, 210, 264, 316317, 344, 666 IDIST keyword, 75, 78, 90, 115, 196-198, 208, 211, 213, 666, 675 INFO keyword, 36, 90, 115, 208, 212, 218, 222-223, 226-227, 256, 634, 675 ITERATION keyword, 264, 316, 318 LIST keyword, 475, 801-804, 816, 817 LOADINGS keyword, 476 LOCATION keyword, 53-54, 90, 115, 208, 213, 221-222, 652, 666 METHOD keyword, 90, 115, 208, 213-214, 216-217, 476, 652, 666, 685, 781, 801804, 817 MISSING option, 477 MLE option, 264, 316317
MOMENTS option, 26, 79, 91, 115, 208, 214215 NAME keyword, 264, 316, 318 NBLOCK keyword, 264 NFAC keyword, 477, 802, 816 NFORM keyword, 91, 115, 208, 215, 219, 220 NOADJUST option, 264, 316, 318 NOPRINT option, 53, 91, 115, 208, 216, 634 NOSCORE option, 264, 316, 319 NQPT keyword, 77, 90, 115, 197, 198, 208, 216, 264, 310-311, 316, 319, 706 NRATER option, 264, 316, 319 PARAM keyword, 477 PFQ keyword, 264, 316, 320, 693 PMN keyword, 75, 90, 115, 208, 214, 217, 219 POP option, 91, 115, 208, 213, 218, 223, 634, 653, 675 PRINT option, 264, 316, 320 PSD keyword, 75, 90, 115, 208, 214, 217219 QPREAD option, 264, 310, 316, 321 QRANGE keyword, 264, 316, 321 QUAD keyword, 478 READF option, 91, 115, 208, 215-216, 219-20
REFERENCE keyword, 91, 115, 208, 215216, 219-220 REPEAT keyword, 264 RESCALE option, 264, 316, 321 RSCTYPE keyword, 5354, 90, 115, 208, 213, 221-222, 652, 659, 675 SAMPLE option, 264, 316, 322 SCALE keyword, 53, 54, 90, 115, 208, 213, 221, 652, 666 SCORING keyword, 264, 316, 322, 706 SMEAN keyword, 264, 316, 323, 693, 696, 707 SPRECISION keyword, 478, 802 SSD keyword, 264, 316, 323, 693, 696, 707 TIME option, 479 WML option, 264, 316317 YCOMMON option, 91, 115, 208, 213, 222, 653, 675 Score estimates summary statistics, 814 Score file naming of, 206 SCORE keyword on SAVE command, 52, 86, 114, 158, 199, 206-207, 216, 243, 263, 312, 315-316, 337, 693, 707, 724 SCORE option on PROBLEM command, 748, 773 Score Options dialog box, 79, 209-210, 215
898
INDEX Score Options option on Technical menu, 79, 91, 209-210, 215 Scores assigning different name, 318 assigning to intervals, 29 assumed prior distributions, 37 Bayes MAP, 35 combining using weights, 285-286, 292 EAP estimation on general factor, 418 estimating, 31, 410, 530 grouping into fractiles, 435, 776 identifying criterion, 428 method of estimating, 214, 317 ML, 34 requesting for individuals or response patterns, 40 requesting printing of, 320 rescaling, 29, 32, 40, 53, 90, 128, 208, 218, 612, 634, 675 saving to external file, 52, 395, 461, 465 scaling to user-supplied values, 53 setting reference group, 220 specifying number of frequency groups, 279 specifying of in TESTFACT, 474 specifying reference form for, 215, 219 specifying type of, 52
suppressing printing, 52, 216 type of prior distribution, 75, 316 using multiple forms, 219 variance, 25, 32, 34-35, 575, 589, 623, 637, 656, 684, 844 SCORES option on FRACTILES command, 435-436, 776 on INPUT command, 430, 444-445, 776, 779, 781, 804 on PROBLEM command, 349, 350, 357, 389, 392 on SAVE command, 469 SCORESEED keyword on SIMULATE command, 487, 820, 822 Scoring, 82 defining, 208 excluding population distribution, 392 importing item parameters, 52 information on prior distributions, 64 input of item parameters for, 475 method of, 52, 695 of group-level data, 316 of respondents, 316 suppressing correction for information function, 319 suppressing of, 319 test, 605, 621 Scoring function, 322 generalized partial credit model, 557-558
nominal model, 558 specifying for partial credit model, 272 SCORING keyword on BLOCK command, 263, 265, 272, 709 on SCORE command, 264, 316, 322, 706 Scoring Only option on Run menu, 82 Scoring Prior Latent Distribution dialog box, 197-198, 209, 212, 217219 Scoring Prior Latent Distribution option on Technical menu, 197-198, 209, 212, 217-219 Scott, E.L., 840 SD keyword on EQUAL command, 379 on FIX command, 385386 on PRIORS command, 393 SELECT command, 418419, 457, 480, 489-490, 504, 781, 818 SELECT keyword on CALIB command, 47, 89, 115-116, 136, 686 on PROBLEM command, 457, 480481, 776, 781, 818 Selection of graphs, 515 of items, 480 testing, 618 Sequential item testing, 632 Server tab on Settings dialog box, 84, 85
899
INDEX Settings dialog box, 83, 85 Editor tab, 83 General tab, 83 Server tab, 84-85 Settings option on Options menu, 83, 85 Setup menu, 40 General dialog box, 41, 59, 93, 108-111, 113, 117, 135, 141, 145, 147, 151-153, 156, 160-163, 165, 170, 174, 176, 183, 225, 229, 233-234 General option, 40, 8589, 93, 100 Item Analysis dialog box, 46, 94, 102, 108111, 113, 117-118, 120-124, 127, 130, 137-138, 140, 145146, 160-161, 185186, 225, 231 Item Analysis option, 40, 86-89, 94, 102 Test Scoring dialog box, 52, 147, 150, 157, 209, 211-214, 216, 221-222 Test Scoring option, 40, 85-86, 90-91 Sheppard's correction, 32, 708 Show Selectors option on File menu, 514 Silvey, S.D., 844 Simon, T., 621, 830-831 SIMULATE command, 410, 461, 474, 482, 819 CHANCE option, 483, 484, 820 ERRORSEED keyword, 483, 820, 822 FILE keyword, 483, 819-820
FORMS keyword, 484, 819-820 GROUP keyword, 484, 819-820 GUESSSEED keyword, 485, 822 LOADINGS option, 414, 485, 820-821 MEAN keyword, 486, 819-820 NCASES keyword, 486, 819-820 NFAC keyword, 486, 819-820 PARM keyword, 487 SCORESEED keyword, 487, 820, 822 SLOPES option, 414, 485, 819 Simulation of responses, 410, 575 and guessing parameters, 483 form of item parameters provided, 485 means of population of scores, 486 number to be generated, 486 user-supplied item parameters, 483 Single-group model response data, 334 SKIP keyword on BLOCK command, 263, 265, 272 on PROBLEM command, 457, 780, 802, 818, 824 SKIPC option on CALIB command, 264, 274, 283 Skipping steps in analysis, 457
SLOPE keyword on TEST command, 47, 68, 87, 114, 224, 226, 227, 231, 263, 325, 327 Slopes, 539 for graded model, 379, 385, 393 initial parameter, 564 requesting single common, 267, 275 selecting prior, 123, 137 starting values for, 231 supplying priors for, 190-191, 306-307 SLOPES option on SIMULATE command, 414, 485, 819 SMEAN keyword on SCORE command, 264, 316, 323, 693, 696, 707 Smith, A.F.M., 589 Smith, M.C., 765 SMOOTH option on BIFACTOR command, 422 on FACTOR command, 434, 779 on SAVE command, 470, 779 Smoothed matrix, 584, 791-792 difference from tetrachoric correlations, 422 number of decimals, 420 printing for bifactor solution, 421 SMU keyword on PRIORS command, 26, 47, 70, 90, 115, 187, 190, 264, 305306
900
INDEX SOPTION option on PRIORS command, 264, 305-306 Sorted file saving, 470 SORTED option on SAVE command, 470 Sorting of response patterns, 495 Spearman, C., 586, 623 SPRECISION keyword on SCORE command, 478, 802 SPRIOR option on CALIB command, 51, 89, 115-116, 124, 137, 140, 264, 274, 721 SQUAD keyword on TECHNICAL command, 496-498 SSD keyword on SCORE command, 264, 316, 323, 693, 696, 707 SSIGMA keyword on PRIORS command, 26, 47, 70, 90, 115, 187, 191, 264, 305, 307 Standard deviation of normal distribution for intercepts, 452 of population distribution, 379, 385, 393 posterior, 35-36, 75, 90, 115, 208, 214, 217219, 561, 588, 607608, 610, 655-656, 693, 707, 731, 814, 828 Standard error, 31, 586, 598, 601, 631, 716
Standard error of measurement, 525-526, 589, 606, 608-609, 622 Standardized difficulties, 810 Standardized difficulty, 797-798 Stanley, J.C., 580 START command, 395396, 748 ALL option, 396 FORMAT option, 396, 397 ITEMS keyword, 396 PARAM keyword, 409 PARM keyword, 396, 397 Starting values, 397 assigning, 64 entering interactively, 65-66 fixing items to, 68, 226, 385 for category parameters, 266 for dispersion, 225 for guessing parameter, 227 for intercept, 229, 326 for slopes, 231 for thresholds, 232, 328 importing from file, 65 user-supplied, 396-397 Statistical test of number of factors, 587 Stats, Calibration and Scoring option on Run menu, 82, 98, 106 Status bar adding or removing, 82 Steinberg, L., 91, 345, 399, 523, 529, 535, 559, 567569, 597, 617, 638, 738, 747, 751-752, 754, 757,
759, 761-763, 768, 770, 835 Stone, C.A., 759 STOP command, 482, 488 Stouffer, S.A., 768, 836 Stroud, A.H., 600 Stuart, A., 598 Subject scores file saving, 315, 337 SUBTEST command, 428, 458, 489 BOUNDARY keyword, 489, 776 NAMES keyword, 490 Subtest Items tab on Item Analysis dialog box, 48, 87 SUBTEST keyword on CRITERION command, 428 on PROBLEM command, 458, 776 SUBTEST option on SAVE command, 471 Subtests boundaries, 489 histogram of scores, 577 naming, 490 number of, 42 partitioning of main test, 489 Subtests tab on Item Analysis dialog box, 46, 86, 89 Summary statistics score estimates, 814 Swaminathan, H., 524-525 Swineford, F., 586 Symonds, P.M., 592 Syntax generating from input, 81-82, 357, 364, 370, 733 generating MULTILOG, 357, 364, 370
901
INDEX opening multiple files, 84-85 transferring changes to dialog boxes, 82
T TABLE option on PROBLEM command, 352, 390391 TAKE keyword on INPUT command, 55, 63, 87, 114, 163, 167, 179, 263, 292, 298, 697 TECHNICAL command, 410, 491 ACCEL keyword, 491 FRACTION option, 491 FREQ option, 492 IQUAD keyword, 492 ITER keyword, 492, 496 ITLIMIT keyword, 493 MCEMSEED keyword, 493, 803 NITER keyword, 494 NOADAPT option, 432, 439, 440, 466, 471, 494, 779, 815, 825 NOSORT option, 495 NSAMPLE keyword, 495 PRECISION keyword, 495, 781 PRV keyword, 496 QSCALE keyword, 496 QUAD keyword, 496, 497 QWEIGHT keyword, 497 SQUAD keyword, 496498 Technical menu, 64
Assign Calibration Prior Latent Distribution dialog box, 75 Assign Calibration Prior Latent Distribution option, 70, 90 Assign Fixed Items option, 88 Assign Fixed Items dialog box, 68, 109, 225, 227 Assign Item Parameter Prior Constraints option, 89-90 Assign Item Parameter Starting Values option, 65, 87-88 Assign Scoring Prior Latent Distribution dialog box, 70, 75 Assign Scoring Prior Latent Distribution option, 90-91 Calibration Options dialog box, 78, 117, 122, 125, 128, 133 Calibration Options option, 89 Calibration Prior Latent Distribution dialog box, 70, 194-195 Data Options dialog box, 77, 117, 127, 163, 167 Data Options option, 87, 89 Item Parameter Prior Constraints dialog box, 69, 117, 134, 189-192 Item Parameter Starting Values dialog box, 65, 109, 225-226, 228, 230, 232 Save Output to File dialog box, 80
Score Options dialog box, 79, 209-210, 215 Score Options option, 91 Scoring Prior Latent Distribution dialog box, 197-198, 209, 212, 217-219 Technical support, 85 Terry, M.E., 835 TEST command, 66, 87, 108-109, 114, 121, 127, 153-154, 183, 185-186, 224, 263, 274, 297, 316, 325, 327, 398 ALL option, 355, 367, 398, 730 BS option, 355, 367, 379, 398-400, 403, 752 DISPERSN keyword, 47, 68, 88, 114, 224225, 232 FIX keyword, 25, 36, 47, 68, 88, 114, 224, 226 GR option, 355, 361, 367, 374, 379, 398400 GRADED option, 735, 742, 748 GUESS keyword, 68, 87, 114, 224, 226-227 HIGH keyword, 367, 399, 738, 752 INAMES keyword, 87, 114, 224, 228, 263, 325-326, 710 INTERCEPT keyword, 263, 325-326, 328 INTERCPT keyword, 47, 68, 87, 114, 224, 226, 229, 232 INUMBERS keyword, 47-48, 87, 114, 224, 230, 233 902
INDEX ITEMS keyword, 263, 325-326, 332, 355, 367, 398, 710 L1 option, 355, 367, 379, 398-400, 730 L2 option, 355, 367, 379, 398-400, 732 L3 option, 355, 367, 379, 398-400, 733 NBLOCK keyword, 263-265, 325, 327, 339-340, 342, 692 NC keyword, 367, 375, 399, 738, 773 NO option, 355, 367, 379, 398-400, 403 NOMINAL option, 738 REPEAT keyword, 264 SLOPE keyword, 47, 68, 87, 114, 224, 226227, 231, 263, 325, 327 THRESHLD keyword, 47, 68, 87, 114, 224, 226-227, 230, 232 THRESHOLD keyword, 263, 325-326, 328 TNAME keyword, 46, 87, 114, 224, 232, 263, 325, 328, 710 Test generalizability, 622 Test information, 32, 33, 35, 37, 40, 200, 207, 212, 222, 256, 509, 525527, 594, 608-609, 615, 617, 620, 623, 625, 652, 675 curves, 505, 525 displaying, 509 editing and saving, 509 expressing in comparable units, 222 function, 33, 525-526, 615, 623, 625 plotting, 208, 212
saving statistics to file, 207 Test Model dialog box, 352-354, 360, 367, 373, 377, 398-400 Test Model group box, 355, 367 Test scoring, 605, 621 Test Scoring dialog box, 52, 147, 150, 157, 209, 211-214, 216, 221-222 General tab, 52, 85-86, 90-91 Rescaling tab, 53, 90 Test Scoring option on Setup menu, 40, 52, 85-86, 90-91, 147, 150, 157, 209, 211214, 216, 221-222 Test validity, 575 TESTFACT and guessing parameters, 582, 805 bifactor analysis, 418, 804, 815 classical factor analysis, 410, 530 criterion score, 429 exploratory factor analysis, 584 full information factor analysis, 410, 432, 437, 442, 446, 530, 778, 818 group indicator in simulation of records, 484 input filename, 443 item statistics, 785 methods of estimation, 410, 530 new features, 410 order of commands, 413, 414 simulating responses, 410, 575
specifying guessing parameters, 437 test form identification in simulation, 484 using external variables, 430 weighted analysis, 447 Testing adaptive, 632 assessment, 618-619 clinical, 618-619 qualification, 618-619 selection, 618 sequential item, 632 Tests allocating items to, 40 assigning items, 46, 224 assigning names, 44 assigning prior latent distributions, 70, 75 assigning variant items, 48 different quad points and weights, 76 different score scaling options, 53 form indicator in simulation of records, 484 indicating number of, 152-153, 296 naming and numbering items in BILOG-MG, 108 naming of, 232 norming results, 625 number of, 24, 325, 352, 354, 360, 367, 373, 528 number of items, 185, 294 number of quadrature points, 216 number of variant items, 186
903
INDEX obtaining information in comparable units, 222 partitioning into subtests, 489 personalizing of, 177 providing quadrature points and weights by, 70 providing separate priors, 69 saving item parameter estimates, 471 selecting for calibration, 46, 136 total number of items, 455 TETRACHORIC command, 499, 779, 806 COMPLETE option, 500-501 CROSS option, 499 LIST option, 499, 779, 781, 803 NDEC keyword, 500, 779, 781, 803 PAIRWISE option, 500501 RECODE option, 500501, 779, 825 TIME option, 477, 479, 501 Tetrachoric correlation matrix, 788 and positive definiteness, 584 printing to output, 499 saving to file, 463 Tetrachoric correlations, 457, 499, 500, 530, 583584, 629, 779, 782, 787, 789, 806, 818, 826, 828829, 840, 842 and MINRES factor analysis, 431 difference from smoothed, 422
factor analysis, 410, 529, 575 number of decimals, 500 specifying count matrix, 499 Text Parameters dialog box, 517-518, 521-522 TGROUPS command, 401, 403, 750, 758 MIDDLE keyword, 401 NUMBER keyword, 401-402 QP keyword, 402 Thissen, D., 91, 209, 345, 399, 523, 529, 535, 545, 559, 567-569, 576, 597, 601, 616-617, 638, 679, 688, 730, 738, 740, 747, 751-752, 754, 757, 759, 761-762, 763, 765, 768, 770, 772, 835, 843, 846 Thorndike, E.L., 592 THRESHLD keyword on TEST command, 47, 68, 87, 114, 224, 226227, 230, 232 THRESHOLD keyword on TEST command, 263, 325-326, 328 THRESHOLD option on CALIB command, 264, 274, 284 Thresholds category parameters, 546, 564, 700, 703 for binary graded items, 379, 385, 393 for graded model, 379, 385, 393 for item-category, 546, 553, 700 item parameter, 624 of item in 1PL model, 539 of item in 2PL model, 539
requesting prior for, 284 selecting prior, 138 starting values for, 232, 328 supplying priors for, 191-192, 307 Thurstone, L.L., 576, 583, 586, 592, 830-832, 835, 842 TIME option on BIFACTOR command, 423 on FULL command, 440 on SCORE command, 479 on TETRACHORIC command, 477, 479, 501 Title of analysis, 42, 234, 330, 502 TITLE command, 42, 85, 114, 234, 263, 330, 482, 502 T-matrix form of, 403-404 TMATRIX command, 754, 767 AK option, 404 ALL option, 403 CK option, 404 DEVIATION option, 404 DK option, 404 ITEMS keyword, 403 POLYNOMIAL option, 404, 738, 763 TRIANGLE option, 404 TMU keyword on PRIORS command, 47, 70, 90, 115, 187, 191, 264, 305, 307 TNAME keyword on TEST command, 46, 87, 114, 224, 232, 263, 325, 328, 710 904
INDEX Toby, J., 768 Tolerance intervals, 605, 649 Total Info option Graphics procedure, 505, 509, 527 Total information curve, 505, 526 Total test information, 509, 746 TPRIOR option on CALIB command, 51, 89, 115-117, 124, 138-139, 264, 274, 284, 666 Trace line, 548-549, 553, 555, 558-559, 567, 593594, 595-597, 738, 742, 747, 751, 757-758, 760761, 768 Trait estimation, 593 Trend model, 546, 560 TRIAL keyword on INPUT command, 446, 472 TRIAL option on SAVE command, 447, 468, 472, 475, 825 Trial values input for full information factor analysis, 446 TRIANGLE option on TMATRIX command, 404 TSIGMA keyword on PRIORS command, 47, 70, 90, 115, 187, 192, 264, 305, 307 TSTAT keyword on SAVE command, 86, 114, 199, 207, 256 Tsutakawa, R.K., 602, 843 Tucker, L.R., 542, 792, 840
Two-stage testing, 24, 112, 134, 531-532, 536-537, 632, 679 Type of analysis in MULTILOG, 389 of data file used as imput, 54 of data file used as input, 55, 180, 445 of model, 398 of quadrature, 492 of rescaling of scores, 221 TYPE keyword on INPUT command, 55, 63, 86, 114, 147148, 150-155, 163, 180, 238, 240-241, 638, 666
U UNFORMAT option on INPUT command, 444 Uniquenesses, 792 beta prior for, 452 random number for generating from distribution, 483 UNROTATE option on SAVE command, 472, 815 Unrotated factor loadings saving to file, 472 Updating of posterior distribution, 281 Urban, F.M., 832 User-supplied file specifying number of factors for scoring, 477
User-Supplied tab on Assign Scoring Prior Latent Distribution dialog box, 76, 90-91
V VAIM keyword on ESTIMATE command, 356, 384 VALUE keyword on FIX command, 386 Variable format statement, 350, 352, 359, 366, 372, 405, 415, 442, 502 Variance estimated error, 32-35, 249, 271, 583, 620, 623, 625, 655-656, 684-685, 699, 838, 843 percentage explained by factors, 809 score, 25, 32, 34-35, 575, 589, 623, 637, 656, 684, 844 Variant items, 24, 42, 46, 109, 115, 153, 185-187, 224, 528, 534, 633, 670, 672 assigning to test, 48 number in test, 186 VARIMAX option on FACTOR command, 413 VARIMAX rotation, 413, 431, 433, 441-442, 468, 576, 583, 588, 629, 781, 799, 802 Verhelst, N., 843 Verhulst, P-F., 834 Vertical equating, 24, 116, 120, 129, 138, 528, 532, 534, 621, 627-628, 658, 661, 845 905
INDEX View menu, 82
W Wainer, H., 91, 535, 586, 616, 638, 760, 763, 770, 772, 844, 847 Warm, T., 615 Warm's weighted ML estimation, 264, 316317, 615 WEIGHT keyword on COMBINE command, 723 on INPUT command, 441, 444, 446-447, 470, 779 WEIGHT option on INPUT command, 263, 292, 298, 333336 Weights for calculating criterion score, 429 for combining subscale scores, 285, 286
for quadrature, 70, 251, 308, 310, 590, 800 for Rater's Effect model, 561 providing information on, 62 specifying in BILOGMG, 154 type of, 447 WEIGHTS keyword on COMBINE command, 264, 285286 on CRITERION command, 428-429 on QUAD command, 51, 75, 77, 90, 115, 193, 195 on QUADP command, 264, 308-309 on QUADS command, 91, 115, 196-197, 264, 310-311 White, P.O., 583 Wilmut, J., 579 Wilson, D.T., 529 Window menu, 85 WML option on SCORE command, 264, 316-317
Wolfowitz, J., 840, 843 Wood, R., 529, 575, 582, 592, 599, 611 Workspace setting in PARSCALE, 259 Wright, W.D., 560
Y Yates, F., 267, 834 YCOMMON option on SCORE command, 91, 115, 208, 213, 222, 653, 675
Z Zimowski, M.F., 25, 209, 528, 531, 537, 576, 589, 591, 679, 688, 844-846 Zwarts, M., 599 Zwick, R., 591 Zyzanski, S.J., 780, 802803
906
